Data-Driven Distributionally Robust Economic Dispatch For Distribution Network With Multiple Microgrids

With a high penetration level of renewable energy resources (RESs) in the distribution network (DN) and microgrids (MGs), how to realise the coordination between the two entities while takes the uncertain RESs into consideration becomes an urgent problem. A data-driven distributionally robust economic dispatch (DRED) model for both DN and MGs is proposed in this study, wherein the 1-norm and infinity-norm are used to construct the confidence set for the probability distribution of the uncertainties based on historical data. The DN and each MG are considered as independent entities to minimise their own operation cost. The alternating direction method of multipliers is utilised to coordinate the power exchange between DN and MGs and realise the autonomy of each entity. The column and constraint generation algorithm is used to solve the proposed data-driven DRED model for each entity. Considering the special structure of the proposed DRED problem, a duality-free decomposition method is adopted. Thus the computational burden is reduced. Numerical results on a modified IEEE 33-bus DN with three MGs validate the effectiveness of the proposed method.