Distributionally Robust Risk Dispatch for Integrated Electricity and Heating System Using Moment and Unimodal Information

The integration of large amounts of uncertainties in renewable energies into integrated electricity and heating systems gives rise to new challenges and threatens security. This paper presents a distributionally robust risk dispatch (DRRD) model designed to mitigate the risks arising from uncertainty, with the objective of achieving controllable risk and cost-effectiveness. Uncertainties associated with the wind power forecast error are encapsulated within an ambiguity set that encompasses moment (e.g., mean and covariance) and unimodal information to produce solutions that are more reliable and less conservative. The proposed model constrained by Conditional Value-at-Risk (CVaR) not only limits the probability of violations but also controls the magnitude of potential violations, improving the out-of-sample performance. We employ tractable reformulations and some approximation techniques to recast the resulting distributionally robust CVaR constraints as second-order cone (SOC) programs. Finally, the numerical outcomes presented utilizing the modified Barry Island system showcase the risk mitigation and economic advantages of the proposed methodology.