Business, Sauder School of
Operations and Logistics (OPLOG), Division of
DSpace
UBCV
Mumcular, Aylin
Master of Science in Business - MScB
University of British Columbia
The University of British Columbia (UBC) has set itself aggressive sustainability targets. In order to reduce the greenhouse gas (GHG) emissions in its Vancouver campus, UBC has been consuming natural gas (both renewable and non-renewable) due to its relatively lower emissions compared to other fossil fuels. This practice has resulted in most of UBC’s campus wide GHG emissions coming from natural gas consumption. UBC is considering various alternatives to reduce its GHG emissions to achieve the 2020 sustainability commitment. Using more renewable natural gas may be a viable solution, but it is scarce and expensive. Xpansiv, a software start-up company, uses blockchain technology to capture and store data about the production and distribution attributes of natural gas. This can allow for differentiated natural gas units based on their environmental impact. In this way, the sustainability features such as GHG emissions, production techniques, or leak rates can be commercialized as environmental attributes. This study aims to understand the potential benefits that Xpansiv’s blockchain technology platform can create for UBC’s natural gas procurement in its Vancouver campus. Can this technology have an impact on total GHG emissions, total costs, and UBC’s success in achieving its sustainability goals? To answer these questions, a fit-gap analysis is performed, and a simulation optimization model is built by using operations research (OR) tools. The fit-gap analysis has shown that the decommoditization of natural gas through the differentiated gas units can increase UBC’s procurement options and the usage of environmental attributes as a market instrument can help UBC potentially save costs. To quantify the impact, a simulation optimization model is created. According to the simulation optimization model results, the average total net GHG emissions are estimated as 258 tonnes, the average total cost of procurement and GHG emissions is calculated as $1,304,737, and UBC can achieve its 2020 sustainability commitment. Comparing these results with the status quo demonstrates that the unit cost savings is approximately $72.4 per tonne of emissions reduction. This implies that Xpansiv’s platform is useful in reducing GHG emissions, achieving the 2020 sustainability commitment, and saving costs.
1 BLOCKCHAIN MEETS NATURAL GAS: A CASE STUDY OF THE UNIVERSITY OF BRITISH COLUMBIA AND XPANSIV FROM AN OPERATIONS RESEARCH PERSPECTIVE by Aylin Mumcular B.Sc., Bilkent University, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Business Administration) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) March 2020 © Aylin Mumcular, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Blockchain Meets Natural Gas: A Case Study of the University of British Columbia and Xpansiv from an Operations Research Perspective submitted by Aylin Mumcular in partial fulfillment of the requirements for the degree of Master of Science in Business Administration Examining Committee: Prof. Harish Krishnan Supervisor Prof. Robin Lindsey Supervisory Committee Member Prof. Anming Zhang Additional Examiner iii Abstract The University of British Columbia (UBC) has set itself aggressive sustainability targets. In order to reduce the greenhouse gas (GHG) emissions in its Vancouver campus, UBC has been consuming natural gas (both renewable and non-renewable) due to its relatively lower emissions compared to other fossil fuels. This practice has resulted in most of UBC’s campus wide GHG emissions coming from natural gas consumption. UBC is considering various alternatives to reduce its GHG emissions to achieve the 2020 sustainability commitment. Using more renewable natural gas may be a viable solution, but it is scarce and expensive. Xpansiv, a software start-up company, uses blockchain technology to capture and store data about the production and distribution attributes of natural gas. This can allow for differentiated natural gas units based on their environmental impact. In this way, the sustainability features such as GHG emissions, production techniques, or leak rates can be commercialized as environmental attributes. This study aims to understand the potential benefits that Xpansiv’s blockchain technology platform can create for UBC’s natural gas procurement in its Vancouver campus. Can this technology have an impact on total GHG emissions, total costs, and UBC’s success in achieving its sustainability goals? To answer these questions, a fit-gap analysis is performed, and a simulation optimization model is built by using operations research (OR) tools. The fit-gap analysis has shown that the decommoditization of natural gas through the differentiated gas units can increase UBC’s procurement options and the usage of environmental attributes as a market instrument can help UBC potentially save costs. To quantify the impact, a iv simulation optimization model is created. According to the simulation optimization model results, the average total net GHG emissions are estimated as 258 tonnes, the average total cost of procurement and GHG emissions is calculated as $1,304,737, and UBC can achieve its 2020 sustainability commitment. Comparing these results with the status quo demonstrates that the unit cost savings is approximately $72.4 per tonne of emissions reduction. This implies that Xpansiv’s platform is useful in reducing GHG emissions, achieving the 2020 sustainability commitment, and saving costs. v Lay Summary This is an applied proof-of-concept study. The key research goal is to understand how Xpansiv’s blockchain technology platform can contribute to the natural gas procurement of the University of British Columbia (UBC) in its Vancouver campus. The reason for analyzing this collaboration is that most of the campus wide greenhouse gas (GHG) emissions stem from natural gas consumption and UBC aims at reducing its emissions to a targeted level by the end of 2020. The potential contributions are first analyzed and then quantified in terms of future emissions, total costs and UBC’s ability to achieve its target. This research concludes that Xpansiv can help UBC reduce its GHG emissions, achieve the 2020 target, and save costs. vi Preface This thesis is original, unpublished, independent work by the author, Aylin Mumcular. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ ix List of Figures .................................................................................................................................x List of Abbreviations ................................................................................................................... xi Acknowledgments ....................................................................................................................... xii Dedication ................................................................................................................................... xiii Chapter 1: Introduction ................................................................................................................1 1.1 Problem Definition.......................................................................................................... 2 1.2 Structure of the Thesis .................................................................................................... 3 Chapter 2: Background Information ...........................................................................................4 2.1 Natural Gas ..................................................................................................................... 4 2.1.1 Natural Gas Supply Chain .......................................................................................... 4 2.1.2 Natural Gas Trading .................................................................................................... 9 2.2 Government Policies ..................................................................................................... 11 2.2.1 Policies in British Columbia ..................................................................................... 11 2.2.2 A Related Policy in the United States ....................................................................... 12 2.3 Blockchain Technology ................................................................................................ 14 Chapter 3: Literature Review .....................................................................................................17 viii 3.1 Energy ........................................................................................................................... 17 3.2 Blockchain and Energy ................................................................................................. 19 3.3 Operations Research and Energy ...................................................................................20 Chapter 4: Xpansiv ......................................................................................................................22 4.1 Xpansiv's Business Model ............................................................................................ 22 4.2 Xpansiv's Product ..........................................................................................................23 4.3 Xpansiv's Challenges .....................................................................................................25 Chapter 5: Fit-Gap Analysis .......................................................................................................27 5.1 Solutions ....................................................................................................................... 27 5.2 Process Improvement Ideas ...........................................................................................30 Chapter 6: Estimating the Impact ..............................................................................................31 6.1 Parameter Estimation .................................................................................................... 31 6.2 Simulation Optimization Model ....................................................................................44 6.3 What-if Scenario Analysis .............................................................................................48 Chapter 7: Conclusion and Future Work ..................................................................................54 7.1 Conclusion .................................................................................................................... 54 7.2 Future Work ...................................................................................................................55 References .....................................................................................................................................57 Appendices ....................................................................................................................................63 Appendix A: Explanation of the Forecasting Models............................................................... 63 Appendix B: Computation of the Forecasting Models ............................................................. 66 Appendix C: Computation of the Simulation Optimization Model .......................................... 69 ix List of Tables Table 1 Summary of the problems and solutions........................................................................29 Table 2 Forecasts from Holt’s models with different trend components ....................................37 Table 3 In-sample and out-of-sample performance of the models .............................................40 x List of Figures Figure 1 Non-renewable natural gas supply chain .......................................................................... 5 Figure 2 Renewable natural gas supply chain................................................................................. 7 Figure 3 Summary of non-renewable and renewable natural gas supply chains ............................ 8 Figure 4 The separation of RECs and the corresponding electricity ............................................ 13 Figure 5 Blocks’ verification mechanism ..................................................................................... 15 Figure 6 Diagram illustrating where this study is positioned ....................................................... 17 Figure 7 Xpansiv’s natural gas differentiation model................................................................... 28 Figure 8 UBC Vancouver annual total and renewable natural gas consumption ......................... 36 Figure 9 Comparison of Holt’s models with different trend components .................................... 43 Figure 10 The change in total cost with changing demand ........................................................... 49 Figure 11 The change in total cost, emissions, and the probability with changing environmental attribute supply................................................................................................................................. 50 Figure 12 The change in total cost, emissions, and the probability with changing environmental attribute price ................................................................................................................................... 52 xi List of Abbreviations BC - British Columbia CLL - Campus as a Living Laboratory EWS - Energy and Water Services GHG - Greenhouse Gas GJ - Gigajoule ITMO - Internationally Transferred Mitigation Outcome MAE - Mean Absolute Error ME - Mean Error OR - Operations Research REC - Renewable Energy Credits RMSE - Root Mean Squared Error UBC - University of British Columbia US - United States xii Acknowledgements I would like to express my special thanks to my supervisor, Prof. Harish Krishnan, for his guidance and feedback. It was a pleasure to work with him. I would like to thank Prof. Robin Lindsey for serving as my supervisory committee member and his feedback. I am also very grateful to Prof. Anming Zhang for serving as my committee member and his support during my master’s study. I would like to thank UBC Clean Energy Research Center, Xpansiv, Fortis BC, and UBC Energy and Water Services as well for answering my questions during this process. Special thanks are owed to my family for their unconditional love, my best friends Gulin and Esma for all the fun they bring into my life, and my dear Siva who has been a pillar of support throughout this journey. xiii Dedication To the people I love 1 Chapter 1: Introduction In the Paris agreement, which was adopted in 2015, countries made commitments and determined their climate targets to reduce greenhouse gas (GHG) emissions and mitigate the risks of climate change. Demand for natural gas projects has been booming ever since because natural gas is seen as a clean energy alternative that can reduce emissions and help countries reach their climate targets (Nace et al., 2019). Renewable and non-renewable natural gas are the two types of natural gas available in the market. These natural gas types mainly differ in the way that they are produced, that is, non-renewable natural gas is obtained from gas wells whereas renewable natural gas is produced from organic resources. Currently, renewable natural gas is scarce and expensive compared to non-renewable natural gas. Renewable natural gas is viewed as carbon-neutral, unlike non-renewable natural gas. Although natural gas is a commodity, the techniques used in the production of non-renewable natural gas units lead them to have a different environmental impact. Canada has been a leader in developing and following strategies to implement the Paris Agreement. Provincial governments within the country have adopted various policies to reduce their carbon footprints. In particular, British Columbia is committed to reducing GHG emissions. Hence, low carbon fuels such as renewable natural gas are promoted across the province. Carbon taxes, carbon offset mechanisms, and carbon neutrality goals are among the policies implemented in British Columbia. 2 The University of British Columbia (UBC) is a public research university in British Columbia with its Point Grey campus in Vancouver and Okanagan campus in Kelowna. UBC supports sustainability initiatives and sets itself sustainability goals to reduce the carbon impact. This study will focus on the natural gas procurement of UBC’s Vancouver campus to reduce campus wide GHG emissions. 1.1 Problem Definition UBC highly values sustainability and develops rigorous climate action plans to accomplish its sustainability goals. According to UBC’s latest climate action plan, it aims to reduce campus wide GHG emissions by 67% by the end of 2020 and 100% by 2050 compared to 2007 levels (UBC Climate Action Plan 2020, 2016). To achieve its 2020 target, UBC focuses its efforts on the use of natural gas since the majority of GHG emissions on campus (approximately 90% of emissions in 2016) stem from natural gas consumption (UBC Climate Action Plan 2020, 2016). Furthermore, GHG emissions incur extra costs for the university due to climate policies in British Columbia. Although renewable natural gas seems to be a viable solution to avoid GHG emissions, its scarcity and higher price motivate UBC to search for other alternatives for its natural gas procurement. Xpansiv leverages blockchain technology to differentiate natural gas units based on their environmental impact. Moreover, Xpansiv captures data from natural gas supply chains to create a novel data format called Digital Feedstock. Digital Feedstock allows for the commercialization of the sustainability features of natural gas units by capturing data about the environmental attributes of natural gas. 3 Within the Campus as a Living Laboratory (CLL) program, UBC engages with industrial partners to accelerate the development of innovative sustainability solutions. This thesis is an applied proof-of-concept study that analyzes UBC and Xpansiv’s collaboration as part of the CLL program. Therefore, this study will answer the following research questions: • What are the potential benefits that Xpansiv’s blockchain technology platform can create for UBC’s natural gas procurement? • What is the estimated impact of this collaboration in terms of total GHG emissions, total costs, and UBC’s future success in achieving its 2020 sustainability commitment? To answer these research questions, a fit-gap analysis is performed, and a simulation optimization model is developed by using Operations Research (OR) tools. 1.2 Structure of the Thesis This thesis consists of seven chapters. The first chapter is the introduction that presents the research problems. The second chapter is the background information. It introduces the topics relevant to this thesis. The third chapter is the literature review that discusses related studies. The fourth chapter discusses Xpansiv in detail. The fifth chapter is the fit-gap analysis where potential benefits are analyzed. The sixth chapter quantifies the impact. Lastly, the seventh chapter concludes the study and discusses future research areas. 4 Chapter 2: Background Information The purpose of this chapter is to make the reader acquainted with the concepts relevant to this thesis. In the natural gas section, natural gas supply chain and current natural gas trading practices are discussed. In the government policies section, clean energy incentives in British Columbia and another related regulation in the United States are presented. In the blockchain technology section, blockchain technology and its key features are explained. 2.1 Natural Gas Growing concern for global warming and climate change has resulted in increasing GHG emissions reduction efforts. Natural gas is a cleaner energy alternative compared to coal and other fossil fuels which are mainly used for heating and electricity generation. This has made natural gas an increasingly popular source of energy for countries conscious about reducing their environmental footprint. 2.1.1 Natural Gas Supply Chain Natural gas can be classified into two types: non-renewable and renewable. The main difference between non-renewable and renewable natural gas is that non-renewable natural gas is produced from gas wells whereas renewable natural gas is produced from organic waste. This section will investigate the non-renewable and renewable natural gas supply chains, which can be defined as a network of entities connected with upstream and downstream links to facilitate the transformation of raw gas into finished natural gas. 5 Non-renewable natural gas can be categorized as conventional or unconventional depending on the geological structure of the place of extraction and hence the extraction technique used during the process. Conventional non-renewable natural gas is obtained from permeable rock forms whereas unconventional non-renewable natural gas is obtained from shale rock formations, coal beds, and tight sands. The non-renewable natural gas supply chain is illustrated in Figure 1. Figure 1: Non-renewable natural gas supply chain (author's construction) After geologists and geophysicists identify natural gas and assess the identified natural gas’ physical characteristics, if the prospect natural gas is found to be valuable, gas companies drill exploration wells and perform risk assessments to understand whether safe, sustainable and economic gas can be obtained (API, 2015; Shell, 2016). Wells are designed to produce hydrocarbons and constructed according to the structure of the region’s geography (API, 2015). Then, natural gas is extracted to the ground through onshore or subsea capabilities (API, 2015; 6 Shell, 2016). The extraction technique used during the extraction process depends on whether non-renewable natural gas is conventional or unconventional. It is usually harder to extract unconventional non-renewable natural gas. Therefore, sophisticated production techniques have been developed for this purpose. For instance, hydraulic fracturing which is a stimulation method aiming at creating cracks in the geological structure by applying high pressures is an unconventional non-renewable natural gas production technique mainly used in the industry. These identifying, exploring, designing, constructing and producing stages constitute upstream processes in the non-renewable natural gas supply chain. Extracted non-renewable natural gas is moved from wells to processing plants (Shell, 2016). This transportation stage constitutes the midstream process in the non-renewable natural gas supply chain. In the processing plants, extracted non-renewable natural gas is processed and purified to generate “pipeline quality dry natural gas” (API, 2015). Produced non-renewable natural gas can move to the liquefaction stage or national hubs. Exported and imported natural gas is in the liquid state since liquefied natural gas shipping is an economical and environmentally friendly way to transfer natural gas for long distances (API, 2015). Imported liquefied natural gas units are vaporized and converted back to the gaseous state through regasification (API, 2015). After their conversion to natural gas, they also move to national hubs. Depending on the market demand, natural gas can be sold to gas companies, stored underground or exported to other countries. Gas companies distribute natural gas to end customers. These processing, liquefaction, regasification, and distribution stages constitute downstream processes in the non-renewable natural gas supply chain. 7 Although non-renewable natural gas is considered clean, its combustion still emits greenhouse gases into the atmosphere. To produce renewable natural gas, methane is captured from organic waste such as agricultural waste, wastewater, landfill gas, dairy, food waste, and wood waste, which are considered carbon-neutral. Hence, renewable natural gas is also considered carbon-neutral due to its zero carbon dioxide contribution to the atmosphere (Fortis BC, 2017). Renewable natural gas can be used “mixed or interchangeably” with non-renewable natural gas (“Renewable Natural Gas Technology Roadmap for Canada”, 2014). Thus, renewable natural gas usage supports greenhouse gas emissions reduction efforts since captured methane is not released into the atmosphere and renewable natural gas decreases the amount of non-renewable natural gas required (Fortis BC, 2017). The renewable natural gas supply chain is illustrated in Figure 2. Figure 2: Renewable natural gas supply chain (author's construction) 8 Landfill gas is captured from methane with the decomposition of waste in landfills (Jaffe et al., 2016). Landfill gas is the largest renewable natural gas source. For the other sources, organic waste is naturally decomposed in an anaerobic environment in pre-treatment plants and gas obtained from these sources is named biogas (Jaffe et al., 2016). Capturing and decomposing organic waste constitute upstream processes in renewable natural gas supply chain. Transporting methane to processing plants for further refinement constitutes the midstream process in the renewable natural gas supply chain. The removal of moisture, hydrogen sulfide and the other trace gases from methane is called purification and removing carbon dioxide from the purified methane is known as upgrading (Hoo et al., 2017). Renewable natural gas refers to upgraded biogas and landfill gas (Hoo et al., 2017). Compressed renewable natural gas can be injected into the natural gas pipeline. Both non-renewable and renewable natural gas share the same downstream processes in their supply chains. A summary of non-renewable and renewable natural gas supply chains is provided in Figure 3. Figure 3: Summary of non-renewable and renewable natural gas supply chains (author's construction) 9 2.1.2 Natural Gas Trading Both non-renewable and renewable natural gas are commodities that are treated the same by consumers irrespective of their producers. The price of non-renewable natural gas is determined by the market primarily based on the balance of its supply and demand. Physical non-renewable natural gas is typically traded in a spot market where it is bought and sold daily or monthly as well as with bilateral agreements for longer terms. Some non-renewable natural gas producers can market their gas and sell it directly to “local distribution companies or to large industrial consumers” whereas others sell it to marketers (API, 2014). Marketers aggregate non-renewable natural gas they buy from different producers and transport the gas to customers according to their individual needs (API, 2014). If customers are directly connected to pipelines, they can purchase non-renewable natural gas from producers or marketers. This is the case for most industrial customers like UBC. Otherwise, customers receive non-renewable natural gas deliveries from a local distribution company, and this holds for “most residential customers” (API, 2014). The sending party of the non-renewable natural gas handles nominations which are official requests to transport gas stating delivery points and quantities. Renewable natural gas is a renewable substitute for non-renewable natural gas. However, compared to non-renewable natural gas, renewable natural gas has a smaller scale distribution model that leads to a higher cost per unit of production. In the case of biogas, the main drivers for the higher renewable natural gas costs are associated with “collecting feedstock and the capital equipment costs to removing impurities from the biogas” (“Renewable Natural Gas Technology Roadmap for Canada”, 2014). In the case of landfill gas, higher costs are related to “collecting and upgrading the gas and connecting to the pipeline” (“Renewable Natural Gas 10 Technology Roadmap for Canada”, 2014). Renewable natural gas is compressed to be delivered by truck to the consuming markets for projects that are distant from the pipeline network (“Renewable Natural Gas Technology Roadmap for Canada”, 2014). The cost of these operations can be too great for renewable natural gas producers, leading renewable natural gas to have limited availability. When marketers purchase renewable natural gas, they can transact it either directly with obligated parties or through a secondary broker market (Element Markets, 2017). Utilities also have started to sell renewable natural gas. For instance, Fortis BC is a renewable natural gas selling utility in British Columbia. Customers can purchase renewable natural gas from utilities only through bilateral agreements and utilities inject the amount of renewable natural gas they obtain from their suppliers as decided by these bilateral agreements. Some utilities make renewable natural gas agreements based on a percent amount of total consumption while some other utilities offer a fixed dollar amount to be added to the bill. For example, UBC purchases renewable natural gas from Fortis BC and the renewable natural gas quantity to be purchased is annually agreed between Fortis BC and UBC Energy and Water Services (EWS, n.d.). Similar to non-renewable natural gas, utilities try to internally balance their actual supply and demand and keep gas deferral accounts to smooth out imbalances. Currently, there are no industry-accepted standards, protocols or a third-party verification mechanism for renewable natural gas. When renewable natural gas consumers make purchase agreements with the gas providers, they have very little oversight on if they actually receive verified renewable natural gas that meets quality and environmental standards. This creates a lack of protection mechanism for customers when they make regulatory claims regarding their 11 sustainable procurement efforts. As an example, to avoid such problems in British Columbia, Fortis BC works with third party carbon management solutions providers to gain the carbon neutrality status for its renewable natural gas and BC government recognizes Fortis BC’s renewable natural gas in assessing customers’ regulatory claims. This practice results in a local protection mechanism for British Columbians. 2.2 Government Policies Governments have started initiating climate policies incentivizing low carbon fuels to reduce GHG emissions and become low carbon economies. Climate policies play a critical role in helping governments achieve their emissions reduction targets to address climate change. 2.2.1 Policies in British Columbia BC government takes climate change seriously and implements aggressive energy policies to reduce fossil fuel reliance within the province. In this section, some of the initiatives BC government takes to decrease GHG emissions will be discussed. In North America, British Columbia became the first jurisdiction in implementing “a consumption-based environmental tax” when carbon taxes were introduced in 2008 (Duff, 2008). Carbon taxes in the province apply to the emissions generated from fossil fuel combustion and exclude carbon-neutral biofuels (Duff, 2008). The carbon tax rate was initially introduced at $10 per ton of GHG emissions (Rhodes & Jaccard, 2013). The current carbon tax rate is $40 per ton of GHG emissions and projected to increase further (Government of BC, 2019). The government of British Columbia has also adopted a policy that the province will be carbon neutral. To achieve carbon neutrality, all public sector organizations within the jurisdiction of 12 British Columbia should have net zero GHG emissions (“2017 Carbon Neutral Year Summary”, 2017). BC government defines carbon neutrality as “measuring operational GHG emissions, planning and taking action to reduce emissions and offsetting the remainder” (Ministry of Environment and Climate Change Strategy, 2018). In this context, a carbon offset is a financial policy tool that represents the removal of one tonne of GHG emissions from the atmosphere (Ministry of Environment and Climate Change Strategy, 2018). By purchasing carbon offsets, public institutions fund high quality overall emissions reductions projects within the province. Carbon offsets provide invaluable flexibility for public institutions to become carbon neutral despite their unavoidable or costly-to-avoid GHG emissions. Currently, BC government only allows purchasing and retiring “BC-based offsets” which cost $25 per tonne of GHG emissions for the public sector institutions (Ministry of Environment and Climate Change Strategy, 2018). 2.2.2 A Related Policy in the United States In the United States, tradable instruments that separate the environmental benefits of renewable energy sources from physical energy have been developed. Renewable Energy Credits (RECs) is one of such instruments. RECs have been introduced as instruments to create economic incentives for renewable electricity generation (Gillenwater, 2008). A REC represents the renewability feature of the corresponding one megawatt-hour of physical electricity. This implies that “electricity cannot be considered renewable without a REC to substantiate its renewable-ness” (United States Environmental Protection Agency, n.d.). This practice of creating RECs by segregating environmental attributes from underlying renewable energy supports sustainable electricity generation projects irrespective of where they are based within the country. 13 RECs are “environmental commodities” that can be traded together (bundled) or separately (unbundled) from electricity (Gillenwater, 2008). REC purchasers hold the right to make environmental claims for using renewable energy even though they do not purchase the associated electricity (United States Environmental Protection Agency, n.d.). The separation of RECs and the corresponding electricity is illustrated in Figure 4. Figure 4: The separation of RECs and the corresponding electricity (author's construction) RECs should be retired by one owner and not circulated in the system after retirement. Electronic tracking systems and auditing practices have been developed to trace the ownership of RECs and avoid possible double counting problems. 14 2.3 Blockchain Technology Blockchain is an emerging technology. In simple terms, this technology consists of immutable data blocks with no central control which allows the transaction data to be transparent to everybody. The immutable, transparent and decentralized nature of blockchain makes it a potential source of digital trust. Blockchain enables the sharing of data across many participants in a network. In blockchain’s distributed database, data is stored and shared on a peer-to-peer network where all participants in the network can see and confirm transactions. It is possible to trade anything with all participants on the network via blockchain as long as they can be represented in a digital form (Gupta, 2017). This feature eliminates the dependency on one central authority to perform these operations. Because of its distributed nature, every new transaction data registered to the ledger is appended to all participants’ existing copy of the blockchain (Gunnarson & Hamber, 2018). Since anybody can view the details of a transaction, this increases the traceability of information. In blockchain technology, data is stored and transferred with blocks. After data is verified, it is permanently secured in a data block. “Every block contains a package of a number of transactions (data) that are sealed together, and every block is linked to each other in a digital chain” (Gunnarson & Hamber, 2018). A block contains details of its transactions, “a digital fingerprint, timestamps, and the hash of the previous block” (Gunnarson & Hamber, 2018). Blocks are linked together by each including the hash value of a former block. A hash value is the result of a mathematical algorithm transforming the information stored in the blocks to an encrypted output. Any change made in the data leads to a different hash value and necessitates a 15 change in the other blocks containing the hash value of that block. Hence, tampering with data records notifies the whole network and leads to the “blockchain’s quality of immutability” (Gunnarson & Hamber, 2018). This verification mechanism is illustrated in Figure 5. Figure 5: Blocks’ verification mechanism (author's construction) In blockchain technology, participants agree on any changes made to the network that include the selection of blocks to be added to the chain. Consensus mechanisms ensure this internal consistency. A transaction is accepted to a block after participants reach a consensus that the data is valid and then becomes a part of the blockchain (Andoni et al., 2019). Consensus mechanisms guarantee that there is only one final version of the database. The fact that multiple participants approve transactions ensures transparency of the system. Participants on the blockchain network have a pair of public and private keys. A public key is used for “identifying users on the network whereas a private key is used for signing transactions” (Gunnarson & Hamber, 2018). Private keys can be used to derive the public keys, but the reverse is not possible. Having public and private keys allows the network to be transparent and private at the same time. Blockchains can be classified differently based on how open and decentralized they are. “The difference between various blockchain types is related to who is allowed to participate in the 16 network, execute the consensus protocol and maintain the shared ledger” (Gunnarson & Hamber, 2018). Public blockchains are open to anybody willing to join. There is no owner for the public blockchain network and trust is achieved through economic incentives in the form of small payments from the verification records that are entered into the system to reach consensus (Gunnarson & Hamber, 2018). However, “a private blockchain network can only be joined by invitation and participants need to be validated by the network initiator” (World Bank Group, 2018). In a private blockchain network, transactions are verified internally (Gunnarson & Hamber, 2018). It is possible to have hybrid blockchain networks to find a balance between public and private blockchains. In general, blockchain tokens represent assets (Chen, 2018). They can be transferred without a central authority and “traded without borders” (Chen, 2018). Overall, blockchain can be summarized as a combination of other technologies such as database technology, cryptography, networking, and hashing which together can create trust (OECD, n.d.). 17 Chapter 3: Literature Review In this chapter, related studies in the literature are discussed. This study combines blockchain technology and OR within the natural gas context as illustrated in Figure 6. Therefore, in the energy section, studies analyzing the major changes that the energy industry has been undergoing will be reviewed. In the blockchain and energy section, studies discussing the integration of blockchain technology into the energy industry will be reviewed. Lastly, in the operations research and energy section, studies applying OR tools to the problems in the energy industry will be reviewed. Figure 6: Diagram illustrating where this study is positioned (author's construction) 3.1 Energy The energy industry has been changing. With the transition to clean energy, utility companies have been increasing their decarbonization efforts to respond to consumers’ demand for cleaner alternatives. Moreover, increasing technological capabilities have been transforming the traditional centralized energy systems into distributed energy resources and giving consumers 18 more control over their energy supplies (Alanne & Saari, 2004). Power utilities are leading the transformation towards decentralization. Power utilities in countries like Germany and Australia have already faced massive disruption in the industry and have been in search of new business models (Sioshansi, 2014). According to Sioshansi (2014), centralized energy systems are often governed by vertically integrated utilities that handle generation, transmission, and retailing of power to “deliver the output of large plants to customers through a massive transmission and distribution network”. However, with the advent of distributed energy resources like “solar photovoltaic systems, batteries, microgrids, and embedded networks”, it has become possible to economically produce power “on small to medium scale” for independent power producers which can be “individuals, small businesses, and local communities” (Sioshansi, 2014; Power Ledger, 2019; Vezzoli et al., 2018). Decreasing costs of distributed energy sources and increasing demand for renewable sources of power such as solar energy have triggered the shift of “power balance from central authorities to the edges of the grid, to where citizens have control” (Power Ledger, 2019). Vezzoli et al. (2018) argue that distributed energy resources have enabled the “democratization of energy access” by allowing all consumers both to receive and supply energy, hence becoming prosumers and getting compensated for their surplus power sold back to the grid, through a network. Consumers are now expecting to use the traditional grid to balance their energy production and consumption and as a back-up when they experience problems in their local power sources (Henderson et al., 2017). Henderson et al. (2017) foresee that traditional centralized utilities in the future will 19 evolve into entities that only install distributed energy resources, aggregate power produced by customers, and arrange for back-up energy. 3.2 Blockchain and Energy Blockchain technology has attracted great attention in the energy industry. Andoni et al. (2019) have reviewed blockchain innovation projects within the energy landscape. According to their study, energy-related use cases of blockchain are mainly related to decentralized trading of energy and energy cryptocurrencies. Blockchain systems have been leveraged to develop decentralized energy trading platforms. The most common applications of the blockchain systems are “wholesale energy trading” and “peer-to-peer energy trading platforms” where consumers and prosumers trade their energy surplus (Andoni et al., 2019). To this end, PONTON has developed a blockchain-based platform to support wholesale cross-regional energy trading (PONTON, 2019). PONTON’s use case has been designed to incentivize local energy production and consumption where only residual energy is transferred to “higher grid level and from there to remote regions” (PONTON, 2019). Furthermore, Power Ledger has created an “interoperable energy trading network” where individuals “buy or sell self-generated electricity” (Power Ledger, 2019; Andoni et al., 2019). Blockchain technology has also started to be used as an underlying mechanism for energy cryptocurrencies which can be created for various purposes. NRGcoin has developed a virtual currency that compensates prosumers when they inject energy into the grid and allows them to either convert the compensation into fiat currencies or purchase electricity from the grid (Mihaylov et al., 2014). SolarCoin has created a reward mechanism using blockchain-based 20 cryptocurrencies for each megawatt-hour of solar energy produced to encourage global solar energy production (SolarCoin, n.d.). EnergiMine has generated Energi Tokens that are granted to prosumers to incentivize energy efficiency when they save energy (lowering their energy consumption, using public transportation, etc.) (EnergiMine, n.d.). 3.3 Operations Research and Energy OR tools have been used to solve a variety of problems in the energy industry such as supply scheduling or energy systems planning. In this section, applications of mathematical modelling, simulation, and forecasting for the problems in the energy domain will be discussed. Mathematical modelling is concerned with translating complex decision-making problems into mathematical expressions to optimize their values based on a defined set of decision variables under certain constraints. In the literature, Guan et al. (2010) have used a mixed integer program to address the scheduling problem of multiple energy supply sources and loads to improve the operational efficiency of energy consumption in buildings. Mavrotas et al. (1998) have used a multi-objective linear program to solve an energy planning problem of expanding a power generation system by considering “multiple policy goals”. Furthermore, Omu et al. (2013) have used a mixed-integer linear program to analyze “the economic and environmental impacts of distributed energy systems” compared to traditional centralized energy systems. Simulation is another commonly used OR technique. Simulation models are usually designed to replicate complex real-world systems and understand their behaviors in response to different scenarios tested on them. Falconett & Nagasaka (2009) have developed a Monte Carlo simulation model to assess the financial impact of certain renewable energy projects under 21 different “renewable energy support mechanisms” including RECs. Mobini et al. (2010) have built a discrete-event simulation model of a biomass supply logistics system to quantify important process outcomes like GHG emissions. In addition, Saif et al. (2013) have proposed a simulation optimization model in determining “the optimal allocation and capacity” of distributed energy resources in a grid-level “power distribution system”. Forecasting is used to predict the future based on past data and/or other explanatory variables that are found to be significant. Within the energy space, forecasting is generally used to predict the future demand for various energy sources. Bianco et al. (2009) have used multiple linear regression models to predict the future electricity consumption of Italy. Similarly, Akpinar & Yumusak (2016) have studied “time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average methods” to forecast Turkey’s future natural gas consumption. 22 Chapter 4: Xpansiv Xpansiv is a software start-up. This chapter will focus on the analysis of Xpansiv’s business model, product, and major challenges. 4.1 Xpansiv’s Business Model Xpansiv extracts and refines production as well as supply chain data of natural gas and transforms it into a new data format called Digital Feedstock. Xpansiv’s Digital Feedstock is the digital representation of natural gas units with a full “attributional profile” that characterizes the process from natural gas’ creation to consumption. In other words, with Digital Feedstock, it is possible to reveal the “physical and environmental realities of production, transportation, storage, and consumption” of natural gas units (Xpansiv, n.d.). Time and location of production, production technique used to extract natural gas, and methane emissions can all be represented as attributes within the Digital Feedstock (Xpansiv, n.d.). Xpansiv has merged with CBL Markets which is a spot exchange operator for global energy and environmental commodities (Xpansiv, 2019). Digital representations of the associated attributes can be “digitally and contractually appended to the physical commodity” and be traded as “unified digital assets” through this registry while the gas flows through a pipeline and changes hands throughout the supply chain (Xpansiv, n.d.). It provides consumers with assurance about the integrity of the products they transact when Digital Feedstock is traded alongside the commodity itself and enables responsible natural gas sourcing (CBL Markets, 2019; Xpansiv, n.d.). However, Digital Feedstock can also be traded as a divisible, “non-financial digital asset” separately from the reference natural gas units (CBL Markets, 2019). 23 Hence, Xpansiv’s platform creates an opportunity for natural gas producers to differentiate themselves based on their actual carbon impact and verify this differentiation with a transparent and auditable platform (Xpansiv, n.d.). Moreover, the platform provides environmentally concerned customers with visibility to direct their natural gas procurement towards more sustainable alternatives. 4.2 Xpansiv’s Product Xpansiv’s product is the data format, Digital Feedstock. Xpansiv leverages advanced data analysis and blockchain technology during the creation and transaction of Digital Feedstock (Xpansiv, n.d.). Digital Feedstock operates on a private permissioned ledger and can be formulated as standardized, quantified, verified, and encrypted impacts for each unit of production. In order to generate Digital Feedstock, Xpansiv collects primary and secondary data. Primary data comprises the data directly related to the production and consumption of natural gas, provided by the producers and other supply chain actors. Secondary data is collected from “environmental standards, drones, satellites, and methane sniffers” (Xpansiv, n.d.). Xpansiv records every event in an append-only ledger which ensures that input data is not altered (Xpansiv, n.d.). The amount of data collected depends on the complexity of attributes producers are willing to trade through the platform (Xpansiv, n.d.). Data collection gives Xpansiv the context on how the facility was constructed. This enables Xpansiv to profile the facility and determine estimates for leak rates, greenhouse gas emissions, co-produced CO2 as well as attributes like water 24 production in real-time. Having a unique profile for natural gas units from each production source combined with Xpansiv’s understanding of the other supply chain actors’ data enables the company to characterize and differentiate the resulting electrons when gas units are burned. Being able to capture real-time data enables Xpansiv to detect anomalies in the process as well. However, Xpansiv should be securely connected to producers’ database systems (Xpansiv, n.d.). After collecting the data, Xpansiv creates the digital representation of original assets and assesses their level of environmental responsibility based on third-party environmental standards (Xpansiv, n.d.). If the data meets performance standards, Xpansiv refines, contextualizes, and digitizes it into Digital Feedstock by defining attributes which can create value for producers. Every contextual data may become an attribute which can be traded on Xpansiv’s platform. A producer can decide which attributes to pack or unpack for trading and data can only be accessed through that producer’s private key. These practices ensure that data is secured confidentially. For instance, a producer can determine which attributes are the most important to base a trade on and unpack the source data including any reports, certifications, equipment specifications or calculations, accordingly. Xpansiv’s platform ensures that “traded products are unique and immutably connected to real production volumes” (Xpansiv, n.d.). Each trading event of a Digital Feedstock should be validated before being performed and this inherits the “chain-of-custody” back to the original Digital Feedstock (Xpansiv, n.d.). “When an end-user wants to extract the claim of an underlying attribute set, they remove the units from the exchange and retire the Digital Feedstock, leading Xpansiv to generate the certificate of utilization” (Xpansiv, n.d.). 25 Xpansiv’s platform can also be used as evidence of the “title transfer” when Digital Feedstock is transacted between accounts (Xpansiv, n.d.). In addition to attributes, the transfer of ownership of a Digital Feedstock product is also captured within the platform. Since every event related to a Digital Feedstock product is appended to an append-only ledger, the platform can be used to prove provenance and authenticity of the data right back to the source and how it was produced as well as who owns the Digital Feedstock each time it is traded right up to retirement. 4.3 Xpansiv’s Challenges In general, blockchain systems are not “at the optimum maturity level” and are “in an early stage of the innovation process” (Wang et al., 2016; World Energy Council & PwC, 2017). This creates uncertainties in the consumer side as well as obstacles for blockchain’s wider adoption. Although Xpansiv has acquired the data of more than 6000 natural gas producers, the company still needs to develop trading volume through market acceptance of differentiated natural gas since the market will decide how the value of this platform will be realized. The acquisition of many data points at essentially real-time speeds generally creates technological challenges for blockchain companies as well. Limitations around scale, speed as well as computational overhead constraints with respect to required resources are among those challenges. Xpansiv collaborates with other blockchain protocols to process data fast and efficiently, and this empowers Xpansiv to overcome scalability challenges. Another common challenge for blockchain companies is to ensure that data entered in the network is not tampered with and is in good quality. Xpansiv overcomes the data accuracy challenge by receiving data from multiple meters as well as the inspection and compliance 26 reports of those meters. As for the data quality, Xpansiv deals with this challenge by creating an incentive mechanism favoring the high-quality data by influencing the price of a product according to the quality of its data. In this model, the higher the integrity and assurances around the data, the more valuable the product is. This attempt incentivizes data providers to invest in improving their data quality in order to sell their products for higher prices in the market. It is also a challenge to link the real and digital systems in this blockchain setting. This is partly because some participants do not have reliable communication systems and technology to interact with blockchain platforms and provide sufficient data. This is a limitation for Xpansiv since what the company gets to work with determines how much context they can extract. In such cases, Xpansiv collaborates with those producers to find ways of aggregating their data efficiently and cost-effectively (Xpansiv, n.d.). Furthermore, currently, the BC government does not recognize Xpansiv’s Digital Feedstock as a market instrument for climate change regulations. Digital Feedstock has the potential to be used like RECs with its environmental attributes. To overcome this challenge, Xpansiv should work with the provincial government to drive new policy discussions in British Columbia for differentiated natural gas units and environmental attributes. 27 Chapter 5: Fit-Gap Analysis This chapter identifies the opportunities for UBC, by assessing the weaknesses in UBC’s natural gas procurement and strengths in Xpansiv’s blockchain technology platform through a fit-gap analysis. These opportunities are categorized as solutions and process improvement ideas in the following sections. 5.1 Solutions As described in the previous chapters, natural gas consumption creates the majority of GHG emissions in UBC’s Vancouver campus. Moreover, under the provincial climate policies, UBC pays carbon taxes for each tonne of GHG emissions emitted and offsets its emissions to achieve carbon neutrality. Hence, campus wide GHG emissions lead to a significant capital hurdle for the university. In order to reduce GHG emissions, UBC aims at reducing the demand for non-renewable natural gas and increasing the consumption of renewable fuels. However, renewable natural gas has limited availability and it is expensive compared to non-renewable natural gas ($7 per GJ of renewable natural gas and $1.549 per GJ of non-renewable natural gas) (Fortis BC, 2020). However, unlike the existing perception of commodities, all natural gas units are not created equal. Xpansiv’s Digital Feedstock demonstrates that different natural gas units have different environmental impacts. For instance, unconventional non-renewable natural gas units have significantly higher production emissions than conventional non-renewable natural gas units (“Science for Environment Policy”, 2011). Xpansiv’s platform can enable the differentiation of gas units based on their environmental impact by identifying sources of low carbon natural gas 28 and create a new marketplace for lower GHG emitting units. Xpansiv’s natural gas differentiation model is illustrated in Figure 7. Figure 7: Xpansiv’s natural gas differentiation model (author's construction) Differentiated natural gas may provide affordable alternatives for UBC as it would give a price signal to natural gas producers related to the impact of their products. With the differentiation, renewable and non-renewable natural gas will not be the only two options available. Hence, it is likely that finding the cleanest non-renewable natural gas alternatives and including them in the natural gas mix will be a new practice followed by UBC to optimize its gas portfolio. Moreover, with differentiated natural gas units, the amount UBC pays for carbon offsets would be less. This is because, with data-driven emissions intensity factors that are calculated based on actual impact, it would be possible to prove that some portion of the natural gas purchased has a lesser impact than it was estimated. 29 Furthermore, as mentioned before, physical natural gas units and their corresponding environmental attributes are traded on Xpansiv’s platform. This feature enables customers to purchase natural gas units and environmental attributes from many verified sources across jurisdictions in a transparent, larger, and more liquid trading environment. Solutions that Xpansiv can offer for UBC’s natural gas procurement problems are summarized in Table 1. Problem Solution Renewable natural gas is scarce. UBC does not necessarily purchase renewable natural gas. Differentiated non-renewable natural gas will provide other alternatives. Renewable natural gas units and environmental attributes created in other jurisdictions will be traded via Xpansiv’s registry and hence increase UBC’s options. Renewable natural gas is expensive. Differentiated non-renewable natural gas units will provide other affordable alternatives. Data-driven emissions intensity factors will help UBC save costs from carbon offsets. Table 1: Summary of the problems and solutions 30 5.2 Process Improvement Ideas Renewable natural gas units are blended with non-renewable natural gas units into pipelines. The resulting mixture of renewable and non-renewable natural gas makes it very difficult to physically track gas units. Xpansiv’s platform can integrate real-time data across the supply chain. Real-time data improves the physical tracking of natural gas units and increases the traceability of the entire supply chain. With the increased traceability, UBC’s supply chain processes can potentially be simplified and made more efficient. Moreover, the integration of real-time data provides UBC with the assurance that it receives verified natural gas units that meet quality and environmental standards from many sources. Xpansiv’s platform can increase third-party institutions’ trust due to transparency, verifiability, and immutability of records when UBC makes a claim. This implies that UBC can claim how much methane is emitted from the natural gas procured for corporate GHG reports by using verifiable blockchain-based records. 31 Chapter 6: Estimating the Impact After acknowledging the potential benefits Xpansiv’s platform can create for UBC’s natural gas procurement, a simulation optimization model is developed to quantify the impact. In order to develop the simulation optimization model, OR tools of forecasting, mathematical modelling, and Monte Carlo simulation are utilized. In the parameter estimation section, parameters of the simulation optimization model will be explained. In the simulation optimization model section, the model and its resultant estimates for the GHG emissions, cost of natural gas and environmental attributes procurement, as well as carbon offsets and carbon tax payments and UBC’s potential success to achieve its 2020 sustainability commitment through Xpansiv’s platform will be discussed. In the what-if scenario analysis section, some what-if scenarios will be performed with the simulation optimization model. 6.1 Parameter Estimation Accurate estimates for the model parameters are critical for obtaining accurate outputs from the model. Model parameters, the reasoning behind the values assigned for those parameters, and assumptions are explained below. M : M (numerical) represents the number of different natural gas products meeting environmental standards on Xpansiv’s platform during the time of the purchase. Reasoning: Xpansiv acquired the data of more than 6000 natural gas producers. Hence, a uniform random number between 1 and 6000 can represent the number of different natural gas products whose environmental attributes are found to be valuable to trade on the platform during the time 32 of the purchase. The reason of selecting the uniform distribution is that M can be any value between these upper and lower limits with equal probability. Ei : E (in tonnes per GJ) represents the emission intensity of natural gas product i during the time of the purchase for each i = 1, ..., M. Furthermore, Emax stands for the emissions intensity of the most carbon-intensive product. Reasoning: BC Ministry of Environment in its best practices methodology for quantifying GHG emissions guideline considers the GHG emissions intensity of non-renewable natural gas as 0.04975 tonnes per GJ (BC Ministry of Environment, 2014). Similarly, Fortis BC takes GHG emissions intensity of non-renewable natural gas as 0.04987 tonnes per GJ in its emissions calculator (Fortis BC, n.d.). Given that differentiated natural gas in addition to traditional renewable and non-renewable natural gas can be available on Xpansiv’s platform, a uniform random number between 0 and 0.05 can represent the emission intensity of a natural gas product on the platform. Hence, Emax is taken as 0.05 in the model. Cgi : Cg (in CAD per GJ) represents the cost of one GJ natural gas product i during the time of the purchase for each i = 1, ..., M. Reasoning: Fortis BC on its website declares the cost of one GJ non-renewable natural gas as $1.549 and one GJ renewable natural gas as $7 (Fortis BC, 2020). In the model, the cost of natural gas will be a decreasing function of emissions intensity. Considering that the cost of differentiated natural gas will be a value in between traditional renewable and non-renewable natural gas, a uniform random number between 1.5 and 7 can represent the cost of a natural gas product in the model. 33 Assumption: For simplicity, the cost of natural gas is determined solely based on its emissions intensity in the model. However, there may be other factors that can determine the price of gas units. For instance, as mentioned in the previous sections, in order to create an incentive mechanism for natural gas producers to capture better quality data, Xpansiv influences the prices of their products. Moreover, in general, natural gas prices are affected by the supply and demand in the market. All these additional effects are ignored in the model. Cai : Ca (in CAD per GJ) represents the cost of a GHG emissions environmental attribute derived from one GJ natural gas product i during the time of the purchase for each i = 1, ..., M. Reasoning: The price of RECs is taken as a reference point to come up with a cost estimate for GHG emissions environmental attributes. From 2015 to 2017, REC prices ranged between $0.0015 and $0.00045 per kWh of electricity produced whereas the price of electricity ranged between $0.1041 and $0.1048 for the same amount (US Environmental Protection Agency, 2018). Since the price of RECs ranged between 0.4% and 1.4% of electricity prices, the cost of GHG emissions environmental attributes is taken as a uniform random number between 0.4% and 1.4% of the price of corresponding natural gas on the platform. Assumption: When calculating the price of environmental attributes, the price is only tied to the worth of their corresponding natural gas units as a percentage. The effects of other factors, such as supply and demand in the market for environmental attributes, are ignored. Sgi : Sg (in GJs) represents the total amount of natural gas type i available during the time of the purchase for each i = 1, ..., M. 34 Assumption: It is assumed that a natural gas producer can supply 50,000 GJs of natural gas at most. So, this value is considered as a uniform random number between 0 and 50,000. Sai : Sa (in GJs) represents the supply of GHG emissions environmental attributes from natural gas product i available during the time of the purchase for each i = 1, ..., M. Reasoning: The supply of GHG emissions environmental attributes from each natural gas product is based on the supply of natural gas the attributes are derived from. The supply of each natural gas product (in GJs) puts an upper bound on how many GJs worth GHG emissions environmental attributes derived from that product are available on the platform. Therefore, the supply of GHG emissions environmental attributes from each natural gas product during the time of the purchase can uniformly range between 0% and 100% of the supply of the corresponding natural gas on the platform. Z : Z (in tonnes) represents the annual emissions target of UBC. Reasoning: In the model, Z is considered as UBC’s 2020 emissions target which is necessary to achieve the 2020 sustainability commitment. Based on UBC’s climate action plan, the UBC Vancouver campus needs to reduce its emissions down to 20,000 tonnes by the end of 2020 in order to accomplish the “67% reduction below 2007 levels” (UBC, 2016). Hence, the value of Z is taken as 20,000. O : O (in tonnes) represents the annual emissions generated other than natural gas consumption. Reasoning: According to UBC’s sustainability reports, total GHG emissions at the UBC Vancouver campus in 2016 is 40,536 tonnes (UBC, 2016). Since approximately 90% percent of 35 emissions come from the natural gas use in that year, this parameter is considered as 10% of 40,536. Therefore, the value of O in the model is taken as 4054. Cof : Cof (in CAD per tonne) represents the cost of carbon offsets per unit. Reasoning: The carbon offset rate is $25 per tonne of GHG emissions in British Columbia for the public sector institutions (Ministry of Environment and Climate Change Strategy, 2018). Hence, the value of Cof is taken as 25 in the model. Tax : Tax (in CAD per tonne) represents the cost of carbon taxes per unit. Reasoning: The carbon tax rate is $40 per tonne of GHG emissions according to BC government regulations (Government of BC, 2019). Hence, the value of Tax is taken as 40 in the model. D : D (in GJs) represents the annual demand for natural gas. Reasoning: In this context, annual demand is considered as UBC’s 2020 total natural gas demand. To predict the future demand, a time series forecasting model is generated based on past total natural gas consumption data (in GJs). Data used in the model is compiled from UBC’s GHG inventory reports from 2006 to 2017. According to the data, UBC starts using renewable natural gas in 2014 and there is an increasing trend in renewable natural gas consumption whereas the trend is decreasing in total natural gas consumption over the years. The consumption of both total natural gas and renewable natural gas is illustrated in Figure 8. 36 Figure 8: UBC Vancouver annual total and renewable natural gas consumption (author's construction) According to Figure 8, the total natural gas consumption of UBC seems to have an observable trend. Moreover, because of annual data, seasonality cannot be estimated. Therefore, an exponential smoothing method of Holt’s model is used to forecast future demands since Holt’s model uses level and trend components to make predictions. Different trend components such as linear, exponential, additive damped, and multiplicative damped are fitted to the data. These models are explained in more detail in Appendix A. Models are compared by their in-sample and 37 cross-validated out-of-sample performance based on mean error (ME), root mean squared error (RMSE), and mean absolute error (MAE) to come up with a good estimate. Models are built in the R environment for statistical analysis and can be found in Appendix B. The results of Holt’s models with different trend components are summarized in Table 2. Year Observation (GJs) Level Trend Forecast (GJs) Holt’s model with linear trend 2005 1235055.7 -33734.33 2006 1282363 1201329.5 -33726.23 1201321.4 2007 1084480 1167595.0 -33734.54 1167603.3 2008 1110520 1133858.1 -33736.87 1133860.4 2009 1063490 1100117.6 -33740.54 1100121.2 2010 1017810 1066372.2 -33745.39 1066377.0 2011 1134530 1032637.0 -33735.20 1032626.8 2012 1062483 998908.1 -33728.85 998901.8 2013 948183 965177.6 -33730.55 965179.3 2014 928673 931446.7 -33730.82 931447.0 2015 862636 897712.4 -33734.33 897715.9 2016 817766 863973.5 -33738.95 863978.1 2017 870677 830238.6 -33734.91 830234.5 2018 796503.6 2019 762768.7 2020 729033.8 38 Year Observation (GJs) Level Trend Forecast (GJs) Holt’s model with exponential trend 2005 1237846.3 0.9687642 2006 1282363 1199199.4 0.9687789 1199181.2 2007 1084480 1161742.2 0.9687648 1161759.0 2008 1110520 1125451.7 0.9687620 1125455.0 2009 1063490 1090289.1 0.9687568 1090294.9 2010 1017810 1056216.6 0.9687492 1056225.0 2011 1134530 1023233.2 0.9687721 1023209.0 2012 1062483 991295.4 0.9687873 991279.8 2013 948183 960351.7 0.9687846 960354.4 2014 928673 930373.6 0.9687842 930374.0 2015 862636 901322.9 0.9687752 901331.3 2016 817766 873167.1 0.9687618 873179.2 2017 870677 845896.4 0.9687680 845891.0 2018 819477.3 2019 793883.4 2020 769088.8 Holt’s model with additive damped trend 2005 1235057.0 -33735.26 2006 1282363 1203885.7 -31171.71 1201996.4 2007 1084480 1171248.6 -32636.68 1173337.4 39 Year Observation (GJs) Level Trend Forecast (GJs) 2008 1110520 1138588.9 -32659.53 1139264.6 2009 1063490 1105569.5 -33019.14 1106582.6 2010 1017810 1071908.4 -33660.84 1073210.8 2011 1134530 1041168.4 -30740.53 1038920.8 2012 1062483 1012251.9 -28916.72 1011042.7 2013 948183 983073.6 -29178.15 983913.5 2014 928673 953872.4 -29201.11 954479.0 2015 862636 923783.2 -30088.82 925255.3 2016 817766 892497.1 -31285.73 894296.2 2017 870677 862044.9 -30452.25 861837.1 2018 832201.7 2019 802955.3 2020 774293.9 Holt’s model with multiplicative damped trend 2005 1236031.0 0.9656663 2006 1282363 1194436.6 0.9663484 1194427.8 2007 1084480 1155025.3 0.9670043 1155032.3 2008 1110520 1117663.4 0.9676528 1117664.1 2009 1063490 1082219.7 0.9682877 1082221.5 2010 1017810 1048572.5 0.9689091 1048575.6 2011 1134530 1016625.2 0.9695326 1016613.4 40 Year Observation (GJs) Level Trend Forecast (GJs) 2012 1062483 986269.0 0.9701402 986261.4 2013 948183 957398.7 0.9707277 957399.6 2014 928673 929925.6 0.9713045 929925.8 2015 862636 903763.0 0.9718659 903767.1 2016 817766 878831.7 0.9724139 878837.8 2017 870677 855068.0 0.9729599 855066.5 2018 832403.2 2019 810339.0 2020 789283.7 Table 2: Forecasts from Holt’s models with different trend components Forecasts of the models for the years in which total gas consumption data are available are used to measure the forecast accuracy. In-sample and out-of-sample performance of the models can be seen in Table 3. Trend Component ME RMSE MAE In-sample performance of the models Linear Trend -479.6452 55862.75 48307.74 Exponential Trend 2089.768 56350.19 46325.57 Additive Damped Trend -15043.78 60341.34 54419.78 Multiplicative Damped Trend 4818.176 57390.8 44795.81 41 Trend Component ME RMSE MAE Out-of-sample performance of the models Linear Trend 90.13772 88982.05 71988.67 Exponential Trend -7358.554 88101.28 73474.72 Additive Damped Trend -285.4483 89840.29 69185.99 Multiplicative Damped Trend -3876.58 86049.94 66806.06 Table 3: In-sample and out-of-sample performance of the models All data points, hence the forecast errors, are used to estimate the forecasting performance measures when calculating the in-sample performance. On the other hand, one-step forecast errors of training data with respect to test data are estimated to perform time series cross-validation and have an idea about the out-of-sample performance of the models. In this method, there is a single observation in the test data and all the other observations realized before that data generate the training data for each data point. Compared models are illustrated in Figure 9. For the in-sample performance, Holt’s model with linear trend performs better according to the ME and RMSE measures whereas Holt’s model with multiplicative damped trend performs better based on the MAE measure. However, in general, out-of-sample performance is a better determinant for models’ accuracy in estimating future data points. Therefore, the model which has a better out-of-sample performance will be selected to forecast the 2020 demand. For the out-of-sample performance, Holt’s models with linear trend performs better according to the ME measure while Holt’s model with multiplicative damped trend performs better based on the 42 RMSE and MAE measures. Therefore, Holt’s models with multiplicative damped trend is selected as it performs better in more measures for out-of-sample prediction. In order to reflect the variation in historical data, the distribution of the future demand should be incorporated into the simulation optimization model rather than the point forecast. Therefore, in the model, parameter D is considered as a normally distributed random number with mean 789,284 (value of the point forecast) and standard deviation 57,000 (approximate variability around the prediction intervals) to represent the 2020 total natural gas demand of UBC. Assumption: When calculating UBC’s 2020 natural gas demand, only past natural gas consumption data is considered to build the forecasting model. However, there may be other predictors that can have an impact on the demand for natural gas such as weather forecasts for that year. In the model, other potential predictors are ignored. 43 Figure 9: Comparison of Holt’s models with different trend components (author's construction)44 6.2 Simulation Optimization Model The simulation optimization model combines mathematical modelling and Monte Carlo simulation. Model parameters that come from certain probabilistic distributions as described in the previous section are generated from scratch for each run to incorporate randomness into the model. These parameters are used to solve an optimization model in each run. Results obtained from the simulation optimization model are used to estimate total emissions and the total cost for natural gas, environmental attributes, carbon offsets, and carbon taxes. Moreover, it provides business insights regarding UBC’s future success to achieve its 2020 sustainability commitment when the university optimizes its gas portfolio through Xpansiv’s platform. The simulation optimization model is created in the R environment and can be found in Appendix C. A pseudo code that explains the model is as follows. Pseudo Code 1. Define cost, emissions, and probability arrays 2. For each run a. Define the model parameters according to their individual distributions b. Solve the optimization model c. Find the amount of natural gas, environmental attributes, and carbon offsets purchases as a result of the optimization model d. Find the total cost of natural gas and environmental attributes procurement in addition to carbon offsets and carbon tax payments as a result of the optimization model 45 e. Find the amount of emissions generated from natural gas consumption and other activities and deduct the amount of emissions avoided by environmental attributes that are associated with the relative renewability of the corresponding natural gas units f. Add total cost to the cost array and total emissions to the emissions array g. If total emissions emitted in that run are less than the emissions target, assign 1 to a performance variable; otherwise, assign 0 h. Add the performance variable to the probability array 3. Calculate the average of emissions and cost arrays to find the average emissions generated and average costs incurred as a result of all runs 4. Calculate the average of the probability array to find the probability of UBC achieving its emissions target The optimization model used in the simulation optimization is a linear program. The model tries to find the optimal amount of natural gas, environmental attribute, and carbon offset purchases while minimizing costs. Information about the inputs of the optimization model and the model itself are as follows. 46 Decision Variables Xi : The amount of natural gas product i to be purchased (in GJs) for each i = 1, ..., M Yi : The amount of GHG emissions environmental attributes derived from natural gas product i to be purchased (in GJs) for each i = 1, ..., M F : The amount of carbon offsets to be purchased (in tonnes) Model Min ∑ (𝑋𝑖𝐶𝑔𝑖 + 𝑌𝑖𝐶𝑎𝑖) + 𝐹 𝐶𝑜𝑓 + 𝑀𝑖=1 𝑇𝑎𝑥 (∑ (𝑋𝑖𝐸𝑖 − 𝑌𝑖(𝐸𝑚𝑎𝑥 − 𝐸𝑖)) 𝑀𝑖=1+ 𝑂) (1) s.t. ∑ (𝑋𝑖) 𝑀𝑖=1 ≥ D (2) ∑ (𝑋𝑖𝐸𝑖 − 𝑌𝑖(𝐸𝑚𝑎𝑥 − 𝐸𝑖)) 𝑀𝑖=1+ 𝑂 – F = 0 (3) Xi ≤ Sgi ; i = 1, ..., M (4) Yi ≤ Sai ; i = 1, ..., M (5) Xi ≥ 0 ; i = 1, ..., M (6) Yi ≥ 0 ; i = 1, ..., M (7) F ≥ 0 (8) 47 The objective function of the optimization model (1) minimizes the total cost of natural gas, GHG emissions environmental attributes, and carbon offsets to be purchased as well as carbon taxes to be paid. Constraint (2) states that sum of the purchased natural gas amount should be greater than or equal to the annual demand. Constraint (3) ensures that carbon neutrality is satisfied by offsetting the remaining emissions. Constraint (4) ensures that each natural gas product can only be purchased as much as its supply. Constraint (5) indicates that GHG emissions environmental attributes from each natural gas product can only be purchased as much as their supplies. Finally, constraints (6), (7), and (8) imply the non-negativity of decision variables. As a result of the simulation optimization model, total GHG emissions that would be emitted by UBC in 2020 are estimated as nearly zero in 98% of the simulation runs, with an average of 258 tonnes. This implies that environmental attributes and differentiated natural gas options are effective instruments to reduce UBC’s environmental footprint, leading to significantly less carbon offset and carbon tax payments. The total cost of natural gas and environmental attribute purchases in addition to carbon offset and carbon tax payments is found to be $1,304,737 on average. Moreover, the probability of achieving the 2020 sustainability target is calculated as 1. To assess the impact of the results from the simulation optimization model, a comparative analysis is performed with the status quo. Assume that UBC purchases the point forecast of 789,284 GJs of natural gas in 2020. Since currently there are two types of natural gas, some of this amount is comprised of renewable natural gas. To find an estimate for 2020 renewable natural gas demand, the same forecasting techniques are applied to UBC’s renewable natural gas consumption data. Based on the results, demand for renewable natural gas is estimated as 72,229 GJs for 2020. Therefore, without the differentiated 48 natural gas concept, UBC purchases 717,055 GJs non-renewable natural gas. This amount corresponds to approximately 35,853 tonnes of GHG emissions from the natural gas procurement. After including emissions generated from other activities as well, total GHG emissions of the UBC Vancouver campus amount to 39,907 tonnes. This result is significantly higher than the estimated average emissions of 258 tonnes. The total cost of natural gas procurement, as well as carbon offsets and carbon tax payments in the current system is $4,175,141 whereas this cost is estimated as $1,304,737 in the proposed system. Furthermore, the proposed system almost guarantees the achievement of future sustainability commitments without being constrained by the availability of renewable natural gas. According to the results obtained from the comparative analysis, UBC’s unit cost savings to meet its environmental goals is approximately $72.4 per tonne of emissions reduction. 6.3 What-if Scenario Analysis In order to understand the behavior of the simulation optimization model with respect to certain model parameters, some hypothetical scenarios are tested and analyzed as follows. During the experimentation, the seed of the random number generator in the model is set to a particular value to eliminate the randomness effect and observe changes that only result from the changing parameters. Scenario 1 – What happens if demand goes up, but natural gas supplies available on the platform remain the same? To analyze this scenario, the value of parameter D in the model is changed from 700,000 GJs levels to 1,300,000 GJs levels. The reason for this upper limit is that it is even higher than the 49 maximum natural gas consuming year in the past. Given that natural gas consumption of UBC is decreasing over time, it is unlikely to see a demand value going beyond that level. After experimenting with D, it is observed that only the total cost of natural gas, environmental attributes, carbon offsets, and carbon taxes changes whereas emissions and the probability of achieving the sustainability target remain constant as D changes. The change in the total cost with respect to experimented D values is illustrated in Figure 10. Figure 10: The change in total cost with changing demand (author's construction) While total cost goes up, emissions are estimated as nearly zero, and the probability of achieving the sustainability target is constantly found to be 1. This implies that the amount of natural gas available on the platform is sufficient to cover the increasing demand when all the other parameters are kept the same. Scenario 2 – What happens when the supply of GHG emissions environmental attributes changes? To analyze the outcomes, the value of parameter Sa is altered in the model. The parameter is experimented with different values between 0% and 8% (no change is observed after 8%) of the 50 natural gas supplies available on the platform when all the other parameters stay the same. As a result of these experiments, it is seen that total cost, emissions, and the probability of achieving the sustainability target change depending on the availability of GHG emissions environmental attributes. The change in total cost, emissions, and the probability with changing environmental attribute supply is illustrated in Figure 11. 51 Figure 11: The change in total cost, emissions, and the probability with changing environmental attribute supply (author's construction) Since purchasing GHG emissions environmental attributes is a cheaper way of reducing GHG emissions compared to purchasing carbon offsets, the total cost goes down as the supply of GHG emissions environmental attributes goes up. Moreover, it is sufficient to have GHG emissions environmental attributes that correspond to 8% of the natural gas supplies to achieve zero GHG emissions and 4% of the natural gas supplies to achieve the 2020 sustainability target with a probability of 1. Scenario 3 – What happens if the cost of environmental attributes goes up? To assess this scenario, parameter Ca in the model is assigned different values that change between 40% and 50% (before and after the breakeven point) of the cost of natural gas units. As a result of these trials, it is observed that total cost, emissions, and the probability of achieving the sustainability target change according to the price of environmental attributes. The change in total 52 cost, emissions, and the probability with changing environmental attribute price is illustrated in Figure 12. 53 Figure 12: The change in total cost, emissions, and the probability with changing environmental attribute price (author's construction) The total cost increases as the cost of environmental attributes goes up. However, once environmental attributes reach a breakeven point between 46% and 47% of the cost of natural gas units, they become more expensive than carbon offsets. For that reason, the total cost stays constant when environmental attribute prices go beyond the breakeven point as the model selects purchasing carbon offsets instead. A similar result can also be deduced from the behavior of emissions with respect to changing environmental attribute prices. Before the breakeven point, environmental attributes are used as an instrument to lower GHG emissions, leading to achieving zero overall GHG emissions. After the breakeven point, the model switches to carbon offsets to achieve carbon neutrality and emissions stay constant. In addition, the probability of achieving the 2020 sustainability target changes from 1 to zero after the breakeven point due to the switch from environmental attributes to carbon offsets. 54 Chapter 7: Conclusion and Future Work This chapter includes the conclusion and future work sections. In the conclusion section, findings of the study are explained, and research questions are answered. In the future work section, further research areas are presented. 7.1 Conclusion The research objectives of this study are to identify the potential benefits that Xpansiv’s blockchain technology platform can create for UBC’s (Vancouver campus) natural gas procurement and quantify the impact. To fulfill these research objectives, a fit-gap analysis is performed and a simulation optimization model is built through the use of OR tools. As a result of the fit-gap analysis, it is found that Xpansiv can decommoditize natural gas units by using data collected from their production and consumption processes to reveal the resulting environmental impact and market their environmental attributes on a blockchain-based platform. UBC can benefit from the differentiated natural gas units and the use of environmental attributes as a market instrument because of the scarcity and expensiveness of renewable natural gas. Given that UBC is trying to reduce its GHG emissions to achieve its sustainability commitments in the most cost-effective way, an estimation of GHG emissions, total costs and UBC’s potential success in achieving the sustainability commitments are important metrics to quantify. To perform this quantification, a simulation optimization model is created for 2020. According to the results, the average total GHG emissions are estimated as 258 tonnes, the average total cost is calculated as $1,304,737, and the probability of achieving the sustainability commitment is found as 1. 55 After comparing these results with the status quo, it is found that the unit cost savings is approximately $72.4 per tonne of emissions reduction. In other words, Xpansiv’s platform is useful in reducing GHG emissions and achieving the 2020 sustainability commitment. It is also less expensive than the current system. The availability of renewable natural gas units is currently a constraint and Xpansiv’s platform can uniquely eliminate this constraint. However, UBC can continue developing other strategies to reduce the demand for natural gas to decrease total costs further. In addition, since GHG emissions are estimated to be much lower than the targeted level for 2020, Xpansiv’s platform can provide great benefits in achieving the 2050 target of zero emissions as well. 7.2 Future Work Further research studies can explore the separation of physical products and their process attributes for trading through the use of blockchain technology. Process attributes comprise, but are not limited to, the way a product is produced, what it is produced from, where it is produced, and who worked for its production. For certain products, whether they are commodities or not, process attributes carry upmost importance for the value and legitimacy of the final product. In the natural gas setting, environmental attributes can be considered as process attributes where natural gas is the physical product. This idea can be applied to other settings as well such as whether child labor is used during the manufacturing of a garment or unsustainable methods are used for palm oil production that can lead to deforestation in a certain region. Blockchain can be helpful to capture process attributes and keep them for the use of network participants, other third parties, and consumers. 56 Moreover, future research studies can discuss the global applications of environmental attributes. For instance, Paris Agreement mentions carbon based internationally transferred mitigation outcomes (ITMOs) that represent the emissions reduction achievements of countries. Blockchain based platforms can digitize ITMOs and support their transfer between different nations. This can be a chance to enhance collaboration between countries and create global emissions reductions opportunities. 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Available at: https://www.xpansiv.com/2019/08/20/cbl-markets-and-xpansiv-merge-to-create-xchg-the-worlds-first-data-driven-commodity-marketplace/ 63 Appendices Appendix A: Explanation of the Forecasting Models Exponential smoothing methods provide a weighted average of past data points by giving more importance (weight) to recent observations compared to older ones, where weights assigned to older observations decrease exponentially. Holt’s model is an exponential smoothing method and it is used to forecast data points with only level and trend components. In Holt’s model, α (0 ≤ α ≤ 1) represents the smoothing parameter for the level and β (0 ≤ β ≤ 1) represents the smoothing parameter for the trend. In the damped trend models, there is an additional damping parameter φ (0 ≤ φ ≤ 1). In order to apply exponential smoothing methods, smoothing parameters and initial values are first estimated by minimizing the sum of squared one-step-ahead forecast errors (Hyndman & Athanasopoulos, 2018). Notation Ft : Forecast for observation t yt : Actual observation t ℓt : Estimate of the level at time t bt : Estimate of the trend at time t 64 Holt’s Method with Linear Trend Component Forecast: Ϝt+n = ℓt + nbt Level: ℓt = αyt + (1−α)(ℓt−1 + bt−1) Trend: bt = β(ℓt − ℓt-1) + (1−β)bt−1 Holt’s Method with Exponential Trend Component In Holt’s method with exponential trend component, level and trend are multiplied instead of being added. Forecast: Ϝt+n = ℓt bt n Level: ℓt = αyt + (1−α)(ℓt−1 bt−1) Trend: bt = β(ℓt / ℓt-1) + (1−β)bt−1 Holt’s Method with Additive Damped Trend Component Additive damped trend component is an extention to Holt’s method with linear trend. The rationale behind this extention is to “dampen the trend to a flat line some time in the future” to avoid over-forecasts (Hyndman & Athanasopoulos, 2018). Forecast: Ϝt+n = ℓt + (φ + φ2 + φ3 + … + φn)bt Level: ℓt = αyt + (1−α)(ℓt−1 + φbt−1) Trend: bt = β(ℓt − ℓt-1) + (1−β)φbt−1 65 Holt’s Method with Multiplicative Damped Trend Component Multiplicative damped trend component is an extention to Holt’s method with exponential trend. Forecast: Ϝt+n = ℓt 𝑏𝑡(𝜑 + 𝜑 2+ 𝜑 3+⋯+ 𝜑 𝑛) Level: ℓt = αyt + (1−α)(ℓt−1 𝑏𝑡−1𝜑 ) Trend: bt = β(ℓt / ℓt-1) + (1−β) 𝑏𝑡−1𝜑 66 Appendix B: Computation of the Forecasting Models require(readxl), require(dplyr), require(tidyverse), require(forecast), require(plyr), require(TTR), require(fpp2) gas_data <- read_xlsx("Forecast Data.xlsx") gas_data <- gas_data %>% mutate(Year=factor(Year)) #Create time series time_series_gas <- ts(gas_data$`Usage GJ`, start=2006, end=2017, frequency=1) time_series_gas plot(time_series_gas, col = "blue", xlab = "Time Series", ylab = "Total Natural Gas Consumption") time_series_rng <- ts(gas_data$`RNG Portion`[9:12], start=2014, end=2017, frequency=1) time_series_rng plot(time_series_rng, col = "blue", xlab = "Time Series", ylab = "Renewable Natural Gas Consumption") #Holt's method with linear trend holt_forecast_lin <- holt(time_series_gas, h=3) holt_forecast_lin autoplot(holt_forecast_lin, PI = FALSE) + theme_bw() + ylab("Natural Gas Consumption") summary(holt_forecast_lin) accuracy(holt_forecast_lin) holt_forecast_lin$model$states #Holt's method with exponential trend holt_forecast_exp <- holt(time_series_gas, h=3, exponential = TRUE) holt_forecast_exp autoplot(holt_forecast_exp, PI = FALSE) + theme_bw() + ylab("Natural Gas Consumption") summary(holt_forecast_exp) accuracy(holt_forecast_exp) holt_forecast_exp$model$states #Holt's method with additive damped trend holt_forecast_add <- holt(time_series_gas, h=3, damped = TRUE) holt_forecast_add 67 autoplot(holt_forecast_add, PI = FALSE) + theme_bw() + ylab("Natural Gas Consumption") summary(holt_forecast_add) accuracy(holt_forecast_add) holt_forecast_add$model$states #Holt's method with multiplicative damped trend holt_forecast_mult <- holt(time_series_gas, h=3, exponential = TRUE, damped = TRUE) holt_forecast_mult autoplot(holt_forecast_mult) + theme_bw() + ylab("Natural Gas Consumption") summary(holt_forecast_mult) accuracy(holt_forecast_mult) holt_forecast_mult$model$states library(ggplot2) forecast_data <- read_xlsx("Forecast.xlsx") forecast_data <- forecast_data %>% mutate(Year=factor(Year)) colors <- c("Linear" = "purple", "Exponential" = "blue", "Additive" = "green", "Multiplicative" = "red", "Original" = "black") ggplot() + geom_point(data = forecast_data, aes(x = Year, y = Linear, group = 1, colour = "Linear")) + geom_point(data = forecast_data, aes(x = Year, y = Exponential, group = 1, colour = "Exponential")) + geom_point(data = forecast_data, aes(x = Year, y = Additive, group = 1, colour = "Additive")) + geom_point(data = forecast_data, aes(x = Year, y = Multiplicative, group = 1, colour = "Multiplicative")) + geom_point(data = forecast_data, aes(x = Year, y = Original, group = 1, colour = "Original")) + geom_line(data = forecast_data, aes(x = Year, y = Linear, group = 1, colour = "Linear")) + geom_line(data = forecast_data, aes(x = Year, y = Exponential, group = 1, colour = "Exponential")) + geom_line(data = forecast_data, aes(x = Year, y = Additive, group = 1, colour = "Additive")) + geom_line(data = forecast_data, aes(x = Year, y = Multiplicative, group = 1, colour = "Multiplicative")) + geom_line(data = forecast_data, aes(x = Year, y = Original, group = 1, colour = "Original")) + 68 labs(x = "Time", y = "Total Natural Gas Consumption", color = "Legend") + scale_color_manual(values = colors) + theme_bw() # Time series cross-validation holt_forecast_lin_cv <- tsCV(time_series_gas, holt, h=1) holt_forecast_lin_cv holt_forecast_exp_cv <- tsCV(time_series_gas, holt, exponential = TRUE, h=1) holt_forecast_exp_cv holt_forecast_add_cv <- tsCV(time_series_gas, holt, damped = TRUE, h=1) holt_forecast_add_cv holt_forecast_mult_cv <- tsCV(time_series_gas, holt, exponential = TRUE, damped = TRUE, h=1) holt_forecast_mult_cv #ME mean(holt_forecast_lin_cv, na.rm=TRUE) mean(holt_forecast_exp_cv, na.rm=TRUE) mean(holt_forecast_add_cv, na.rm=TRUE) mean(holt_forecast_mult_cv, na.rm=TRUE) #RMSE sqrt(mean(holt_forecast_lin_cv^2, na.rm=TRUE)) sqrt(mean(holt_forecast_exp_cv^2, na.rm=TRUE)) sqrt(mean(holt_forecast_add_cv^2, na.rm=TRUE)) sqrt(mean(holt_forecast_mult_cv^2, na.rm=TRUE)) #MAE mean(abs(holt_forecast_lin_cv), na.rm=TRUE) mean(abs(holt_forecast_exp_cv), na.rm=TRUE) mean(abs(holt_forecast_add_cv), na.rm=TRUE) mean(abs(holt_forecast_mult_cv), na.rm=TRUE) 69 Appendix C: Computation of the Simulation Optimization Model require(dplyr), require(tidyverse), require(stats), require(lpSolve), require(ompr), require(ompr.roi), require(glpkAPI), require(ROI), require(ROI.plugin.glpk) cost_array <- NULL emissions_array <- NULL prob_array <- NULL for (run in 1:100){ #Define simulation parameters M = sample(1:6000, 1, replace = TRUE) E = NULL k = 0 for (x in 1:M){ E = c(E, runif(1)*0.05) k = k + 1} Cg = NULL a = 0 for (x in 1:M){ Cg = c(Cg, 7-110*E[x]) a = a + 1} Ca = NULL l = 0 perc = (0.4+runif(1))/100 for (x in 1:M){ Ca = c(Ca, perc*Cg[x]) l = l + 1} Sg = NULL b = 0 for (x in 1:M){ Sg = c(Sg, runif(1)*50000) b = b + 1} Sa = NULL c = 0 70 for (x in 1:M){ Sa = c(Sa, runif(1)*Sg[x]) c = c + 1} D = rnorm(1, mean=789284, sd=57000) Z = 20000 O = 4054 Cof = 25 Tax = 40 #Set up the optimization model model <- MIPModel() %>% add_variable(X[i], i = 1:M, type = "continuous") %>% add_variable(Y[i], i = 1:M, type = "continuous") %>% add_variable(F, type = "continuous") %>% set_objective(sum_expr(X[i]*Cg[i] + Y[i]*Ca[i], i = 1:M) + F*Cof + Tax ıııııı*(sum_expr(X[i]*E[i] - Y[i]*(0.05-E[i]), i = 1:M) + O), "min") %>% add_constraint(sum_expr(X[i], i = 1:M) >= D) %>% add_constraint(sum_expr(X[i]*E[i] - Y[i]*(0.05-E[i]), i = 1:M) + O - F1 ıııııı== 0) %>% add_constraint(X[i] <= Sg[i], i = 1:M) %>% add_constraint(Y[i] <= Sa[i], i = 1:M) %>% add_constraint(X[i] >= 0, i = 1:M) %>% add_constraint(Y[i] >= 0, i = 1:M) %>% add_constraint(F >= 0) %>% solve_model(with_ROI(solver = "glpk", verbose = TRUE)) gas <- get_solution(model, X[i]) total_gas_purchase <- sum(gas$value) attribute <- get_solution(model, Y[i]) total_attribute_purchase <- sum(attribute$value) offset <- get_solution(model, F) cost <- model$objective_value cost_array <- c(cost_array, cost) emissions <- sum(gas$value %*% E) - sum(attribute$value %*% (0.05-E)) + O emissions_array <- c(emissions_array, emissions) if (offset <= Z) { p = 1 } else { p = 0 } 71 prob_array <- c(prob_array, p) } mean(cost_array) mean(emissions_array) mean(prob_array) plot(cost_array, col = "blue") plot(emissions_array, col = "blue") plot(prob_array, col = "blue")
Thesis/Dissertation
eng
Business Administration - Transportation and Logistics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Blockchain meets natural gas : a case study of the University of British Columbia and Xpansiv from an operations research perspective
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