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Distributionally robust offering strategy of the aggregator integrating renewable energy generator and energy storage considering uncertainty and connections between the mid-to-long-term and spot electricity markets

Author

Listed:
  • Li, Bingkang
  • Zhao, Huiru
  • Wang, Xuejie
  • Zhao, Yihang
  • Zhang, Yuanyuan
  • Lu, Hao
  • Wang, Yuwei

Abstract

As an emerging entity, the aggregator integrating renewable energy generator and energy storage (REG-ES aggregator) can promote the consumption of renewable energy by participating in electricity market, which is an important way to deal with global climate change and achieve the “carbon neutrality” target. To solve the problems of multiple market connection and REG output uncertainty faced by the aggregator in the offering process, this paper develops a comprehensive offering strategy decision-making model for REG-ES aggregator, and the Wasserstein based two-stage distributionally robust optimization (DRO) technique is proposed to deal with the uncertainty in the model. Linear affine technique and strong duality theory are applied to reconstruct the DRO model into an easy-to-solved form. The simulation results and further discussions show that (1) the aggregator can effectively suppress the output uncertainty through ES system, but the adjustment effect in certain periods is not significant due to ES's power balance constraint. (2) Considering market connection, aggregator will offer over 90% of the output in mid-to-long-term market and tend to reduce ES capacity to gain more stable profits. Therefore, market regulator need to formulate necessary ES configuration requirements and market incentive mechanisms to guarantee aggregators' orderly participation in multiple markets. (3) When ignoring uncertainty, aggregator's revenue will increase by 3.46% while its profit will decrease by 1.85% due to higher default risk and deviation penalty costs. (4) By comparing the results of stochastic optimization, robust optimization and DRO methods, the DRO model is proved to have significant advantages in data-driven, low computational complexity and result conservativeness.

Suggested Citation

  • Li, Bingkang & Zhao, Huiru & Wang, Xuejie & Zhao, Yihang & Zhang, Yuanyuan & Lu, Hao & Wang, Yuwei, 2022. "Distributionally robust offering strategy of the aggregator integrating renewable energy generator and energy storage considering uncertainty and connections between the mid-to-long-term and spot elec," Renewable Energy, Elsevier, vol. 201(P1), pages 400-417.
    • Handle: RePEc:eee:renene:v:201:y:2022:i:p1:p:400-417
    DOI: 10.1016/j.renene.2022.10.117
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    References listed on IDEAS

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