Resilience-oriented planning of integrated electricity and heat systems: A stochastic distributionally robust optimization approach

The resilience management of energy and power systems is of utmost importance in mitigating the impact of extreme events, which have resulted in devastating disasters and substantial economic losses. We present a novel stochastic distributionally robust optimization approach for the resilience-oriented planning of integrated electricity and heat systems (IEHSs). Firstly, A resilience-oriented planning model is developed for the IEHS, which incorporates the hardening of both electricity and heat networks, while also considering the deployment of both electric and thermal energy storages to enhance the resilience of the IEHS as a whole. Then, the stage-by-stage uncertainties associated with extreme weather events faced by the IEHS are accounted for by a stochastic distributionally robust optimization approach, where the uncertainty in the intensity of contingent extreme events is addressed via a stochastic optimization approach, while the uncertainty in the occurrence of outages resulting from a specific extreme event is addressed by a distributionally robust optimization approach. Finally, the stochastic distributionally robust optimization model is transformed into an equivalent three-level model, which is solved using a customized column-and-constraint generation algorithm. The effectiveness and superiority of the proposed approach according to the enhanced resilience and reduced costs are demonstrated by numerical simulations.