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Volume 10 Issue 2
Feb.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
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
Citation: 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

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

doi: 10.1109/JAS.2023.123216
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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  • 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.

     

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  • [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]
    [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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    Highlights

    • 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

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