An Optimization Framework for Collaborative Control of Power Loss and Voltage in Distribution Systems With DGs and EVs Using Stochastic Fuzzy Chance Constrained Programming

被引：10

作者：

Tang Huiling ^{[1
]}

Wu Jiekang ^{[2
]}

Wu Fan ^{[3
]}

Chen Lingmin ^{[2
]}

Liu Zhijun ^{[4
]}

Yan Haoran ^{[5
]}

机构：

[1] Guangdong Univ Technol, Sch Phys & Optoelect Engn, Guangzhou 510006, Peoples R China

[2] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China

[3] Guangxi Hongshen Elect Power Design Co Ltd, Nanning 530023, Peoples R China

[4] Guangzhou Maritime Univ, Sch Shipping & Marine Engn, Guangzhou 510008, Peoples R China

[5] China Southern Power Grid Co Ltd, Liuzhou Power Supply Bur, Guangzhou 540005, Peoples R China

来源：

IEEE ACCESS | 2020年 / 8卷

基金：

中国国家自然科学基金;

关键词：

Programming; Stochastic processes; Uncertainty; Optimization; Voltage control; Electric vehicles; Renewable energy sources; Distribution system with renewable energy; fuzzy collaborative control of power loss and voltage; non-dominated sorting genetic algorithm with normal distribution crossover; distributed generation; controllable load; electric vehicle;

D O I：

10.1109/ACCESS.2020.2976510

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A stochastic fuzzy chance-constrained programming model with multi-objective optimization for coordinated control of power loss and voltage in distribution systems with renewable energy is presented by taking power output of DGs and charging-discharging power of EVs as random fuzzy variables and load power as random variables. Considering the fuzziness and randomness of active power output of distributed generation systems with wind and solar energy and charging power of electric vehicles, the key parameters of probability density function are determined by fitting incomplete data of uncertainties such as wind speed, sunlight intensity and charging power of electric vehicles. According to the principle of random fuzzy compatibility, the probability density function of the uncertainties is transformed into the probability distribution function of the uncertainties. The NDC(Normal Distribution Crossover)-based non-dominated sorting genetic algorithm is used to solve the optimization problem, and the Pareto solution set of the multi-objective optimization problem is obtained. The feasibility and applicability of the proposed model and algorithm are verified by simulating IEEE-33 and IEEE-118 distribution system.

引用

页码：49013 / 49027

页数：15

共 49 条

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