Community decision-makers’ choice of multi-objective scheduling strategy for integrated energy considering multiple uncertainties and demand response

Abstract

• Construct the RE output uncertainty model by the probability box theory. • Use complex networks to classify the community residents. • A CDR model is built to mobilize various users to join in IESs scheduling. • Propose a utility model for decision-makers based on the S-shaped utility function. • Select the second optimal strategy by Muirhead mean operator. To address the impact of uncertainty on scheduling strategy in community integrated energy systems, a multi-objective optimal scheduling model under the constraint of various uncertainties and demand response is suggested. Firstly, a source-load uncertainty model and a comprehensive demand response model are built. Secondly, construct a multi-objective satisfaction model and utility model based on the energy supplier profit, resident cost, carbon treatment amount, and renewable energy utilization rate. Then, the best strategy is determined using the entropy weight method and the Muirhead mean operator. Finally, a multi-scene case of a residential area is used to validate the model's effectiveness. The results show that: 1) The source-load uncertainty model improves overall robustness. 2) The resident cost is reduced by 7.59%-9.84%, the carbon treatment amount is reduced by 17.71%-95.635%, and the energy supplier profit and the renewable energy utilization rate are increased. 3) The strategy with the best satisfaction or the best utility may not be the best choice among the objective functions of different relationships, so decision-makers need to choose according to the actual situation. The model is beneficial to the economy and environment, and it serves as a guide for community decision-makers in selecting a comprehensive energy multi-objective scheduling strategy.