Exploiting Flexibility in Multi-Energy Systems through Distributionally Robust Chance-Constrained Optimization

Although the increasing demand due to the electri-fication of other sectors, such as transportation and heating due to the transition towards carbon-neutral energy supply, presents challenges to grid operators, it also offers high flexibility potential in grid operation. Therefore, the distribution system operators must attempt to exploit these sectors' flexibility. The flexibility offered by electric heat pumps and thermal storage can play a vital role in providing much-needed affordable energy storage and potential for load shifting. However, this flexibility comes with uncertainty as it depends on changing weather conditions and customer behavior. In this paper, a scheme for providing the optimal operation of electricity and heat networks under uncertainty in weather conditions is proposed. The system uncertainty is modeled through distributionally robust chance constraints that limit the probability of insecure operation due to uncertainty in the weather forecast. The proposed scheme is demonstrated using actual weather data on a low-voltage residential grid coupled to a district heating system through an electric heat pump. The results are compared to a conventional deterministic optimal power flow, and the grid's robustness is evaluated. The proposed scheme provides the optimal exploitation of flexibility through grid coupling and prevents insecure operation against uncertainty.