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Dynamic pricing in electricity and natural gas distribution networks: An EPEC model

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  • Chen, Sheng
  • Sun, Guoqiang
  • Wei, Zhinong
  • Wang, Dan

Abstract

Independent clearing of coupled retail electricity and natural gas markets generally results in a loss of market efficiency. This study investigates the problem of dynamic pricing in retail electricity and natural gas markets in the presence of network constraints by developing a two-leader multiple-follower bi-level model. Here, upper-level electricity and gas utility companies serve as leaders in the model, and determine their optimal dynamic retail prices in anticipation of the demand response (DR) provided by multiple lower-level integrated third-party DR providers or aggregators. Moreover, the model considers the dynamic gas prices provided by gas utility companies to electric utility companies for distributed gas-fired power units, which coordinates the operations of the separate utilities. The resulting model is recast as an equilibrium problem with equilibrium constraints (EPEC). Numerical results for a test system composed of an electric grid with distributed gas-fired power units, a gas distribution network, and multiple integrated DR aggregators indicate that the economic value of the proposed dynamic pricing scheme is up to 5.5% compared with existing regular pricing schemes.

Suggested Citation

  • Chen, Sheng & Sun, Guoqiang & Wei, Zhinong & Wang, Dan, 2020. "Dynamic pricing in electricity and natural gas distribution networks: An EPEC model," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s0360544220312457
    DOI: 10.1016/j.energy.2020.118138
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    References listed on IDEAS

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    1. Tang, Rui & Wang, Shengwei & Li, Hangxin, 2019. "Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids," Applied Energy, Elsevier, vol. 250(C), pages 118-130.
    2. Liu, Peiyun & Ding, Tao & Zou, Zhixiang & Yang, Yongheng, 2019. "Integrated demand response for a load serving entity in multi-energy market considering network constraints," Applied Energy, Elsevier, vol. 250(C), pages 512-529.
    3. Bao, Shiyuan & Yang, Zhifang & Yu, Juan & Dai, Wei & Guo, Lin & Yu, Hongxin, 2019. "Probabilistic energy flow and risk assessment of Electricity–Gas systems considering the thermodynamic process," Energy, Elsevier, vol. 189(C).
    4. Srinivasan, Dipti & Rajgarhia, Sanjana & Radhakrishnan, Bharat Menon & Sharma, Anurag & Khincha, H.P., 2017. "Game-Theory based dynamic pricing strategies for demand side management in smart grids," Energy, Elsevier, vol. 126(C), pages 132-143.
    5. Wang, Jianxiao & Zhong, Haiwang & Ma, Ziming & Xia, Qing & Kang, Chongqing, 2017. "Review and prospect of integrated demand response in the multi-energy system," Applied Energy, Elsevier, vol. 202(C), pages 772-782.
    6. RUIZ, Carlos & CONEJO, Antonio J. & SMEERS, Yves, 2012. "Equilibria in an oligopolistic electricity pool with stepwise offer curves," LIDAM Reprints CORE 2395, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Bai, Linquan & Li, Fangxing & Cui, Hantao & Jiang, Tao & Sun, Hongbin & Zhu, Jinxiang, 2016. "Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 270-279.
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    Cited by:

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    3. Wang, Jian & Xin, Hao & Xie, Ning & Wang, Yong, 2022. "Equilibrium models of coordinated electricity and natural gas markets with different coupling information exchanging channels," Energy, Elsevier, vol. 239(PA).
    4. Khojasteh, Meysam & Faria, Pedro & Lezama, Fernando & Vale, Zita, 2022. "Optimal strategy of electricity and natural gas aggregators in the energy and balance markets," Energy, Elsevier, vol. 257(C).
    5. Lv, Si & Wei, Zhinong & Chen, Sheng & Sun, Guoqiang & Wang, Dan, 2021. "Integrated demand response for congestion alleviation in coupled power and transportation networks," Applied Energy, Elsevier, vol. 283(C).
    6. Tsimopoulos, Evangelos G. & Georgiadis, Michael C., 2021. "Nash equilibria in electricity pool markets with large-scale wind power integration," Energy, Elsevier, vol. 228(C).
    7. Uribe, Jorge M. & Mosquera-López, Stephania & Arenas, Oscar J., 2022. "Assessing the relationship between electricity and natural gas prices in European markets in times of distress," Energy Policy, Elsevier, vol. 166(C).
    8. Huang, Gang & Wang, Jianhui & Wang, Cheng & Guo, Chuangxin, 2021. "Cascading imbalance in coupled gas-electric energy systems," Energy, Elsevier, vol. 231(C).
    9. Yang, Jie & Ma, Tieding & Ma, Kai & Yang, Bo & Guerrero, Josep M. & Liu, Zhixin, 2021. "Trading mechanism and pricing strategy of integrated energy systems based on credit rating and Bayesian game," Energy, Elsevier, vol. 232(C).

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