Dynamic modeling and adaptive variable constraint optimization for integrated energy systems based on double-loop framework

Abstract

Integrated energy systems (IESs) provide support for improving energy utilization and have become an inevitable choice for the energy revolution. However, because the dynamic characteristics of different energy conversion devices vary considerably, the system operation exhibits source-load energy deviation, which affects the optimal performance of IESs. This paper proposes a dynamic optimization dispatch strategy based on double-loop optimization framework for IESs. Firstly, a nonlinear difference equation model is developed to characterize dynamic response capacity of different devices, and the optimization dispatch model is established. Next, a double-loop optimization framework is proposed to separate the devices with different dynamic response capacity and time scales. Then, the nonlinear difference equation is transformed into an algebraic equation by power time-domain integration, and an adaptive variable constraint particle swarm optimization (AVCPSO) algorithm is proposed to solve the dispatch plan, which achieves constraints updating by continuous feedback of the device state. Finally, an IES case study is conducted to perform optimization analysis, the simulation results show that the optimized strategy can reduce the daily operation cost by 6.25% and carbon emission by 2.90%, increase the source-load matching by 3.06%, and reduce total power consumption by 29.36% and peak-valley load difference by 19.97% to power grid.