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Remote community electrification using woody biomass Wilson, Sonja 2012

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Remote Community Electrification using Woody Biomass by Sonja Wilson BSc. Mechanical Engineering, The University of Alberta, 2004  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Resource Management and Environmental Studies)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October, 2012  © Sonja Wilson, 2012  Abstract In British Columbia (BC) there are approximately 60 communities not connected to the regional electricity grid, these communities are classified as “remote”. The prevailing technology for generating electricity in these remote communities is the diesel generator. Diesel generators are polluting, noisy, costly and unsustainable; yet communities rely on them for lack of a suitable alternative. However, many remote communities in BC are surrounded by forests, and may have access to a wood supply sufficient to meet community power requirements. In addition to displacing diesel for electricity generation, biomass power plants may also displace fuels for space and hot water heating.  Co-generation with woody biomass in grid-connected applications at large-scale is well established. However, remote community electrification with woody biomass is an emerging field requiring a different approach to risk/benefit analysis, technology selection, sizing and demand management. This thesis examines the benefits, risks, and technoeconomic feasibility of generating electricity from woody biomass in the context of offgrid communities in BC.  Technology options were reviewed for their suitability to remote community applications. The finding of the review is that a Thermal Oil boiler coupled with an Organic Rankine Cycle turbine is the best choice for remote community power plants. Specifications and pricing for these technologies were applied in the techno-economic assessment and optimization study for Tsay Keh Village in British Columbia. The results of the analysis  ii  suggest that a bioenergy plant in Tsay Keh Village would significantly reduce air pollution, soil contamination due to spills, and noise, while also reducing the 25-year net present value (NPV) of energy expenditures.  Provided that the many forms of risk are recognized and managed effectively, a Bandowned bioenergy power plant at Tsay Keh Village could result in many benefits to the community; including improved respiratory health, employment, protection from escalating fossil fuel costs, revenue and energy self sufficiency. However, in light of recent BCHydro Zone II electricity price increases, the stepped rate price structure and a trend of falling populations in remote communities, the economic risk of a community owned bioenergy plant in Tsay Keh Village appears to outweigh the potential benefits.  iii  Table of Contents  Abstract ...................................................................................................................................... ii Table of Contents.......................................................................................................................iv List of Tables........................................................................................................................... viii List of Figures .............................................................................................................................x Acknowledgements ....................................................................................................................xi Dedication................................................................................................................................. xii  1. Introduction................................................................................................................ 1 1.1. Objectives of the Thesis......................................................................................................2  2. Remote Communities and Bioenergy Technologies: Context, Options, Benefits and Risks............................................................................................................................ 3 2.1. Remote Communities in BC...............................................................................................3 2.2. Implications of the BCHydro Remote Community Electrification Program ...............4 2.3. The Risk-Benefit Paradox of Renewable Energy Technologies in Remote Communities ...............................................................................................................................5 2.4. Introduction to Community Bioenergy Technology........................................................6 2.4.1. Default Technology: Diesel Generators ......................................................................6 2.4.2. Biomass to Electricity Technologies ...........................................................................7 2.4.2.1. High Pressure Steam Boiler to Micro Steam Turbine .................................................8 2.4.2.2. Low Pressure Thermal Oil Boiler to Organic Rankine Cycle (ORC) Turbine .........11 2.4.2.3. Gasification to Internal Combustion Engines ...........................................................14 2.5. Benefits of Remote Community Bioenergy Systems......................................................16 2.5.1. Improved Air Quality and Public Health ..................................................................16  iv  2.5.2. Economic Benefits ....................................................................................................26 2.5.3. Social Benefits...........................................................................................................27 2.6. The Risks of Remote Community Bioenergy Systems ..................................................27 2.6.1. Health, Safety and Environmental Contamination Risks of the Technologies .........30 2.6.2. Health and Safety Risks of the Feedstock Supply.....................................................31 2.6.3. Technological Risks ..................................................................................................32 2.6.4. Power Quality Risks ..................................................................................................33 2.6.5. Resource Risks ..........................................................................................................34 2.6.6. Economic Risks .........................................................................................................35 2.6.7. Social & Institutional Risks.......................................................................................36  3. Techno-Economic Feasibility Assessment ............................................................. 38 3.1. Quantifying the Feedstock Supply ..................................................................................39 3.2. Determining System Requirements.................................................................................40 3.3. Application of the Framework to a Case Study of a Remote Community in BC .......41 3.3.1. History of the Community.........................................................................................41 3.3.2. Feedstock Supply ......................................................................................................45 3.3.3. Village Demand Characteristics ................................................................................46 3.3.4. Near-term Community Growth .................................................................................47 3.3.5. Implementation Strategy ...........................................................................................52 3.3.6. Storage Electric Heaters ............................................................................................57 3.4. Technology Recommendation for Tsay Keh Village .....................................................59 3.5. Optimizing system configuration ....................................................................................59 3.5.1. Feedstock Price Sensitivity Analysis ........................................................................65 3.5.2. Impact of GHG Offset Revenue on Plant Economics ...............................................69 3.6. Economic Risk Analysis ...................................................................................................71 3.7. Recommendations For Tsay Keh Village .......................................................................72  v  4. Discussion.................................................................................................................. 79 4.1.  The Decision Making Framework in Context .............................................................79  4.2.  Avenues for Further Investigation ...............................................................................81  4.3.  Comments on Strengths & Weaknesses of the Thesis.................................................83  4.4.  Conclusion ...................................................................................................................84  5. Tsay Keh Village Case Study: Epilogue................................................................. 86 Works Cited..................................................................................................................... 89 Appendix A: Inputs into the Tsay Keh Village Case Study completed in BEAT ..... 99 1.  Project Budget & Financing Data Provided to BEAT....................................................99  2.  Rates Charged for Energy Delivered............................................................................100  Appendix B: Methodology and an Explanation of BEAT......................................... 100 1.  The Structure of BEAT.................................................................................................100  2.  Database Files...............................................................................................................101  3.  Location Data................................................................................................................102  4.  Building Data................................................................................................................103  5.  Electrical Demand Estimate/Data.................................................................................105  6.  Selection of a Bioenergy System..................................................................................106  7.  Feedstock Processing....................................................................................................106  8.  Integrated Electrical Demand Profile ...........................................................................106  9.  Estimating Capital Costs ..............................................................................................107  10.  Estimating Operating Costs and Revenue ..................................................................107  11.  Estimating the Net Reduction in Greenhouse Gas Emissions ....................................108  12.  Cash Flow Analysis and Other Financial Indicators ..................................................108  13.  BEAT Output File ......................................................................................................109  vi  Appendix C: BEAT Output Files from Case Study................................................... 110  vii  List of Tables Table 1: Emissions factors for different combustion systems and fuels........................... 23 Table 2: Change in PM emissions estimated by conversion to biomass CHP.................. 26 Table 3: Summary of community bioenergy technology options..................................... 29 Table 4: Current and future building stock and demand conditions modeled in BEAT... 48 Table 5: Cost of energy before and after the RECP.......................................................... 56 Table 6: Tradeoffs of base demand and peak demand sizing of remote community power systems ...................................................................................................................... 60 Table 7: Summary of current Tsay Keh Village building stock scenarios modeled in BEAT ................................................................................................................................... 62 Table 8: Results of Part A scenario analysis in BEAT ..................................................... 63 Table 9: Summary of future Tsay Keh Village building stock scenarios modeled in BEAT ................................................................................................................................... 67 Table 10: Results of Part B scenario analysis in BEAT ................................................... 68 Table 11: GHG emissions reduction achieved through the implementation of the biomass CHP plant in Tsay Keh Village. ............................................................................... 71 Table 12: Decision making framework for the community energy options at Tsay Keh Village; fulfillment of community objectives........................................................... 76 Table 13: Decision making framework for community energy options at Tsay Keh Village; consideration of risks. ............................................................................................... 78 Table 14: Context of the decision making framework for RCE using woody biomass in BC ................................................................................................................................... 80  viii  Table 15: Capital Costs of the Tsay Keh Village bioenergy plant .................................. 99 Table 16: Financing structure for Tsay Keh Village bioenergy plant ............................ 100 Table 17: Rates applied to energy sources for the Tsay Keh Village community bioenergy case study ................................................................................................................ 100 Table 18: Database files and their contents .................................................................... 102 Table 19: Sources of GHGs from fossil fuel and bioenergy based community energy systems .................................................................................................................... 108  ix  List of Figures  Figure 1: Biomass fueled fire tube boiler producing steam for power generation ............. 8 Figure 2: Biomass fueled thermal oil boiler providing heat input to the organic rankine cycle for power generation........................................................................................ 12 Figure 3: A wood gasifier producing fuel for a spark ignition internal combustion engine to generate power .......................................................................................................... 15 Figure 4: Debris piles along the shore of Williston Reservoir.......................................... 43 Figure 5: Debris accumulation around Williston Reservoir ............................................. 43 Figure 6: Location of Tsay Keh Village on Williston Reservoir...................................... 44 Figure 7: Tsay Keh Village 2011 load duration curve...................................................... 49 Figure 8: Duration of peak demand periods in 2011 ........................................................ 49 Figure 9: Heating load duration curve and demand duration curve for Tsay Keh Village51 Figure 10: Map of Tsay Keh Village ................................................................................ 54 Figure 11: 24 hour electrical demand for in 2011............................................................. 56 Figure 12: Cut-away view of a storage electric heater ..................................................... 58 Figure 13: Impact of feedstock transport distance and chipper motor on supply cost of feedstock ................................................................................................................... 66 Figure 14: Flow diagram of decision making framework for RCE using woody biomass81 Figure 15: Flow of information in BEAT ...................................................................... 101 Figure 16: Population centres in British Columbia. Locations highlighted in green are included in the BEAT climate database.................................................................. 103 Figure 17: Required format of building data files .......................................................... 104 Figure 18: Building heating load as a function of insulation and design temperature ... 105  x  Acknowledgements First and foremost I must acknowledge the unwavering support and guidance of my supervisor, Dr. Hadi Dowlatabadi; his vision and confidence in my abilities has driven me to dive deeper into this topic and accomplish more than I believed I could. Second, a thesis of this sort is meaningless without a case study, and I sincerely thank Chief Dennis Izony and the Tsay Keh Dene Band for their participation and the sharing of community data to inform the work completed herein. Many thanks also, to VAS Energy Systems for their willingness to share detailed product information and inform this work. I am very grateful for the support of the UBC Bridge Program for both the research skills I acquired through the course work and the financial support. Finally, I want to thank NSERC, the BC Bioenergy Network, MITACS, INAC ecoEnergy and Green Erg Technologies for their support of this research.  xi  Dedication To my family, friends and colleagues who have supported, inspired and commiserated with me throughout this process.  xii  1. Introduction Remote communities in British Columbia (BC) typically have local electricity grids supplied by diesel generators, or in some cases small hydro-electric schemes (Government of British Columbia, 2003; Ministry of Energy, 2008). However, the prevalence of diesel generators in these communities is beginning to be challenged by a growing interest in renewable energy sources. The type of renewable energy best suited to a community depends on local resources, but generally speaking, small hydro, wind and biomass are viable possibilities in BC (Skoda, Doherty, & Harding, 2002). Woody biomass (hereafter referred to as biomass) stands out as an appealing energy option because it is a local resource for many BC communities; it is renewable on a long time frame; and it has the ability provide a reliable electricity supply to meet base load demand, as opposed to solar and wind which are intermittent and require significant and costly storage. At a first glance, it may seem that wood fueled Combined Heat and Power (CHP) systems in remote communities are an ideal solution to the problems of escalating fossil fuel costs, reliance on delivered fuel, and local pollution from fuel spills and leaks.  However, there are a number of risks associated with renewable based community energy systems, and biomass energy (bioenergy) systems in particular. These risks fall under five broad categories: Health, Safety & Environmental Risk, Technological Risk, Resource Risk, Economic Risk and Social & Institutional Risk. These risks must be carefully considered and addressed in both the decision making process and, if a project is to proceed, in the implementation phase.  1  Chapter One of this thesis provides an introduction to remote community electrification in BC and the primary bioenergy technologies that are becoming available; focusing on an explanation of the benefits, risks, and risk management strategies for this family of emerging technologies in a remote community context. Chapter Two explores the technoeconomic feasibility and optimization of a biomass CHP system for a case community in northern BC, completed with the MATLAB-based BioEnergy Analysis Tool (BEAT) developed for the purpose of evaluating such projects, and concludes with recommendations for the case community. Finally, Chapter Three discusses the strengths and weakness of the thesis and draws some general conclusions with respect to remote community electrification with woody biomass in BC.  1.1. Objectives of the Thesis The first objective of this work is to examine purposefully, all the significant factors which may contribute to the success or failure of an off-grid bioenergy heat and power plant in British Columbia, in order to develop both a framework for conducting feasibility assessments, and a strategy for successful implementation of such technology in a remote community. The second objective is to apply the feasibility assessment framework to the case community of Tsay Keh Village, in order to assess the feasibility, optimize the system configuration, and evaluate the risks and benefits, of implementing a bioenergy system in the small, remote community. The third objective is to provide recommendations to the Tsay Keh Dene on the technology choice, sizing, and configuration of a bioenergy plant for their community, and inform their decision making process.  2  2. Remote Communities and Bioenergy Technologies: Context, Options, Benefits and Risks 2.1. Remote Communities in BC In the context of electricity supply, remote communities are classified as such because they are not connected to a regional electricity grid. British Columbia has approximately 60 remote communities (Hawley, Oliver, & Pelletier, 2010; MacDonald, 2010b), many of which are unlikely to ever become grid connected because they are geographically isolated and the monetary and environmental cost of grid extension is unjustifiable, given the small size of the community’s electrical demand. Remote communities in BC can be classified into four main categories; BCHydro Non-Integrated Service Areas (NISA), meaning that BCHydro is responsible for operating and maintaining the generators and power distribution system within the community under contract; First Nations communities which historically have been responsible for generating their own electricity with costs subsidized by Aboriginal and Northern Development Canada (AANDC); Forestry and mining operations that typically generate their own power; and communities, First Nations or civic, which have become customers of BCHydro through the BCHydro Remote Community Electrification Program (RCEP).  Eligibility for the RCEP is restricted to communities that have been in existence for 20 years or more, have at least 25 permanent residents (Hawley, 2007) and have “10 or more permanent, principal residences clustered in a given area”(BCHydro, 2011; Ministry of Energy, 2008). BCHydro estimates that there are 30 to 40 communities eligible for the RCEP.  3  2.2. Implications of the BCHydro Remote Community Electrification Program Under the RCEP, communities are entitled to electricity at the Zone II rate1 and at the same level of reliability as the regional electricity grid. Remote communities that are not part of the RCEP pay the full cost of electricity generation, which in the case of diesel generation, at a fuel cost of $0.88/L, is approximately 0.31 $/kWh (2008 figures), excluding maintenance and capital cost recovery (Hawley et al., 2010). Under the RCEP, BCHydro takes over the ownership, operation and maintenance of the community’s generation and transmission systems, and the community becomes a customer of BCHydro; responsible only for paying the electricity bill. Through the RCEP the low cost power benefits of BCHydro’s Heritage Contract (Government of British Columbia, 2003; MacDonald, 2010b) are extended to off grid communities; BCHydro subsidizes the cost of electricity in remote communities and is responsible for administration, customer billing and cost overruns (Hawley, 2007; Hawley et al., 2010). In First Nations communities, the subsidy from AANDC is transferred directly to BCHydro.  BCHydro has a target of 50% (Hawley et al., 2010) renewable energy in communities under the RCEP, and proposes four modes of development and operation of a renewable energy system in remote communities. 1. The community builds, owns and operates the system and BCHydro has an Energy Purchase Agreement (EPA) with the community. 2. The community selects an Independent Power Producer (IPP) to build, own and operate the system with the community as a partner and BCHydro has an  1  7.84¢/kWh for the first 1500kWh per month, 13.47¢/kWh for additional kWh (British Columbia Hydro and Power Authority, 2011) 4  EPA with the IPP. 3. BCHydro selects an IPP to build, own and operate the system and BCHydro has an EPA with the IPP. 4. BCHydro builds, owns and operates the system. (MacDonald, 2010a; 2010b) Scenarios one and two expose the community to more risk. However, they also have the potential for greater community benefit. Under the RCEP, an EPA between a community and BCHydro creates the opportunity for the community to sell power to BCHydro at the foregone cost of diesel electricity and buy it from BCHydro at Zone II prices. The implication of this is that provided the cost of power from the community owned and operated renewable power system is not greater than the price paid by BCHydro, the community can generate revenue from the sale of electricity. Furthermore, the presence of BCHydro’s diesel generators in the community provides a source of backup and peak power, reducing the necessary size and redundancy of the bioenergy system, and therefore its capital cost.  2.3. The Risk-Benefit Paradox of Renewable Energy Technologies in Remote Communities Remoteness simultaneously makes renewable energy technologies more appealing and more risky. The potential benefits of fuel cost savings, revenue generation and selfsufficiency are all amplified in a remote setting, but so, however, are the risks, primarily because remote communities may be lacking in resources (both human and monetary) to build, operate and maintain the bioenergy system. Furthermore, remoteness limits access to the specialized support, parts and labour that may be necessary to keep a system running. In BC, the conditions of the RCEP and EPA serve to mitigate some of the economic and technological risk of renewable energy technologies in remote  5  communities by providing a guaranteed price for energy sold to BCHydro, and by providing backup power in a community.  2.4. Introduction to Community Bioenergy Technology The technology for generating electricity from biomass in large power plants, such as the one in Williams Lake, BC that produces more than 60 MW (Kumar, Flynn, & Sokhansanj, 2008) is well established. However, there are numerous technical and economic challenges to producing power from biomass on the scale of 100 to 1000 kW (typical of BC remote communities), and the benefits of switching to an emerging technology from the proven alternative must be weighed against the risks. The following section describes benefits and costs of diesel generators and then introduces the emerging biomass-to-electricity technologies that may be suitable for community scale power plants. 2.4.1. Default Technology: Diesel Generators Diesel generators are the primary technology of choice for producing electricity in BC’s remote communities for many reasons; they have a low capital cost relative to renewable technologies; they do not require a full time, or even a part time operator; they are capable of load following (responding to changes in electrical demand); they are reliable, and multiple units can be installed to suit different load patterns and provide redundancy; finally, they are an established technology with easily sourced spare parts and labour to maintain them.  Although the capital cost of diesel generators is low, they have a high operating cost due to fuel consumption and maintenance; and diesel generator operating costs will continue 6  to increase as diesel prices rise. Furthermore, the delivery and storage of diesel fuel in remote communities is a health and environmental hazard. Diesel spills occur during tank refueling as the delivery truck nozzle is disconnected from the tank, and repeated spills over time can cause significant site contamination; likewise a slow leak from a large tank may go unnoticed for years, releasing thousands of liters of fuel into the soil. Hydrocarbon contamination of soil affects water retention and nutrient cycling, and hydrocarbons migrate readily through soil, potentially contaminating a community’s ground water. The lighter fractions of diesel hydrocarbons (benzene, toluene, ethylbenzenes and xylenes) migrate most readily through soil and have been shown to have adverse effects on the human central nervous system (Environment Canada, 2008a; 2008b; 2008c; 2008d).  In addition to the risks posed by fuel leaks and spills, there is evidence to suggest that acute exposure to diesel exhaust can cause irritation of the eyes, nose and throat, lightheadedness, nausea, coughing, phlegm production and exacerbation of asthma-like symptoms. Diesel exhaust has been judged to pose a “chronic respiratory hazard to humans” and there is also evidence supporting a causal link between diesel exhaust exposure and lung cancer (USEPA, 2002). 2.4.2. Biomass to Electricity Technologies The production of electricity from biomass is a multistage process that also produces heat; therefore, most proposed bioenergy systems are CHP systems sized to supply space and hot water heating to buildings on a network, in addition to producing electricity. Heat is produced through either direct combustion of wood in a burner, or as a co-product  7  of wood gasification followed by gas combustion in an internal combustion engine. There are three distinct cycles by which the energy in the biomass feedstock can be converted into heat and electricity. Each cycle requires a different plant design. The following sections explain the operating principles, and primary advantages or disadvantages of each bioenergy system in a remote community power plant context. 2.4.2.1.  High Pressure Steam Boiler to Micro Steam Turbine  In high pressure steam boiler to steam turbine systems, biomass is burned and the heat from combustion produces steam at high pressure inside a boiler. The steam is then expanded in a turbine coupled to a generator. The energy stored in the high pressure steam is converted to mechanical power in the spinning turbine blades and shaft, the rotating turbine shaft is coupled to a generator that produces electricity.  Figure 1: Biomass fueled fire tube boiler producing steam for power generation  There are two fundamental boiler designs, classified as water tube and fire tube; in water tube boilers the water and steam flows through small diameter tubes that are heated from the outside by combustion gases, and in fire tube boilers combustion gases flow through  8  small diameter tubes that are surrounded by water contained in a larger outer shell, as shown in Figure 1. Water tube boilers are used in applications requiring very high pressure (greater than 27 bar) and are common in large power plants. However, a fire tube boiler is more suitable for a small community power plant (SpiraxSarco, 2012a).  In fire tube steam boilers, radiation and convective heat transfer from the burning fuel pile and combustion chamber heats the water inside the boiler shell from below, while the exhaust gases flow through the firetubes of the boiler shell, further heating the water. High pressure is maintained within the system so that the water remains in a liquid state at temperatures much greater than the atmospheric boiling point of 100oC. In the top of the boiler, or superheater, the high temperature water is converted into superheated steam (SpiraxSarco, 2012b). The stored energy in the large volume of high temperature, high pressure water inside the boiler shell is very hazardous. Therefore, very strict regulations govern the design, installation and operation of high pressure steam power plants. In British Columbia the Safety Standards Act requires a qualified steam engineer to be present at steam power plants at all times (BC Safety Authority, 2007; Government of British Columbia, 2011) and the operating cost of employing such highly qualified personnel is unsustainable for a small community power plant. There is potential to circumvent the supervision requirement of steam boilers if they meet certain risk assessment criteria. However, it does not appear that such boilers are designed for burning biomass or for operating under varying load conditions.  9  Another disadvantage of steam boilers is that they consume water. Steam boilers require chemical water treatment to prevent scaling, corrosion and foaming, and over time these chemicals concentrate inside the boiler to form sediment. Accumulation of sediment on heat transfer surfaces inhibits heat transfer from the boiler shell to the water, and eventually causes the boiler to rupture. Therefore, regular partial flushing of the boiler (known as blowdown) is necessary to remove sediment; blowdown water is “pressurized, hot, and dirty” (SpiraxSarco, 2012c) and it may pose a disposal problem, especially in a remote community. Furthermore, water lost to blowdown must be replaced with treated make-up water; treatment chemicals must be stored on site, and the treatment of boiler water is a “matter for expert advice and professional analysis” (SpiraxSarco, 2012d)  In general, steam power plants have a high degree of complexity in their design and operation, and economies of scale are prominent. At a large scale, steam turbine power plants can achieve an electrical efficiency of 39% (Flynn & Kumar, 2005). However, the additional process stages and expensive equipment and materials required to achieve this level of efficiency are not economical at a small scale. A community-scale steam power plant is not likely to achieve such a high efficiency, and some estimates of small scale biomass steam power plant efficiency are in the order of 6 to 10% (Bibeau, Smith & Tampier, 2005; Yablecki, Bibeau & Smith, 2011). Finally, steam power plants are not well suited to load-following applications because sudden and sustained changes in steam demand will affect the boiler pressure, and therefore power production considerably (SpiraxSarco, 2012e).  10  2.4.2.2. Low Pressure Thermal Oil Boiler to Organic Rankine Cycle (ORC) Turbine An example of a biomass ORC power plant is shown in Figure 2. The combustion of biomass provides heat input to the Organic Rankine Cycle (ORC), which operates much like a steam cycle in that a high pressure vapor expands in a turbine coupled to a generator (Pratt & Whitney Power Systems, Inc., 2009). However, in ORC systems the working fluid in the turbine is not steam, but vaporized silicone oil indirectly heated by biomass combustion via a heat exchanger.  The source of heat input to the ORC heat exchanger is a high-temperature thermal oil that is heated to approximately 300oC by the hot combustion gas from the biomass burner as it flows through the thermal oil boiler. The advantage of using high temperature thermal oil to carry heat to the ORC is that the oil is in a liquid state below 359oC (Solutia, 2004) and the thermal oil boiler can be operated at atmospheric pressure.  11  Figure 2: Biomass fueled thermal oil boiler providing heat input to the organic rankine cycle for power generation  12  Although a thermal oil boiler is regulated by the same legislation as a steam boiler, the low-pressure design of the thermal oil boiler is much safer than a high pressure steam boiler, therefore the required operator oversight and qualifications are far less stringent. In fact, an ORC requires only 3 to 5 man-hours per week (Obernberger, Thonhofer, & Reisenhofer, 2002) during normal operation. Daily operating requirements of the thermal oil plant are limited to loading feedstock into the automatic feeding system, a walkthrough checklist of the plant, and periodic emptying of ash containers (Thurner, 2012). The limited operator requirements of ORC plants reduce the dual burdens of operating costs and human resource requirements in community power plants. Furthermore, the use of thermal oil rather than water eliminates water consumption, the need for chemical treatment of boiler water, and the necessity of blowdown. The use of silicone oil in the ORC system eliminates corrosion of the turbine blades, and extends turbine life relative to steam power plants. Although both thermal oil and silicone oil pose some health and environmental risks (Solutia, 2011; Solvay North America, 2011) both the thermal oil boiler and the ORC operate as closed loops. Therefore disposal of these fluids is not required as part of the normal operation of the plant. The silicone oil in the ORC has the same lifetime as the ORC itself (Obernberger et al., 2002) while the high temperature thermal oil eventually cracks and separates. Cracked thermal oil can be disposed of by combusting it in the thermal oil boiler (Thurner, 2012).  The net electrical efficiency of an ORC power plant is in the range of 15% to 21% including thermal losses in the biomass combustion system and the internal electricity consumption of the ORC (Obernberger et al., 2002; Turboden, 2011a; VAS Energy  13  Systems GmbH, 2012a). The energy stored in the large volume of thermal oil means that the ORC is more capable of responding to changes in demand than a steam power plant, and an ORC can be operated down to 10% of its nominal capacity with good performance at partial load (Pratt & Whitney Power Systems, Inc., 2012) making it more suitable to load following applications in off-grid communities. 2.4.2.3.  Gasification to Internal Combustion Engines  Wood gasification reduces solid biomass to a flammable gas, known as producer gas, and char. Gasification occurs inside a reactor, such as the one shown in Figure 3, at a temperature between 700 – 1000oC (Devi, Ptasinski, & Janssen, 2003), under oxygenstarved conditions; the partial combustion of the wood provides heat to drive the gasification reaction. The low calorific value producer gas, primarily composed of CO and H2, is then either combusted in a boiler coupled with a steam turbine or an ORC system, or used as fuel in a spark ignition internal combustion engine coupled to a generator (Arena, Di Gregorio, & Santonastasi, 2010).  14  Figure 3: A wood gasifier producing fuel for a spark ignition internal combustion engine to generate power  Gasification to Internal Combustion (IC) engine systems offer several theoretical advantages compared to the steam cycle and ORC systems; they are available in smaller capacities than either a micro-steam turbine or an ORC system (Community Power Corporation, 2012); the engines may operate in a mode similar to diesel engines, offering load following capability; and they offer higher efficiencies than either small scale steam or ORC systems. The Community Power Corporation claims an efficiency of 22% on their BioMax 100kW units, and Arena et al. (2010) provide a range of 13-28% net electrical efficiency. However, gasification to IC engine technology is the least mature of the three technology options and a great deal of uncertainty about the technical feasibility of implementing such a system in a remote community remains. Wood gasification results in a producer gas that contains vaporized tar and entrained fine particulate matter; these contaminants must be removed before the gas can be injected into an IC engine, or  15  the gas compressor and engine will be swiftly destroyed by erosion and tar condensation. This gas clean up stage adds complexity and cost to the system, increases the service requirements, and decreases the overall reliability and efficiency (Arena et al., 2010; BTG Biomass Technology Group BV, 1999; Devi et al., 2003). Gasifiers are also more sensitive to feedstock quality than biomass burners; requiring consistent wood chip size and lower moisture content.  2.5. Benefits of Remote Community Bioenergy Systems 2.5.1. Improved Air Quality and Public Health The health benefits of remote community electrification using bioenergy technology are more or less pronounced depending on the baseline conditions for heat and power generation in the community. In remote communities in BC, where electricity is an expensive source of energy derived from diesel fuel, and propane is only slightly cheaper, wood remains a primary fuel for residential space heating purposes. However, there is little evidence to support this claim given that both provincial and federal surveys on household energy consumption routinely exclude households on First Nations Reserves and households of “certain remote regions” (Stats Canada, 2007). A 2008 publication by BCStats on home heating in BC reported that only 3.5% of BC households were heated with wood in 2006 (Norton, 2008), however this survey includes only metropolitan areas; therefore it focuses on homes with inexpensive alternative sources of heating, such as grid electricity and natural gas. Furthermore, in some municipalities bylaws restrict residential wood combustion to control air pollution. It is likely that the fraction of households heated with wood is much higher than 3.5% in rural communities.  16  Although wood is a relatively cheap, reliable and abundant source of energy for space heating, it is not without its drawbacks. The products of wood combustion include the inorganic gases CO, NOX; un-combusted hydrocarbons including the human carcinogen benzo[a]pyrene (BaP); oxygenated organics such as aldehydes and organic alcohols; free radicals; traces of SO2; and particulate matter (PM). PM is composed of condensed combustion gases and entrained ash; the size of PM is variable. The fraction of particles less than 2.5µm in diameter (PM2.5) is particularly harmful as these particles can evade the natural defenses of the human respiratory system and penetrate deep into the respiratory tract; most PM in wood smoke is in the PM2.5 range. Many of the products of wood combustion are irritants, allergens and suspected carcinogens. However, studies have shown that the toxicological effects of exposure to wood smoke are most strongly associated with particulate pollutants (Naeher et al., 2007).  A number of epidemiological studies have demonstrated that young children are particularly sensitive to the effects of wood smoke exposure. Honicky, Osborne, & Akpom, (1985) found that children living in homes heated with wood burning stoves are more likely to develop severe respiratory tract symptoms (such as persistent coughing at night, more than four days of coughing consecutively, and wheezing) than children living in homes heated by other means; these results were reinforced by a follow-up study by Honicky & Osborne in 1991. In a study of Navajo and Hopi children living on reserves in Arizona, Morris et al. (1990) found that the presence of a wood burning stove in the home increased the risk of developing bronchiolitis and pneumonia, and Robin et al. (1996) conducted another study of Navajo children in which children diagnosed with  17  acute lower respiratory illnesses (ALRI) were paired with healthy children, and the PM levels in their homes were measured. Robin et al. concluded that at a larger number of the children with ALRI lived in homes with higher concentrations of PM. Triche (2002) conducted a unique study relating hours of wood stove or fireplace use to respiratory symptoms in infants (1-12 months); they found that for every 8-hour increase in the operation of a wood stove there was a 10% increase in the rate of cough days. In a study of asthmatic children living in a wood smoke impacted area of Seattle, Washington, Allen et al. (2008) found that exposure to ambient PM resulted in airway inflammation and decrements in lung function, and a similar study of asthmatic children in the Netherlands found that there was a 3-5% reduction in lung volume following episodes of high PM concentrations (Pierson, Koenig, & Bardana, 1989). An intervention study by Allen et al. (2011) found that the use of indoor HEPA filters in a woodsmoke impacted area of BC reduced indoor PM2.5 concentrations by 60%, and that this reduction in exposure to PM2.5 was associated with a decrease in two predictors of cardiovascular morbidity; impaired endothelial function and inflammation.  Finally, in a 10-year study tracking PM pollution and hospital admissions in Christchurch, New Zealand (a city in which more than 90% of winter time PM has been attributed to residential wood combustion), it was found that there was a 3.3% increase respiratory admissions, and a 1.2% increase in cardiac hospitalizations for every interquartile increase of 14.8 µg/m3 of PM pollution, across all age groups (McGowan et al., 2002). These effects were more pronounced in children.  18  There is a lack of coherence in the results of studies that measure the contribution of wood-burning stoves to indoor air pollution through direct emissions into the house. In a study of a small community in Vermont, where residential wood combustion is the primary source of airborne particles in residential areas of the town Sexton et al. (1984b) found that the average outdoor level of respirable suspended particles (RSP) was 22µg/m3. In a follow-up study on indoor air quality in the same community (Sexton et al. 1984a), it was found that indoor concentrations of RSP were significantly higher than outdoor concentrations, and the mean indoor RSP levels in wood burning homes (24µg/m3) tended to be elevated compared to those without wood burning appliances (18µg/m3), although not significantly so. Traynor et al. (1987) conducted a study of emissions from four wood burning stoves inside a single house and concluded that properly operated and maintained airtight stoves emits only minor amounts of pollution into the home. Similarly, a 2009 study by Allen et al. measured indoor PM2.5 concentrations in 15 homes in northern British Columbia before and after old wood burning stoves were replaced with high efficiency EPA certified stoves; they found that upgrading the stoves did not have a consistent impact on indoor levels of PM2.5 and that the majority of indoor PM2.5 is generated by other activities within the home, rather than stove leakage or infiltration from the outdoors. They found that although outdoor concentrations of PM2.5 were lower following the stove exchange, this change was the result of changes in the ambient concentration in the air shed and could not be attributed to the local impact of the stove exchange. Conversely, a 2008 study by Gustafson, Östman, & Sällsten found that BaP levels in wood burning homes were significantly higher than both outdoor levels and indoor levels  19  in non-wood burning homes. Furthermore they found that the equivalent BaP levels exceeded the guideline value of 0.1ng/m3 by a factor of five, both inside the wood burning homes, and outdoors.  Allen et al. (2009) conclude that in homes with high infiltration efficiencies, indoor PM2.5 concentrations are influenced by the ambient PM2.5 concentration more than the local outdoor concentration, therefore efforts to reduce woodsmoke exposure indoors may only be effective if woodstove change out programs are regional in scale. A before/after study of a woodstove change out program in Libby Montana where 1200 of 1300 “dirty” stoves were replaced with EPA certified stoves supports this conclusion (Ward & Noonan, 2008). In Libby, Montana 82% of the wintertime ambient PM is attributed to residential wood combustion; following the change out program the average indoor level of PM2.5 in 16 sample houses was reduced by 71%, and peak levels were reduced by 76% after the old stoves were replaced. During the change-out period Noonan et al. (2012) conducted prospective surveys of parents of school-aged children in the community to asses the impact of the change out program on respiratory health; they found that the reduction in PM was associated with reduced odds of reported wheeze, cold infection, bronchitis, influenza, and throat infection and reduced frequency of itchy/watery eyes and sore throat. Following the completion of the change out program, Ward et al. (2010) estimated that there was a 28% reduction in wood smoke PM in the community.  Given the association between residential wood combustion, outdoor air pollution, indoor air pollution and respiratory symptoms resulting from exposure, there appears to be an  20  opportunity to improve respiratory well being in communities by reducing the prominence of residential wood combustion for space heating purposes. A District Heating Network (DHN) or community wide electric heating, fueled by a centralized biomass CHP plant, has the potential to reduce the indoor and outdoor wood smoke PM concentrations dramatically.  Although the PM emissions from a wood burning stove or furnace are highly dependent on stove design, fuel characteristics and operating conditions, a number of authors have provided estimates of emissions factors based on experimental work. The results have been reported in a variety of units, such as Total Suspended Particles (TSP), particles ranging from 0.1 to 30 µm in diameter; PM2.5 (U S EPA, 1969); and PM1, particles less than 1 µm in diameter (Sippula et al., 2009). Sanborn & Blanchet (1982) tested 14 wood burning appliances to determine particulate emission rates and found that the average TSP emissions for all 14 appliances was 19.1 g/kg (1005 mg/MJ) of fuel burned, with a range of 6.6 to 42.3 g/kg (347 to 2226 mg/MJ), they found that the average TSP emission rate from two wood furnaces was 9.6 g/kg (505mg/MJ). A very thorough study by McDonald et al. (2000) developed emission profiles for representative appliances and wood species used in wood burning communities; their experimental results yielded an average PM2.5 emission rate of 4.35 g/kg of dry fuel (229 mg/MJ) for wood burning stoves.  The PM emissions from small residential wood burners are high in soot and organic matter as a result of poorly controlled and incomplete combustion. However, in larger  21  scale district heating boilers, combustion is well controlled, and the PM emissions consist of inorganic ash material (Sippula et al., 2009). Furthermore, district heating boilers typically have emissions control equipment, such as cyclones, multicyclones, electrostatic precipitators (ESP) and baghouses, to remove PM from the exhaust stream. Finally, the high stack height of large boilers allows for better mixing and dissipation of pollution than residential chimneys.  In a study of 150 kW and 500 kW pellet boilers Chandrasekaran et al. (2011) found that PM2.5 emissions after the cyclone exhaust gas cleanup were approximately 26 mg/MJ for both boiler sizes. The study also found that burning a lower quality fuel, such as woodchips from forestry residues, produced a higher emission rate of 41 mg/MJ in a third 150 kw boiler. Sippula et al. (2009) measured the TSP and PM1 emitted from four different boilers with varying emission control equipment; a 5 MW rotating grate boiler burning a mixture of sawdust and bark with a multicylone and a wet scrubber, a 15 MW rotating grate boiler burning forest residue wood chips with a multicyclone and an ESP, a 10 MW rotating grate boiler burning sawdust and bark with a multicyclone and ESP, and a 7 MW gasifying boiler burning forest residue wood chips with a monocylone. A significant finding of this study was that grate combustion boilers produce much higher concentrations of coarse and fine particles in the flue gas than gasifying boilers. At the outlet of the emissions control equipment the measured TSP and PM1 were was 54 mg/MJ and 31 mg/MJ for the 5 MW boiler, 16 mg/MJ and 6 mg/MJ for the 15 MW, and 52 mg/MJ and 13 mg/MJ for the 7 MW gasifying boiler (no measurement was reported for the 10 MW boiler). VAS Energy Systems, a manufacturer of gasifying thermal oil  22  boilers, specify TSP emissions of less than 20 mg/Nm3 downstream of the ESP; at the given feed rate and exhaust flow conditions of the boiler, this emission rate is equal to approximately 13.5 mg/MJ or less (VAS Energy Systems GmbH, 2012a). Table 1: Emissions factors for different combustion systems and fuels Combustion System  TSP (mg/MJ)  PM2.5 (mg/MJ)  PM1 (mg/MJ)  Wood Furnace  505  229  Gasifying Boiler with ESP  13.6  -  13  Diesel Generator  69.4  -  -  Propane Combustion*  1.9  Open Pile Burning  353.0  -  *Based on LPG emission factor of 0.05 kg/m3 (Acurex Env. Corp.,2008) 46.6MJ/kg (USDOE, 2012) and 552 kg/m3 (Indian Oil Corp., 2010)  It is not appropriate to directly compare the emission factor for a residential wood stove to the emission factor for a biomass CHP plant as shown in Table 1 because the two systems will operate very differently. The CHP plant will consume more MJ of fuel than the total community residential wood consumption since it is also providing heat to generate electricity, and this process is, at best, 24.7% efficient (Turboden, 2011a). However the CHP plant is also displacing the PM contribution from the diesel generators in the community (Environment Canada, 2005), so this reduction is included in the calculation. Due to the indirect nature of the comparison between the two scenarios Although it is harder to quantify, if feedstock for the bioenergy system is sourced from forest residues or other waste wood that has been diverted from open pile burning near the community, an additional reduction in atmospheric TSP can be expected. Springsteen et al. (2011) provide a review of open pile TSP emission factors, the average of which is 353 mg/MJ (range: 173 – 736 mg/MJ), therefore, for an annual feedstock consumption of  23  5500 dry tonnes diverted from open pile burning, the reduction in TSP may be in the order of 38 tonnes/year. Table 2 is provided as an example of the change in PM emissions that could be expected when replacing residential wood combustion and diesel powered electricity with a centralized biomass CHP plant. It is important that a biomass boiler with low emissions be selected if any air quality improvement is to be realized. A poor choice of boiler would actually increase particulate air pollution in the community under the electric heating scenario. However, the reduction in emissions does not fully describe the impact on local air quality for three reasons: first, the organic and inorganic chemical species emitted from a residential wood stove are more reactive than the inorganic emissions from a large biomass boiler; second, the proximity of the population to the emission source is much closer when individual residential wood stoves are used, compared to a tall exhaust stack venting high into the atmosphere; third, individuals are exposed to acute levels of wood smoke during wood stove refueling. A full explanation of the biomass CHP plant specification and the community demand details used in this calculation are provided in Chapter Two of this thesis.  Despite the fact that there is insufficient data to directly compare the CHP system PM2.5 emissions to residential wood burning PM2.5 emissions, it can be inferred that since the TSP emissions from both CHP options are less than the PM2.5 emissions in the residential wood burning case, net PM2.5 emissions from the CHP system will also be less than the residential wood burning case. Therefore, it is highly likely that the air quality in the  24  community would be improved by eliminating residential wood burning for space heating purposes.  Although it is harder to quantify, if feedstock for the bioenergy system is sourced from forest residues or other waste wood that has been diverted from open pile burning near the community, an additional reduction in atmospheric TSP can be expected. Springsteen et al. (2011) provide a review of open pile TSP emission factors, the average of which is 353 mg/MJ (range: 173 – 736 mg/MJ), therefore, for an annual feedstock consumption of 5500 dry tonnes diverted from open pile burning, the reduction in TSP may be in the order of 38 tonnes/year.  25  Table 2: Change in PM emissions estimated by conversion to biomass CHP Residential Wood Heating, C&I Propane Heating & Diesel Electricity Generation  Scenario  Biomass CHP and Community Wide DHN  Residential Heat Consumption (MJ/yr)  6,336,000  Commercial & Institutional Heat Consumption (MJ/yr)  5,508,000  Community Electricity Consumption (MJ/yr)  5,472,000  Biomass CHP, C&I DHN, Residential Electric Heating  Wood Consumed by Wood Furnaces* (t/yr)  513  0  136  Wood Consumed by Boiler* (t/yr)  0  2700  5900  Particulate Emissions Estimate TSP Wood Furnace Emissions (kg/yr)  4923  0  1303  TSP Boiler Emissions (kg/yr)  0  698  1525  TSP Propane Emissions (kg/yr)  13  0  0  TSP Diesel Emissions (kg/yr)  380  0  0  Total TSP Emissions (kg/yr)  5316  698  2828  PM2.5 Emissions (kg/yr)  2232  PM1 Emissions (kg/yr)  insufficient data to estimate  insufficient data to insufficient data to estimate estimate 667  1457  * Assumes a wood stove efficiency of 65%;LPG furnace efficiency of 80%; wood energy content of 19MJ/kg, dry; CHP plant feedstock consumption as determined by BEAT in Appendix A  2.5.2. Economic Benefits Under the RCEP, if a community owned and operated bioenergy system is sized properly and operates as expected; the community should generate revenue from the sale of electricity to BCHydro through an EPA. Furthermore, if a community is currently paying for power from diesel generators, they may save several hundred thousand dollars per  26  year on diesel fuel costs. In addition, replacing propane boilers in large buildings with a district heating system can significantly increase the total savings from fuel displacement. The potential for energy expenditure savings and revenue generation is explored further in Chapter Two. 2.5.3. Social Benefits A biomass fueled CHP system provides tangible and intangible social benefits to the community. Depending on the size and complexity of the system, a number of new jobs will be created within the community to collect/harvest, transport and process feedstock for the bioenergy system, and at least two individuals will be trained to oversee the daily operation and routine maintenance of the plant. The provision of inexpensive thermal energy may enable the operation of a greenhouse, improving the availability of fresh vegetables and creating even more jobs. A less tangible but still significant social benefit of community owned and operated bioenergy systems is the reduced reliance on fossil fuels and increased self-sufficiency.  2.6. The Risks of Remote Community Bioenergy Systems The risks associated with implementing a bioenergy system in a remote community are numerous, and can be classified in six categories: •  Health, Safety & Environmental Risks are those stemming directly from the presence and operation of the technology itself.  •  Technological Risks result from the implementation of an emerging technology in a remote setting.  •  Power Quality Risks result from the challenge of meeting a rapidly varying load.  27  •  Resource Risks are associated with the sustainability, access, quality and cost of the biomass supply.  •  Economic Risks stem from uncertainty about the potential to generate a profit from the sale of electricity, and the operating and maintenance costs of the system.  •  Finally, Social & Institutional Risks result from inadequate communication between system developers and the community, failure to clearly define ownership and responsibility for maintenance of the system, inadequate ongoing developer support following commissioning, and the lack of capacity within local institutions to manage the administrative aspects of the bioenergy system, such as metering and billing.  Table 3 summarizes features of each of the three bioenergy technologies discussed in Section 2.4.2 as they relate to Health, Safety & Environmental, Technological and Economic risk.  28  Table 3: Summary of community bioenergy technology options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Table 3: Continued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ealth, Safety and Environmental Contamination Risks of the Technologies There are health and safety hazards inherent in all the bioenergy technologies options described in section 2.4.2, high temperature and operating pressures in steam systems pose a risk to employees who operate or maintain the system. Furthermore, boilers require the addition of chemicals to the feed water for control of corrosion, sedimentation and fouling of heat exchange surfaces. These chemicals must be stored on site, and their ongoing disposal is problematic (SpiraxSarco, 2012f). Similarly, the high temperature thermal oil and silicone oil in ORC systems are classified as hazardous for disposal and  30  pose some non-fatal health risks to employees if they are exposed through skin and eye contact or inhalation of fumes (Solutia, 2011; Solvay North America, 2011). Gasifiers also operate at high temperature, and pose the additional risk of gas leaks, which would release deadly CO, as well as H2 into the gasifier and engine rooms, and pose a fire or explosion hazard.  Fortunately, the risks associated with boilers and pressure vessels can be managed through adherence to codes and the enforcement of safety regulations by the BC Safety Authority. Engineered control systems monitor boiler operation and will shut a boiler system down safely if necessary, greatly reducing the risk of catastrophic failure. These regulations and controls apply to both steam boilers and thermal oil boilers. The risk posed by gas leaks from gasifiers is recognized, and the risks can be mitigated by operating the gasifier below atmospheric pressure so that a seal failure would result in atmospheric air leaking into the reactor, rather than producer gas leaking out. Due to their emerging technology status there are no safety codes and regulations developed specifically for the design and operation of such systems in British Columbia. However, they are governed under the Alternative Safety Standards Regulation as part of the Safety Standards Act (Babich, 2012). 2.6.2. Health and Safety Risks of the Feedstock Supply Working with heavy machinery while harvesting/collecting and processing feedstock poses some safety risk, and there may be health risks associated with exposure to dust  31  and fungal pathogens2 in the feedstock, especially in an enclosed storage facility. There are also risks associated with feedstock storage; hog fuel piles have a possibility to spontaneously combust when fuel is stored in large piles over long periods of time. The risks associated with the feedstock supply operation can be managed through proper training of employees and adherence to safety requirements, and the health risks posed by the inhalation of dust and pathogens can be reduced through the use of proper ventilation and mandatory use of masks or respirators. Finally the risk of spontaneous fire or explosion in feedstock storage areas can be mitigated by storing fuel as round wood, maintaining only a small quantity of chipped wood to feed the system and by keeping fuel piles dry and well ventilated (Friesen, 2011). 2.6.3. Technological Risks All of the technologies described in 2.4.2 can be classified as emerging; as such they are at a higher risk of failure in untested operating conditions, and the consequences of a failure are exaggerated in a remote setting. Frequent, unplanned shutdowns may result from sub-optimal operating conditions, poor quality feedstock, or inexperienced operators. A sensitive and highly tuned system will require the attention of an expert technician to keep it running, and it is unlikely that such individuals will be available in remote communities (since, at the emerging stage, this sort of expertise typically exists only within the companies who have developed the technology, or those with experience operating similar facilities). The failure of system components can result in significant down time because the availability of parts and labour for a non-commercialized 2  In 1999 the tropical fungus Cryptococcus gattii associated with eucalyptus trees (Campbell et al., 2005) made its way to Vancouver Island through an unknown vector and by 2006 had caused 176 cases of infection and 8 deaths in BC (Galanis, Hoang, Kibsey, Morshed, & Phillips, 2009) Although exposure to the fungus is unlikely to be found outside of the lower mainland in BC, there is a risk that individuals working within forests or with forestry debris may be exposed to this pathogen.  32  technology may be limited, especially in a remote location. The consequences of frequent shutdowns can be dire; under the RCEP the community would still receive electricity from BCHydro generators, but they would not be able to sell electricity to BCHydro and recover the capital cost of the system. Furthermore, the community would have to pay for the electricity from BCHydro. If the community was not part of the RCEP, a system failure could leave a community with only their backup source of power until the bioenergy system was repaired.  Technology risks can be mitigated to some extent through good system design, employee training, feedstock quality control and the selection of a technology with a good track record of performance and a higher level of adoption. Furthermore, a service network for annual maintenance and an asset management plan must be developed in order to maintain the plant over the long term. Negotiation of a robust contract with the technology supplier is also essential. A comprehensive warranty and good customer support reduce technological risk. However the manufacturer must also be in good financial standing in order to fulfill warranty obligations and provide support. 2.6.4. Power Quality Risks In addition to the risk of unplanned shutdowns, there is a risk that the technology may not be capable of responding to changes in community demand, affecting the power quality and leading to damaged electrical equipment. Off-grid communities have highly variable demand and not all technologies are capable of load-following under such operating conditions. Diesel generators have a proven track record in meeting highly variable demands, but the other technologies have yet to be put to such a test.  33  2.6.5. Resource Risks The primary resource risk of bioenergy systems is the depletion or loss of the feedstock supply. Although biomass is classified as a renewable resource, the timescale on which it is renewable can be much longer than the lifetime of the bioenergy system. Therefore, if feedstock is to be harvested from a standing forest near the community, a sustainable forest management and harvesting plan for the duration of the plant lifetime is essential (Richardson, 2002). Forest fire fuel load management, and logging in the urban/wildland interface may also be source of feedstock for a community power plant (Daugherty & Fried, 2007; Evans & Finkral, 2009), as can Mountain Pine Beetle killed wood (Kumar et al., 2008).  Alternatively, forestry residues could provide a source of feedstock for a communityscale system. Residues consisting of tree top stems and branches constitute 15 to 25% of a tree’s biomass (Kumar, 2009); a recent study of forestry residue production in Canada estimated that the total production, taking into account ecological and economic limitations, is 20 Tg per year, 7 Tg of which were attributed to British Columbia (Dymond et al., 2010). However, reliance on logging operations for a supply of feedstock exposes the bioenergy plant to risks related to economic downturns or other disruptions in the forestry industry; for example, the biomass district heating network in Prince George, BC lost its feedstock supply in 2012 when the sawmill which was supplying mill residues was destroyed in an explosion and fire (Ostergaard, 2012).  Regardless of the source, sustainability and long-term access to the feedstock resource must be assured, and any conflicts over the use of the resource must be resolved. If the 34  feedstock is to be supplied under contract by a third party, the risks of a disruption in supply, or exposure to escalating feedstock prices must be mitigated through the negotiation of a long-term supply contract. Furthermore, the feedstock supplier must not lose money on the supply contract or the whole operation will be unsustainable, and the supplier may walk away from the contract. The bioenergy system at Dockside Green in Victoria lost its original feedstock supplier due to unsustainable economic conditions and is currently operating only its natural gas boilers (Ostergaard, 2012). 2.6.6. Economic Risks The installation of a bioenergy system is a considerable investment for a community. Generally, larger systems have a higher capital cost, and if community demand is overestimated, the revenue from the EPA will also be overestimated (since it is based on a fixed price per kWh sold to BCHydro). If a community sells less power than expected to BCHydro, it may not be able to recover the capital cost of the bioenergy system, resulting in economic loss. A second source of economic risk is higher-than-anticipated operating costs, which can result from unscheduled maintenance, more frequent (than anticipated) replacement of parts, high transport cost for spare parts and repair technicians, or higher-than-expected labour and feedstock costs.  Literature on remote community electrification using renewable energy technologies in developing countries repeatedly points out the risk that consumers will not be able to pay for electricity (Kumar, Mohanty, Palit, & Chaurey, 2009; Senelwa & Sims, 1999). This is indeed a risk in BC as well. However, under the RCEP customers are not exposed to the full cost of electricity generated by renewables. Therefore, the RCEP can be viewed as  35  mitigating some of the economic risk inherent in renewable energy systems, provided that the systems are technologically sound and sized appropriately. 2.6.7. Social & Institutional Risks A lack of clearly defined ownership and responsibility for maintenance of the renewable energy system is a frequently identified risk in the literature on remote community electrification in developing countries (Kumar et al., 2009; Zhang & Kumar, 2011). There is a similar risk in BC regarding bioenergy systems in remote communities; the daily operation of a bioenergy system requires supervision and occasional intervention, in addition to daily maintenance. It is of utmost importance that the human resource requirements of the bioenergy system be considered at the earliest stages of project planning; and that the skills necessary to operate the bioenergy system can be developed within the community so that the new jobs go to community members. In addition, the ownership structure and responsibility for operation and maintenance must be defined before the project proceeds beyond the feasibility assessment stage, since these factors have a profound influence on the economic benefits and risks for the community (Fischer et al., 2005). The RCEP and an EPA provide a foundation for establishing such arrangements.  Another frequently cited risk in the literature is the lack of local institutional capacity to collect tariffs to cover the cost of electricity (ITDG, 2001; Kumar et al., 2009; Zhang & Kumar, 2011). It is critical that the community has the capacity to operate the system as a business, as well as operating the physical infrastructure. Under the RCEP and with an EPA with BCHydro, some of this risk is mitigated because BCHydro takes responsibility  36  for electricity metering and customer billing. However, the administration of a DHN is not within BCHydro’s responsibility, and other aspects of plant operation (such as the feedstock supply) must be managed by the community.  If the community is not actively involved with the planning of the bioenergy system and its administrative structure, and good communication is not maintained between the developer and the community, there is a risk that the community will be dissatisfied with the system once it has been implemented. Furthermore, if community members are not prepared for the realities of a bioenergy system in their midst, there is a real risk that the community may reject the system. A bioenergy system complete with feedstock processing and storage will have a significantly larger footprint than a set of diesel generators. Furthermore, if the plant is to also provide heat to a DHN, this footprint will have to be close to the heat loads.  The management of Social & Institutional Risks depends primarily on the involvement of the community at every stage of project development. Motivation for the project should arise from within the community and their objectives should be fully understood; furthermore, the project should have the full support of informed community members. Communication between the community and the developer must be ongoing, effective and bilateral; taking into account community demand, expectations, and resources, with the developer clearly detailing system requirements and limitations. Good communication enables communities to make well informed decisions and allows the developers to provide a system that will fulfill community objectives (Cherni et al.,  37  2007). The development of institutional and administrative capacity within the community, and the training of local employees should be supported by the developer and occur concurrently with system design, installation and commissioning. Furthermore, the relationship between the developer and the community must not end with the commissioning of the plant, there must be a program in place for ongoing support, remote supervision and annual servicing of the community power plant (Thurner, 2012).  3. Techno-Economic Feasibility Assessment Prior to the commencement of EPA negotiations, or any capital investment in a community power plant, the feasibility of a community biomass CHP system must be thoroughly assessed in order to determine whether a plant can be sized to suit the community demand, and whether the community electricity consumption can support the capital cost of a bioenergy plant.  Techno-economic evaluation is a strategic assessment which draws on criteria that are not purely economic, also taking into consideration self sufficiency, environmental impact, social factors and technical suitability. The level of detail required for a comprehensive feasibility study is quite high; factors unique to each community, such as the local climate, the feedstock source, the community heat and power consumption and the distribution of buildings all influence the feasibility of a community power plant. A thorough feasibility study must examine the sensitivity of the economic viability to factors such as changes in expected energy consumption, increased feedstock costs, and reduced revenue. The BioEnergy Analysis Tool (BEAT) was developed as part of this  38  thesis to conduct in-depth feasibility analyses of community bioenergy opportunities in BC. It was written in MATLAB (MathWorks, 2010) and is highly flexible in configuration, allowing the user to input data unique to each community. BEAT enables rapid comparison of system sizes and configurations, and the investigation of various feedstock supply and energy consumption scenarios to inform an economic risk assessment. The first steps in the techno-economic assessment of a community bioenergy opportunity are to identify and quantify the source of feedstock, and to characterize the community energy requirements.  3.1. Quantifying the Feedstock Supply In BC the source of feedstock for a community bioenergy system may come from one or many of the following sources; slash and logging residues, thinnings from forest management, mountain pine beetle killed wood, harvesting at the wildland-urban interface for fire management, debris from hydro-electric reservoirs, or sustainable harvesting of local forests. Regardless of the source, the available annual volume and lifetime availability of the feedstock supply must be assessed, and the cost of the feedstock delivery to the plant estimated. If there is no long-term supply of affordable feedstock available for a community bioenergy system, the plant is unlikely to succeed. However, affordability is a function of many factors, including the cost of the energy that the bioenergy system is displacing, and the amount of feedstock that the system will consume. Therefore, in the first stages of a feasibility study, quantifying the supply is of higher priority than estimating the cost. Once a system has been sized, the annual feedstock consumption can be estimated and the impact of feedstock cost on economic viability can be evaluated.  39  3.2. Determining System Requirements A community’s peak electric power demand is defined in terms of kWe (and kWth for heat); it is the maximum power that the community is expected or known to draw. However, demand is always changing, as appliances and lights turn on and off, over the course of a day and over the course of a year. Peak demand lasts for only a small number of hours in a year, however, it is a major determinant of the capacity of the generators required to provide uninterrupted supply to a community.  A community’s energy consumption is the annual energy required to meet the electrical or heating demand, summed over the course of a year. The base load for a community can be defined seasonally or annually. It is defined by the minimum power drawn over that period. The annual base load therefore is the minimum demand multiplied by 8760 (hours per year). This demand is typically made up of chargers, standby demand of various appliances and 24/7 demands such as exit signs. In a community system, periodic but on-going loads such as refrigerators and pumps also contribute to the base load.  In an off-grid community with no secondary system for meeting peak demand, the CHP system must be sized to meet peak power requirements (whether the peak is itself managed or not). Furthermore, in order to assure reliability of supply, redundancy of generating capacity must be assured by having multiple, independent generators. The requirements for peak capacity and reliability imply that a properly sized system will be capable of supplying far more electricity than the community is likely to demand most of the time. This excess of supply capacity results in low load factors (ratio of power 40  consumed to power production capacity). Hence, the capital cost of power divided by total units of power sold is much higher at low load factors. However, under the RCEP, BCHydro’s generators can provide the source of backup and peak power if necessary, thereby reducing the size and capital cost of the bioenergy system.  3.3. Application of the Framework to a Case Study of a Remote Community in BC 3.3.1. History of the Community In the late 1960’s BCHydro completed construction of the largest hydro electric generating station in the Province at the W.A.C. Bennett Dam (BCHydro, 2012b). Construction of the Bennett Dam at Hudson’s Hope flooded the Rocky Mountain Trench and tapped the combined powers of the Finlay and Parsnip Rivers, in addition to the many tributary rivers that feed this remarkable watershed. The reservoir that was created covers more than 1700 square kilometers of submerged forest and destroyed habitat (Pollon & Matheson, 1989). The dam also flooded the traditional territory of the Tsay Keh Dene Band, who lost their waterway, traditional hunting grounds, gathering sites and cemeteries to the rising waters (BCHydro, 2009; St Laurent & Laplante, 2009). The river valley was not logged before it was flooded, and the new lake was soon clogged with floating trees. After the flood, “the part of the [Rocky Mountain] trench that became Williston Lake was transformed from one of the safest, most beautiful areas, to one of the most hazardous and ugly places in British Columbia, in which to boat or operate a float equipped aircraft.” (Pen Powell, Pollon & Matheson,1989, p. 226).  41  However, BCHydro’s water license agreement requires that the lake be maintained in a state that is safe for navigation, so, following the flood, BCHydro undertook a program of debris clearing; collecting floating trees, piling them on the shore, and burning them (Pollon & Matheson, 1989). Originally, the lake was expected to be cleared of debris by 1981 (St Laurent & Laplante, 2009). However, as of 2008 BCHydro had an ongoing debris management program for Williston Reservoir with a budget of more than $8 Million (BCHydro, 2008). In 2008 objections to the burning of debris were raised, and since then, the debris has continued to accumulate on the shoreline (Rowed, 2012). A 2010 survey of the debris on Williston Lake estimated that there is approximately 1.1 million cubic meters of woody debris collected in ribbons and piles on the shore of the reservoir, as shown in Figures 4 and 5 (AECOM Tecsult Inc., 2010)3.  The Tsay Keh Dene now live in a small community at the northernmost point of the reservoir (see map, Figure 6), still adjusting to a life without the river and river valley which was so much a part of their culture. Despite the fact that the Tsay Keh Dene were displaced to make way for the largest hydro electric scheme in British Columbia, their small community remains unconnected to the regional electricity grid; they reap none of its benefits, and have paid a higher price than anyone for its existence (St Laurent & Laplante, 2009). Instead the Tsay Keh Dene suffer dust storms every summer from the exposed glacial silt shoreline of the lake, and make do with the expensive, noisy and polluting diesel generators that provide their village with electricity.  3  The study by AECOM (2010) employed a combination of aerial surveying and 108 ground survey plots, they claim that this method results in an accuracy of 92% with a 90% confidence interval.  42  Figure 4: Debris piles along the shore of Williston Reservoir (AECOM, 2010)  Figure 5: Debris accumulation around Williston Reservoir (AECOM,2010)  43  Figure 6: Location of Tsay Keh Village on Williston Reservoir  However, there is potential to make use of the woody debris from Williston Lake as fuel for a community bioenergy power plant in Tsay Keh Village. Such a power plant would at last allow the Tsay Keh Dene to see some benefit from the waste of so many trees.  44  The sections that follow outline the energy demand characteristics of Tsay Keh Village; describe a strategy for implementing a biomass CHP plant in the village; and, employ BEAT to assess the technical and economic feasibility of such plant in the community. 3.3.2. Feedstock Supply The proposed bioenergy system at Tsay Keh Village intends to rely on the 1.1 million cubic meters of accumulated debris on the shoreline of Williston Lake as its source of feedstock. The debris has been tested and deemed unsuitable for timber or pulp and paper purposes, so there is little concern about contested claims to this resource (AECOM Tecsult Inc., 2010). A recent analysis of the debris carried out by EconoTech labs in Delta (Econotech, 2012), BC showed that the debris has an energy content of approximately 19 MJ/kg on a dry basis which is the average for most softwood species (Biomass Trade Centres, 2008). A recent measurement of wood accumulated near the community indicated that the wood has approximately 14% moisture content; this is consistent with an estimate of moisture content in mountain pine beetle killed wood in northern BC of 13% (Kumar et al., 2008), based on local temperature and relative humidity. Although both energy density and moisture content are expected to vary (by location and season), these values are suitable for estimating the energy content of the feedstock supply. Taking the average density of common conifer species as 475 kg/m3 (Biomass Trade Centres, 2008) it is estimated that the debris on the shoreline of Williston lake is equivalent to approximately 450,000 t, and 8,550 TJ. At an estimated annual feedstock consumption of 5500 dry tonnes per year, the existing supply of beached debris is sufficient to meet the feedstock demands of a community bioenergy system for approximately 81 years. The proportion of debris that has decayed substantially has not  45  been quantified in the debris report. The cold, dry climate of the region are likely to have slowed the rotting process, and piling of round-wood helps to prevent decay. However, debris decay would decrease the calorific value of the feedstock supply, requiring higher rates of feedstock consumption. The mode of delivery of feedstock to the bioenergy plant remains an unanswered question, since the debris will need to be collected from the shores of the reservoir and transported to the plant, however, the Tsay Keh Dene have held the debris clearing contract with BCHydro a number of years, and have much of the equipment and expertise necessary to run the feedstock supply operation. Furthermore, the majority of the beached debris (65%) is located in the Finlay arm of the reservoir (AECOM Tecsult Inc., 2010) closest to Tsay Keh Village, reducing the lifetime average transport distance of the feedstock to the plant. In addition to the existing accumulation of debris, there is a small annual contribution of debris from shoreline erosion and tributary transport (approximately 15,000 m3); prevailing winds over the reservoir drive floating debris towards Tsay Keh Village, reducing the transport distance of newly recruited debris to the power plant.  In this analysis feedstock transport costs have been based on road transport. However, it is possible that feedstock will be delivered by barge, in which case the feedstock transport costs are expected to be lower due to higher volume of feedstock transported per trip, reduced fuel consumption and reduced maintenance costs for the transport equipment. 3.3.3. Village Demand Characteristics Tsay Keh Village is a community of 247 residents (AANDC, 2011), 59 houses, and 16 commercial and institutional buildings located on the northern shore of Williston Lake,  46  near the Finlay River (Government of British Columbia, 2011). The community is part of the BCHydro Non Integrated Service Areas and currently relies on diesel generators, which are operated by BCHydro under contract, for electricity. In 2011, the community’s peak electrical demand was 394 kW and their total annual consumption was approximately 1520 MWh/yr (Pulse Energy; Tsay Keh Village Generator Demand Data). Space and hot water heating is supplied primarily with propane in the commercial and institutional buildings, and with wood and propane (respectively) in individual households. In Tsay Keh Village individual households do not pay for the electricity they consume and the cost of supplying electricity to the entire community is paid for by the Band (with the exception of non-Band owned commercial and institutional buildings). However, individual households do pay for the propane that they consume for supplementary space heating, domestic hot water heating, cooking and clothes drying. At the moment, the Band pays for firewood to be supplied to Elders in the community. In general, individual houses are responsible for obtaining their own firewood for space heating, and they incur personal labour and fuel costs in order to do so.  3.3.4. Near-term Community Growth The community has plans to build three new large (1800 m2) institutional buildings in the near future, in addition to 12 new 111 m2 houses and 10 new 65 m2 houses. Based on incremental peak increases of 5 kWe for each normal house, 2.9 kWe for each small house, and 8 kWe per institutional building (Hawley et al., 2010), this substantial growth is expected to increase the peak electrical demand in the community to approximately  47  507 kW. Correspondingly, annual electrical consumption is anticipated to grow to approximately 1570 MWh/yr. Under the current energy supply scenario, commercial and institutional buildings are heated with propane, while private homes rely on wood burning furnaces for space heat. The present and near future community energy requirements are summarized in Table 4.  !"#$%#&'()*+,-(),.&/0#+ 3./04 5+6(+7(189:(!"#$%#&'4 5+6(+7(;.4#%.&,.4 <./-(189(=>.0?/$(@.?/&% A-BC D&&"/$(189(=>.0?/$(1+&4"?EF+& AGB>HI0C <./-(J$.,*0#,#*I(@.?/&% A-BC D&&"/$(J$.,*0#,#*I(1+&4"?EF+& AGB>HI0C <./-(;.4#%.&F/$(=>.0?/$(@.?/&% A-BC D&&"/$(;.4#%.&F/$(=>.0?/$1+&4"?EF+& AGB>HI0C  1"00.&*  2"*"0.  2010-2014  2014-2039  16  19  59  80  K(9&,0./4.  Table 4: Current and future building stock and demand conditions modeled in BEAT  406  801  97  1530  3030  98  394  507  29  1520  1570  3  466  577  24  1760  2180  24  * C&I = Commercial & Institutional  Two demand scenarios, Current and Future, form the basis of the techno-economic analysis carried out in BEAT to asses the economic viability of a wood fired biomass heat and power plant in Tsay Keh Village, and to determine the optimal system configuration.  48  Figure 7: Tsay Keh Village 2011 load duration curve  Figure 8: Duration of peak demand periods in 2011  The minute-level electrical demand in Tsay Keh Village was recorded by Pulse Energy and the data is plotted as Load Duration Curve (LDC) in Figure 7. It is evident from the  49  LDC that in 2011-12 the absolute demand peak of 394 kW occurred for only very brief intervals, and that the practical peak demand was approximately 80% of the absolute peak, or 315kW. Figure 8 further illustrates the very brief nature of the demand peaks in Tsay Keh Village; demands of more than 315kW persisted for less than 148 minutes per year. The community’s base load was approximately 30% of peak or 118kW. The shape of the LDC is relatively unchanged from year to year allowing insights from available data to be applied to plan the new power system appropriately.  There are no recorded data available for determining the thermal demand profile of the community. However, thermal energy demand (for space heating) is proportional to the outdoor temperature. Therefore, heating degree-day data, coupled with basic information regarding the area and insulation level of community buildings, can be used to estimate the thermal demand of the community (RETScreen, 2005). The heating LDC and heating Demand Duration Curve (DDC) for Tsay Keh Village are shown in Figure 9.  50  Figure 9: Heating load duration curve and demand duration curve for Tsay Keh Village  51  The heating DDC shows that a heating system sized to meet 75% of the peak thermal demand should be sufficient to meet the heating requirements for more than 90% of the year. It is important to note that the shapes of the LDC and DDC are a function of the heating-degree day data for Tsay Keh Village, and remain the same, regardless of how many buildings are included in the analysis; however, the level of demand is determined by building characteristics. 3.3.5. Implementation Strategy In most on-grid applications, woody-biomass CHP plants are sized to meet a given heating load, the heat produced by the CHP plant is the primary product delivered to customers via a DHN, and the electricity is generated as a co-product exported to the electricity grid. However, the capital cost to install DHN pipes is high; a recent feasibility study of a DHN in a remote BC community estimated that the cost of the distribution pipe network is between 300 and 500 $/m (Dubois, 2012) and therefore the economics of district heating are most favorable in high-density, high heat demand areas.4  In Tsay Keh Village the thermal demands are small and widely dispersed. The low area density of thermal demand of private homes could be met by a community-wide DHN. However, the capital cost of such an extensive network would be prohibitive5. Therefore, the CHP system in Tsay Keh Village will be sized to meet the electrical demand. The DHN is only economical for large demands located close to one another. This leads to a 4  When the heat network can be laid down at the same time as water and sewer pipes, the costs are much less. Nevertheless, if energy demand density is low, the pumping energy and energy lost in transit may make such a DHN uneconomical. 5 The additional cost of extending the DHN to all the houses in Tsay Keh Village was estimated to be approximately $2.5 Million. 52  limited DHN serving only the centralized commercial and institutional buildings, located nearest to the power plant. Figure 10 is a map of the community; the region in light pink is the area that will be served by the DHN, the remaining blocks all being private homes.  53  Figure 10: Map of Tsay Keh Village (KEM, 2011)  54  One of the biggest challenges in implementing biomass CHP in remote communities is the lack of systems available in the 400-700 kWe range. The lack of small systems means that plants are likely to be oversized for many remote communities. However, fuel switching from wood heat to electric heat in residences offers an opportunity to increase the load factor on larger systems, while also improving local air quality and reducing the substantial costs associated with wood furnaces; BCHydro estimates the cost of wood heat to be 0.21$/kWh (Hawley et al., 2010). Under the RCEP, and coupled with an EPA, fuel switching to electric heat has the additional benefit of increasing plant revenue from the sale of electricity.  The Band has made it clear that they do not intend to start charging individual households for the electricity they consume in the future, even if households decide to replace propane and wood appliances with electric appliances in order to reduce their household energy costs. Table 5 shows the change in energy costs before and after the RCEP takes effect; given that under the RCEP electric heat becomes cheaper than propane and wood heat, and that households will not have to pay for the electricity they use, it is feasible that electric storage heaters will be well received in many households, since they will decrease household energy expenditures, while providing a source of convenient, automatic and clean heat to the homes. In this study, the cost of installing electric storage heaters in households has been included in the project budget, therefore neither the capital cost nor the operating cost of fuel switching from wood heat to electric heat will be borne by individual households.  55  Table 5: Cost of energy before and after the RECP (Hawley, 2010)  .+/0'+1'!"#$%&'2$#34.!2 .+/0'+1'!"#$%&'2+/034.!2 56789:; 56789:; !"#$%&'$(" )'#*#" +,-. +,+/01 23#&4(" 5&67(8# +,9+,923#&4(" :66; +,<9 +,<9 1=>?@A#$%=%6=*%#77#;=&(%#=(%=46&#=%3(8=9.++=B:3C468%3=$68*?4#;=7#&=36?*#36"; !"#$%&'(&)#  !"#$%&'*+,$-#  The disadvantage of electric heating is that it greatly increases the peak electrical demand on the generator. However, this study proposes that the demand peak can be reduced by installing storage electric heaters, which draw electricity during periods of low demand, and release heat to the home according to thermostat settings (Dimplex North America Ltd., 2012), therefore supply of heat to a space and supply of electricity to the heater do not need to happen simultaneously, allowing the load on the generator to be smoothed  Demand (kW)  out.  Time of day (minutes)  Figure 11: 24 hour electrical demand for January 1 (blue) and June 1(red) in 2011.  56  Figure 11 shows the minute to minute electrical demand in Tsay Keh Village for January 1 and June 1, 2011; although the average demand is lower in summer than in winter, the same pattern of low electrical demand from midnight (0 minutes) until 6 am (360 minutes) followed by an increase over the course of the day, and a decline in demand from 7pm (1140 minutes) to midnight is observable in both seasons. The minute-tominute electrical demand is highly variable and it can change by as much as 40 kW in one minute during the winter. It is proposed that the electrical storage heaters will be operated during the off-peak periods of 12 am to 6 am and 7 pm to 12 am, daily, thereby increasing electrical consumption without driving peak demand above the capacity of the CHP system. Furthermore, the electric storage heaters will behave as dispatchable loads that can be intermittently interrupted during charging to compensate for sudden (but brief) increases in demand, and during sudden decreases in demand surplus electricity can be absorbed by the storage heaters. Remotely controlled storage electric heaters are an integral part of the demand management and power quality strategy for Tsay Keh Village. 3.3.6. Storage Electric Heaters Storage electric heaters consist of a heat storing medium, such as well-insulated bricks, electric heating coils, a circulating fan and a control system, as shown in Figure 12. The storage medium heats up when the heater coils draw electricity, and the electrical energy is stored as heat in the well-insulated thermal mass. When heat is required in the room (as determined by the thermostat) a fan draws air from the room over the heat storage medium, raising the air temperature, and circulates it back into the room to heat the  57  space. Storage electric heaters also provide a continuous source of radiant heating in a space.  !"#$%&'('%")*&+,+  4*5"+)6)+*(- %)5.#(*(# 1$*+7'+). '(&$#26(& 897:"&0 ("#$%), #.#$5-  !"#$%&'()(*+),,-+&.($&,,#/0). (&/*'($*12(#"#)()'$#32*$#/  Figure 12: Cut-away view of a storage electric heater. Source: Reid & Kurtz, 2012  The key distinction between storage electric heaters and radiant electric heaters is that radiant electric heaters only provide heat to a room while they are drawing electricity, while storage electric heaters provide heat to a room even when they are not drawing any electricity from the grid. The advantage of storage electric heaters is that they can provide continuous heating, without a continuous electrical demand. Storage electric heaters are designed to charge for 8 to 12 hours during off-peak electrical demand periods and provide heat for the full 24 hours in a day (Dimplex, 2012).  The charging of storage electric heaters can be controlled in a number of ways; a time-ofday pricing signal on the power line to the house can cause the electric heating elements to turn on or off, charging may be controlled by a timer, or alternatively heaters may also  58  be controlled wirelessly through a radio signal or internet connection (Reid & Kurtz, 2012).  Electric utilities in Europe have been employing storage electric heaters to reduce the peak electricity demand for more than 40 years (Nova Scotia Power, 2012) and it is estimated that in Great Britain, two million homes, or 8% of all households rely on offpeak electric storage heating to meet their primary space heating requirements (Owen et al, 2012).  3.4. Technology Recommendation for Tsay Keh Village Consideration of the benefits and risks of the three technology choices available for remote community bioenergy systems lead to the conclusion that a thermal oil boiler coupled with an ORC is the best option for remote community electrification with biomass. This conclusion is based on the technology’s proven track record in Europe, the limited operator requirements, low operating costs, and high degree of reliability and safety. Therefore, the analysis carried out in BEAT is based on the sizing and prices of thermal oil boilers and ORC turbogenerators from reputable vendors with strong track records.  3.5. Optimizing system configuration Selecting the electrical capacity of the CHP system requires consideration of many factors, including community growth over the lifetime of the plant, uncertainty in predicting electrical demand, ability to manage demands, and ability of the system to load  59  follow. Although there is not a large size range of CHP plants available to choose from, the advantages and disadvantages of the options that are available must be evaluated.  In assessing the feasibility of biomass CHP system in Tsay Keh Village, three electrical capacities were considered. The smallest system has a net electric output of approximately 720 kW, the medium system a net output of 828 kW and the largest system under consideration a net output of 1330 kW electric (Pratt & Whitney Power Systems, Inc., 2012; VAS Energy Systems GmbH, 2012b). Each of these three systems is matched to a different demand scenario in Tsay Keh Village; scenarios that represent varying capacities of installed residential electric heating, and the presence or absence of demand management. Choosing the appropriate electrical capacity involves the consideration of many tradeoffs. Some of these tradeoffs are summarized in Table 6. Table 6: Tradeoffs of base demand and peak demand sizing of remote community power systems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onsideration of these tradeoffs leads to the conclusion that the optimized system configuration is one in which the output of the generator is capable of meeting the peak  60  demand, but also one in which the peak demand does not depart wildly from the average demand of the community. Such a system requires the design of not only the electricity supply, but also some control over the electricity demand.  61  Table 7: Summary of current Tsay Keh Village building stock scenarios modeled in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Table 8: Results of Part A scenario analysis in BEAT  )H.'+E,1%EI%"3, 1#&'(J,2E&:5  K#%&,L, )*#"M,@(%J,N%"+(&  K#%&,O%P,@(%J,N%"+(&, =#%"G#J  !"#  !"#  !"#  !"#  L  .'$  /(//)*$+  $0  %'(+1)*$+  $(21  3'4  $('2  Q  .'$  -($')*$+  2(40  %-(-.)*$2  $(2+  '-  $(,  X  -,,$  -($,)*$.  -20  %2(21)*$+  $('+  1  $(,+  '-  Y  .'$  /(14)*$+ ',($0  +(+4)*$+  $(',  +  $(+4  R  .'$  1('.)*$+ '4($0  .(4.)*$+  $('-  +  Z  .'$  1('.)*$+ ',($0  .($+)*$+  $(''  [  .'$  1('.)*$+ '$($0  +('-)*$+  \  .'$  1('.)*$+ -1($0  ]  .'$  LW  7G%$D#,'$,A1B,(0, 7%:G,N*(U:,0&(;, !+%+.:,V.(  @'0#C;#,?9#&%D#,7(:+, (0,)*#"+&'"'+E,2<F34G5  !"#  QR,K#%&,A1B,(0, )$#&DE,7(:+:  1?@A6,A1B,2<5  !"#  QR,K#%&,A1B,(0,4((J, T#%C$D,1%E;#$+:  8==,2>5  $  QR,K#%&,A1B,(0,7S8, 1&(/%$#,1%E;#$+:  6(+%*,7%/'+%*, 8$9#:+;#$+,2<5  !"#  QR,K#%&,A1B,(0, )*#"+&'"'+E,1%E;#$+:  )*#"+&'"%*,-.+/.+,(0, 1*%$+,2345  W  K#%&,L,N##J:+("3, 7($:.;/C($,2+5  !"#$%&'(  =#:.*+:,(0,!"#$%&'(:,O(J#*#J,'$,^)?6, 1%&+,?,_,7.&&#$+,^.'*J'$D,!+("3,^%:':  $  %&'($)*$+  %&,(,)*$+  %&+(')*$+  %&-(')*$.  $  33'4 '-$$  %&'(/)*$+  $  %&+(')*$+  %&1($)*$+  %&-(.)*$4  33'4 '+$$  %&,(4)*$+  $  $  %&,(4)*$+  &/($)*$+  +'$$  %&.(')*$+  $  $  %&.(')*$+  %&-(2)*$4  ''  4/$$  %&.(-)*$+  $  %&-(+)*$+  %&/(.)*$+  &1(4)*$+  $(++  '-  4/$$  %&.(')*$+  $  %&-(+)*$+  %&/(/)*$+  &-($)*$.  +  $(++  '-  4/$$  %&.(')*$+  $  %&-(+)*$+  %&/(/)*$+  &1(/)*$+  $('2  .  $(++  '-  4/$$  %&.(')*$+  $  %&-(+)*$+  %&/(/)*$+  &/(1)*$+  4(.$)*$+  $('2  .  $(++  '-  4/$$  %&.(')*$+  $  %&-(+)*$+  %&/(/)*$+  &/(2)*$+  1('.)*$+ '2($0  .(-4)*$+  $(''  +  $(+4  ''  4/$$  %&.(')*$+  $  %&-(+)*$+  %&/(/)*$+  &1(1)*$+  .'$  1('.)*$+ -+($0  4(12)*$+  $(',  /  $(+4  ''  4/$$  %&.(-)*$+  $  %&-(+)*$+  %&/(/)*$+  &/(.)*$+  LL  .'$  1('.)*$+ --($0  2(4$)*$+  $('2  --  $(+4  ''  4/$$  %&.(-)*$+  $  %&-(+)*$+  %&/(.)*$+  &.(,)*$+  LQ  .'$  1('.)*$+  4(.4)*$+  $(''  1  $(+4  ''  4/$$  %&.(-)*$+  $  %&-(+)*$+  %&/(.)*$+  &/(+)*$+  -40  Table 7 shows the electrical supply and demand sizing scenarios considered for the current building stock in Tsay Keh Village and Table 8 summarizes the results of the analysis completed in BEAT; all scenarios in both tables incorporate an annual energy 63  consumption growth factor of 1.5% for both heat and electricity. BCHydro uses a growth factor of 1 to 3% in their planning documents (Hawley, 2010; BCHydro, 2012a). Scenario 0 defines the energy expenditures that the Tsay Keh Dene Band will face if they do not choose to invest in a bioenergy plant, and continue to rely on diesel electricity generation, propane and wood heating for the next 25 years. Scenarios 1 and 2 examine the feasibility of the system without any additional electric heating installed, and scenario 2 looks at the feasibility of extending the DHN to all homes in the community; since the electric heaters would act as a dump load in the winter time, a 40 kW nominal electric output from a diesel generator has been included to provide load following capacity in scenarios 1 and 2. Neither option looks particularly lucrative, although the revenue from the DHN is enough to net a positive return on investment in the second case. It is clear from these two cases that the electricity sales are too low to justify the capital cost of a system that is greatly oversized for the existing demand.  Scenario 3 models the effect of installing electric storage heaters in all of the existing homes; the heaters are sized to meet 100% of the peak heat demand, and the CHP system is sized to meet 100% of the peak electrical demand, including that of the storage heaters. A 40 kW dump load has been included for load following purposes, and it is operated during the non-heating season. Sizing the CHP system this way leads to the selection of a plant with an electrical capacity of 1330 kW; such a system is oversized, even for the new demand, and leads to low load factors and a low IRR.  64  In scenario 4 the electric storage heaters are sized to meet 75% of the peak residential heat demand, and it is assumed that 20% of the combined electrical peak can be shaved through demand management, thus allowing for the selection of a smaller CHP system. This configuration produces a modest, but acceptable, IRR (23%), a positive NPV 6  ($6.6M) and a cost of electricity equal to 0.23$/kW, which is less than the nominal  BCHydro EPA price of 0.31$/kWh (based on the cost of producing electricity with diesel in 2008 (Hawley et al., 2010)). 3.5.1. Feedstock Price Sensitivity Analysis Scenarios 1 through 4 assume that the feedstock for the plant is supplied as wood chips by an external contractor at a rate of $25/t and 25% moisture content. However in reality it is more likely that the feedstock supply operation will be integrated into the plant operation; therefore the cost of feedstock is a variable that will change depending on the distance that must be traveled to collect the wood. Scenarios 5 through 10 examine the impact of integrating the feedstock supply operation (collection, transport, chipping) with the plant, and the lifetime average transport distance of the feedstock on plant economics. At a salvage stumpage rate of 0.25$/m3, an average transport distance of 25 km, and using a mobile diesel chipper, the average cost of feedstock is approximately $20/t. Given that 65% of the debris is collected in the Finlay arm of the Williston Reservoir, a 25 km average feedstock transport distance is a reasonable estimate. However, even at a lifetime average transport distance of 80 km plant economics are favorable.  6  Discount rate used in NPV calculation is the assumed cost of capital, 4.5% 65  Figure 13 illustrates the impact of feedstock transport distance and type of chipper motor on plant economics. By using an electric chipper, the plant may save approximately $2/t in diesel costs. However, the overwhelming cost of the feedstock is the fuel used for transportation. There are benefits and costs to both chipping at the point of collection and chipping at the bioenergy plant site. Transporting whole logs to a stationary electric chipper allows for storage of the feedstock as round wood (a better choice for long term storage) while chipping at the collection point with a diesel chipper may allow for more efficient loading of the transport vehicle. It is recommended that further analysis of the feedstock supply operation be carried out before a decision on the choice of chipper is made. &!"  ,-./012/"342552678"9..:;0</=" >?55-@"3<;0"AB0" C2.;.-"342552678"9..:;0</=" >?55-@"3<;08"AB0"  !""#$%&'()*++,-(.%#$(/01$2(  %!"  $!"  #!"  !" !"  #!"  $!"  %!"  &!"  '!"  (!"  )!"  *!"  +!"  !""3#$&'(4567#+%5$(89#$67&"(/':2( Figure 13: Impact of feedstock transport distance and chipper motor on supply cost of feedstock  66  Since Tsay Keh Village is expected to grow substantially in the near future, a second series of scenarios, described in Table 9, were modeled in order to confirm that the plant is sized appropriately for the planned community growth. Although the growth is planned for the near term, it could be unwise to depend on community growth (and therefore demand growth) for the financial viability of the plant. Table 9: Summary of future Tsay Keh Village building stock scenarios modeled in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Table 10: Results of Part B scenario analysis in BEAT  QR,K#%&,A1B,(0, )*#"+&'"'+E,1%E;#$+:  QR,K#%&,A1B,(0,7S8, 1&(/%$#,1%E;#$+:  QR,K#%&,A1B,(0,4((J, T#%C$D,1%E;#$+:  QR,K#%&,A1B,(0,)$#&DE, 7(:+:  7G%$D#,'$,A1B,(0, 7%:G,N*(U:,0&(;, !+%+.:,V.( $  %&/(/*+$,  $  $  %&/(/*+$,  &)(/*+$-  /1$$  %&/($*+$,  $  %&'($*+$,  %&)($*+$-  &)(-*+$-  )'  ,,$$  %&-(/*+$,  $  %&'($*+$,  %&4(/*+$,  &)(4*+$-  $(-1  )'  ,,$$  %&-(/*+$,  $  %&'($*+$,  %&4(/*+$,  &)(4*+$-  $(-1  )'  ,,$$  %&-(/*+$,  $  %&'($*+$,  %&4(/*+$,  &)(-*+$-  !"#  !"#  !"#  !"#  )($-*+$- '/($0  )($$*+$-  $(''  1  $(2)  33'1 -)$$  /'/  4(-/*+$, .2($0  )($-*+$-  $(')  1  $(,1  ''  LY  -'$  4(..*+$, 2'($0  )(''*+$-  $('  2  $(-1  LZ  -'$  4(..*+$, 22($0  )(',*+$-  $('  2  L[  -'$  4(..*+$, '.($0  )($1*+$-  $('  ,  6(+%*,7%/'+%*, 8$9#:+;#$+,2<5  !"#  )*#"+&'"%*,-.+/.+,(0, 1*%$+,2345  !"#  !"#$%&'(  K#%&,O%P,?(%J,N%"+(&, =#%"G#J  %&)(,*+$-  K#%&,L, )*#"M,?(%J,N%"+(&  %&-(-*+$,  )H.'+E,1%EI%"3,1#&'(J, 2E&:5  %&,(,*+$,  ?'0#C;#,@9#&%D#,7(:+, (0,)*#"+&'"'+E,2<F34G5  %&'()*+$,  1?@A6,A1B,2<5  $  8==,2>5  K#%&,L,N##J:+("3, 7($:.;/C($,2+5  =#:.*+:,(0,!"#$%&'(:,O(J#*#J,'$,\)@6, 1%&+,\,],N.+.&#,\.'*J'$D,!+("3,\%:':  LW  !"#  $  LX  )..$  LR  The results of this second set of scenarios, given in Table 10, indicate that the 720 kW CHP system selected in Part A of the analysis is suitable for the growth in demand, provided that a greater degree of peak shaving (30%) can be achieved. Scenario 16 indicates that an IRR of approximately 42% is achievable if the community does grow as planned in the near term.  Based on the preceding analysis, the optimal strategy for meeting the electrical and thermal energy requirements of Tsay Keh Village is to meet 100% of the heat demand of the commercial and institutional buildings with a DHN; heat private homes with electric storage heaters sized to meet 75% of the peak thermal demand, while relying on wood  68  heating for the extremely cold periods; and to meet 100% of the community’s electrical demand with the electrical output of the CHP system. 3.5.2. Impact of GHG Offset Revenue on Plant Economics An often-cited benefit of wood based bioenergy is that it is carbon neutral; meaning that the combustion of wood releases carbon stored by trees during their lifetime resulting in a net-zero change in atmospheric carbon dioxide. There are, however, still carbon emissions associated with the generation of power and/or heat from biomass. There are emissions from land use changes if the wood is harvested from a standing forest, and there are emissions associated with replanting and fertilizing the re-growth. There are also emissions from the harvesting and transport of feedstock to the site of the bioenergy plant, and emissions from any feedstock processing equipment that consume fossil fuels. In addition, the biomass firing system will release a small amount of N2O and CH4, which are potent greenhouse gases (Wihersaari, 2005). Despite the feedstock supply chain and bioenergy system GHG emissions, the net reduction in GHG emissions achieved by switching to biomass from fossil fuels for heat and power in remote communities is significant, relative to the community’s baseline fossil fuel emissions.  Table 11 summarizes the changes GHG emission that can be expected as a result of implementing a bioenergy CHP system in Tsay Keh Village. The most significant change in overall GHG emissions is the combustion of reservoir debris under controlled conditions, rather than in open piles. If the Tsay Keh Village biomass CHP plant can obtain recognition for the GHG offsets achieved from the avoided use of diesel and propane, and more significantly, for the avoided climate forcing effect of black carbon  69  emissions from open burning of the biomass that they consume in the power plant the sale of the offset credits could become a substantial source of additional revenue for the plant.  Comparison of scenarios 16 and 17 illustrates the impact of the sale of GHG offset credits for $13/t, to an institution such as Pacific Carbon Trust or Offsetters, on the financial performance of the plant. The calculation of revenue from GHG emission offsets only includes reductions in CO2, CH4 and nitrogen oxides (converted to CO2 equivalent on a 100 year time horizon), since there is no recognized protocol for black carbon emissions accounting at this time. The two scenarios are operationally identical in terms of feedstock transportation distance, consumption and cost, and electricity and heat sold. However, when revenue from the sale of GHG offsets is included in the plant cash flow, the IRR of the plant increases from 42% to 44%. Revenue from the sale of GHG offsets is estimated to be $82,000 in the first year (of the future building stock scenario), increasing in proportion to the electricity and heat consumed by the community in subsequent years7.  7  The total revenue from the sale of GHG offsets is less than the total offsets multiplied by the sale price of $13/t because under the RCEP, BCHydro takes credit for the GHG offsets related to the avoided use of diesel for electricity generation.  70  Table 11: GHG emissions reduction achieved through the implementation of the biomass CHP plant in Tsay Keh Village.  GHG Offset Sources Avoided Diesel Consumption Avoided Propane Consumption Avoided Diesel Transport Emissions Avoided Propane Transport Emissions Avoided Emissions from Open Pile Burning of Biomass GHG Contributions Emissions from Boiler Emissions from Feedstock Transport & Processing Net Change in GHG Emissions  (t CO2e/yr) 1100 800 27 23 5,800 (t CO2e/yr) 70 72 -7,608  3.6. Economic Risk Analysis Uncertainty about project and capital costs, actual operating costs, and revenues can never be entirely eliminated. Therefore it is important to evaluate the economic viability of the project under less than ideal conditions. The economic risk analysis carried out in this section is concerned with the impact of higher capital costs and lower than predicted revenues from the sale of heat and electricity. Although a great deal of effort has gone into estimating the total cost of the community bioenergy system, there is always potential for cost overruns; therefore, scenario 10 examines the effect of a capital cost increase of $1 million or loss of the $1 million dollars in funding that has been provisionally approved for this project. According to the results from BEAT, a $1 million cost overrun will decrease the IRR of the plant by 8% (to 16%). However, provided that revenues and operating costs are as expected, the plant would still be economically viable.  Another possible economic risk is that for the first one or two years, the plant may not sell as much heat or power as anticipated, due to difficulties in operation, demand management or unplanned shutdowns. This possibility was modeled by introducing a 71  factor which reduced heat and power sales by 50% and increased operating costs by 50% for the first two years of plant operation, for both the current and future building stock cases in scenarios 12 and 18. Under current building stock conditions, a two-year learning period would decrease the overall project IRR from 24% to 15%; and under future building stock conditions the IRR would be reduced from 42% to 23%. It is hoped that the choice of a robust technology, good system design and a strong training program will prevent economic losses due to learning. However, it is informative to model the economic impact of such a scenario and reassuring to know that even if the plant underperforms initially, the venture can still be successful in the long term.  A “worst case” economic risk scenario was modeled in scenario 11 by combing a $1 Million cost overrun with a two year learning period; this set of circumstance has the effect of reducing the IRR to only 11%.  3.7. Recommendations For Tsay Keh Village Results from the analysis carried out in BEAT indicated that the best system for Tsay Keh Village has a 720 kW net electric output. A system of this size will be sufficient for meeting the current electrical demand of the community, and when combined with modest demand management, should be sufficient to meet demand in the near future when 22 new homes and 3 new institutional buildings are added to the current demand. The estimated capital cost of such a system is detailed in Table 15 in Appendix A.  However, the current level of electricity consumption in the community will not result in enough revenue from electricity sales to pay for the cost of the bioenergy plant. It is clear  72  that a biomass CHP plant in Tsay Keh Village is only viable if the community converts residential space heating from wood burning stoves to electric storage heating; therefore it is recommended that storage heaters of 5 to 6 kW capacity be installed in all homes. Under the current building stock condition, such a system is predicted to have an IRR of 24%. Under the future building stock condition, this system could generate up to 42%. These figures are based on a feedstock cost of 20$/t resulting from a transportation distance of 25 km, and the use of a diesel chipper for feedstock preparation. The economic performance of the plant would improve if feedstock costs turned out to be lower.  The “best case” scenario for the proposed system in Tsay Keh Village is an IRR of 44%, which includes revenue from the sale of GHG emission offsets in the future building stock scenario. The “worst case” scenario is one that combines a one million dollar cost over-run with a two year learning period in which operating costs are increased, revenue is decreased, and the planned community growth does not materialize. The worst case scenario is predicted to result in an IRR of only 11%.  Another important point to consider is the cost of not investing in a renewable energy system at Tsay Keh Village. Although the annual cost of electricity will be reduced significantly by the RCEP, regardless of whether the bioenergy system is implemented, the Band will make considerable savings on the cost of propane for heating buildings on the district heating network and the cost residential heating with wood furnaces.  73  In order to understand the net benefits or costs of the proposed CHP system, the 25 year Net Present Values (NPV) of the costs of electricity and heat are estimated based on both the current and future building stock conditions for each of the scenarios modeled. The NPV of bioenergy plant cash flow, NPV of payments to BCHydro and the NPV of propane and wood expenditures are provided for comparison.  The results in Table 8 and Table 10 indicate that the lifetime savings on energy costs achieved by installing a bioenergy plant at Tsay Keh Village are quite considerable. Under the current building stock scenario, if the band did not invest in a bioenergy CHP plant, the combined NPV of the Band’s energy costs for a 25 year period is $1.15 Billion (Scenario 0); however, if the Band were to invest in their own power plant, their 25-year NPV of total energy costs would be reduced by 23% to $8.79 Million. When the NPV the bioenergy plant cash flow is considered, the net change in the 25 year NPV of cash flow related to energy consumption in Tsay Keh Village is a savings of $2.73 Million and a revenue of $7.15 Million. Therefore, by investing in a community-owned power plant, the Tsay Keh Dene could recognize a net benefit $9.9 Million relative to the status quo.  There is some uncertainty around the accounting of revenue from the district heating network; if the utility bills for the commercial and institutional buildings on the DHN are paid for by the Band, then the Band will not recognize any net savings. However, if the bills are paid by a third party, such as a service provider in the community, there will be net revenue to the Band from the sale of heat on the DHN (via the Band owned power plant).  74  Although the lifetime of the plant, as modeled in BEAT is 25 years, the plant can be expected to last longer than this, especially since the supply of feedstock can sustain a further 56 years of operation. However, after 12 years the electrical consumption of the community is likely to exceeded the production of the bioenergy plant, and the community would need to rely on supplemental sources of power, or demand management to meet their energy needs. In order to avoid exceeding the electrical capacity of the CHP plant, it is recommended that the community expands the DHN incrementally, to reduce the demand for electric heating as other electrical demands increase. The DHN should be designed with this potential future expansion in mind. The electrical heating demand could also be reduced by building envelope upgrades in existing homes.  Finally, the impact of removing a large amount of woody debris from the shoreline of Williston reservoir on the severity of dust storms at Tsay Keh Village must be investigated (AECOM Tecsult Inc., 2010). BCHydro carries out on-going dust control activities at Williston reservoir, and it is possible that the debris clearing, dust control and feedstock harvesting operations could be coordinated. Dust management on Williston reservoir is an area which requires further research and field studies.  75  Table 12: Decision making framework for the community energy options at Tsay Keh Village; fulfillment of community objectives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Table 12: Continued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ased on the findings of this research, it is recommended that the Tsay Keh Dene apply the decision making framework outlined in Table 12 and consider the risks presented in Table 13 when deciding whether or not to proceed with investing in a bioenergy plant for their community. The first seven objectives listed are identified as priorities in the 2010 BCHydro Remote Community Electricity Plan for the Community of Tsay Keh (Hawley et al., 2010). The last two are based on discussion with community members with respect to a bioenergy plant at Tsay Keh Village.  77  Table 13: Decision making framework for community energy options at Tsay Keh Village; consideration of risks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According to this assessment, the choice to construct a bioenergy plant in Tsay Keh Village is both high in benefits, and carries a higher risk than the alternatives. It is recommended that the community consider the objectives and options in the tables carefully, and assign their own scores. Weighting the different objectives depending on their relative priority, and risks according to community tolerance of each risk, and summing the weights could also provide more clarity to the decision analysis (Keeney & McDaniels, 1992). The decision to invest in a community bioenergy plant at Tsay Keh Village is one that must not be taken lightly; it has been the objective of this work to provide clear, and unbiased information to support a well-informed decision by the community.  4. Discussion 4.1. The Decision Making Framework in Context The stages of research and analysis carried out in this thesis can be generalized into a broader framework for decision making on RCE with woody biomass in the context of British Columbia. The context for the decision making process comprises regulatory, market and community aspects and is summarized in Table 14. In the regulatory context, the BC Utilities Commission approves the rates that BCHydro can charge for electricity under the RCEP, and approves the purchase price of electricity in the EPA between the community and BCHydro. Provincial safety legislation limits the range of available technologies, and determines the operator qualifications for each. Market factors determine the range of systems available, the cost of alternative energy sources and influence feedstock costs. Finally, the community specific context includes objectives  79  with respect to energy supply and economic development, demand characteristics for both electrical and thermal energy, and the distribution of energy costs in the community. Table 14: Context of the decision making framework for RCE using woody biomass in BC !"#$%&'()*+ •  !"#$%&'%()#"*++'))'*,#-!"$".# /001*2()#1/3()#4*1#(&(531'5'36#7,8(1#39(# :";<## •  !"$"#/001*2()#;<=#>(3?((,# 5*++7,'36#@#!"A681*# •  <1*2',5'/&#B/4(36#B3/,8/18)#=53# 8(3(1+',()#*0(1/3*1#C7/&'D5/%*,)#  ,&)-"'+ •  •  •   E'53/3()#39(#1/,F(#*4#/2/'&/>&(# 3(59,*&*F'()# E(+/,8#4*1#4((8)3*5G#/,8H*1#?**8# 01*8753)#8'53/3()#4((8)3*5G#01'5()# E'53/3()#5*)3#*4#/&3(1,/%2(#(,(1F6# )*715()#4*1#9(/3#/,8#0*?(1#',#39(# 5*++7,'36#  .(//$01'*+ •  •  •  •   ;&(531'5/&#8(+/,8#59/1/53(1')%5)# I9(1+/&#(,(1F6#8(+/,8# 59/1/53(1')%5)# J>K(5%2()#@#1')G#3*&(1/,5(#0/1%/&&6# 8(3(1+',()#)6)3(+#360(#@#)'L',FM# E')31'>7%*,#*4#(,(1F6#5*)3)# 8(3(1+',()#01*0(,)'36#3*?/18)#47(&# )?'359',F#  A flow diagram of the decision making framework is shown in Figure 14. First, a choice of the technology type based on the assessment criteria outlined in Table 3 defines the range of system sizes and capital costs for that particular technology. In addition to quantifying the bioenergy system capacities and costs, the potential supply of feedstock and the community energy consumption must also be quantified. These three features of the RCE scenario provide the basis for the system sizing and techno-economic feasibility assessment.  If a technically and economically feasible bioenergy plant configuration is identified for the community, an economic risk assessment is carried out to determine the economic viability under less-than-ideal operating conditions. The results of the techno-economic feasibility and risk assessments inform a community decision, based on community objectives, as to whether or not they want to invest in a biomass combined heat and power plant.  80  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igure 14: Flow diagram of decision making framework for RCE using woody biomass  4.2. Avenues for Further Investigation There are varying characterizations of the risks associated with remote community electrification using renewable energy technologies within the peer-reviewed literature. The renewable and sustainable energy literature focuses on case studies of communities, and tends to omit or gloss over a discussion of risks in favour of a focus on economic and social benefits (Mamphweli & Meyer, 2009; Mukhopadhyay, 2004; Yablecki et al., 2011). However, papers that provide lessons learned from unsuccessful remote community electrification projects also exist; their primary finding is that Social & Institutional Risks are often only identified after the project has failed in one way or another (Cherni et al., 2007; Fischer et al., 2005). In addition, there are a number of  81  authors who offer frameworks for decision making and project standardization which can be viewed as risk management strategies (Cherni et al., 2007; Kumar et al., 2009). In the science and engineering literature, the focus is on the design and performance of the technology, and risks are rarely explicitly addressed. It appears that there is a gap between the technology implementation case study literature and the science and engineering literature which explains how the technologies came to be installed in the case communities in the first place. What criteria were used in the feasibility assessment? What was the decision making process? Who were the decision makers? Perhaps a study in this area would offer more insights into how to manage risks proactively.  The completion of the process of remote community electrification with biomass; from feasibility study to the sale of electricity to BCHydro is the only true means of confirming the validity of the claims made in this thesis. The process of acquiring the necessary permissions, designing, building, commissioning and successfully operating a bioenergy plant in a remote community will generate more knowledge than any model based study ever will.  An additional avenue for investigation is the modeling of a variety of different communities and bioenergy systems in an effort to develop some general guidelines for community bioenergy in BC. The circumstances at Tsay Keh Village are quite unique. Therefore the recommendations of this thesis cannot be generalized to other communities. However, the design of BEAT is flexible enough to accommodate a wide range of heat and power demand scenarios, feedstock supply conditions and market  82  values for energy. Given that there are 29 to 39 additional remote communities in BC that could benefit from participating in the RCEP with BCHydro, a study aimed at identifying communities where bioenergy is a viable alternative would be a worthwhile avenue of investigation. Furthermore, modeling the economic viability of a much larger, gridconnected bioenergy plant is also possible in BEAT and would make an interesting contribution to understanding the economics of bioenergy in BC. 4.3. Comments on Strengths & Weaknesses of the Thesis The design of a model is an exercise in compromise between adding detail to better understand sensitivities, and creating a model that is overwhelmingly complex, opaque, and difficult to verify or troubleshoot. However, models that over-simplify or make too many assumptions can be blind to factors that may have a significant effect on outcomes, and like all human enterprises, models are equipped with the biases of their creators.  All models are only approximations of real systems, and, as such, they represent our best estimates of real world outcomes. It is important that engineers, analysts and decision makers regard models as tools for aiding decision-making (Craig, Gadgil, & Koomey, 2002) and not as the decision making agent. Models allow us to compare possible scenarios, and determine the scale of uncertainty in a situation relative to the decision criteria. A hazard of models is that results can be mistaken for facts, and rather than using a model as a tool to explore potential outcomes, individuals may anchor on one particular result, rather than exploring the range of possible outcomes inherent in a situation. In decision-making, it is crucial to consider the range of possible consequences of a choice,  83  and then decide whether or not that range is acceptable, based on the benefits and risks associated with that decision.  The primary strength of this thesis is that it is informed by the participation of the Tsay Keh Dene, and their interest in pursuing a bioenergy system for their community. The weight of responsibility for providing accurate information to this pursuit, and the implications of decisions that this thesis may inform have driven the author to repeatedly question and verify assumptions, and to pursue implementable solutions. The second strength of this thesis is that it is based on a model created by the author, and the foremost objectives in the design of the model were simplicity, modularity, transparency and flexibility. These qualities have enabled the exploration of many subtleties and have allowed the model to be configured to incorporate a great deal of community-specific detail. 4.4. Conclusion The objective of this thesis was to develop a decision making framework for RCE using woody biomass in BC. In developing this framework an integrated assessment of remote community electrification in British Columbia using woody biomass was carried out; the assessment began by examining the benefits, risks and risk management strategies of RCE with biomass, followed by a critical examination of the various technology options available for communities to choose from, and finally, the techno-economic feasibility of RCE with biomass was explored for the remote community of Tsay Keh Village. This multi-faceted assessment examines RCE with biomass through several lenses, identifies  84  objectives which may not be made explicit otherwise, and informs a structured decision making approach to RCE with biomass.  In general, the following conclusions regarding RCE with woody biomass can be made: •  Feedstock reliability is of the utmost importance. The margins of economic viability for community power plants are narrow, and escalating feedstock costs could cause the plant to lose money.  •  Feedstock sustainability is of equal importance to reliability; the environmental impact of the feedstock supply must be carefully considered and discussed openly.  •  Technical suitability is critical. The additional cost of overcoming technical difficulties can be extreme in a remote location; high operating costs will cause the plant to lose money, and technical problems will result in an unreliable supply of heat and power.  •  The implementation of renewable energy systems in remote locations requires a different philosophy than renewable integration with regional electricity grids.  •  Air quality impacts are an important criteria and should be given weight in the decision making process.  •  Revenue from GHG emission reduction credits can provide a significant increase in the bottom-line of the bioenergy plant economics, and the province should continues to support GHG accountability.  The implementation of biomass combined heat and power in remote communities is challenged by the fact that remote communities consume relatively little electricity while biomass to electricity technologies are typically sized for larger loads (in order to take advantage of the economies of scale in manufacturing and installation), and can only be paid for with high capacity utilization (high load factor). Smaller, less costly biomass CHP systems may be available for less proven technologies, however the risk inherent in implementing an emerging technology in a remote community is very high.  85  In order for RCE with woody biomass to displace the use of diesel generators in remote communities, the barrier of high system capital cost must be overcome through sufficient electricity sales to the community (or through a higher EPA price than either BCHydro or the BCUC is able or willing to approve8); therefore, there is a minimum annual electricity consumption threshold below which RCE with woody biomass is not economically viable. Furthermore, this electricity must be lower in cost to produce than the BCHydro EPA price, thus a suitably sized community must also have a reliable and inexpensive source of feedstock which is large enough to meet their substantial electricity demand. The case of Tsay Keh Village serves to illustrate this point, for despite the fact that the community has a surplus supply of inexpensive biomass, their current annual energy consumption is too low to support the high capital cost of a biomass power plant. Matching community demand to available local resources and proven technologies is the biggest challenge facing RCE with woody biomass in British Columbia.  5. Tsay Keh Village Case Study: Epilogue In August and September of 2012, the Tsay Keh Dene began EPA negotiations with BCHydro as the first stage in pursuing a biomass power plant for their community. Meetings with the BCHydro Non-Integrated Service Areas team, which the author participated in, shed light on additional project risks which were not considered in the techno-economic analyses carried out as part of this thesis. These risks would all result in  8  The BCUC enforces a limit of 15% project IRR on BCHydro EPAs with independent power producers. This level of return is unacceptable for RCE with woody biomass, given the level of economic risk involved. 86  lower-than-anticipated electricity consumption, and therefore lower-than-expected plant revenues. The risks are •  Falling community population; BCHydro has witnessed negative population growth rates and decreasing electricity consumption in other off-grid communities.  •  Lower than expected fuel switching due to recently approved Zone II electricity rate increases that put the cost of electric heating nearly on par with propane heat.  •  Stepped rate or “conservation” pricing which discourages the consumption of electricity for space heating purposes.  •  Lower than expected propane prices.  Further analysis was carried out to assess the impact of falling demand and increasing electricity prices on the project feasibility. The results indicated that a community owned biomass power plant in Tsay Keh Village is a high risk, low reward investment for the community. The overall economic benefit to the community is eroded by the stepped rate pricing, and there is a very real risk that the community might never break even on such a project. In light of this additional information and analysis, the author cannot recommend that the community pursue such an investment.  The conservation rate pricing is particularly difficult to overcome in the case of a bioenergy plant because it discourages the utilization of available, off-peak generating capacity through the use of storage electric heaters. The marginal cost of generating with woody biomass in Tsay Keh Village is low because feedstock is inexpensive, while the capital cost of a biomass power plant is high. The stepped rate pricing unnecessarily  87  penalizes utilization of plant capacity, and makes capital cost recovery next to impossible.  BCHydro stands to benefit from load-following biomass combined heat and power plants in remote communities through reduced exposure to increasing diesel prices, reduced exposure to large electric heating loads through the implementation of a district heating network, reduced investment in generating capacity, and lower operation and maintenance costs of their diesel generators. These benefits should be reflected in the allocation of project risk as determined by the terms of the EPA between BCHydro and the community; either in the form of a higher EPA price or a take-or-pay agreement. Furthermore, if BCHydro wishes to encourage the implementation of load-following biomass energy systems in remote communities, it may be necessary to eliminate the stepped rate structure in the EPA, and allow communities to recover the capital cost of the plant through increased off-peak electricity sales.  88  Works Cited AANDC. (2011, November 11). 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Microdata User Guide: Households and the Environment Survey, 1–51. Thurner, N. (2012, February 4). VAS Energy Systems GmbH. Company Visit. Traynor, G. W., Apte, M. G., Caruthers, A. R., Dillworth, J. F., Grimsrud, D. T., & Gundel, L. A. (1987). Indoor Air Pollution due to Emissions from Wood-Burning Stoves, 1–7.  96  Triche, E. W. (2002). Infant Respiratory Symptoms Associated with Indoor Heating Sources. American Journal of Respiratory and Critical Care Medicine, 166(8), 1105–1111. doi:10.1164/rccm.2202014 Turboden. (2011a). Turboden High Efficiency (HRS) Units Standard Sizes and Typical Performances (pp. 1–1). Turboden. (2011b, December 7). Turboden Reference List. www.turboden.eu. Retrieved December 13, 2011, from http://www.turboden.eu/en/references/references.php?country=all&application=0&power =all U S Environmental Protection Agency. (1969, December 31). Module 3: Characteristics of Particles - Particle Size Categories. epa.gov. Retrieved June 13, 2012, from http://www.epa.gov/apti/bces/module3/category/category.htm#pm2.5 United States Environmental Protection Agency. (2002). Health Assessment Document For Diesel Engine Exhaust, 1–669. VAS Energy Systems GmbH. (2012a). VAS Performance Data Sheet (pp. 1–6). VAS Energy Systems GmbH. (2012b). VAS Budget Price Sheet for Standardized Series. Ward, T. J., Palmer, C. P., & Noonan, C. W. (2010). Fine Particulate Matter Source Apportionment Following a Large Woodstove Changeout Program in Libby, Montana. Journal of the Air & Waste Management Association, 60(6), 688–693. doi:10.3155/1047-3289.60.6.688 Ward, T., & Noonan, C. (2008). Results of a residential indoor PM 2.5sampling program before and after a woodstove changeout. Indoor Air, 18(5), 408–415. doi:10.1111/j.1600-0668.2008.00541.x Wihersaari, M. (2005). Greenhouse gas emissions from final harvest fuel chip production in Finland. Biomass and Bioenergy, 28(5), 435–443. doi:10.1016/j.biombioe.2004.11.007 Yablecki, J., Bibeau, E. L., & Smith, D. W. (2011). Community-based model for bioenergy production coupled to forest land management for wildfire control using combined heat and power. Biomass and Bioenergy, 35(7), 2561–2569. doi:10.1016/j.biombioe.2011.02.011 Zhang, X., & Kumar, A. (2011). Evaluating renewable energy-based rural electrification program in western China: Emerging problems and possible scenarios. Renewable and Sustainable Energy Reviews, 15(1), 773–779. doi:10.1016/j.rser.2010.08.023  97  Zwart, R. W. R., van der Drift, A., Bos, A., Visser, H. J. M., Cieplik, M. K., & Könemann, H. W. J. (2009). Oil-based gas washing-Flexible tar removal for highefficient production of clean heat and power as well as sustainable fuels and chemicals. Environmental Progress & Sustainable Energy, 28(3), 324–335. doi:10.1002/ep.10383  98  Appendix A: Inputs into the Tsay Keh Village Case Study completed in BEAT 1. Project Budget & Financing Data Provided to BEAT The budget for the entire community bioenergy plant includes estimates for the cost of the DHN, the construction of the power plant building, the transport of capital equipment to Tsay Keh Village, installation and commissioning costs, and non-capital costs such as permitting, engineering and project management which are assumed to remain constant over the size range of the bioenergy plants under consideration. Although the cost of the bioenergy system and the DHN depend on the size of the system selected, the cost of engineering, project management and permitting, building, transportation, installation and commissioning should all remain relatively constant. Table 15 lists the elements that contribute to the total capital cost of a community CHP system in Tsay Keh Village. Table 15: Capital Costs of the Tsay Keh Village bioenergy plant  !"#$%&'()*(+'$,(-".(/%&&$0"(1%)2$'(345(5&$6#(3)'#(7'82$#"' CHP system capital cost District heating network capital cost Electric heating capital cost Taxes on bioenergy system Shipping Costs Site costs Chipper capital cost Feedstock transport vehicle(s) capital cost Project implementation costs Total Project Cost  $4,700,000 $570,000 $70,000 $230,000 $380,000 $1,900,000 $100,000 $220,000 $1,130,000 $9,300,000  The Tsay Keh Village bioenergy plant is likely to be financed through a combination of Band equity, provincial and federal funding, and a loan from a financial institution. If the Tsay Keh Dene were to obtain a loan from the First Nations Finance Authority they should be able to get a 20-year loan at a rate of 4.5% (“First Nations Finance Authority,”  99  2012). The economic analysis carried out in BEAT uses the financing structure shown in Table 16. Table 16: Financing structure for Tsay Keh Village bioenergy plant Total Project Cost Source of Funding Government Grants Loan Amount Band Equity Invested Terms of Loan  $9,270,000 Amount $1,211,111 60% of Total Project Costs Total Project Costs - Funding - Loan Amount 4.5% interest for 20 years  2. Rates Charged for Energy Delivered Table 17 outlines the rates charged for the many forms of energy taken into consideration in BEAT. These rates are used to estimate operating costs, revenues, and savings from fuel switching. Table 17: Rates applied to energy sources for the Tsay Keh Village community bioenergy case study Value  Rate ($/kWh)  Comment/Reference  Price paid by BCHydro under EPA for electricity  0.310  Based on cost of generating with diesel (Hawley et al.,2010)  Price Paid by third parties for thermal energy from plant  0.116  Priced at the cost of heating with electricity under the RCEP  Cost of residential heating with wood furnaces  0.210  (Hawley et al., 2010)  Price paid by Tsay Keh Dene for electricity consumed under RCEP  0.116  Average of stepped rate based on projected annual consumption (British Columbia Hydro and Power Authority, 2008)  Cost of heating with LPG  0.13  (Hawley et al., 2010)  Appendix B: Methodology and an Explanation of BEAT 1. The Structure of BEAT BEAT, the evaluation tool developed specifically for assessing community bioenergy opportunities in BC, is written in the mathematical software package, MATLAB. The purpose of BEAT is to perform the calculations necessary to assess the financial viability of a community bioenergy project, optimize the system configuration, and understand the economic risks of the project. The process of estimating community energy demand and consumption, sizing a bioenergy system, determining feedstock requirements, and  100  calculating cash flow is often iterative. The purpose of BEAT is to automate this process and allow for optimization and sensitivity analysis. Figure 15 illustrates the flow of information through the calculations performed in BEAT. The user is queried for inputs such as community location, indoor temperatures, data files containing information about the buildings in the community and the current electrical demand, and the proposed feedstock supply. Values that are treated as constants throughout the analysis are declared in database files and the user is not expected to need to modify database values during a case study for one community. BEAT is designed to perform techno-economic analyses for both heat only and CHP systems under on-grid as well as off-grid circumstances.  Figure 15: Flow of information in BEAT  2. Database Files Database files contain all the numerical values that are treated as constants in BEAT and database values may be edited within the MATLAB editor, in order to suit a specific community. Comments within the code (available in Appendix D) provide further detail  101  regarding the units of each value. Table 18 lists the database file names, a description of their contents, and references. Table 18: Database files and their contents File Name  Description of Contents  basic_db.m  Basic numerical assignments such as y=1, n=0  chipper_db.m  Sizes/capacities, costs, electrical demand/fuel consumption etc. of a range of chippers.  Biomass Trade Centres, 2008  chp_systems_db.m  Electrical output, feedstock requirements, number of operators, costs, etc. of a range of CHP systems.  climate_db.m  Heating degree-day, design temperature, average air temperature and average relative humidity data for 16 population centres within BC. Installation cost data for DHNs  Pratt & Whitney Power Systems, 2012; VAS Energy Systems, 2012b NASA, 2012  dhn_db.m dryer_db.m  Reference  Dubois, 2012  Basic design, operation and energy demand data for a feedstock dryer. Cost to install electric heating in buildings. Density values and density factors relating to the type and form of biomass feedstock. Financial data, project life time, interest rates, etc.  Brammer & Bridgwater, 1999  ghgs_db.m  Greenhouse gas emissions factors for bioenergy system components/stages, feedstock supply, and displaced electrical & fossil fuel energy systems.  Springsteen et al., 2011; Whiersaari, 2005; CN, 2011; IPCC 2011.  logistics_db.m  Data regarding the transport of feedstock: capacities, number of drivers, average speed, fuel consumption.  electric_heat_db.m feedstock_db.m finance_db.m  Biomass Trade Centres, 2008  3. Location Data Sixteen locations throughout British Columbia were chosen as representative climates for the determination of heating loads in BEAT; they are highlighted in green in Figure 16. The range of locations was chosen to represent the climactic variations in B.C; southern coastal, northern coastal, southern interior, northern interior, and high and low altitudes. The co-ordinates of each community were used to search for climate data in NASA’s Surface Meteorology and Solar Energy database (NASA, 2011) furthermore it is possible to add climate data for any community location to the program’s climate database file. 102  Figure 16: Population centres in British Columbia. Locations highlighted in green are included in the BEAT climate database (Environment Canada)  4. Building Data BEAT requires specific formatting of the excel file containing the building data for heating network load and cost calculations. There must be no column or row headings, and the order of the data in the columns is critical. Figure 17 is an example of a properly formatted building data file.  Building area, in the first column, is the total floor area, in square meters, of an individual building. Insulation level, in the second column, is a rating of the effectiveness of the insulation in the individual building (1=poor, 2=medium, 3=good), according to the RETScreen function relating design temperature and insulation level to building heat loss, as shown in Figure 18 (RETScreen, 2005).  103  Building type, in the third column, is a specification of whether a building is residential (1), or commercial/institutional (2), and is a rough determinant of the capital cost of installing new radiators connected to the DHN (Dubois, 2012; RETScreen, 2005). Length of main DHN line, in the fourth column, is the length in meters, of the main distribution line connecting the individual building to the DHN. Length of secondary DHN line, in the fifth column, is the length, in meters, of the secondary line connecting the individual building to the DHN. Note that most buildings will be connected via a secondary branch line off the main line; therefore the direct mainline connection length is zero.  Figure 17: Required format of building data files  104  Figure 18: Building heating load as a function of insulation and design temperature (RETScreen International, 2005).  RETScreen does not provide a standard for determining poor, medium and good insulation in buildings. In absence of any standard, the following estimate is recommended: Buildings older than 20 years, or with single glazed windows likely have “poor” insulation, buildings between 20 and 5 years old likely have “medium” insulation, and buildings less than 5 years old likely have “good” insulation. If the user has more specific insulation data for the buildings of interest, it should be utilized. 5. Electrical Demand Estimate/Data If hourly, or minute-to-minute demand-peak data are available from a metered community, it is possible to generate an LDC for that community in BEAT. The data file should not contain any headers, footers or labels. The file should be saved as a comma separated value (.csv) file, and entered into BEAT in single quotes when prompted. BEAT will generate an LDC, calculate the total consumption, and read the peak demand value.  105  If metered data are not available for a community, peak demand must be estimated. BEAT will use an LDC representative of an off grid residential community to estimate the total electricity consumption, based on the estimated peak. 6. Selection of a Bioenergy System BEAT uses the electrical demand to size a CHP bioenergy system that will meet the community demand. The system is chosen so that the net electrical output of the system meets the maximum load specified by the user as a fraction of peak demand. Once the bioenergy system is selected, the maximum feed rate, required feedstock form and maximum acceptable feedstock moisture content are all defined within BEAT. It should be noted that BEAT can also be configured to complete analysis on heat-only bioenergy systems, although this functionality was not used for the case study in this thesis. 7. Feedstock Processing The user defines the condition of the feedstock supply in terms of the species of feedstock (coniferous or deciduous), the estimated moisture content, and the annual volume available. This information, coupled with the specific feedstock requirements of the selected bioenergy system, allows BEAT to determine what steps of feedstock processing (if any) are necessary to prepare the feedstock . 8. Integrated Electrical Demand Profile BEAT adds the electrical consumption from feedstock processing, the biomass firing system and the district heating system pumps to the electrical consumption of the community to determine the total energy required from the CHP system. The total annual consumption is then used to determine the total electrical load factor on the CHP system. If the total load factor is greater than one, the program will check to see if the next largest  106  CHP system in the database is more suitable for the community. If the combined demands produce a load factor of 0.5 or greater on the larger system, BEAT will repeat the feedstock processing requirements and demand calculation to determine the new combined consumption. If the combined demands produce a load factor less than 0.5 on the larger system, it will not select the next largest CHP system for the community. 9. Estimating Capital Costs BEAT estimates the capital cost of the bioenergy system, feedstock transportation, storage and processing equipment, and the cost of the DHN, including any electric heaters that will be installed. The capital costs of equipment are retrieved from the corresponding database files. Fixed costs, such as engineering, permitting and equipment transportation are entered by the user. 10. Estimating Operating Costs and Revenue The operating cost of the entire system is broken down into the following categories: Fuel costs, utility costs, labour costs, maintenance costs and insurance costs. Fuel, heat and power consumption are proportional to operational hours, and therefore these costs are estimated based on the number of hours a particular piece of equipment operates in a year. The operational hours of both the bioenergy system and the feedstock processing equipment are proportional to the load factor on the bioenergy system. The maintenance and insurance costs are assumed to be a fixed value, and are therefore calculated separately and added to the load factor-varying costs.  107  Revenue may be generated through the sale of heat, electricity, processed feedstock and carbon offsets. Revenue from the sale of heat and electricity includes the electricity and heat consumed in the preparation of feedstock, as well as energy sold to the community. 11. Estimating the Net Reduction in Greenhouse Gas Emissions The program estimates the change in greenhouse gas emissions (as tCO2 equivalent) resulting from the adoption of a biomass energy source for heating and/or electricity generation in a community. The sources of emissions included in this calculation are summarized in Table 19. The user is required to specify whether the community is on or off grid, how far fossil fuel must be transported to the community, and whether the potential feedstock will be harvested, or if the feedstock will come from a waste wood stream that is currently burned in open piles (Springsteen et al., 2011; Wihersaari, 2005). Table 19: Sources of GHGs from fossil fuel and bioenergy based community energy systems Reduced or Eliminated GHG Emissions  GHG Emissions Resulting from the Bioenergy System & Feedstock Supply  On-grid communities: • Emissions related to use of grid electricity. • Emissions from the combustion of natural gas for heating. Off-grid communities:  Direct emissions from the bioenergy system Emissions from feedstock transportation Emissions from feedstock processing  • •  Emissions from diesel generators Emissions from the combustion of propane for heating.  Emissions resulting from land use changes when trees are harvested for feedstock Emissions related to fertilization of re-planted forests. Emissions from diesel generator for load following.  • Delivery of propane and diesel On-grid and off-grid: • Open pile burning of waste wood  12. Cash Flow Analysis and Other Financial Indicators The annual cash flow resulting from investment in the bioenergy system is calculated for every year of the plant lifetime. A demand growth factor specified in the finance database file is applied to estimate the increased heat and/or power consumption over the lifetime of the system. The projected future consumption is used to estimate load factors in future 108  years, and future feedstock consumption. The annual load factor is used to scale the annual operating costs and revenue up from the year one values. The cash flow calculation also includes annual payments on borrowed capital and the year one cash outflow for equity invested in the project. There is an option to include project funding as part of the total capital invested. The annual cash flow over the lifetime of the project is used to calculate the internal rate of return (IRR), the net present value of the bioenergy plant, and the annual cost of electricity produced by the system.  13. BEAT Output File Once the analysis has been completed BEAT will ask the user to provide a name for file that the results will be saved in. The filename must be entered in single quotes with an .xls file extension; for example ‘filename.xls’.  The output file contains a summary of all the user defined input data; a financial indicators summary that includes IRR and NPV; a summary of the heat and electricity consumption; specification of the bioenergy system and feedstock requirements; the required feedstock processing equipment; and the net change in greenhouse gas emissions.  The output file also reports the annual operating costs and revenue streams over the project lifetime, as well the details of equipment and facility capital costs; the annual load factors, details of feedstock processing equipment and the annual energy consumption of each piece of equipment. These details are useful once a specific system configuration  109  has been chosen. However, initially much of the information necessary to inform decisions regarding system configuration can be found in the first two pages of the BEAT output file.  Appendix C: BEAT Output Files from Case Study (Electronic Appendix) All the output files from the scenarios modeled in Table 7 and Table 9 are numbered according to their scenario number and can be viewed in Excel.  Appendix D: BEAT Code (Electronic Appendix) To run the generic version of BEAT simply type BEAT in the MATLAB prompt window. To run analyses related to Tsay Keh Village type BEATTKV in the MATLAB prompt window, this file contains some Tsay Keh Village specific inputs that would normally be entered every time, but have been written in to speed up the process and avoid errors. Current commercial and institutional building data are in the file ‘TKVDHN.xls” and the future commercial and institutional building data are in the file “TKVF-DHN”. Similarly, the residential building data are in the files “TKV-BLDG.xls” and “TKV-BLDG.xls”. The generator data are in “TKVGEN.csv”.  110  

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