Risk-Quantified Distributed Optimal Dispatch of Combined Electricity and Heating System

The increasing penetration of renewable energy integration poses a threat to the safe, reliable operation of coupled electricity and heating systems under uncertain power supply. Additionally, there are limitations on the sharing of private data between the two system operators, while managing uncertainty escalates the intricacy of this issue. Therefore, we propose a risk-quantified distributed optimal dispatch model for combined electricity and heating systems. We utilize moment-based distributionally robust chance constraints to describe the uncertain risk caused by the stochastic wind power output. The adaptive penalty factor-based alternating direction method of multipliers is employed to decouple the centralized model in a distributed manner. A second-order cone programming problem can be derived to enhance a more tractable reformulation. Multidimensional risk indices are proposed to quantify the operational risk, achieving a balance between the controllable risk and the economical dispatch. Finally, numerical results, based on the Barry Island system, validate the effectiveness and feasibility of the proposed approach while selecting risk coefficient ε=0.1 to achieve the optimal distributed risk operation.