  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.
