计及风电场并网的机会约束规划的机组组合优化

陈璟华; 梁丽丽; 丁林军; 周俊; 刘国祥

doi:10.12052/gdutxb.160087

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

计及风电场并网的机会约束规划的机组组合优化

综合研究 | 更新时间：2026-05-27

- 计及风电场并网的机会约束规划的机组组合优化
- Unit Commitment Optimization Based on Chance-constrained Programming in Wind Power Integrated System
- 广东工业大学学报 2017年34卷第1期页码：50-54,64
- 作者机构：
- 作者简介：
  
  陈璟华(1974-),女,副教授,博士,主要研究方向为电力系统运行与优化.
- 基金信息：
  
  广东省自然科学基金资助项目（S2013040013776）;广东省教育厅育苗工程项目（2013LYM_0019）
- DOI：10.12052/gdutxb.160087
  中图分类号： TM731
- 网络首发：2017-01-09，
  
  纸质出版：2017
- 稿件说明：
移动端阅览
陈璟华, 梁丽丽, 丁林军, 周俊, 刘国祥. 计及风电场并网的机会约束规划的机组组合优化[J]. 广东工业大学学报, 2017, 34(1): 50-54,64.

Chen Jing-hua, Liang Li-li, Ding Lin-jun, et al. Unit Commitment Optimization Based on Chance-constrained Programming in Wind Power Integrated System[J]. 2017, 34(1): 50-54,64.
陈璟华, 梁丽丽, 丁林军, 周俊, 刘国祥. 计及风电场并网的机会约束规划的机组组合优化[J]. 广东工业大学学报, 2017, 34(1): 50-54,64. DOI： 10.12052/gdutxb.160087.

Chen Jing-hua, Liang Li-li, Ding Lin-jun, et al. Unit Commitment Optimization Based on Chance-constrained Programming in Wind Power Integrated System[J]. 2017, 34(1): 50-54,64. DOI： 10.12052/gdutxb.160087.

摘要

由于风电出力的随机性，提出一种基于机会约束规划的含风电场的机组组合优化模型.该模型在考虑系统约束和发电机自身约束条件下，采用混沌离散粒子群优化算法安排机组的启停策略，再利用混沌文化粒子群优化算法实现负荷的经济分配.在此基础上，建立了以燃料耗量及污染气体排放量为最小的多目标优化模型，并引入蒙特卡洛随机模拟技术对机会约束条件进行校验，分析了机会约束条件在不同置信度要求下协调方案利润和风险的优势，为调度人员根据实际情况协调风险、利润及环境因素，实现最优化决策提供参考.1个风电场和10台火电机组的仿真试验证明了该方法的正确性和有效性.

Abstract

Because of the random nature of wind power output

a unit commitment optimization model with wind farm based on chance-constrained programming is proposed. The constraints of both power system and generator are considered. Chaos Discrete Particle Swarm Optimization algorithm (CDPSO) is used to arrange thermal unit commitment startup and shutdown and cultural chaos particle swarm optimization algorithm (CCPSO) is proposed to solve economic load dispatch. Based on it

Multi-objective optimization unit commitment optimization problem in wind power integrated system under the energy saving and lower emission is considered. The Monte Carlo stochastic simulation techniques verified opportunity constraints and the superiority of opportunities constraints coordination programs profit and risk under different confidence level are analyzed. It provides a new way of thinking to coordinating profit

risk and environment benefits. A system with one wind power farm and 10 coal-fired plants was taken as a study example and the results show that the model is correct.

关键词

Keywords

references

周双喜, 鲁宗相. 风力发电与电力系统[M]. 北京:中国电力出版社, 2012.

陈海焱, 陈金富, 段献忠. 含风电场电力系统经济调度的模糊建模及优化算法[J]. 电力系统自动化, 2006, 30(2):22-26. CHEN H Y, CHEN J F, DUAN X Z. Fuzzy modeling and optimization algorithm on dynamic economic dispatch in wind power integrated system[J]. Automation of Electric Power Systems, 2006, 30(2):22-26.

江岳文, 陈冲, 温步瀛. 含风电场的电力系统机组组合问题的随机模拟粒子群算法[J]. 电工技术学报, 2009, 24(6):129-137. JIANG Y W, CHEN C, WEN B Y. Particle swarm research of stochastic simulation for unit commitment in wind farms integrated power system[J]. Transactions of China Electrotechnical Society, 2009, 24(6):129-137.

孙元章, 吴俊, 李国杰, 等. 基于风速预测和随机规划的含风电场电力系统动态经济调度[J]. 中国电机工程学报, 2009, 29(4):41-47. SUN Y Z, WU J, LI G J, et al. Dynamic economic dispatch considering wind power penetration based on wind speed forecasting and stochastic programming[J]. Proceeding of the CSEE, 2009, 29(4):41-47.

Saleh M. Knowledge-based solution dynamic optimization problems using cultural algorithms[D]. Detroit MI, Wayne State University, 2001.

刘纯青. 文化算法及其应用研究[D]. 黑龙江, 哈尔滨工程大学信息与通讯工程学院, 2007.

Chung C. Knowledge-based approaches to self-adaptation in cultural algorithms[D]. Detroit MI, Wayne State University, 1997.

Li T Y, Yorke J A. Period three implies chaos[J]. American Mathematical Monthly, 1975, 82(10):985-992.

罗萍, 刘伟, 周述波. 自适应混沌变异的万有引力搜索算法[J]. 广东工业大学学报, 2016, 33(1):57-61. LUO P, LIU W, ZHOU S B. Adaptive chaos gravitational search algorithm[J]. Joural of Guangdong Universit of Teachnology, 2016, 31(1):57-61.

陈保颖, 高学军. 一种新的三维二次自治型混沌系统的分类准则[J]. 广东工业大学学报, 2016, 33(1):26-28. CHEN B Y, GAO X J. New classification of chaos in 3-D autonamous quadratic systems[J]. Joural of Guangdong Universit of Teachnology, 2016, 31(1):26-28.

胡家声, 郭创新, 曹一家. 一种适合于电力系统机组组合问题的混合粒子群优化算法[J]. 中国电机工程学报, 2004, 24(4):24-28. HU J S, GUO C X, CAO Y J. A hybrid particle swarm optimization method for unit commitment problem[J]. Proceeding of the CSEE, 2004, 24(4):24-28.

Dhillon J S, Parti S C, Kothari D P. Fuzzy decision-making in stochastic multi-objective short-term hydrothermal scheduling[J]. IEE Proceedings:Generation Transmission and Distribution, 2002, 149(2):191-200.

张晓花, 赵晋泉, 陈星莺. 节能减排下含风电场多目标机组组合建模及优化[J]. 电力系统保护与控制, 2011, 39(17):33-39. ZHANG X H, ZHAO J Q, CHEN X Y. Multi-objective unit commitment modeling and optimization for energy-saving and emission reduction in wind power integrated system[J]. Power System Protection and Control, 2011, 39(17):33-39.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

人工噪声辅助无人机太赫兹安全通信的鲁棒设计

基于PSI技术的福州市地面形变监测与影响因素分析

具守恒量波动方程的循环双子网物理信息神经网络

基于高效时序建模的RGB-T跟踪

双模态迭代交叉注意力融合集成框架