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

TL;DR

This paper introduces a data-driven inverse optimization method to accurately recover generator marginal offer prices in electricity markets, leveraging market-clearing data and gradient descent for efficient solutions.

Contribution

It develops a novel, closed-form inverse optimization model that is computationally feasible and rigorously proves its optimality and robustness under noisy conditions.

Findings

Effective recovery of marginal offer prices demonstrated in IEEE and NYISO systems

The method provides error bounds and guarantees convergence

Robustness analysis confirms reliability under data noise

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.