Research On Energy Management In Smart Grid Based On Distributed Optimization Algorithm

The smart grid,which is composed of various intelligent agents,has emerged in response to several factors.These include the growing demand for electricity in society,the rapid integration of traditional power grid systems and new energy generation technologies,as well as the adoption of new technologies such as the Internet of Things,artificial intelligence,and blockchain.With the capability to improve energy efficiency,reduce energy consumption,and carbon emissions,the smart grid plays a crucial role in achieving smart cities,low-carbon economies,and sustainable development,particularly against the backdrop of overall carbon neutrality and carbon peaking.One important component of the smart grid is energy management,which utilizes information technology and intelligent equipment to comprehensively coordinate the power supply,demand,and energy storage of the grid,ensuring its stability and reliability.The purpose of this article is to investigate energy management strategies in distributed energy systems with the objective of enhancing operational efficiency,reliability,and scalability between the supply and demand sides of the system.This research has significant practical implications for the development of smart grids.The main objectives of this study are as follows:Firstly,we focus on the distributed energy management problem with line transmission losses in multi-agent systems under the network topology of an undirected network.To solve this problem,we propose an algorithm based on the distributed gradient tracking algorithm for undirected topology.The algorithm utilizes a constant step size to update the state of multiple agents,and is proven to have global sublinear convergence.Moreover,the algorithm can accelerate to linear convergence when the average value of its consensus variables falls within a certain range.Therefore,it is capable of converging to the optimal value quickly.The simulation analysis validates the effectiveness of the proposed algorithm.Secondly,we extend the undirected network topology to a directed network topology and investigates the distributed energy management problem in multi-agent systems under the directed network topology,focusing on the energy scheduling problem under an unbalanced communication network.To address this challenge,we propose a solution based on the distributed gradient tracking algorithm for directed and unbalanced topology.The proposed algorithm enables each agent to estimate the optimal value of the overall social welfare maximization model by exchanging information with other agents only through a directed communication link.Subsequently,the algorithm’s global sublinear convergence is analyzed,and it is shown that the algorithm can accelerate to linear convergence when the average value of its consensus variables falls within a certain range.Additionally,the effectiveness of the algorithm is verified through simulation examples.