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A multi-agent model of urban microgrids: Assessing the effects of energy-market shocks using real-world data

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  • Madler, Jochen
  • Harding, Sebastian
  • Weibelzahl, Martin

Abstract

The shift towards renewable energy sources (RES) in energy systems is becoming increasingly important. Residential energy generation and storage assets, smart home energy management systems, and peer-to-peer (P2P) electricity trading in microgrids can help integrate and balance decentralized renewable electricity supply with an increasingly electrified power, heat, and transport demand, reducing costs and CO2 emissions. However, these microgrids are difficult to model because they consist of autonomous and interacting entities, leading to emergent phenomena and a high degree of complexity. Agent-based modeling is an established technique to simulate the complexity of microgrids. However, the existing literature still lacks real-world implementation studies and, as a first step, models capable of validating the existing results with real-world data. To this end, we present an agent-based model and analyze the corresponding microgrid performance with real-world data. The model quantifies economic, technical, and environmental metrics to simulate microgrid performance holistically and, in line with state-of-the-art research, consists of self-interested, autonomous agents with specific load profiles, RES generation, and demand-response potential. The model can simulate a P2P marketplace where electricity is traded between agents. In the second part of the paper, we validate the model with data from a medium-sized German city. In this case study, we also compare microgrid performance in 2022, during the energy market crisis in Europe, with historical data from 2019 to assess the effects of energy market shocks. Our results show how microgrids with P2P trading can reduce electricity costs and CO2 emissions. However, our trading mechanism illustrates that the benefits of energy-community trading are almost exclusively shared among prosumers, highlighting the need to consider distributional issues when implementing P2P trading.

Suggested Citation

  • Madler, Jochen & Harding, Sebastian & Weibelzahl, Martin, 2023. "A multi-agent model of urban microgrids: Assessing the effects of energy-market shocks using real-world data," Applied Energy, Elsevier, vol. 343(C).
    • Handle: RePEc:eee:appene:v:343:y:2023:i:c:s0306261923005445
    DOI: 10.1016/j.apenergy.2023.121180
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    References listed on IDEAS

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    1. Keirstead, James, 2007. "Behavioural responses to photovoltaic systems in the UK domestic sector," Energy Policy, Elsevier, vol. 35(8), pages 4128-4141, August.
    2. Nguyen, Hieu Trung & Battula, Swathi & Takkala, Rohit Reddy & Wang, Zhaoyu & Tesfatsion, Leigh, 2019. "An integrated transmission and distribution test system for evaluation of transactive energy designs," Applied Energy, Elsevier, vol. 240(C), pages 666-679.
    3. Nilsson, Anders & Stoll, Pia & Brandt, Nils, 2017. "Assessing the impact of real-time price visualization on residential electricity consumption, costs, and carbon emissions," Resources, Conservation & Recycling, Elsevier, vol. 124(C), pages 152-161.
    4. Neves, Diana & Scott, Ian & Silva, Carlos A., 2020. "Peer-to-peer energy trading potential: An assessment for the residential sector under different technology and tariff availabilities," Energy, Elsevier, vol. 205(C).
    5. 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.
    6. Lutz Kilian, 2014. "Oil Price Shocks: Causes and Consequences," Annual Review of Resource Economics, Annual Reviews, vol. 6(1), pages 133-154, October.
    7. Hamilton, James D, 1983. "Oil and the Macroeconomy since World War II," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 228-248, April.
    8. Colak, Ilhami & Sagiroglu, Seref & Fulli, Gianluca & Yesilbudak, Mehmet & Covrig, Catalin-Felix, 2016. "A survey on the critical issues in smart grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 396-405.
    9. Zhang, Chenghua & Wu, Jianzhong & Zhou, Yue & Cheng, Meng & Long, Chao, 2018. "Peer-to-Peer energy trading in a Microgrid," Applied Energy, Elsevier, vol. 220(C), pages 1-12.
    10. Xiong, Linyun & Li, Penghan & Wang, Ziqiang & Wang, Jie, 2020. "Multi-agent based multi objective renewable energy management for diversified community power consumers," Applied Energy, Elsevier, vol. 259(C).
    11. Reis, Inês F.G. & Gonçalves, Ivo & Lopes, Marta A.R. & Antunes, Carlos Henggeler, 2020. "A multi-agent system approach to exploit demand-side flexibility in an energy community," Utilities Policy, Elsevier, vol. 67(C).
    12. Wang, Xiaoxue & Wang, Chengshan & Xu, Tao & Guo, Lingxu & Li, Peng & Yu, Li & Meng, He, 2018. "Optimal voltage regulation for distribution networks with multi-microgrids," Applied Energy, Elsevier, vol. 210(C), pages 1027-1036.
    13. Inês F. G. Reis & Ivo Gonçalves & Marta A. R. Lopes & Carlos Henggeler Antunes, 2021. "Assessing the Influence of Different Goals in Energy Communities’ Self-Sufficiency—An Optimized Multiagent Approach," Energies, MDPI, vol. 14(4), pages 1-32, February.
    14. Meng, Lexuan & Sanseverino, Eleonora Riva & Luna, Adriana & Dragicevic, Tomislav & Vasquez, Juan C. & Guerrero, Josep M., 2016. "Microgrid supervisory controllers and energy management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1263-1273.
    Full references (including those not matched with items on IDEAS)

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