Strategic Bidding for Energy Hubs Based on Hybrid Stochastic/Distributionally Robust Optimization

Abstract

The synergy among multi-energy carriers enables the flexible operation of energy hubs (EHs) under uncertain environment. To address the uncertain density function associate with scenario trees, a novel risk-aversion optimal bidding strategy is proposed for the energy hub operator (EHO) to minimize the day-ahead cost, considering the uncertainties of day-ahead prices, real-time prices, loads, photovoltaic output, and ambient temperature. A novel scenario tree with uncertain density functions is proposed to approximate these uncertainties under total variation distance. The bidding problem is formulated as a two-stage distributionally robust risk-aversion optimization problem. With duality, it is reformulated to a linear programming problem, which is further solved by the multi-cuts Benders decomposition scheme. Simulations are performed on a test EH system, and numerical results have verified the effectiveness of the proposed method, which is able to provide risk-averse bidding strategies for EHOs.