Multi-objective Optimization Scheduling Method for Integrated Energy System Considering Uncertainty

In recent years, with the rapid development of integrated energy systems (IES), the complementary coupling of multiple energy sources has been deepening. The power spot market is running, high ratio of new energy is connecting to the grid, and demand-side management technology is developing those lead to various uncertainties in the building-level IES (BIES). Under this environment, how to dispatch the output of each unit in BIES to reduce the operation cost and carbon emission, and increase the overall energy efficiency have become an urgent problem to be solved. This paper uses Latin hypercube sampling (LHS) and fuzzy c-means (FCM) clustering algorithm to obtain typical scenes that can effectively reflect uncertainty. A multi-objective optimal scheduling model including minimum operating cost, minimum carbon emission, and maximum energy utilization is established, and a nondominated sorting genetic algorithm II (NSGA-II) with elite strategy is used to solve the model. Finally, the effectiveness of the proposed model and the algorithm is verified by simulation analysis. The proposed method is beneficial to both the economy and efficiency of BIES.