Optimal bidding strategy for integrated energy system participating in spot power market: A Wasserstein metric based DRO method

The ongoing reforms in electricity markets have pushed energy producers and consumers to confront an increasingly uncertain market environment. The integrated energy systems (IES), designed to integrate a wide range of energy resources and carriers, have the potential to address the uncertainties of market trading. To this end, this paper innovatively introduces the Wasserstein metric-based distributionally robust optimization theory to construct a bidding model for IES participating in the spot power market. Firstly, the bidding mechanisms of the spot market across day-ahead, intraday and real-time market are analyzed, the decision-making keys for IES operators are proposed. Secondly, a data-driven Wasserstein metric ambiguity set is constructed to deal with price uncertainty, taking the minimization of expected cost and conditioned value-at-risk as objective, a distributionally robust optimization (DRO) bidding model is proposed for IES participating in the market. Thirdly, addressing the inherent complexity of the infinite-dimensional worst-case expectation problem, the proposed model is reformulated to a finite-dimensional mixed integer linear programming problem for computationally tractable, with rigorous reformulation process and final model presented in detail. Finally, taking the spot power market in Shandong Province, China, as an example, a case study is carried out to verify the feasibility and superiority of the proposed model. Comparison with stochastic programming and robust optimization show that the proposed model has better out-of-sample performance.