Optimal Vehicle-to-Grid Strategies for Energy Sharing Management Using Electric School Buses
Abstract
:1. Introduction
- A V2G model incorporating ESBs in a community is proposed which includes the additional cost model of UC and the utility model of schools that possess a number of ESBs. The UC tries to minimize the additional cost to cover peak demand periods, while schools in a community try to maximize their utility using the stored energy inside their ESBs.
- A two-level Stackelberg game model is introduced to capture the interaction between two types of players, that is a UC and schools who possess a number of ESBs, both of the players trying to negotiate to establish the optimal incentive price and the discharged energy for the peak shaving program. Also, the existence and uniqueness of the Stackelberg equilibrium is proven and guaranteed.
- The optimal energy-price (OEP) equilibrium algorithm is introduced to achieve the Stackelberg equilibrium, where no player can increase their utility by unilaterally changing their strategies from this equilibrium.
2. Related Works
3. System Model
3.1. Preliminaries
3.1.1. Overview of the Model
- Utility company (UC): It is assumed that the utility company (UC) can accurately forecast the upcoming peak demand and predict the additional energy required for generation. However, power plants generating peak energy during peak durations typically incur very high running costs, posing a considerable financial burden for UC if it was to generate all the required energy during peak times independently. In a community comprising several schools, each with multiple ESBs equipped with battery storage, these ESBs can play a crucial role in supplying the needed energy. Consequently, UC initiates communication by sending incentive price signals and peak duration information to schools before the peak period, engaging in negotiations for the provision of discharged energy from their ESBs. The incentive price offered by UC is set lower than the unit cost it would incur to generate all electricity independently. While UC still needs to generate the remaining energy that ESBs do not cover, the quantity is reduced, resulting in a substantial decrease in unit costs compared to when UC generates all the energy. Therefore, UC can effectively minimize the cost of generating additional energy during peak periods with the assistance of ESBs in schools within the community.
- Schools: Within a community, there exists a collection of schools, each consisting of a number of ESBs. These ESBs possess the capability to offer V2G services to the UC, specifically engaging in peak shaving activities by receiving incentive prices for providing discharged energy to the UC. The unit incentive price that UC sends to schools will be higher than the unit price at which the schools charge their ESBs during non-peak periods, meaning the school will benefit from providing this service to the UC. Upon receiving the incentive signal from UC, each school responds by determining the optimal quantity of the discharged energy to submit back to UC. Additionally, the school strategically identifies the most favorable time-duration to provide the V2G service, aiming to maximize their overall benefit from participating in the program.
3.1.2. Technologies for Implementation
- Bidirectional converter: To enable electric school buses (ESBs) to charge from the grid and discharge back to it, a bidirectional converter is essential at the charging point. This converter consists of an AC/DC converter and a DC/DC converter. When ESBs charge from the grid, the AC/DC converter converts electricity from AC to DC, which then passes through the DC/DC converter to match the voltage of the ESBs. Conversely, during discharge from ESBs to the grid, the DC electricity from the ESBs’ batteries is converted back to AC form and supplied to the grid, as illustrated in Figure 2.Figure 2. Power flow diagram for G2V and V2G [33].Figure 2. Power flow diagram for G2V and V2G [33].
- Advanced metering infrastructure (AMI): Smart meters are crucial components in the smart grid, playing an essential role not only in accurately measuring electrical parameters but also in facilitating communication and control. They might even be capable of providing timely forecasts of energy demand, either day-ahead or during the day. Smart meters for users and prosumers will be installed at sites where the demand and generation occur, such as homes, charging stations, and buildings. The communication capabilities of smart meters can be enhanced through various technologies. Wireless options like WiMAX or wired solutions through power line communication (PLC) enable efficient two-way communication between different entities in the system. This connectivity is essential for real-time data exchange and grid management. The effective operation of the smart grid requires specific functionalities from AMI, such as bidirectional energy measurement and communication, seamless connectivity, and adequate memory storage. These capabilities ensure that all parts of the grid are integrated and that data flows smoothly to support grid management and optimization.
- Communication protocol: In future complex smart grid networks, there will be a high frequency of communication, including numerous requests and acknowledgments between entities. The communication between sensors and smart meters can be achieved using wireless communication technologies or PLC. These methods enable efficient data exchange and monitoring within the smart grid infrastructure. The communication between smart meters and aggregators or operators can utilize wireless networks such as WiMAX, 3G, and 4G. These technologies provide reliable and rapid data transmission, essential for effective grid management and operations. The selected communication technologies should be capable of covering wide areas, offer connectivity at any time, and incur low operational costs. The standard for EV charging station communication is ISO/IEC 15118 [34], which outlines the protocol for communication between electric vehicles and charging stations. Meanwhile, the standard for communication between charging stations and the grid is IEC 61850 [35], which is used for substation automation and enables interoperability and communication between various grid components.
3.2. Utility Company
3.2.1. Energy Cost Function
- (1)
- (2)
- The cost function is strictly convex [38].
3.2.2. Utility of UC
3.3. Schools
3.3.1. Battery Dynamics
- (1)
- SoC at the beginning of time slot, ;
- (2)
- The amount of charged SoC in time slot t;
- (3)
- The amount of discharged SoC in time slot t;
- (4)
- The amount of SoC usage for the travel of ESBs in school n in time slot t.
3.3.2. Utility of Schools
- (1)
- The satisfaction function is a non-decreasing function. Each school is always more satisfied with more energy stored inside their ESBs’ battery until it reaches the maximum level of stored energy.
- (2)
- The marginal benefit is a non-increasing function, meaning that the satisfaction level increases less per unit of stored energy as the level of energy in storage increases, eventually reaching a saturated level.
4. Stackelberg Game among Players
4.1. Game Formulation
- (1)
- Players: The set of schools owning ESBs as followers determines their optimal discharged energy for the peak shaving program, and the UC as the leader decides on the incentive price paid to schools for providing peak shaving service.
- (2)
- Strategies: Each school n decides on the amount of discharged energy, , to maximize its payoff. The UC decides on the optimal incentive price, , to minimize its additional cost in meeting the demand within a community.
- (3)
4.2. Existence and Uniqueness of the Equilibrium
- (1)
- Lower level: optimal discharged energy of schools
- (2)
- Upper level: optimal incentive price of UC
4.3. Algorithm for Reaching the Equilibrium
Algorithm 1 Optimal Energy-Price (OEP) Equilibrium Algorithm |
|
4.4. Implementation Process
4.4.1. G2V for Valley Filling
- (1)
- First, assume that the UC has accurate forecasting capabilities for the energy profile of a community on a day-ahead basis and announces the valley period that needs to be filled by charging at the school.
- (2)
- Each school then requests the amount of energy needed to charge its ESBs from the UC.
- (3)
- The UC allocates the demand to flatten the valley period and sends the relevant information back to the schools for implementation.
4.4.2. V2G for Peak Shaving
- (1)
- The utility company (UC) announces the peak periods and sends an incentive signal to schools before the peak period begins.
- (2)
- Each school submits the amount of energy they plan to discharge from their electric school buses (ESBs) during the peak period.
- (3)
- UC calculates its own utility and announces a new price to the schools based on this calculation.
- (4)
- The process returns to steps 2 and 3 and repeats until an equilibrium is reached.
5. Evaluation Studies
5.1. Simulation Setup
5.2. Energy Profile Results and Discussion
- Charge-after-use (CAU) [52]: The ESBs are charged immediately until full after their pick-up and delivery service in the morning and afternoon, following typical human behavior.
- Grid-to-Vehicle (G2V) [53]: This charging involves the ESBs being charged only during the valley period, where the electricity price is the lowest, until full. This typically occurs after midnight until early morning before the service starts, and no discharging energy is provided to the grid.
- Centralized approach (CEN) [22]: This scenario employs centralized control from the UC to maximize the reduction in generation costs by fully utilizing all ESBs to discharge energy during peak periods. This approach does not consider the schools’ efforts to maximize their own benefits, but the schools still receive monetary compensation for providing the discharged energy.
- The proposed optimal energy-price (OEP): This scenario utilizes both charging during the lowest price period and discharging energy during peak periods to receive monetary benefits from the utility company.
5.3. Sensitivity Analysis Results and Discussion
5.3.1. Number of Schools
5.3.2. Travel Distance
5.3.3. Battery Capacity of Each ESB
5.3.4. Optimal Battery Capacity of ESB
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ercan, T.; Noori, M.; Zhao, Y.; Tatari, O. On the front lines of a sustainable transportation fleet: Applications of vehicle-to-grid technology for transit and school buses. Energies 2016, 9, 230. [Google Scholar] [CrossRef]
- Krivoshto, I.N.; Richards, J.R.; Albertson, T.E.; Derlet, R.W. The toxicity of diesel exhaust: Implications for primary care. J. Am. Board Fam. Med. 2008, 21, 55–62. [Google Scholar] [CrossRef] [PubMed]
- Pourmirza, Z. An ICT Architecture for the Neighbourhood Area Network in the Smart Grid; The University of Manchester: Manchester, UK, 2015. [Google Scholar]
- Lim, Y.; Javaid, S.; Khwanrit, R.; Tan, Y. Seasonal storage capacity design for distributed power flow system with safe operation conditions. In Proceedings of the 2022 IEEE International Conference on Consumer Electronics-Taiwan, Taipei, Taiwan, 6–8 July 2022; pp. 577–578. [Google Scholar]
- Khwanrit, R.; Lim, Y.; Charoenlarpnopparut, C.; Kittipiyakul, S.; Javaid, S.; Tan, Y. Optimal Vehicle-to-Grid Strategies for Electric School Bus using Game-Theoretic Approach. In Proceedings of the 2023 International Conference on Consumer Electronics-Taiwan (ICCE-Taiwan), PingTung, Taiwan, 17–19 July 2023; pp. 191–192. [Google Scholar]
- Liu, Y.; Liang, H. A three-layer stochastic energy management approach for electric bus transit centers with PV and energy storage systems. IEEE Trans. Smart Grid 2020, 12, 1346–1357. [Google Scholar] [CrossRef]
- Elliott, M.; Kittner, N. Operational grid and environmental impacts for a V2G-enabled electric school bus fleet using DC fast chargers. Sustain. Prod. Consum. 2022, 30, 316–330. [Google Scholar] [CrossRef]
- Noel, L.; McCormack, R. A cost benefit analysis of a V2G-capable electric school bus compared to a traditional diesel school bus. Appl. Energy 2014, 126, 246–255. [Google Scholar] [CrossRef]
- Beatty, T.K.; Shimshack, J.P. School buses, diesel emissions, and respiratory health. J. Health Econ. 2011, 30, 987–999. [Google Scholar] [CrossRef]
- Neff, J.; Dickens, M. Public Transportation Fact Book; American Public Transportation Association: Washington, DC, USA, 2015. [Google Scholar]
- Khwanrit, R.; Lim, Y.; Javaid, S.; Kittipiyakul, S.; Tan, Y. Study of energy loss for distributed power-flow assignment in a smart home environment. Designs 2022, 6, 99. [Google Scholar] [CrossRef]
- Tushar, W.; Saad, W.; Poor, H.V.; Smith, D.B. Economics of electric vehicle charging: A game theoretic approach. IEEE Trans. Smart Grid 2012, 3, 1767–1778. [Google Scholar] [CrossRef]
- Kim, J.; Lee, J.; Park, S.; Choi, J.K. Power scheduling scheme for a charging facility considering the satisfaction of electric vehicle users. IEEE Access 2022, 10, 25153–25164. [Google Scholar] [CrossRef]
- Yu, Y.; Su, C.; Tang, X.; Kim, B.; Song, T.; Han, Z. Hierarchical game for networked electric vehicle public charging under time-based billing model. IEEE Trans. Intell. Transp. Syst. 2020, 22, 518–530. [Google Scholar] [CrossRef]
- Laha, A.; Yin, B.; Cheng, Y.; Cai, L.X.; Wang, Y. Game theory based charging solution for networked electric vehicles: A location-aware approach. IEEE Trans. Veh. Technol. 2019, 68, 6352–6364. [Google Scholar] [CrossRef]
- Wang, Y.; Su, Z.; Xu, Q.; Yang, T.; Zhang, N. A novel charging scheme for electric vehicles with smart communities in vehicular networks. IEEE Trans. Veh. Technol. 2019, 68, 8487–8501. [Google Scholar] [CrossRef]
- Rui, T.; Hu, C.; Li, G.; Tao, J.; Shen, W. A distributed charging strategy based on day ahead price model for PV-powered electric vehicle charging station. Appl. Soft Comput. 2019, 76, 638–648. [Google Scholar] [CrossRef]
- Guo, D.; Zhou, C. Utility Optimization Using Game-based SG-DR Algorithm in V2G. In Proceedings of the 2018 IEEE Vehicle Power and Propulsion Conference (VPPC), Chicago, IL, USA, 27–30 August 2018; pp. 1–6. [Google Scholar]
- Shokouhmand, E.; Ghasemi, A. Stochastic optimal scheduling of electric vehicles charge/discharge modes of operation with the aim of microgrid flexibility and efficiency enhancement. Sustain. Energy Grids Netw. 2022, 32, 100929. [Google Scholar] [CrossRef]
- Ren, H.; Zhang, A.; Li, W. Study on optimal V2G pricing strategy under multi-aggregator competition based on game theory. In Proceedings of the 2019 IEEE Sustainable Power and Energy Conference (iSPEC), Beijing, China, 21–23 November 2019; pp. 1027–1032. [Google Scholar]
- Chen, P.; Han, L.; Xin, G.; Zhang, A.; Ren, H.; Wang, F. Game theory based optimal pricing strategy for V2G participating in demand response. IEEE Trans. Ind. Appl. 2023, 59, 4673–4683. [Google Scholar] [CrossRef]
- Aguilar Dominguez, D. The Influence of Electric Vehicle Availability on Vehicle-to-Grid Provision within a Microgrid. Ph.D. Thesis, University of Sheffield, Sheffield, UK, 2023. [Google Scholar]
- Zhou, H.; Liu, C.; Yang, B.; Guan, X. Optimal dispatch of electric taxis and price making of charging stations using Stackelberg game. In Proceedings of the IECON 2015-41st Annual Conference of the IEEE Industrial Electronics Society, Yokohama, Japan, 9–12 November 2015; pp. 004929–004934. [Google Scholar]
- Ma, K.; Hu, X.; Yang, J.; Yue, Z.; Yang, B.; Liu, Z.; Guan, X. Electric taxi charging strategy based on stackelberg game considering hotspot information. IEEE Trans. Veh. Technol. 2022, 71, 2427–2436. [Google Scholar] [CrossRef]
- Yang, Y.; Zhang, W.; Niu, L.; Jiang, J. Coordinated charging strategy for electric taxis in temporal and spatial scale. Energies 2015, 8, 1256–1272. [Google Scholar] [CrossRef]
- Yang, Z.; Guo, T.; You, P.; Hou, Y.; Qin, S.J. Distributed approach for temporal–spatial charging coordination of plug-in electric taxi fleet. IEEE Trans. Ind. Inform. 2018, 15, 3185–3195. [Google Scholar] [CrossRef]
- Fei, F.; Sun, W.; Iacobucci, R.; Schmöcker, J.D. Exploring the profitability of using electric bus fleets for transport and power grid services. Transp. Res. Part Emerg. Technol. 2023, 149, 104060. [Google Scholar] [CrossRef]
- Yang, S.X.; Wang, X.F.; Ning, W.Q.; Jia, X.f. An optimization model for charging and discharging battery-exchange buses: Consider carbon emission quota and peak-shaving auxiliary service market. Sustain. Cities Soc. 2021, 68, 102780. [Google Scholar] [CrossRef]
- Wu, Z.; Guo, F.; Polak, J.; Strbac, G. Evaluating grid-interactive electric bus operation and demand response with load management tariff. Appl. Energy 2019, 255, 113798. [Google Scholar] [CrossRef]
- Manzolli, J.A.; Trovão, J.P.F.; Antunes, C.H. Electric bus coordinated charging strategy considering V2G and battery degradation. Energy 2022, 254, 124252. [Google Scholar] [CrossRef]
- El-Taweel, N.A.; Ayad, A.; Farag, H.E.; Mohamed, M. Optimal energy management for battery swapping based electric bus fleets with consideration of grid ancillary services provision. IEEE Trans. Sustain. Energy 2022, 14, 1024–1036. [Google Scholar] [CrossRef]
- Haces-Fernandez, F. Framework to Develop Electric School Bus Vehicle-to-Grid (ESB V2G) Systems Supplied with Solar Energy in the United States. Energies 2024, 17, 2834. [Google Scholar] [CrossRef]
- Tan, K.M.; Ramachandaramurthy, V.K.; Yong, J.Y. Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques. Renew. Sustain. Energy Rev. 2016, 53, 720–732. [Google Scholar] [CrossRef]
- ISO 15118-20; Road Vehicles—Vehicle to Grid Communication Interface—Part 20: Network and Application Protocol Requirements. International Electrotechnical Commission (IEC): Geneva, Switzerland, 2022.
- IEC 61850; Communication Networks and Systems for Power Utility Automation—All Parts. International Electrotechnical Commission (IEC): Geneva, Switzerland, 2024.
- Samadi, P.; Mohsenian-Rad, A.H.; Schober, R.; Wong, V.W.S.; Jatskevich, J. Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid. In Proceedings of the 2010 First IEEE International Conference on Smart Grid Communications, Gaithersburg, MD, USA, 4–6 October 2010; pp. 415–420. [Google Scholar] [CrossRef]
- Mohsenian-Rad, A.H.; Wong, V.W.S.; Jatskevich, J.; Schober, R.; Leon-Garcia, A. Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid. IEEE Trans. Smart Grid 2010, 1, 320–331. [Google Scholar] [CrossRef]
- Boyd, S.P.; Vandenberghe, L. Convex Optimization; Cambridge University Press: Cambridge, UK, 2004. [Google Scholar]
- Charoen, P.; Sioutis, M.; Javaid, S.; Charoenlarpnopparut, C.; Lim, Y.; Tan, Y. User-centric consumption scheduling and fair billing mechanism in demand-side management. Energies 2019, 12, 156. [Google Scholar] [CrossRef]
- Chai, B.; Chen, J.; Yang, Z.; Zhang, Y. Demand Response Management with Multiple Utility Companies: A Two-Level Game Approach. IEEE Trans. Smart Grid 2014, 5, 722–731. [Google Scholar] [CrossRef]
- Paudel, A.; Chaudhari, K.; Long, C.; Gooi, H.B. Peer-to-peer energy trading in a prosumer-based community microgrid: A game-theoretic model. IEEE Trans. Ind. Electron. 2018, 66, 6087–6097. [Google Scholar] [CrossRef]
- Von Stackelberg, H. Market Structure and Equilibrium; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2010. [Google Scholar]
- U.S. Energy Information Administration. Hourly Electric Grid Monitor. Available online: https://www.eia.gov/electricity/gridmonitor (accessed on 12 December 2023).
- Rochest Community Schools. School Buses—General Information. Available online: https://www.rochester.k12.mi.us/about-us/departments/transportation/test-page-photos-content (accessed on 12 December 2023).
- US Census Bureau. American Community Survey 5-Year Data Estimates. Available online: http://censusreporter.org/profiles/97000US2629940-rochester-community-school-district-mi/ (accessed on 12 December 2023).
- Santos, A.; McGuckin, N.; Nakamoto, H.Y.; Gray, D.; Liss, S. Summary of Travel Trends: 2009 National Household Travel Survey; Technical Report; Federal Highway Administration: Washington, DC, USA, 2011. [Google Scholar]
- Long, C.; Wu, J.; Zhou, Y.; Jenkins, N. Peer-to-peer energy sharing through a two-stage aggregated battery control in a community Microgrid. Appl. Energy 2018, 226, 261–276. [Google Scholar] [CrossRef]
- Khwanrit, R.; Lim, Y.; Javaid, S.; Charoenlarpnopparut, C.; Tan, Y. Incorporating a Load-Shifting Algorithm for Optimal Energy Storage Capacity Design in Smart Homes. Designs 2024, 8, 11. [Google Scholar] [CrossRef]
- Sadiq, S.; Pritchard, E.; Dulaney, K.; Emadi, A. Plug-in hybrid market transformation by leveraging a niche market: School buses. In Proceedings of the 2007 IEEE Vehicle Power and Propulsion Conference, Arlington, TX, USA, 9–12 September 2007; pp. 483–492. [Google Scholar]
- Al-Ogaili, A.S.; Al-Shetwi, A.Q.; Al-Masri, H.M.; Babu, T.S.; Hoon, Y.; Alzaareer, K.; Babu, N.P. Review of the estimation methods of energy consumption for battery electric buses. Energies 2021, 14, 7578. [Google Scholar] [CrossRef]
- Moore, A.; Crolius, S. Vehicle-to-Grid Electric School Bus Commercialization Project; Technical Report; Blue Bird Corporation: Fort Valley, GA, USA, 2022. [Google Scholar]
- Kim, D.S.; Chung, Y.M.; Chung, B.J. Statistical Analysis of Electric Vehicle Charging Based on AC Slow Chargers. Energies 2023, 16, 2735. [Google Scholar] [CrossRef]
- Ching, T.W. Design of electric vehicle charging station in Macau. J. Asian Electr. Veh. 2011, 9, 1453–1458. [Google Scholar] [CrossRef]
- Green Power Motor Company. BEAST School Bus. Available online: https://greenpowermotor.com/gp-products/beast-school-bus (accessed on 12 January 2024).
- BLUEBIRD. All American ELectric. Available online: https://www.blue-bird.com/all-american-electric (accessed on 12 January 2024).
- LION Electric. Lion D. Available online: https://pages.thelionelectric.com/liond-the-all-electric-type-d-school-bus-usa (accessed on 12 January 2024).
- Peterson, S.B.; Apt, J.; Whitacre, J. Lithium-ion battery cell degradation resulting from realistic vehicle and vehicle-to-grid utilization. J. Power Sources 2010, 195, 2385–2392. [Google Scholar] [CrossRef]
- Lunz, B.; Yan, Z.; Gerschler, J.B.; Sauer, D.U. Influence of plug-in hybrid electric vehicle charging strategies on charging and battery degradation costs. Energy Policy 2012, 46, 511–519. [Google Scholar] [CrossRef]
- Noori, M.; Zhao, Y.; Onat, N.C.; Gardner, S.; Tatari, O. Light-duty electric vehicles to improve the integrity of the electricity grid through Vehicle-to-Grid technology: Analysis of regional net revenue and emissions savings. Appl. Energy 2016, 168, 146–158. [Google Scholar] [CrossRef]
% Reduction | Winter | Spring | Fall | ||||||
---|---|---|---|---|---|---|---|---|---|
G2V | CEN | OEP | G2V | CEN | OEP | G2V | CEN | OEP | |
Cost of UC | 0.11 | 0.71 | 0.59 | 0.15 | 0.79 | 0.58 | 0.17 | 0.85 | 0.63 |
Bill of schools | 7.75 | 19.56 | 22.57 | 9.10 | 20.05 | 22.10 | 9.80 | 20.03 | 22.61 |
peak-to-average ratio | 6.76 | 11.11 | 9.48 | 5.01 | 8.34 | 7.12 | 4.53 | 8.51 | 7.24 |
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Khwanrit, R.; Javaid, S.; Lim, Y.; Charoenlarpnopparut, C.; Tan, Y. Optimal Vehicle-to-Grid Strategies for Energy Sharing Management Using Electric School Buses. Energies 2024, 17, 4182. https://doi.org/10.3390/en17164182
Khwanrit R, Javaid S, Lim Y, Charoenlarpnopparut C, Tan Y. Optimal Vehicle-to-Grid Strategies for Energy Sharing Management Using Electric School Buses. Energies. 2024; 17(16):4182. https://doi.org/10.3390/en17164182
Chicago/Turabian StyleKhwanrit, Ruengwit, Saher Javaid, Yuto Lim, Chalie Charoenlarpnopparut, and Yasuo Tan. 2024. "Optimal Vehicle-to-Grid Strategies for Energy Sharing Management Using Electric School Buses" Energies 17, no. 16: 4182. https://doi.org/10.3390/en17164182