IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i12p3202-d374051.html
   My bibliography  Save this article

Optimal Coordination Strategies for Load Service Entity and Community Energy Systems Based on Centralized and Decentralized Approaches

Author

Listed:
  • Longxi Li

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China
    Center for Energy Environmental Management and Decision-Making, China University of Geosciences, Wuhan 430074, China)

Abstract

The energy interaction among a load service entity and community energy systems in neighboring communities leads to a complex energy generation, storage, and transaction problem. A load service entity is formed by a local electricity generation system, storage system, and renewable energy resources, which can provide ancillary services to customers and the utility grid. This paper proposes two coordination schemes for the interaction of community-based energy systems and load service entities based on game-theoretic frameworks. The first one is a centralized coordination scheme with full cooperation, in which the load service entity and community energy systems jointly activate the local resources. The second one is set as a decentralized coordination scheme to obtain a relative balance of interests among the market participants in a Stackelberg framework. Two mathematical models are developed for the day-ahead decision-making of the above energy management schemes. The Shapley value method, Karush-Kuhn-Tucker conditions, and strong dual theory are applied to solve the complex coordination problems. Numerical study shows the effectiveness of the coordination strategies that all stakeholders benefit from the proposed coordination schemes and create a win–win situation. In addition, sensitivity analysis is conducted to study the effects of system configuration, energy demand, and energy prices on the economic performance of all stakeholders. The results can serve as references for business managers of the load service entity.

Suggested Citation

  • Longxi Li, 2020. "Optimal Coordination Strategies for Load Service Entity and Community Energy Systems Based on Centralized and Decentralized Approaches," Energies, MDPI, vol. 13(12), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3202-:d:374051
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/12/3202/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/12/3202/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jalali, Mehdi & Zare, Kazem & Seyedi, Heresh, 2017. "Strategic decision-making of distribution network operator with multi-microgrids considering demand response program," Energy, Elsevier, vol. 141(C), pages 1059-1071.
    2. Sinha, Surabhi & Sinha, S. B., 2002. "KKT transformation approach for multi-objective multi-level linear programming problems," European Journal of Operational Research, Elsevier, vol. 143(1), pages 19-31, November.
    3. Li, Longxi & Mu, Hailin & Li, Nan & Li, Miao, 2016. "Economic and environmental optimization for distributed energy resource systems coupled with district energy networks," Energy, Elsevier, vol. 109(C), pages 947-960.
    4. Kang, Ligai & Yang, Junhong & An, Qingsong & Deng, Shuai & Zhao, Jun & Wang, Hui & Li, Zelin, 2017. "Effects of load following operational strategy on CCHP system with an auxiliary ground source heat pump considering carbon tax and electricity feed in tariff," Applied Energy, Elsevier, vol. 194(C), pages 454-466.
    5. Hélène Le Cadre & Ilyès Mezghani & Anthony Papavasiliou, 2019. "A game-theoretic analysis of transmission-distribution system operator coordination," LIDAM Reprints CORE 2996, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Li, Bei & Roche, Robin & Paire, Damien & Miraoui, Abdellatif, 2019. "A price decision approach for multiple multi-energy-supply microgrids considering demand response," Energy, Elsevier, vol. 167(C), pages 117-135.
    7. Shapley, L. S. & Shubik, Martin, 1954. "A Method for Evaluating the Distribution of Power in a Committee System," American Political Science Review, Cambridge University Press, vol. 48(3), pages 787-792, September.
    8. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
    9. Gately, Dermot, 1974. "Sharing the Gains from Regional Cooperation: A Game Theoretic Application to Planning Investment in Electric Power," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(1), pages 195-208, February.
    10. Liu, Zhen & Zhang, Xiliang & Lieu, Jenny, 2010. "Design of the incentive mechanism in electricity auction market based on the signaling game theory," Energy, Elsevier, vol. 35(4), pages 1813-1819.
    11. Lo Prete, Chiara & Hobbs, Benjamin F., 2016. "A cooperative game theoretic analysis of incentives for microgrids in regulated electricity markets," Applied Energy, Elsevier, vol. 169(C), pages 524-541.
    12. Jin, Ming & Feng, Wei & Marnay, Chris & Spanos, Costas, 2018. "Microgrid to enable optimal distributed energy retail and end-user demand response," Applied Energy, Elsevier, vol. 210(C), pages 1321-1335.
    13. Le Cadre, Hélène & Mezghani, Ilyès & Papavasiliou, Anthony, 2019. "A game-theoretic analysis of transmission-distribution system operator coordination," European Journal of Operational Research, Elsevier, vol. 274(1), pages 317-339.
    14. Mendes, Gonçalo & Ioakimidis, Christos & Ferrão, Paulo, 2011. "On the planning and analysis of Integrated Community Energy Systems: A review and survey of available tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4836-4854.
    15. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Ren, Jianxing & Lao, Changshi, 2017. "Profit allocation analysis among the distributed energy network participants based on Game-theory," Energy, Elsevier, vol. 118(C), pages 783-794.
    16. Du, Yan & Wang, Zhiwei & Liu, Guangyi & Chen, Xi & Yuan, Haoyu & Wei, Yanli & Li, Fangxing, 2018. "A cooperative game approach for coordinating multi-microgrid operation within distribution systems," Applied Energy, Elsevier, vol. 222(C), pages 383-395.
    17. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
    18. von Appen, J. & Braun, M., 2018. "Strategic decision making of distribution network operators and investors in residential photovoltaic battery storage systems," Applied Energy, Elsevier, vol. 230(C), pages 540-550.
    19. Zhou, Yizhou & Wei, Zhinong & Sun, Guoqiang & Cheung, Kwok W. & Zang, Haixiang & Chen, Sheng, 2018. "A robust optimization approach for integrated community energy system in energy and ancillary service markets," Energy, Elsevier, vol. 148(C), pages 1-15.
    20. Koirala, Binod Prasad & Koliou, Elta & Friege, Jonas & Hakvoort, Rudi A. & Herder, Paulien M., 2016. "Energetic communities for community energy: A review of key issues and trends shaping integrated community energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 722-744.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuzhe Xie & Yan Yao & Yawu Wang & Weiqiang Cha & Sheng Zhou & Yue Wu & Chunyi Huang, 2022. "A Cooperative Game-Based Sizing and Configuration of Community-Shared Energy Storage," Energies, MDPI, vol. 15(22), pages 1-17, November.
    2. Anne A. Gharaibeh & Deema A. Al-Shboul & Abdulla M. Al-Rawabdeh & Rasheed A. Jaradat, 2021. "Establishing Regional Power Sustainability and Feasibility Using Wind Farm Land-Use Optimization," Land, MDPI, vol. 10(5), pages 1-32, April.
    3. Almendra Awerkin & Paolo Falbo & Tiziano Vargiolu, 2023. "Optimal Investment and Fair Sharing Rules of the Incentives for Renewable Energy Communities," Papers 2311.12055, arXiv.org.
    4. Fernando V. Cerna & Mahdi Pourakbari-Kasmaei & Luizalba S. S. Pinheiro & Ehsan Naderi & Matti Lehtonen & Javier Contreras, 2021. "Intelligent Energy Management in a Prosumer Community Considering the Load Factor Enhancement," Energies, MDPI, vol. 14(12), pages 1-24, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Longxi, 2021. "Coordination between smart distribution networks and multi-microgrids considering demand side management: A trilevel framework," Omega, Elsevier, vol. 102(C).
    2. Li, Longxi & Cao, Xilin & Wang, Peng, 2021. "Optimal coordination strategy for multiple distributed energy systems considering supply, demand, and price uncertainties," Energy, Elsevier, vol. 227(C).
    3. Jing, Rui & Wang, Meng & Liang, Hao & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2018. "Multi-objective optimization of a neighborhood-level urban energy network: Considering Game-theory inspired multi-benefit allocation constraints," Applied Energy, Elsevier, vol. 231(C), pages 534-548.
    4. Li, Na & Hakvoort, Rudi A. & Lukszo, Zofia, 2022. "Cost allocation in integrated community energy systems — Performance assessment," Applied Energy, Elsevier, vol. 307(C).
    5. Churkin, Andrey & Bialek, Janusz & Pozo, David & Sauma, Enzo & Korgin, Nikolay, 2021. "Review of Cooperative Game Theory applications in power system expansion planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    6. Tuomo Joensuu & Markku Norvasuo & Harry Edelman, 2019. "Stakeholders’ Interests in Developing an Energy Ecosystem for the Superblock—Case Hiedanranta," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
    7. de Wildt, T.E. & Chappin, E.J.L. & van de Kaa, G. & Herder, P.M. & van de Poel, I.R., 2019. "Conflicting values in the smart electricity grid a comprehensive overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 184-196.
    8. Gao, Lei & Hwang, Yunho & Cao, Tao, 2019. "An overview of optimization technologies applied in combined cooling, heating and power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    9. Sun, Lu & Xu, Qingshan & Song, Yun, 2022. "Game-theoretic genetic-priced optimization of multiple microgrids under uncertainties," Applied Mathematics and Computation, Elsevier, vol. 426(C).
    10. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Ren, Jianxing, 2017. "Benefit allocation for distributed energy network participants applying game theory based solutions," Energy, Elsevier, vol. 119(C), pages 384-391.
    11. Binod Prasad Koirala & Ellen van Oost & Henny van der Windt, 2020. "Innovation Dynamics of Socio-Technical Alignment in Community Energy Storage: The Cases of DrTen and Ecovat," Energies, MDPI, vol. 13(11), pages 1-22, June.
    12. Pickering, B. & Choudhary, R., 2019. "District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles," Applied Energy, Elsevier, vol. 236(C), pages 1138-1157.
    13. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    14. Wang, Yongli & Li, Jiapu & Wang, Shuo & Yang, Jiale & Qi, Chengyuan & Guo, Hongzhen & Liu, Ximei & Zhang, Hongqing, 2020. "Operational optimization of wastewater reuse integrated energy system," Energy, Elsevier, vol. 200(C).
    15. Li, Ruonan & Mahalec, Vladimir, 2022. "Integrated design and operation of energy systems for residential buildings, commercial buildings, and light industries," Applied Energy, Elsevier, vol. 305(C).
    16. Shariat Torbaghan, Shahab & Madani, Mehdi & Sels, Peter & Virag, Ana & Le Cadre, Hélène & Kessels, Kris & Mou, Yuting, 2021. "Designing day-ahead multi-carrier markets for flexibility: Models and clearing algorithms," Applied Energy, Elsevier, vol. 285(C).
    17. Correa-Florez, Carlos Adrian & Michiorri, Andrea & Kariniotakis, Georges, 2018. "Robust optimization for day-ahead market participation of smart-home aggregators," Applied Energy, Elsevier, vol. 229(C), pages 433-445.
    18. Scheller, Fabian & Bruckner, Thomas, 2019. "Energy system optimization at the municipal level: An analysis of modeling approaches and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 444-461.
    19. Zhang, Youjun & Hao, Junhong & Ge, Zhihua & Zhang, Fuxiang & Du, Xiaoze, 2021. "Optimal clean heating mode of the integrated electricity and heat energy system considering the comprehensive energy-carbon price," Energy, Elsevier, vol. 231(C).
    20. Haji Bashi, Mazaher & De Tommasi, Luciano & Le Cam, Andreea & Relaño, Lorena Sánchez & Lyons, Padraig & Mundó, Joana & Pandelieva-Dimova, Ivanka & Schapp, Henrik & Loth-Babut, Karolina & Egger, Christ, 2023. "A review and mapping exercise of energy community regulatory challenges in European member states based on a survey of collective energy actors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3202-:d:374051. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.