Low-Carbon Economic Multi-Objective Dispatch of an Integrated Energy System Based on GAPSO

Abstract

In recent years, several countries have proposed targets for carbon neutrality in energy, and the transformation of energy systems has become a research hotspot. As a system capable of coupling multi-energy, achieving high penetrations of renewable energy, and improving energy efficiency, the integrated energy system will take on more responsibility under the carbon neutrality target. This paper uses GAPSO (which combines genetic algorithm with particle swarm optimisation algorithm, has a faster iteration speed, and avoids local optimisation) to solve the Pareto frontier set considering the system operation costs and carbon emission. The system operation costs are described using Latin hypercube sampling (LHS) to predict the stochastic output of the renewable energy source and a penalty function based on the predicted mean vote (PMV) model to describe the thermal comfort of the user, which is solved using the genetic algorithm (GA) algorithm. The carbon emission is calculated using the carbon accounting method.