A Demand Response-Based Electric Load Clustering Method

Abstract

The invention discloses a demand response-based power load clustering method, which belongs to the field of smart grids. The present invention clusters various large users into limited categories by constructing characteristic indexes and synthesizing and calculating similarity coefficients. When constructing the characteristic index of large user, creatively put forward the characteristic indexes such as load fluctuation coefficient, load distribution index, load adjustment limit and critical peak-to-valley electricity price ratio, and make a systematic analysis of a large user from two aspects of power consumption characteristic and response characteristic description, so the present invention is more accurate and scientific to the clustering of large users, conforms to reality, improves the accuracy of clustering, and realizes more accurate load scheduling for major users in the power grid, and the calculation formula of characteristic index and similarity coefficient is simple at the same time, The required data is close to reality, and it is easier to implement in actual projects.

Description

technical field [0001] The invention relates to the field of smart grids, in particular to a demand response-based power load clustering method. Background technique [0002] In the current distribution network, large users are the main consumers of electricity. The so-called large users currently mainly refer to various industrial users, shopping malls, office buildings and other large electricity consumers. The load of large users accounts for the vast majority of the total load of the power grid, so it is particularly important to monitor the power consumption of large users. [0003] However, due to the wide range of users involved in large users, the power consumption characteristics and demand response characteristics of different types of users are very different, the current centralized power load clustering method has poor clustering results, and cannot provide large users accurately and efficiently. Making decisions cannot meet the needs of the current power syste...

AI Technical Summary

Problems solved by technology

[0003] However, due to the wide range of users involved in large users, the power consumption characteristics and demand response characteristics of different types of users are very different, the current centralized power load clustering method has poor clustering results, and cannot provide large users accurately and efficiently. Make decisions that cannot meet the needs of the current power system

Images