Power distribution network frame fuzzy programming method taking DG uncertainty into account

Abstract

The invention discloses a power distribution network frame fuzzy programming method taking DG uncertainty into account. The method includes the following steps of according to the uncertainty of a distributed power source, constructing a first-type distributed power source output model and a second-type distributed power source output model; according to the second-type distributed power source output model, adopting a power distribution network topology structure constraint, a node voltage upper limit, a permeability constraint and a system power balance constraint as constraint conditions tocreate a power distribution network frame programming target function which involves the distributed power source and is based on a fuzzy expected value, wherein the fuzzy expected value is obtainedby means of fuzzy power flow calculation; adopting a genetic algorithm for solution, and obtaining power distribution network frame fuzzy programming of the distributed power source. According to theuncertainty of the distributed power source, power distribution network frame fuzzy programming involving the distributed power source is achieved; with a fuzzy expected value of the annual average expense as the target, more convenience is provided for utilizing the genetic algorithm for optimization searching, and the practicability is high.

Description

technical field [0001] The invention relates to a distribution network grid planning method, in particular to a distribution network grid fuzzy planning method considering DG uncertainty. Background technique [0002] At present, gradually replacing fossil energy with renewable energy, increasing the share of clean energy in primary energy supply, promoting energy transformation, and building a clean, low-carbon, safe and efficient modern energy system are the main goals of my country's energy revolution. The "Energy Production and Consumption Revolution Strategy (2016-2030)" proposes that by 2030, the proportion of non-fossil energy power generation in total power generation should reach 50%. As an important part of clean energy power generation, distributed power generation will increase its proportion in the power system significantly, and the access of a large number of distributed power sources will bring severe challenges to the development of distribution networks. ...

AI Technical Summary

Problems solved by technology

[0007] According to the uncertainty of distributed power generation, distributed power generation is divided into two categories: one type of distributed power generation mainly includes wind power generation and photovoltaic power generation, and its access location and capacity are scientifically planned, output prediction and control strategy It is relatively mature, and the power generation output has a certain certainty; the second type of distributed power generation, including wind power generation, photovoltaic power generation and biomass power generation, etc., users build distributed power generation within the limited capacity according to their own wishes and needs, and the construction capacity and The distribution has obvious uncertainty, and the power generation output is difficult to predict

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