Distributed Multi-Objective Optimal Power Flow Algorithm For Power System Based On Fast Adaptive Analytic Expression

Under the situation of open power market,cooperation and competition coexist among regional power grids.The relationship between regional power grids is becoming increasingly close,and it is particularly critical to ensure the privacy of regional information.In the era of big data information,due to the growing demand for data computing,storage and information transmission in the operation and scheduling of large-scale interconnected power systems,centralized processing methods have been unable to meet the needs.Therefore,using distributed optimization method to solve power system optimization scheduling problem is particularly critical.In addition,the current operation of the power grid needs to be considered from the aspects of economy,reliability and environmental protection,that is,the optimization of power system is not only a single-objective optimization problem,but also the influence of multiple factors such as power generation cost and carbon dioxide emissions.In order to solve the above problems,this paper proposes a distributed multiobjective optimal power flow optimization algorithm for power system based on fast adaptive analytic expression.The algorithm can solve the distributed multiobjective power flow optimization problem only by exchanging a small amount of boundary information,and meet the demand of information privacy between regions.In this paper,the algorithm is applied to the multi-objective power flow optimization model of complex interconnected power system.The convergence,stability and rapidity of the algorithm are discussed and analyzed through the simulation results.The specific research contents and results are as follows :Firstly,the power generation cost and carbon dioxide emissions generated during the operation of the power system are selected as the objective function to optimize.The proposed algorithm uses fuzzy optimization method to change the multi-objective optimization problem into a single objective optimization problem for maximizing the minimum membership function.The advantages and disadvantages of the compromise solution are evaluated by comparing the maximum satisfaction function value.Secondly,the mathematical models of alternating direction method of multipliers(ADMM)and auxiliary problem principle(APP)are derived.On the basis of these two algorithms,the iteration method iteration method is improved to the adaptive variable step iteration method,namely,the adaptive exchange objective value based on alternating direction method of multipliers method(AIOV-ADMM)and the adaptive exchange objective value based on auxiliary problem principle method(AIOV-APP).The improved algorithm can achieve the effect of adaptively updating the iterative step size,which can improve the convergence rate of the algorithm and obtain the satisfaction function value that is more in line with the requirements.Finally,this paper builds five complex simulation models by bus tearing and constructing virtual contact lines.Among them,two-area simulation models include IEEE 118-bus,IEEE 300-bus and 1472-bus power systems,and threearea simulation models include IEEE 118-bus and 418-bus power systems.The proposed algorithm is applied to the constructed simulation model.By analyzing the simulation results,it is verified that the proposed algorithm has the characteristics of convergence,rapidity and stability in solving the distributed multi-objective power flow optimization problem.Compared with the original algorithm,the proposed algorithm can achieve better optimization results,and the feasibility of the proposed algorithm in distributed multi-objective power flow optimization of power system is proved.