Research On Optimal Scheduling Strategy Of Interruptible Load With Consideration Of Generation Cost

With the rapid development of society and economy,the demand for electric power in our country is increasing.The relative lag of power supply construction leads to the power shortage during the peak period.This problem caused the sluice power limit and peak stagger.Based on the requirements of environmental protection,electricity demand has abandoned the way of increasing installed capacity.In such an environment,interruptible load becomes an effective way to solve this problem.In this paper,interruptible load contract,interruptible load cost and the multi-objective optimization are researched.Firstly,the concept of interruptible load is introduced in this paper.According to the development of power market in our country and the electricity demand of industrial and commercial users,a model of interruptible load contract is proposed and the compensation mechanism is analyzed based on summing up the implementation methods,contract design and market mechanism of interruptible load in foreign power market.Secondly,the concept and components of power generation cost are expounded.The relationship between consumption cost and load of generating set is studied.The cost calculation model is established and the curve fitting of cost function is carried out.Based on the analysis of reserve quotation of generator sets,the optimization model of reserve capacity on generator side is constructed,and the factors affecting generation cost are studied and analyzed.Finally,aiming at the optimal scheduling of interruptible load,which is an optimization problem,a mathematical model is established,and the interruption characteristics of users and different contents are considered.In this paper,genetic algorithm and quantum genetic algorithm are used to solve the problem.The application of different algorithms in interruptible load scheduling problem are analyzed based on the simulation of interruptible optimal scheduling problems in 16 periods with 19 users,and in comparison with the results of genetic algorithm and quantum genetic algorithm.