UBC Undergraduate Research

AMS sustainable food truck : technology assessment & energy management Sens, Guilherme Ono Aug 1, 2014

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
18861-Sens_Guilherme_SEEDS_2014.pdf [ 989.7kB ]
Metadata
JSON: 18861-1.0108752.json
JSON-LD: 18861-1.0108752-ld.json
RDF/XML (Pretty): 18861-1.0108752-rdf.xml
RDF/JSON: 18861-1.0108752-rdf.json
Turtle: 18861-1.0108752-turtle.txt
N-Triples: 18861-1.0108752-rdf-ntriples.txt
Original Record: 18861-1.0108752-source.json
Full Text
18861-1.0108752-fulltext.txt
Citation
18861-1.0108752.ris

Full Text

 UBC Social Ecological Economic Development Studies (SEEDS) Student ReportGuilherme Ono SensAMS Sustainable Food Truck: Technology Assessment & Energy ManagementVOL 400August 01, 201411571677University of British Columbia Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report”.         AMS Sustainable Food Truck: Technology Assessment & Energy Management     By: Guilherme Ono Sens Supervisor: Dr. Paul Lusina Date: August 2014   2  Abstract   The AMS at UBC has a goal to drastically reduce the Green House Gas emission in the next decade. For such manner, a sustainable food truck fits their purpose and the culture of the city of Vancouver. This project involves a simulation system as well as an energy management algorithm to predict the behaviour of the electrical system of the truck and to best suit its needs, considering all the equipment is powered  by solar panels (as a primary source), fuel cells (secondary source) and a battery (back-up source). Also, the project includes the implementation of each subsystem and their integration to the point where the relevant data can be displayed on the screen. The user will be able to customize the simulations, choosing which subsystem will be turned on or off.  Scenarios for the simulations were tested, such as a busy sunny day (during summer), providing enough data to state that the mobility system of the truck may not be supported by the sources. Moreover, unless the technologies for equipment and energy generation are well chosen, energy shortage might be expected in bad-case scenarios, such as a cloudy day.  The results of these simulations will be used to create and design specifications of the truck's energy systems in future stages of the AMS Sustainable Food Truck project.   3  Contents Abstract .................................................................................................................................. 2 1.INTRODUCTION .............................................................................................................. 4 2. METHODS ........................................................................................................................ 5 2.1. The Sources ................................................................................................................. 5 2.1.1. Solar Panels .......................................................................................................... 5 2.1.2. Fuel Cell ............................................................................................................... 6 2.1.3. Battery .................................................................................................................. 7 2.2. Loads ........................................................................................................................... 8 2.3. The Management Algorithm and Subsystems .......................................................... 10 2.4. Fully assembled simulation System and Plotting System ......................................... 13 3.RESULTS ......................................................................................................................... 15 3.1. Scenario 1 : Sunny busy day (Summer) .................................................................... 15 4. CONCLUSION ................................................................................................................ 18 5.APPENDIX ....................................................................................................................... 18 5.1. Appendix 1 (Solar Panel code) ................................................................................. 18 5.2. Appendix 2 (Fuel cell Limit MATLAB function block) .......................................... 19 5.3. Appendix 30 (Management Algorithm Code) .......................................................... 19 5.4. Appendix 4 (MATLAB codes for generating input profiles) ................................... 21 6.REFERENCES .................................................................................................................. 27  Figure 1 - Solar Panel Subsystem .................................................................................... 5 Figure 2 - Fuel Cell Subsystem ......................................................................................... 7 Figure 3 - Battery subsystem ............................................................................................. 8 Figure 4 - Loads subsystem .............................................................................................. 9 Figure 5 - User switch interface for the loads ............................................................... 10 Figure 6 - Management subsystem ................................................................................ 12 Figure 7 - Management Algorithm .................................................................................. 13 Figure 8 - Fully Assembled Simulation System ............................................................ 14 Figure 9 - Plotting system (displaying the highlighted scopes) .................................. 14 Figure 10 - Final Power Balance [ Power(W) vs. Time(h) ] ........................................ 16 Figure 11 - Sources and loads power profiles [  Power (W) vs. Time (h) ] .............. 17 Figure 12 - Battery activities [ Energy (Wh) vs. Time (h) ] .......................................... 17  Table 1 - Sunny busy day scenario ................................................................................ 15  4   1.INTRODUCTION    The Alma Mater Society (AMS) at UBC has been engaged in the environmental matters since 2007, when its Environmental Policy was created [1]. Thus, in 2008, the AMS Lighter Footprint Strategy was presented in order to “provide a framework for fostering environmental justice in our own operations and lobbying for sustainability practices through our relationships with the University community and broader society” [2]. Within this strategy, the goal of reducing the Green House Gas (GHG) emission by 33 percent until 2020 was set.  Considering the dependency on fossil based fuel technologies (which not only disrupt the planet's carbon cycle, but also are finite, meaning the resources may one day no longer be available for human daily use), it is necessary to switch to newer and greener technologies.  In a city like Vancouver, where food-trucks are commonly seen in the streets, the project of a sustainable food-truck that considers zero GHG emission is one step to achieve the goals established by AMS. This project considered powering the truck's equipment (such as microwave, fridge, deep fryer, griddle, ventilation and a variety of devices) with green sources of energy, including solar panels, fuel cells and a battery.  Starting with a custom energy evaluation tool, designed using MATLAB and Simulink, to simulate operation scenarios for the truck, this project is capable of estimating high, medium and low energy usage, which may include sunny and cloudy day operations (to simulate the high and low limits of the solar energy generated), busy and quiet days, in order to define how the loads (equipment) and energy generation/storage system will behave in these situation. Moreover an energy management strategy was included in the simulations with the purpose to find the optimal operation of the system.  A energy management algorithm  was implemented prioritizing the energy usage of each source in a way that the solar energy, fuel cell and battery work together to best suit the loads' energy requirements.   Furthermore, this project does not include financial evaluation of the system 5  and technologies used nor the truck's mobility, meaning that the motor was not considered as a load to the system. However, this possibility can be added to the algorithm in a further stage of the project. 2. METHODS    The system described in this project was developed using MATLAB and Simulink to create subsystems capable of simulating the behavior of the sources and loads energy usage as well as the power profiles of such. 2.1. The Sources 2.1.1. Solar Panels  The solar panel subsystem was made simple on a MATLAB function block that receives inputs from a Radiation.mat file and from the user (number of panels, area of each panel, efficiency), multiplying these inputs to generate a Solar_Power signal.  Figure 1 shows the subsystem that represents the solar panel. Appendix 1demonstrates the code for the function block multiplying the inputs.  Figure 1 - Solar Panel Subsystem   26     The MATLAB function block was chosen over a simple multiplying block to implement this subsystem due to its capacity to be customized, therefore, modified if needed in the future.  2.1.2. Fuel Cell  The Fuel cell subsystem needed more attention than the previous source, once the powered required had to be updated constantly and a limit to the maximum power output had to be added.  Figure 3 shows the subsystem, which receives an input from the management system that includes the amount of power required by the loads that the primary source (solar cells) was not able to provide. This input ranges  between 0 (zero) and the maximum output rate provided by the user. Because the fuel in the cell is finite, another limiter had to be implemented, this time in the form of a switch that turns the cell off when its energy consumption reaches the maximum energy provided for the period of simulation. The discrete integration block integrates the instant power generating an ascendant energy usage curve that is limited to the maximum fuel energy times the efficiency of the cell.  In Figure 3, outputs 2 and 3 will not be used by the management system. They will be used by a plotting system instead, in order to better analyze the data generated and to follow the behaviour of this source. 7   Figure 2 - Fuel Cell Subsystem 2.1.3. Battery   The battery subsystem was the most complex one of the project to be implemented considering its needs and parameters. The subsystem needed to be updated on the power required by the loads, and should be able to discharge and charge, also updating its own available energy.  Figure 4 shows the battery model. The small system on the top is the representation of the discharging system, in which the power required is the input and it is limited by the available energy, once it reaches 0 (zero), the signal is stopped, assuring that the battery does not provides more energy than its maximum capacity.  The system below represents the charging and storage systems integrated. Input 2 provides the energy going in, being limited by the energy available, one it reaches its maximum capacity. Integrating the signal provides the energy curve which will be sent to a sum block (adding the energy_in curve and the initial energy in the battery and subtracting the energy_out curve, thus generating a curve that shows the energy available).  All the outputs other than 1 will be used by the plotting system to generate the battery usage profile. 8   Figure 3 - Battery subsystem 2.2. Loads  The Loads subsystem receives Load Profiles files as inputs. These profiles were created off-line (on MATLAB using the code in Appendix 4) based estimates of usage patterns and each load's power.   This subsystem basically sums all the loads profiles and generates in single signal to be analyzed by the management algorithm. It also added a switch to each load as seen in Figure 5, which can be operated by the user (who is now capable of choosing the loads that will be operating in the simulation). Figure 6 demonstrates the interface that allows the user to turn on or off the loads by typing 1 or 0 respectively.  9   Figure 4 - Loads subsystem 10   Figure 5 - User switch interface for the loads 2.3. The Management Algorithm and Subsystems  The Management strategy is the main part of the project, assuming it adopts a suitable solution to the source prioritization in order to fulfill the energy needs or to demonstrate the viability of the system in terms of energy consumption and storage.  In this algorithm, the solar power was considered primary, which means it will always be on, unless switched off by the user. As a secondary source, the fuel cells provide the energy needed by the loads when the solar power is not sufficient. And finally, a battery was used as a back-up source to assist both primary and secondary sources when they cannot satisfy the demand. 11   In Figure 7, it is possible to note that the strategy adopted utilizes many switches to manage the sources and loads as well as to give the user the power to choose which subsystem will be operating during a simulation. The outputs of the management block labeled "s1, s2, s3, s4 and s5" are the decisions taken by the algorithm, these are sent to the switches, which will activate or deactivate the adjacent subsystems. It can be noted that "s5" has no connection, this is due to its purpose to deactivate the loads when the system is overloaded. This function not implemented. For simulation purposes, it is useful to know at which point the energy provided by the loads was not enough, in addition to the amount of energy that couldn't be satisfied. Therefore, the loads' emergency shutdown was not implemented, although the entry for such is ready. To do so, it would only require a switch between the loads and the management block, as well as some minor changes on the final lines of the management code.   Figure 8 demonstrates the flowchart for the MATLAB function block that characterizes the management algorithm. The code to this block is represent in Appendix 3. It is important to notice that the sequence repeats for every time step as it is necessary to constantly compare the sources to the loads.   Furthermore, the management system provides as outputs the final power balance when power from sources and loads have already been processed, the fuel and battery power needed to satisfy the demand in addition to the excessive power that is sent to the battery charging system.   12   Figure 6 - Management subsystem 13     Figure 7 - Management Algorithm 2.4. Fully assembled simulation System and Plotting System  When fully assembled (Figure 9), the system picks specific points of the subsystems (as seen in purple tags) and sends this information to the plotting system (Figure 10) that will generate the graphs and data to be analyzed by the user.  In Figure 10, the yellow scope blocks represent the most relevant information to be evaluated, therefore, they are highlighted for easy identification. 14   Figure 8 - Fully Assembled Simulation System  Figure 9 - Plotting system (displaying the highlighted scopes) 15  3.RESULTS 3.1. Scenario 1 : Sunny busy day (Summer)  For this scenario it was considered an operation time of 8 hours and day time from 6:00AM to 9:00PM (which determines the amount of solar energy available to power the loads and recharge the battery). Table 1 shows some specifications for this scenario which includes the operation time from 10:00AM to 6:00PM. All the parameters  displayed on this table were used to create the loads profiles and program the sources.  Simulation Scenario 1 Busy Sunny day Operation Profiles (Summer) Power Source Max Output  Operating Time (hours) Solar panels 10*0.24kWh*15h = 36kWh 6AM - 9PM Fuel cells 30kWh with a maximum output rate of 5000Wh Depends on Power Output Rates Battery 10kWh Depends on demand Loads Power (W) Simulation Intervals (5 minutes/cycle) Operating Time (hours) Deep fryer 5000 ON During operation time 10AM - 6PM Griddle 5000 ON 90 minutes OFF 30 minutes 10AM - 6PM Microwave oven 1000 ON 5 minutes OFF 5 minutes 10AM - 6PM Fridge 700 ON 10 minutes OFF 50 minutes 10AM - 6PM Ventilation 200 ON During operation time 10AM - 6PM Others 150 ON During operation time 10AM - 6PM Table 1 - Sunny busy day scenario   In the graphs below (Figures 11 to 13) , it was used 14 (fourteen) solar 16  panels with an area of 0.5m^2 each, which happen to be the first value to satisfy the loads when added to a 10kWh Battery and 30kWh Fuel cell (with a maximum output rate of 5kWh). It is important to notice that the x-axis of every graph represents the time in hours as in a full day and for Figures 11 and 12, the y-axis represents power (in Watts) in each step, However, Figure 13 represents the battery energy usage (in Wh). The energy was found by integrating the power by the time step. Figure 11 shows the final power balance through the day, after the loads have been subtracted from the sources. Moreover, it is possible to view how the each subsystem behaved during the operation time in Figure 12.   As additional data to the analysis, Figure 13 illustrates the battery energy usage (Energy stored during the day, discharging and charging systems operations).   Figure 10 - Final Power Balance [ Power(W) vs. Time(h) ] 17   Figure 11 - Sources and loads power profiles [  Power (W) vs. Time (h) ]  Figure 12 - Battery activities [ Energy (Wh) vs. Time (h) ]  18   Running this scenario, all the sources had to be used simultaneously to meet the loads requirements. However, the energy stored in the battery was not used to its maximum, making it possible to almost fully recover the energy used only using the solar power that was left after the operation time. 4. CONCLUSION   In summary, the results of the simulator were consistent and they reveal that it is possible to run a sustainable food truck with heavy loads on solar, fuel cell and battery energy on a sunny day using the parameters selected and the load profiles assumed. However, the data acquired also states that it is not possible to integrate the motor to this system in order to help move the truck, considering the energy was short and there was not enough left in the battery to complete such task. Which also reveals that a cloudy and busy day could face problems if all the loads were to be working at the same time. It can be deduced from this scenario that another sunny day scenario not happening during summer  would have few changes, such as less energy charged into the battery after the operation hours.  Although the project of a sustainable food truck requires zero GHG emission. The AMS should consider other technologies to implement to the truck's mobility system as well as the electrical system in order to prevent energy shortage while operating the truck. Bio-fuels might be a suitable option to be used in the motor or even in a generator (one could make good use of another source of energy).  5.APPENDIX 5.1. Appendix 1 (Solar Panel code)  19  5.2. Appendix 2 (Fuel cell Limit MATLAB function block)  5.3. Appendix 30 (Management Algorithm Code) 20    21  5.4. Appendix 4 (MATLAB codes for generating input profiles) 22  23  24  25  26     27  6.REFERENCES  [1] “AMS Sustainability Initiatives”. Available: http://amssustainability.ca/initiatives/;  [2] “The AMS Lighter Footprint Strategy”. Available: http://amssustainability.ca/the-ams-lighter-footprint-strategy/;  [3]  Shau Hua (Raymond) Hou. “'Peak Shave'  Energy Managing tool for Energy Subsystems Design of AMS Sustainable Food Truck.”, Final Report, Electrical and Computer Engineering Department, University of British Columbia, 2014   

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.18861.1-0108752/manifest

Comment

Related Items