Study On The Optimization And Management Of Electricity Purchase In Jiangxi Province Based On The Electricity Markets

With the development of the electricity market, how to share power in a numberof electricity markets become a problem in the purchase of electricity providers. Howto distribute the electricity’s proportion in each market according to the total neededpower, so that the power purchase costs and risk are minimized,become scholars andpurchase of electricity providers’ research focus.This paper studies how to districbute the electricity which is bought by JiangxiPower Company in the long-term market and real-time market, so that costs and risksare achieving a minimum at the same time. Meanwhile this paper research the powerpurchase management, develop a reasonable purchase management process, establishan efficient management system, combined with the power supply and demand aswell as the status of Jiangxi purchase patterns, make recommendations for thepurchase of electricity shortage in Jiangxi Power Company Management Department.At first,this paper analyzed the status of power supply and demand in JiangxiProvince.In the "Twelve Five" period, Jiangxi Province’s power load were predictedby using Elastic coefficient method and gray simulation method, and on this basis, thebalance of Jiangxi electricity market was predicted and analyzed too. Using MonteCarlo simulation of the multiplicative congruential method and rejection method toshort-term market and real-time market’s price simulation. Markowitz model is thenpresented according to Jiangxi Province in the electricity market environment powerpurchase patterns and modern investment theory, which is a dual objectiveprogramming problems, including the benefits and risks of the two performanceindicators. Fuzzy set theory transforms the double objective problem into a singleobjective planning problems, and then using the fuzzy algorithm and improved tabusearch algorithm for the single objective optimization problem. Finally, examples ofJiangxi power purchase proves the feasibility of the proposed model and the.algorithm.