A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

Bingyun Li; Qinmin Yang; Innocent Kamwa

doi:10.1109/JAS.2023.123216

Volume 10 Issue 2

Feb. 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(2): 462-473

B. Y. Li, Q. M. Yang, and I. Kamwa, “A novel Stackelberg-game-based energy storage sharing scheme under demand charge,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 462–473, Feb. 2023. doi: 10.1109/JAS.2023.123216

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B. Y. Li, Q. M. Yang, and I. Kamwa, “A novel Stackelberg-game-based energy storage sharing scheme under demand charge,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 462–473, Feb. 2023. doi: 10.1109/JAS.2023.123216

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A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

doi: 10.1109/JAS.2023.123216

1.
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China. Q. M. Yang is also with Huzhou Institute of Zhejiang University, China
2.
Department of Electrical and Computer Engineering, Laval University, Quebec City, QC, G1V 0A6, Canada

Funds: This work was supported by the National Natural Science Foundation of China (U21A20478), Zhejiang Provincial Nature Science Foundation of China (LZ21F030004), and Key-Area Research and Development Program of Guangdong Province (2018B010107002)

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Author Bio:
Bingyun Li (Graduate Student Member, IEEE) re-ceived the B.S. degree in automation from Zhejiang University in 2016, where he is currently pursuing the Ph.D. degree in control science and engineering.He is a Member of the Group of Networked Sensing and Control, State Key Laboratory of Industrial Control Technology, Zhejiang University. His research interests include nonlinear adaptive control, optimization, smart grid, wind power, and energy storage

Qinmin Yang (Senior Member, IEEE) received the B.S. degree in electrical engineering from the Civil Aviation University of China in 2001, the M.S. degree in control science and engineering from the Institute of Automation, Chinese Academy of Sciences, in 2004, and the Ph.D. degree in electrical engineering from the University of Missouric Rolla, USA, in 2007. From 2007 to 2008, he was a Postdoctoral Research Associate at University of Missouri Rolla. From 2008 to 2009, he was a System Engineer with Caterpillar Inc. From 2009 to 2010, he was a Postdoctoral Research Associate with the University of Connecticut, USA. Since 2010, he has been with the State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, where he is currently a Professor. He has also held visiting positions with the University of Toronto, Canada and Lehigh University, USA. His research interests include intelligent control, renewable energy systems, smart grid, and industrial big data. Prof. Yang has been serving as an Associate Editor for the IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Neural Networks and Learning Systems, Transactions of the Institute of Measurement and Control, and IEEE/CAA Journal of Automatica Sinica

Innocent Kamwa (Fellow, IEEE) received the Ph.D. degree in electrical engineering from Laval University in 1989. He is a Full Professor in the Department of Electrical Engineering and Tier 1 Canada Research Chair in Decentralized Sustainable Electricity Grids for Smart Communities at Laval University. He was previously a Researcher at Hydro-Québec’s Research Institute, specializing in the dynamic performance and control of power systems. He was also the Chief Scientist for Hydro-Québec’s Smart Grid Innovation Program and an international consultant in power grid simulation and network stability. Dr. Kamwa is a past Editor-in-Chief of IET Generation, Transmission and Distribution, and is currently the Editor-in-Chief of IEEE Power and Energy Magazine and an Associate Editor of IEEE Transactions on Power Systems. A Fellow of the Canadian Academy of Engineering and Fellow of the IEEE for his innovations in power system control, he is also the 2019 recipient of the IEEE Charles Proteus Steinmetz and Charles Concordia Awards
Corresponding author: Qinmin Yang, e-mail: qmyang@zju.edu.cn
Received Date: 2022-01-26
Revised Date: 2022-04-05
Accepted Date: 2022-05-06

Available Online: 2022-12-05

Abstract

Abstract

Demand response (DR) using shared energy storage systems (ESSs) is an appealing method to save electricity bills for users under demand charge and time-of-use (TOU) price. A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this study. In this scheme, the interactions between selfish users and an operator are characterized as a Stackelberg game. Operator holds a large-scale ESS that is shared among users in the form of energy transactions. It sells energy to users and sets the selling price first. It maximizes its profit through optimal pricing and ESS dispatching. Users purchase some energy from operator for the reduction of their demand charges after operator’s selling price is announced. This game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization model. The upper-level optimization maximizes operator’s profit and the lower-level optimization minimizes users’ costs. The bi-level model is transformed and linearized into a mixed-integer linear programming (MILP) model using the mathematical programming with equilibrium constraints (MPEC) method and model linearizing techniques. Case studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.
- Bi-level optimization,
- demand charge,
- energy storage system (ESS) sharing,
- energy transaction,
- mathematical program with equilibrium constraints (MPEC),
- stackelberg game

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References

[1]	R. Hledik, “Rediscovering residential demand charges,” The Electricity Journal, vol. 27, no. 7, pp. 82–96, 2014. doi: 10.1016/j.tej.2014.07.003
[2]	Siano, “Demand response and smart grids – A survey,” Renewable and Sustainable Energy Reviews, vol. 30, pp. 461–478, 2014. doi: 10.1016/j.rser.2013.10.022
[3]	M. Yu and S. H. Hong, “Supply-demand balancing for power management in smart grid: A Stackelberg game approach,” Applied Energy, vol. 164, pp. 702–710, 2016. doi: 10.1016/j.apenergy.2015.12.039
[4]	D. Liu, Y. Xu, Q. Wei, and X. Liu, “Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 36–46, 2018.
[5]	J. Liu, N. Zhang, C. Kang, D. Kirschen, and Q. Xia, “Cloud energy storage for residential and small commercial consumers: A business case study,” Applied Energy, vol. 188, pp. 226–236, 2017. doi: 10.1016/j.apenergy.2016.11.120
[6]	R. Carli and M. Dotoli, “Decentralized control for residential energy management of a smart users’ microgrid with renewable energy exchange,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 641–656, 2019. doi: 10.1109/JAS.2019.1911462
[7]	K. Paridari, A. Parisio, H. Sandberg, and K. H. Johansson, “Demand response for aggregated residential consumers with energy storage sharing,” in Proc. IEEE 54th Annu. Conf. Decision and Control, 2015, pp. 2024–2030.
[8]	E. Oh and S.-Y. Son, “A framework for consumer electronics as a service (CEaaS): A case of clustered energy storage systems,” IEEE Trans. Consumer Electronics, vol. 63, no. 2, pp. 162–168, 2017. doi: 10.1109/TCE.2017.014846
[9]	C. Kang, J. Liu, and N. Zhang, “A new form of energy storage in future power system: Cloud energy storage,” Automation of Electric Power Systems, vol. 41, no. 21, pp. 2–16, 2017.
[10]	J. Liu, N. Zhang, and C. Kang, “Research framework and basic models for cloud energy storage in power system,” Proc. the CSEE, vol. 37, no. 12, pp. 3361–3371, 2017.
[11]	J. Liu, N. Zhang, C. Kang, D. S. Kirschen, and Q. Xia, “Decision-making models for the participants in cloud energy storage,” IEEE Trans. Smart Grid, 2017. DOI: 10.1109/TSG.2017.2689239
[12]	Y. Yang, G. Hu, and C. J. Spanos, “Optimal sharing and and fair cost allocation of community energy storage,” arXiv preprint arXiv: 2010.15455, 2020.
[13]	Y. Li, “Research on power load optimal scheduling based on user-side energy storage system,” Master’s thesis, Zhejiang University, 2018.
[14]	K. Rahbar, M. R. V. Moghadam, S. K. Panda, and T. Reindl, “Shared energy storage management for renewable energy integration in smart grid,” in Proc. IEEE Power Energy Society Innovative Smart Grid Technologies Conf., 2016, pp. 1–5.
[15]	A. Taşcıkaraoğlu, “Economic and operational benefits of energy storage sharing for a neighborhood of prosumers in a dynamic pricing environment,” Sustainable Cities and Society, vol. 38, pp. 219–229, 2018. doi: 10.1016/j.scs.2018.01.002
[16]	B. S. K. Patnam and N. M. Pindoriya, “Centralized stochastic energy management framework of an aggregator in active distribution network,” IEEE Trans. Industrial Informatics, vol. 15, no. 3, pp. 1350–1360, 2019. doi: 10.1109/TII.2018.2854744
[17]	Y. Tao, J. Qiu, S. Lai, and J. Zhao, “Integrated electricity and hydrogen energy sharing in coupled energy systems,” IEEE Trans. Smart Grid, 2020. DOI: 10.1109/TSG.2020.3023716
[18]	H. Chen, Y. Yu, Z. Hu, H. Luo, C.-W. Tan, and R. Rajagopal, “Energy storage sharing strategy in distribution networks using bi-level optimization approach,” in Proc. IEEE Power Energy Society General Meeting, 2017, pp. 1–5.
[19]	B. H. Zaidi, D. M. S. Bhatti, and I. Ullah, “Combinatorial auctions for energy storage sharing amongst the households,” Journal of Energy Storage, vol. 19, pp. 291–301, 2018. doi: 10.1016/j.est.2018.08.010
[20]	A. Fleischhacker, H. Auer, G. Lettner, and A. Botterud, “Sharing solar PV and energy storage in apartment buildings: Resource allocation and pricing,” IEEE Trans. Smart Grid, 2018. DOI: 10.1109/TSG.2018.2844877
[21]	A. Valibeygi and R. A. de Callafon, “Cooperative energy scheduling for microgrids under peak demand energy plans,” in Proc. IEEE 58th Conf. Decision and Control, 2019, pp. 3110– 3115.
[22]	A. Elkasrawy and B. Venkatesh, “Demand response cooperative and demand charge,” IEEE Trans. Smart Grid, 2020. DOI: 10.1109/TSG.2020.2979435
[23]	J. Yang, J. Zhao, F. Luo, F. Wen, and Z. Y. Dong, “Decision-making for electricity retailers: A brief survey,” IEEE Trans. Smart Grid, vol. 9, no. 5, pp. 4140–4153, 2017.
[24]	T. Namerikawa, N. Okubo, R. Sato, Y. Okawa, and M. Ono, “Realtime pricing mechanism for electricity market with built-in incentive for participation,” IEEE Trans. Smart Grid, vol. 6, no. 6, pp. 2714–2724, 2015. doi: 10.1109/TSG.2015.2447154
[25]	L. Jia and L. Tong, “Dynamic pricing and distributed energy management for demand response,” IEEE Trans. Smart Grid, vol. 7, no. 2, pp. 1128–1136, 2016. doi: 10.1109/TSG.2016.2515641
[26]	I. Momber, S. Wogrin, and T. G. San Román, “Retail pricing: A bilevel program for PEV aggregator decisions using indirect load control,” IEEE Trans. Power Systems, vol. 31, no. 1, pp. 464–473, 2015.
[27]	T. Hahn, Z. Tan, and W. Ko, “Design of time-varying rate considering CO₂ emission,” IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 383–389, 2013. doi: 10.1109/TSG.2012.2234771
[28]	B. W. Griffiths, “Reducing emissions from consumer energy storage using retail rate design,” Energy Policy, vol. 129, pp. 481–490, 2019. doi: 10.1016/j.enpol.2019.01.039
[29]	“Pecan street dataport,” https://dataport.cloud/.
[30]	“Commercial rates,” https://austinenergy.com/ae/rates/commercial-rates/commercial-electric-rates-and-line-items.
[31]	C. Ruiz, A. J. Conejo, and Y. Smeers, “Equilibria in an oligopolistic electricity pool with stepwise offer curves,” IEEE Trans. Power Systems, vol. 27, no. 2, pp. 752–761, 2012. doi: 10.1109/TPWRS.2011.2170439
[32]	Y. Ye, D. Papadaskalopoulos, and G. Strbac, “An MPEC approach for analysing the impact of energy storage in imperfect electricity markets,” in Proc. IEEE 13th Int. Conf. European Energy Market, 2016, pp. 1–5.
[33]	C. Ruiz and A. J. Conejo, “Pool strategy of a producer with endogenous formation of locational marginal prices,” IEEE Trans. Power Systems, vol. 24, no. 4, pp. 1855–1866, 2009. doi: 10.1109/TPWRS.2009.2030378
[34]	J. Fortuny-Amat and B. McCarl, “A representation and economic interpretation of a two-level programming problem,” Journal of the Operational Research Society, vol. 32, no. 9, pp. 783–792, 1981. doi: 10.1057/jors.1981.156
[35]	X. Jiao, Q. Yang, and B. Xu, “Hybrid intelligent feedforward-feedback pitch control for VSWT with predicted wind speed,” IEEE Trans. Energy Conversion, vol. 36, no. 4, pp. 2770–2781, 2021. doi: 10.1109/TEC.2021.3076839
[36]	Y. Bao and Q. Yang, “A data-mining compensation approach for yaw misalignment on wind turbine,” IEEE Trans. Industrial Informatics, vol. 17, no. 12, pp. 8154–8164, 2021. doi: 10.1109/TII.2021.3065702
[37]	Q. Yang, G. Liu, Y. Bao, and Q. Chen, “Fault detection of wind turbine generator bearing using attention-based neural networks and voting-based strategy,” IEEE/ASME Trans. Mechatronics, vol. 27, no. 5, pp. 3008–3018, 2022.
[38]	Z. Wang, Z. Xu, B. Liu, Y. Zhang, and Q. Yang, “A hybrid cleaning scheduling framework for operations and maintenance of photovoltaic systems,” IEEE Trans. Systems,Man,and Cybernetics: Systems, vol. 52, no. 9, pp. 5925–5936, 2022.
[39]	A. Sheikhi, M. Rayati, S. Bahrami, and A. M. Ranjbar, “Integrated demand side management game in smart energy hubs,” IEEE Trans. Smart Grid, vol. 6, no. 2, pp. 675–683, 2015. doi: 10.1109/TSG.2014.2377020
[40]	A. Sheikhi, M. Rayati, and A. M. Ranjbar, “Demand side management for a residential customer in multi-energy systems,” Sustainable Cities and Society, vol. 22, pp. 63–77, 2016. doi: 10.1016/j.scs.2016.01.010

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A novel Stackelberg-game-based ESS sharing scheme is proposed
All the participants are assumed to be selfish and not collaborating, as in the practical scenarios
Demand charge and Time-of-Use (TOU) price are considered
Bi-level optimization and linearization methods are designed along with strict analysis

A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

doi: 10.1109/JAS.2023.123216

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