TR2020-142

CVaR-constrained Stochastic Bidding Strategy for a Virtual Power Plant with Mobile Energy Storages


    •  Xiao, D., Sun, H., Nikovski, D.N., Shoichi, K., Mori, K., Hashimoto, H., "CVaR-constrained Stochastic Bidding Strategy for a Virtual Power Plant with Mobile Energy Storages", IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), November 2020.
      BibTeX TR2020-142 PDF
      • @inproceedings{Xiao2020nov,
      • author = {Xiao, Dongliang and Sun, Hongbo and Nikovski, Daniel N. and Shoichi, Kitamura and Mori, Kazuyuki and Hashimoto, Hiroyuki},
      • title = {CVaR-constrained Stochastic Bidding Strategy for a Virtual Power Plant with Mobile Energy Storages},
      • booktitle = {IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-142}
      • }
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  • Research Areas:

    Electric Systems, Optimization

This paper proposes a stochastic optimizationbased energy and reserve bidding strategy for a virtual power plant (VPP) with mobile energy storages, renewable energy resources (RESs) and load demands at multiple buses. In the proposed bidding strategy, the energy markets include the dayahead and real-time energy markets, and the reserve markets include operating, regulation up and regulation down reserve markets. In view of the differences of energy and reserve prices, renewable generations and load demands between buses on the next day, the mobile energy storages can be delivered to different buses for maximizing the VPP’s total expected profit considering its risk preference. In the stochastic optimization model for generating the bidding strategies, the uncertain market prices, renewable power productions and load demands are represented via scenarios, and the conditional value at risk (CVaR) is used as the risk measure to manage the VPP’s risks in the worst case scenarios related to a confidence level. Since the VPP may need to manage the risks related to multiple confidence levels, the proposed model maximizes multiple CVaRs with different confidence levels. Finally, case studies are carried out to verify the effectiveness of proposed bidding strategy with mobile energy storages and multiple CVaRs.