Multi-objective optimization of an integrated energy system with high proportion of renewable energy under multiple uncertainties

With the development of integrated energy systems, the system energy demands become more complicated and the renewable energy proportion becomes higher. Under this background, integrated energy systems gradually present a state of high randomness and strong uncertainty, which affects the system collaborative optimization. In order to handle the multiple uncertainties’ effects effectively, multi-objective optimization considering uncertainties of energy demand and renewable energy using information gap decision theory (IGDT) method was carried out. By analyzing the optimization results and comparing the results of different weight coefficient of multiple uncertainties, the renewable energy uncertainty has a strong effect, while the energy demand uncertainty’s effect is more complex.