Decentralized Dispatch with Distributionally Robust Joint Chance Constraints for Integrated Electrical and Heating System via Dynamic Boundary Response

With the widespread application of combined heat and power (CHP) units, the economic dispatch of integrated electric and district heating systems (IEHSs) has drawn increasing attention. Because the electric power system (EPS) and district heating system (DHS) are generally managed separately, the decentralized dispatch pattern is preferable for the IEHS dispatch problem. However, many common decentralized methods suffer from the drawbacks of slow and local convergence. Moreover, the uncertainties of renewable generation cannot be ignored in a decentralized pattern. Additionally, the most commonly used individual chance constraints in distributionally robust optimization cannot consider safety constraints simultaneously, so the safe operation of an IEHS cannot be guaranteed. Thus, distributionally robust joint chance constraints and robust constraints are jointly introduced into the IEHS dispatch problem in this paper to obtain a stronger safety guarantee, and a method combined with Bonferroni and conditional value at risk (CVaR) approximation is presented to transform the original model into a quadratic program. Additionally, a dynamic boundary response (DBR)-based distributed algorithm based on multiparametric programming is proposed for a fast solution. Case studies showcase the necessity of using mixed distributionally robust joint chance constraints and robust constraints, as well as the effectiveness of the DBR algorithm.