Data-Driven Inverse Optimization for Marginal Offer Price Recovery in Electricity Markets

Abstract

This paper presents a data-driven inverse optimization (IO) approach to recover the marginal offer prices of generators in a wholesale energy market. By leveraging underlying market-clearing processes, we establish a closed-form relationship between the unknown parameters and the publicly available market-clearing results. Based on this relationship, we formulate the data-driven IO problem as a computationally feasible single-level optimization problem. The solution of the data-driven model is based on the gradient descent method, which provides an error bound on the optimal solution and a sub-linear convergence rate. We also rigorously prove the existence and uniqueness of the global optimum to the proposed data-driven IO problem and analyze its robustness in two possible noisy settings. The effectiveness of the proposed method is demonstrated through simulations in both an illustrative IEEE 14-bus system and a realistic NYISO 1814-bus system.