Integrated Energy System Operation Optimization Based on Reinforcement Learning

Abstract

Each subject in the integrated energy system has different interests and demands, and it is necessary to optimize the energy dispatching with the help of multi-subject game theory. In order to solve the above problems, this paper proposes a reinforcement learning-based multi-object operation optimization method for integrated energy systems. Firstly, a multi-subject integrated energy system model including energy suppliers, park service providers and users is constructed; secondly, a game search method based on reinforcement signals is proposed to improve the speed of multi-subject game solution; finally, a simulation is conducted with an integrated energy system as an example to verify the effectiveness and rapidity of the proposed method.

Supplier Selection for a Power Generator Sustainable Supplier Park: Interval-Valued Neutrosophic SWARA and EDAS Application

“…Furthermore, there is an emphasis on the "energy/power industry" of the supplier park literature [20,59] as a research gap. Additionally, the "supplier park for energy sector" literature discusses energy management and material flows, energy cascading, and pricing issues [60].…”