A novel multi-objective optimization framework for optimal integrated energy system planning with demand response under multiple uncertainties

Abstract

The planning of integrated energy systems (IES) faces significant challenges due to the presence of multiple uncertainties caused by stochastic demands and renewable energy generation. Particularly, how to balance different conflicted objectives in IES planning under multiple uncertainties is a major obstacle with few studies. Thus, this paper proposes a novel multi-objective optimization framework (MOOF) for uncertain IES planning with demand response to achieve the synergistic enhancement of multiple performance indicators of the system while ensuring its flexibility and safety. The proposed MOOF encompasses several key steps. Firstly, a multi-objective optimization model under various uncertainties is constructed, with the minimization of investment and operating costs, maximization of exergy efficiency, and minimization of carbon emissions as optimization objectives. Secondly, the chance constraint approach is used to convert the constraint conditions into deterministic ones. Subsequently, the Pareto dominance concept is incorporated into robust, interval, and opportunistic optimization techniques to obtain three deterministic transformation methods for multi-objective optimization with uncertainty. Further, a high-efficiency constrained multi-objective coevolutionary algorithm (CMCA) is developed to solve the proposed planning model, which has the characteristics of nonlinearity, and high-dimensional complexity. Finally, the effectiveness of the proposed MOOF and CMCA is verified through numerous case studies.