Multi-objective dynamic optimal power flow of wind integrated power systems considering demand response

Rui Ma; Xuan Li; Yang Luo; Xia Wu; Fei Jiang

doi:10.17775/CSEEJPES.2017.00280

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Multi-objective dynamic optimal power flow of wind integrated power systems considering demand response

Regular Papers | 更新时间：2025-12-29

- Multi-objective dynamic optimal power flow of wind integrated power systems considering demand response
- Multi-objective dynamic optimal power flow of wind integrated power systems considering demand response
- 中国电机工程学会电力与能源系统学报(英文) 2019年5卷第4期页码：466-473
- 作者机构：
  
  School of Electrical and Information Engineering, Changsha University of Science & Technology,Hunan,Changsha,410114
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2017.00280
  中图分类号：
- 纸质出版：2019
- 稿件说明：
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Rui Ma, Xuan Li, Yang Luo, 等. Multi-objective dynamic optimal power flow of wind integrated power systems considering demand response[J]. 中国电机工程学会电力与能源系统学报(英文), 2019,5(4):466-473.

Rui Ma, Xuan Li, Yang Luo, et al. Multi-objective dynamic optimal power flow of wind integrated power systems considering demand response[J]. CSEE Journal of Power and Energy Systems, 2019, 5(4): 466-473.
Rui Ma, Xuan Li, Yang Luo, 等. Multi-objective dynamic optimal power flow of wind integrated power systems considering demand response[J]. 中国电机工程学会电力与能源系统学报(英文), 2019,5(4):466-473. DOI： 10.17775/CSEEJPES.2017.00280.

Rui Ma, Xuan Li, Yang Luo, et al. Multi-objective dynamic optimal power flow of wind integrated power systems considering demand response[J]. CSEE Journal of Power and Energy Systems, 2019, 5(4): 466-473. DOI： 10.17775/CSEEJPES.2017.00280.

摘要

Abstract

This paper studies the economic environmental energy-saving day-ahead scheduling problem of power systems considering wind generation (WG) and demand response (DR) by means of multi-objective dynamic optimal power flow (MDOPF). Within the model

fuel cost

carbon emission and active power losses are taken as objectives

and an integrated dispatch mode of conventional coal-fired generation

WG and DR is utilized. The corresponding solution process to the MDOPF is based on a hybrid of a non-dominated sorting genetic algorithm-II (NSGA-II) and fuzzy satisfaction-maximizing method

where NSGA-II obtains the Pareto frontier and the fuzzy satisfaction-maximizing method is the chosen strategy. Illustrative cases of different scenarios are performed based on an IEEE 6-units\30-nodes system

to verify the proposed model and the solution process

as well as the benefits obtained by the DR into power system.

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