Power load peak-valley difference control method based on power integration mechanism
A power integration and power load technology, applied in the field of power system demand response control, can solve the problems of waste of resources, long implementation period and high cost, and achieve the effects of alleviating power supply pressure, ensuring power supply quality, and reducing equipment investment.
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 Such as figure 1 As shown, the power load peak-valley difference control method based on the power integration mechanism includes the following steps:
 Step 1. Analysis of the adjustable potential of multiple user loads.
 Based on N s According to the user load adjustable potential data of a sample, the probability density distribution model of the user load adjustable potential is estimated by the kernel density estimation method. For a certain moment, N s Among sample users, sample user i s load adjustable potential According to N s The load adjustable potential sample data of a sample user, for any load adjustable potential P, use the following formula to estimate N s Probability Density of Sample User Load Adjustable Potential
 In the formula, K( ) represents the kernel function, which is non-negative and the integral value is equal to 1. h is the bandwidth of the kernel function K(·). According to the central limit th...
 For verifying the feasibility and correctness of the present invention, make following example:
 In this example, the average daily load data of users in Nanjing City, Jiangsu Province in June 2020 is selected. In order to reduce the peak-to-valley difference, the comparison between the target load curve and the initial load curve is set. The electricity price adopts the time-of-use electricity price information of Jiangsu Province, and the conversion factor μ of a single integral is 0.01 yuan / kWh.
 According to the steps of the kernel density estimation method, input the sample data of 300 users, and calculate the bandwidth h=0.3487 according to the empirical rule bandwidth estimation method, and obtain the load adjustable potential probability density of group users as follows: image 3 As shown, the expected value of load adjustable potential of all users is 255.956kW.
 Taking peak load reduction as an example, Monte Carlo simulation is used ...
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