Research On Optimized Operation Of Integrated Energy Microgrid System Based On Multiagent Reinforcement Learning

Building an integrated energy microgrid system can help further improve the utilization rate of renewable energy such as wind power and photovoltaic and reduce the adverse impact of grid stability when large-scale grid connects of distributed power.And helping realize the“Dual Carbon” goal.What’s more,by integrated energy microgrid systems,effective collaborative coupling of multiple energy sources can be achieved,thereby improving the economic benefits of the system,which is also an important direction of future energy research.However,integrated energy microgrid systems has the complex operation and control because of their multiple spatiotemporal scales,multiple uncertainties,deep coupling of multiple heterogeneous energy flows and uncertainty of users’ requirements for energy.To solve the above problems,this paper introduces a multi-agent reinforcement learning algorithm.Under the premise of ensuring the voltage stability of the integrated energy microgrid system,it optimizes the operation modes of multiple distributed power sources to achieve energy balance and reduce the system operating costs.Firstly,the article briefly describes the development background and current situation of integrated energy microgrid systems and introduces the research status of multi-agent reinforcement learning algorithms.By introducing multi-agent reinforcement learning algorithms,optimizing operational issues arising in integrated energy microgrid systems are solved.Since the scheduling time scales vary due to different types of energy characteristics in integrated energy microgrid systems,this paper constructs a multi time scale integrated energy microgrid system scheduling based on multi-agent reinforcement learning,which improves the reliability and economy of system operation;Then,in order to address the interests of different energy investment and operation entities in the integrated energy microgrid system,the article further studies the implementation of the multi-entity optimization operation strategy of the integrated energy microgrid system based on heterogeneous multi-agent.This article has successively used the optimal scheduling model of ordinary multi-agent and the optimal operation model based on heterogeneous multi-agent.According to different types of issues about optimal operation,ordinary multi-agent algorithms are applied to the optimal scheduling of multiple time scales integrated energy microgrid systems,and heterogeneous multi-agent reinforcement learning algorithms are applied to the multi-entity optimal operation of integrated energy microgrid systems.The above research has positive guiding significance for achieving low-carbon energy transformation,efficient utilization,and promoting the actual implementation of clean energy industrial parks and comprehensive energy projects.Example validation: Taking the optimization operation of a comprehensive energy system in northern China and a multi-agent microgrid system in northwestern China as examples,through experimental results analysis and comparison,it is proven that the multi-time scale scheduling model based on multi-agent reinforcement learning and the multi-agent microgrid optimization operation model can effectively improve the economy and reliability of the operation of the integrated energy microgrid.