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An integrated two-level demand-side management game applied to smart energy hubs with storage

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  • Sobhani, Seyed Omid
  • Sheykhha, Siamak
  • Madlener, Reinhard

Abstract

Energy hubs, an important component of future energy networks employing distributed demand-side management, can play a key role in enhancing the efficiency and reliability of power grids. In 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 congestion game. Each energy hub operator (player) participates in a dynamic energy pricing market and tries to maximize his/her own payoff when satisfying energy demand. We propose a distributed algorithm based on a congestion game, which guarantees the existence of a Nash equilibrium. Furthermore, two different types of signaling are developed (price-based, load-based) and simulation results 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 dominate the other one with regard to generation costs, convergence rate, price level, and stability. Hence, energy providers and consumers can choose a favorable setup based on their respective needs.

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  • Sobhani, Seyed Omid & Sheykhha, Siamak & Madlener, Reinhard, 2020. "An integrated two-level demand-side management game applied to smart energy hubs with storage," Energy, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:energy:v:206:y:2020:i:c:s0360544220311245
    DOI: 10.1016/j.energy.2020.118017
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    5. 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).
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    7. 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.
    8. 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.
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    10. 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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    More about this item

    Keywords

    Smart energy hub (SEH); Integrated demand-response program; Distributed demand-side management; Storage system;
    All these keywords.

    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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