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An Integrated Two-Level Demand-Side Management Game Applied to Smart Energy Hubs with Storage

Author

Listed:
  • S. Omid Sobhani

    (Sharif University of Technology)

  • Siamak Sheykhha

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

  • Reinhard Madlener

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

Abstract

The integration of energy hubs – as an important component of future energy networks that will employ demand-side management techniques – has a key role in the process of efficiency improvement and reliability enhancement of power grids. In such power grids, energy hub operators need to optimally schedule the consumption, conversion, and storage of available resources based on their own utility functions. In sufficiently large networks, scheduling an individual hub can affect the utility of the other energy hubs. In this paper, the interaction between energy hubs is modeled as a potential game. Each energy hub operator (player) participates in a dynamic energy pricing market and tries to maximize his own payoff with regard to energy consumption satisfaction. We propose a distributed algorithm based on a potential game, which guarantees the existence of a Nash equilibrium. Furthermore, two different types of signaling are developed and simulation results are compared. Simulation results show that with the implementation of either setup the peak-to-average ratio between electricity networks and natural gas networks diminishes. An analysis of the results shows that either setup can have superiority over the other one with regard to generation costs, convergence rate, price level, and the stability perspective. Hence, energy providers and consumers can choose a favorable setup based on their respective needs.

Suggested Citation

  • S. Omid Sobhani & Siamak Sheykhha & Reinhard Madlener, 2018. "An Integrated Two-Level Demand-Side Management Game Applied to Smart Energy Hubs with Storage," FCN Working Papers 14/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
  • Handle: RePEc:ris:fcnwpa:2018_014
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    Cited by:

    1. Stefanie Wolff & Reinhard Madlener, 2019. "Charged up? Preferences for Electric Vehicle Charging and Implications for Charging Infrastructure Planning," FCN Working Papers 3/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    2. Jiang, Meihui & Xu, Zhenjiang & Zhu, Hongyu & Hwang Goh, Hui & Agustiono Kurniawan, Tonni & Liu, Tianhao & Zhang, Dongdong, 2024. "Integrated demand response modeling and optimization technologies supporting energy internet," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    3. Nguyen, Hai-Tra & Safder, Usman & Loy-Benitez, Jorge & Yoo, ChangKyoo, 2022. "Optimal demand side management scheduling-based bidirectional regulation of energy distribution network for multi-residential demand response with self-produced renewable energy," Applied Energy, Elsevier, vol. 322(C).
    4. Dong, Zihang & Zhang, Xi & Strbac, Goran, 2021. "Evaluation of benefits through coordinated control of numerous thermal energy storage in highly electrified heat systems," Energy, Elsevier, vol. 237(C).
    5. Zhu, Xu & Sun, Yuanzhang & Yang, Jun & Dou, Zhenlan & Li, Gaojunjie & Xu, Chengying & Wen, Yuxin, 2022. "Day-ahead energy pricing and management method for regional integrated energy systems considering multi-energy demand responses," Energy, Elsevier, vol. 251(C).
    6. Li, Songrui & Zhang, Lihui & Nie, Lei & Wang, Jianing, 2022. "Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game," Energy, Elsevier, vol. 249(C).
    7. Artur Felipe da Silva Veloso & José Valdemir Reis Júnior & Ricardo de Andrade Lira Rabelo & Jocines Dela-flora Silveira, 2021. "HyDSMaaS: A Hybrid Communication Infrastructure with LoRaWAN and LoraMesh for the Demand Side Management as a Service," Future Internet, MDPI, vol. 13(11), pages 1-45, October.
    8. Ting Zhang & Shuaishuai Cao & Lingying Pan & Chenyu Zhou, 2020. "A Policy Effect Analysis of China’s Energy Storage Development Based on a Multi-Agent Evolutionary Game Model," Energies, MDPI, vol. 13(23), pages 1-35, November.
    9. Esmaeil Valipour & Ramin Nourollahi & Kamran Taghizad-Tavana & Sayyad Nojavan & As’ad Alizadeh, 2022. "Risk Assessment of Industrial Energy Hubs and Peer-to-Peer Heat and Power Transaction in the Presence of Electric Vehicles," Energies, MDPI, vol. 15(23), pages 1-24, November.
    10. Oskouei, Morteza Zare & Mohammadi-Ivatloo, Behnam & Abapour, Mehdi & Shafiee, Mahmood & Anvari-Moghaddam, Amjad, 2021. "Privacy-preserving mechanism for collaborative operation of high-renewable power systems and industrial energy hubs," Applied Energy, Elsevier, vol. 283(C).
    11. Qi, Haijie & Yue, Hong & Zhang, Jiangfeng & Lo, Kwok L., 2021. "Optimisation of a smart energy hub with integration of combined heat and power, demand side response and energy storage," Energy, Elsevier, vol. 234(C).

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Demand and Supply; Prices
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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