Identification of Joint Strategic Bidding Considering Spatial Correlation of Electricity Prices

Prices in electricity market exhibit tight spatial correlations due to transmission constraints. Bidding deviations at one bus can significantly influence prices at others, which shows the high potential of strategic bidding to obtain excessive profits. However, this property is not considered in related studies regarding the identification of joint strategic bidding. To address this issue, this paper first formulates the spatially correlated strategic bidding problem as a bilevel optimization problem. The key challenge for identifying spatially correlated strategic bidding is that the number of solutions required for the bilevel optimization calculation is a combinatorial function of the scale of market participants. Theoretical derivation shows that the joint profit considering spatial correlations can be decomposed into separate profits within a neighborhood. Then, we present a criterion for effective decomposition. A practical bound of the deviation incurred by such decomposition is provided. Utilizing these properties, a computationally efficient identification method of the spatially correlated strategic bidding is presented. The combinations of market participants that most likely to acquire excessive joint profit are listed. Numerical experiments indicate that the proposed method is capable of effectively identify the spatially correlated strategic bidding potentials with an accuracy of over 97%, while notably accelerating the efficiency.