An intelligent network approach to renewable energy management decisions: A comparative study of integrated energy system

Abstract

This paper presents an analysis of how intelligent network approaches can be used to inform renewable energy scheduling decisions. Integrated energy system (IES) optimum planning problems are characterized by high dimensional nonlinearity. To this end, an electricity and heating combined demand response (DR) scheme is proposed that takes dispatchers into account when calculating dispatch capabilities. This study constructs a load group aggregation scheme based on incentive DR and constructs the electrical load response scheme to minimize deviations between the dispatch signal and the characteristic model. Continuous intelligent deep searching can be realized by jumping outside the local optimum and improving the algorithm's capability to do so. Chicken swarm optimization is deployed as the optimizer and it is shown that it is more effective in comparison to the conventional intelligent optimizer. The paper also identifies key elements of a successful network approach based on digital twin and discusses how the proposed approach can be used to inform renewable energy decisions in other contexts. As a result of incorporating the dispatcher's preferred signal into the integrated DR, new energy is accommodated and the system's operational costs are reduced, as well as the resource-load relationship is improved.