Bidding strategy of load-side resource integrators participating in energy and peak regulation markets considering distributional robust optimization

Compared with traditional resources, load-side resources are diverse, and their regulatory capacity is uncertain, making it difficult for them to participate in energy and peak regulation markets simultaneously. To address the challenge that the uncertainty of load-side resources cannot be accurately described by a known probability distribution, a day-ahead joint bidding strategy of electricity energy and peak regulation markets based on the distributional robust chance constraint (DRCC) and risk expectation is proposed. Firstly, a data-driven approach is employed to characterize the uncertainty in the adjustable capacity of load-side resources. An ambiguity set based on the Wasserstein distance is constructed, which does not require prior assumptions about the specific probability distribution of the underlying random variables. Then, the bidding strategy of load-side resource integrators is proposed to minimize the risk expectation. Finally, the effectiveness of the proposed model is assessed by case studies. The proposed method overcomes the problem that the robust model is too conservative, and its computational adaptability is better than that of the stochastic model, achieving a good balance between robustness and economy.