Collaborative optimization and scheduling of source, load and storage of distribution networks considering distributed energy and load uncertainty

Abstract Because distributed energy is affected by natural environmental influences, its power output has certain uncertainties, and it is challenging to achieve effective scheduling after it is incorporated into the distribution network. Based on this, a collaborative optimization scheduling strategy for the distribution network source storage load and distribution network is proposed. The scheduling strategy fully considers the randomness of the output of the distributed power supply and the uncertainty of the load demand, builds a target model to minimize the operating cost of the distribution network, uses the slime mold optimization algorithm to solve it, and finally verifies it based on actual cases. The results show that the predicted values of distributed power supplies and energy storage devices can be obtained, proving the accuracy of the proposed model and the proposed algorithm, and providing reference value for the collaborative optimization of actual source-load storage.