Optimal self-scheduling and market involvement with electricity price uncertainty

With increasing electricity market complexity and electricity price volatility, self-scheduling and market involvement problem has become a significant challenge for power producers. Instead of previous studies focusing solely on single-market optimization problem, we propose a self-scheduling and market involvement problem that integrates both the forward market and the spot market under price uncertainty. In our approach, the forward market determines unit commitment and electricity transaction decisions for future periods, while the spot market dictates generation scheduling and real-time electricity transaction. The objective is to maximize profit from both markets, while managing the risks associated with price uncertainty using the mean conditional value-at-risk (mean-CVaR). This risk measure captures the potential losses in profit over all spot price distributions, enabling a balance between profit maximization and risk aversion. To address electricity price uncertainty, we introduce two distributionally robust optimization (DRO) models. The first, M-DRO, utilizes the mean, support, and mean absolute deviation to define the ambiguity set, ensuring tractable and efficient optimization. The second, W-DRO, employs the 1-Wasserstein distance to capture more complex and data-driven uncertainties. A decomposition-based algorithm is proposed to solve the reformulated max–min problems. Extensive numerical experiments compare the performance of the proposed DRO models against traditional stochastic programming methods, providing key managerial insights for power producers in multi-market involvement.