To ensure the timely restoration of energy supply in post-disaster cities

a stochastic optimization method for coordinated load restoration of electricity-transportation systems considering the thermal inertia of buildings is proposed

taking building energy as an example. First

based on the cooling storage characteristics of the demand-side building envelope and the operational constraints of the power grid and the road network

a coordinated load restoration mathematical model for electricity-transportation systems considering the thermal inertia of buildings is constructed. Next

in order to maximize the weighted load supply time and minimize the total network loss

a multi-temporal coordinated load restoration method for electricity-transportation systems is proposed

leveraging the cooling flexibility of air conditioners (ACs) and the spatio-temporal flexibility of emergency power supply vehicles (EPSVs). Then

considering uncertainties in outside temperature and EPSV speed

the original optimization problem is transformed into a mixed integer second-order cone programming problem by using chance-constrained programming. Finally

taking post-disaster fault scenarios as cases

the impacts of different strategies on load restoration effectiveness are compared and analyzed. The results show that in uncertain environments

the proposed method can improve the critical load restoration effect by exploiting energy complementary coupling characteristics between buildings and EPSVs in the restoration process.