Classification Strategy And Optimal Dispatching Without Capacity Increase For Electric Load In Villages House

With the widespread application of clean energy in the northern villages and towns of my country,electric heating has attracted people’s attention because of its flexibility,stability,and safety.However,the increase in the number of electric heating users in villages and towns has brought new challenges to the safe and stable operation of the distribution network.The change in the characteristics of electrical load increases the peak-to-valley difference of electrical load,and there is a maximum power range that can be tolerated in the actual operation of rural residential electricity.For the situation where the maximum power range cannot meet the total power load of electric heating users,the principle is The power capacity needs to be increased.However,the cost of power capacity increase is very high and the idle capacity increase equipment during the nonheating period will cause a certain degree of waste.Therefore,proposing a non-capacity control strategy and subsequent optimization methods are very important to solve this problem.This paper firstly surveys the electricity usage habits of users in villages and towns,analyzes the influencing factors of electricity usage habits and designs questionnaires accordingly,establishes an average electricity usage habits model,and plots household appliances other than electric heating equipment(below Referred to as household appliances),the total load changes over time to obtain the usage of electric heating users on their household appliances.The EnergyPlus energy consumption simulation software was used to simulate the energy consumption of cities in different climate zones(Nanjing,Jinan,Shenyang).By establishing the dynamic heat balance equation of indoor air,the electric heating heat load and indoor temperature changes with time can be obtained,according to the design specifications Set outdoor weather parameters,indoor design parameters,building envelope parameters,and indoor thermal disturbance parameters with the actual situation of the building,and analyze the heat load of electric heating in Nanjing,Jinan and Shenyang respectively.Adding the household appliances load of electric heating users and the heating load of electric heating to obtain the electric load of electric heating users in different climate zones(abbreviated as electric load),the analysis found that the electric load of Nanjing,Jinan,and Shenyang all exceeded the maximum power.During the time period,according to the different needs of users,the power load is classified and two non-increasing strategy methods are proposed.They are to give priority to the use of household appliances and to ensure the use of electric heating equipment to achieve no increase in power,that is,the use of electricity.The load does not exceed the maximum power number of the family.According to the building dynamic heat balance equation,the electricity load and indoor temperature changes after the implementation of the strategy method are obtained.According to the actual situation of electric heating operation,the optimization goal of electricity load is determined,and two optimization methods are proposed,which are the method of ensuring minimum comfortable temperature and the method of direct heat storage optimization.It aims to satisfy users’ heating comfort on the basis of no increase in power capacity,while reducing the electricity costs for heating operation and improving the economics of electric heating.The genetic algorithm is used as the realization algorithm to solve the optimal solution of the optimization goal,and finally the result of the optimized electricity load and the running electricity bill is obtained.