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Fair trading strategy in multi-energy systems considering design optimization and demand response based on consumer psychology

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  • Li, Li
  • Fan, Shuai
  • Xiao, Jucheng
  • Zhou, Huan
  • Shen, Yu
  • He, Guangyu

Abstract

Multi-energy systems can complement and convert various energy sources, bring opportunities and challenges to community energy markets. However, energy system configuration and trading prices change as a result of demand response behavior interventions and have a reverse effect on them, which is generally ignored. Therefore, it is necessary to provide an energy trading strategy that considers interactions between the supply and demand sides. To this end, this study proposes a holistic approach for electricity, cooling and heating trading, which is based on the Nash-type game to guarantee energy trading fairness under such interactions. The integrated demand response model is established considering uncertainty based on consumer psychology, where the capacity of flexible energy demand is a critical decision variable influenced by the supply side. Based on this, the design optimization model of multi-energy systems is developed, where commonly used energy supply technologies are considered. The impact of interaction with the demand side on the design optimization of multi-energy systems is highlighted. The case study shows that the energy retailer can avoid sacrificing 21 % of its profits to achieve 9 % energy cost savings for the demand response aggregator considering trading fairness; both players have better economic performance when demand-side interactions are considered.

Suggested Citation

  • Li, Li & Fan, Shuai & Xiao, Jucheng & Zhou, Huan & Shen, Yu & He, Guangyu, 2024. "Fair trading strategy in multi-energy systems considering design optimization and demand response based on consumer psychology," Energy, Elsevier, vol. 306(C).
    • Handle: RePEc:eee:energy:v:306:y:2024:i:c:s0360544224021674
    DOI: 10.1016/j.energy.2024.132393
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    1. 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).
    2. Wang, Yongli & Ma, Yuze & Song, Fuhao & Ma, Yang & Qi, Chengyuan & Huang, Feifei & Xing, Juntai & Zhang, Fuwei, 2020. "Economic and efficient multi-objective operation optimization of integrated energy system considering electro-thermal demand response," Energy, Elsevier, vol. 205(C).
    3. Wang, Yongli & Liu, Zhen & Wang, Jingyan & Du, Boxin & Qin, Yumeng & Liu, Xiaoli & Liu, Lin, 2023. "A Stackelberg game-based approach to transaction optimization for distributed integrated energy system," Energy, Elsevier, vol. 283(C).
    4. Li, Yuxin & Wang, Jiangjiang & Zhou, Yuan & Wei, Changqi & Guan, Zhimin & Chen, Haiyue, 2023. "Multi-dimension day-ahead scheduling optimization of a community-scale solar-driven CCHP system with demand-side management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    5. Zhou, Yuan & Wang, Jiangjiang & Yang, Mingxu & Xu, Hangwei, 2023. "Hybrid active and passive strategies for chance-constrained bilevel scheduling of community multi-energy system considering demand-side management and consumer psychology," Applied Energy, Elsevier, vol. 349(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. Matschegg, Doris & Carlon, Elisa & Sturmlechner, Rita & Sonnleitner, Andrea & Fuhrmann, Marilene & Dißauer, Christa & Strasser, Christoph & Enigl, Monika, 2023. "Investigation of individual motives and decision paths on residential energy supply systems," Energy, Elsevier, vol. 281(C).
    8. Yu, Heyang & Zhang, Jingchen & Ma, Junchao & Chen, Changyu & Geng, Guangchao & Jiang, Quanyuan, 2023. "Privacy-preserving demand response of aggregated residential load," Applied Energy, Elsevier, vol. 339(C).
    9. Okur, Özge & Heijnen, Petra & Lukszo, Zofia, 2021. "Aggregator’s business models in residential and service sectors: A review of operational and financial aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    10. Duan, Jiandong & Liu, Fan & Yang, Yao, 2022. "Optimal operation for integrated electricity and natural gas systems considering demand response uncertainties," Applied Energy, Elsevier, vol. 323(C).
    11. Meng, Yan & Fan, Shuai & Shen, Yu & Xiao, Jucheng & He, Guangyu & Li, Zuyi, 2023. "Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy," Applied Energy, Elsevier, vol. 350(C).
    12. Ouyang, Tiancheng & Zhang, Mingliang & Wu, Wencong & Zhao, Jiaqi & Xu, Hua, 2023. "A day-ahead planning for multi-energy system in building community," Energy, Elsevier, vol. 267(C).
    13. Li, Li & Wang, Jing & Zhong, Xiaoyi & Lin, Jian & Wu, Nianyuan & Zhang, Zhihui & Meng, Chao & Wang, Xiaonan & Shah, Nilay & Brandon, Nigel & Xie, Shan & Zhao, Yingru, 2022. "Combined multi-objective optimization and agent-based modeling for a 100% renewable island energy system considering power-to-gas technology and extreme weather conditions," Applied Energy, Elsevier, vol. 308(C).
    14. Durisch, Wilhelm & Tille, Dierk & Wörz, A. & Plapp, Waltraud, 2000. "Characterisation of photovoltaic generators," Applied Energy, Elsevier, vol. 65(1-4), pages 273-284, April.
    15. Jing, Rui & Xie, Mei Na & Wang, Feng Xiang & Chen, Long Xiang, 2020. "Fair P2P energy trading between residential and commercial multi-energy systems enabling integrated demand-side management," Applied Energy, Elsevier, vol. 262(C).
    16. Li, Miao & Mu, Hailin & Li, Nan & Ma, Baoyu, 2016. "Optimal design and operation strategy for integrated evaluation of CCHP (combined cooling heating and power) system," Energy, Elsevier, vol. 99(C), pages 202-220.
    17. Kim, Hansung & Cheon, Hyungkyu & Ahn, Young-Hwan & Choi, Dong Gu, 2019. "Uncertainty quantification and scenario generation of future solar photovoltaic price for use in energy system models," Energy, Elsevier, vol. 168(C), pages 370-379.
    18. Wu, Xiaomin & Cao, Weihua & Wang, Dianhong & Ding, Min & Yu, Liangjun & Nakanishi, Yosuke, 2021. "Demand response model based on improved Pareto optimum considering seasonal electricity prices for Dongfushan Island," Renewable Energy, Elsevier, vol. 164(C), pages 926-936.
    19. Zhang, Di & Samsatli, Nouri J. & Hawkes, Adam D. & Brett, Dan J.L. & Shah, Nilay & Papageorgiou, Lazaros G., 2013. "Fair electricity transfer price and unit capacity selection for microgrids," Energy Economics, Elsevier, vol. 36(C), pages 581-593.
    20. Jing, Rui & Wang, Meng & Brandon, Nigel & Zhao, Yingru, 2017. "Multi-criteria evaluation of solid oxide fuel cell based combined cooling heating and power (SOFC-CCHP) applications for public buildings in China," Energy, Elsevier, vol. 141(C), pages 273-289.
    21. Yousefi, Shaghayegh & Moghaddam, Mohsen Parsa & Majd, Vahid Johari, 2011. "Optimal real time pricing in an agent-based retail market using a comprehensive demand response model," Energy, Elsevier, vol. 36(9), pages 5716-5727.
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