Multi-objective optimization of multi-energy complementary integrated energy systems considering load prediction and renewable energy production uncertainties

Multi-energy complementary integrated energy system (MCIES) is considered as a promising solution to mitigate carbon emissions and promote carbon peaking and carbon neutrality. Currently, the capacities of a MCIES are sized according to the deterministic load and parameters of the system model. However, uncertainty may lead to the failure to achieve the desired performance and affect the sizing of the MCIES. This study explored an optimization model for the proper sizing of the MCIES considering uncertainties to achieve the best economic, environmental and thermal comfort benefits. The non-dominated sorting genetic algorithm-II (NSGA-II) combined with a technique for order preference by similarity to an ideal solution (TOPSIS) and Shannon entropy method were adopted to solve the optimization. Case studies, an actual swimming pool building with MCIES, as the prototype, were used to illustrate the procedure. Moreover, the effects of uncertainty degree and scenario setting were investigated. The results show the benefits of the proposed approach against the traditional deterministic optimization method for comprehensive consideration of economy, environment and thermal comfort. It also suggests that uncertainty and scenario setting should be carefully and properly considered during the design stage, as they have a significant impact on the results of sizing.