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Rewarding cooperative virtual power plant formation using scoring rules

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  • Robu, Valentin
  • Chalkiadakis, Georgios
  • Kota, Ramachandra
  • Rogers, Alex
  • Jennings, Nicholas R.

Abstract

Virtual Power Plants (VPPs) are fast emerging as a viable means of integrating small and distributed energy resources (DERs), like wind and solar, into the electricity supply network (Grid). VPPs are formed via the aggregation of a large number of DERs, so that they exhibit the characteristics of a traditional generator in terms of predictability and robustness. In this work, we promote the formation of such “cooperative” VPPs (CVPPs) using techniques from the field of distributed Artificial Intelligence and game theory. In particular, we design a payment mechanism that encourages DERs to join CVPPs with increased size and visibility to the network operator. Our method is based on strictly proper scoring rules and incentivises the provision of accurate predictions of expected electricity generation from member DERs, which aids in the planning of the supply schedule at the Grid. We empirically evaluate our approach using the real-world setting of 16 commercial wind farms in the UK, and we show that it incentivises real DERs to form CVPPs, and outperforms the current state of the art payment mechanism developed for this problem.

Suggested Citation

  • Robu, Valentin & Chalkiadakis, Georgios & Kota, Ramachandra & Rogers, Alex & Jennings, Nicholas R., 2016. "Rewarding cooperative virtual power plant formation using scoring rules," Energy, Elsevier, vol. 117(P1), pages 19-28.
    • Handle: RePEc:eee:energy:v:117:y:2016:i:p1:p:19-28
    DOI: 10.1016/j.energy.2016.10.077
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    1. James E. Matheson & Robert L. Winkler, 1976. "Scoring Rules for Continuous Probability Distributions," Management Science, INFORMS, vol. 22(10), pages 1087-1096, June.
    2. Tarroja, Brian & Mueller, Fabian & Eichman, Joshua D. & Samuelsen, Scott, 2012. "Metrics for evaluating the impacts of intermittent renewable generation on utility load-balancing," Energy, Elsevier, vol. 42(1), pages 546-562.
    3. Shafie-khah, Miadreza & Parsa Moghaddam, Mohsen & Sheikh-El-Eslami, Mohamad Kazem & Rahmani-Andebili, Mehdi, 2012. "Modeling of interactions between market regulations and behavior of plug-in electric vehicle aggregators in a virtual power market environment," Energy, Elsevier, vol. 40(1), pages 139-150.
    4. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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    Cited by:

    1. Jingjing Luo & Yajing Gao & Wenhai Yang & Yongchun Yang & Zheng Zhao & Shiyu Tian, 2018. "Optimal Operation Modes of Virtual Power Plants Based on Typical Scenarios Considering Output Evaluation Criteria," Energies, MDPI, vol. 11(10), pages 1-22, October.
    2. Hany Elgamal, Ahmed & Kocher-Oberlehner, Gudrun & Robu, Valentin & Andoni, Merlinda, 2019. "Optimization of a multiple-scale renewable energy-based virtual power plant in the UK," Applied Energy, Elsevier, vol. 256(C).
    3. Lee, Kyungeun & Lee, Hyesu & Lee, Hyoseop & Yoon, Yoonjin & Lee, Eunjung & Rhee, Wonjong, 2018. "Assuring explainability on demand response targeting via credit scoring," Energy, Elsevier, vol. 161(C), pages 670-679.
    4. Levieux, Luis Ignacio & Ocampo-Martinez, Carlos & Inthamoussou, Fernando A. & De Battista, Hernán, 2021. "Predictive management approach for the coordination of wind and water-based power supplies," Energy, Elsevier, vol. 219(C).
    5. Yetuo Tan & Yongming Zhi & Zhengbin Luo & Honggang Fan & Jun Wan & Tao Zhang, 2023. "Optimal Scheduling of Virtual Power Plant with Flexibility Margin Considering Demand Response and Uncertainties," Energies, MDPI, vol. 16(15), pages 1-14, August.
    6. Kirli, Desen & Couraud, Benoit & Robu, Valentin & Salgado-Bravo, Marcelo & Norbu, Sonam & Andoni, Merlinda & Antonopoulos, Ioannis & Negrete-Pincetic, Matias & Flynn, David & Kiprakis, Aristides, 2022. "Smart contracts in energy systems: A systematic review of fundamental approaches and implementations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    7. Andoni, Merlinda & Robu, Valentin & Früh, Wolf-Gerrit & Flynn, David, 2017. "Game-theoretic modeling of curtailment rules and network investments with distributed generation," Applied Energy, Elsevier, vol. 201(C), pages 174-187.
    8. Mohammad Mohammadi Roozbehani & Ehsan Heydarian-Forushani & Saeed Hasanzadeh & Seifeddine Ben Elghali, 2022. "Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    9. Zhang, Ying & Robu, Valentin & Cremers, Sho & Norbu, Sonam & Couraud, Benoit & Andoni, Merlinda & Flynn, David & Poor, H. Vincent, 2024. "Modelling the formation of peer-to-peer trading coalitions and prosumer participation incentives in transactive energy communities," Applied Energy, Elsevier, vol. 355(C).
    10. Chen, Yujia & Pei, Wei & Ma, Tengfei & Xiao, Hao, 2023. "Asymmetric Nash bargaining model for peer-to-peer energy transactions combined with shared energy storage," Energy, Elsevier, vol. 278(PB).
    11. Burger, Scott & Chaves-Ávila, Jose Pablo & Batlle, Carlos & Pérez-Arriaga, Ignacio J., 2017. "A review of the value of aggregators in electricity systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 395-405.
    12. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    13. 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.

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