广东工业大学学报 ›› 2017, Vol. 34 ›› Issue (01): 50-54,64.doi: 10.12052/gdutxb.160087

• 综合研究 • 上一篇    下一篇

计及风电场并网的机会约束规划的机组组合优化

陈璟华1, 梁丽丽1, 丁林军1, 周俊2, 刘国祥3   

  1. 1. 广东工业大学 自动化学院, 广东 广州 510006;
    2. 国网江西省电力公司萍乡市安源区供电分公司, 江西 萍乡 337000;
    3. 国网江西萍乡供电分公司, 江西 萍乡 337000
  • 收稿日期:2016-06-20 出版日期:2017-01-09 发布日期:2017-01-09
  • 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.
  • 基金资助:

    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）

Unit Commitment Optimization Based on Chance-constrained Programming in Wind Power Integrated System

Chen Jing-hua1, Liang Li-li1, Ding Lin-jun1, Zhou Jun2, Liu Guo-xiang3   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou, 510006, Guangdong Province, China;
    2. State Grid Pingxiang City Anyuan District Electric Power Supply Branch, Pingxiang, 337000, Jiangxi Province, China;
    3. State Grid Power Jiangxi Pingxiang Supply Branch Company, Pingxiang, 337000, Jiangxi Province, China
  • Received:2016-06-20 Online:2017-01-09 Published:2017-01-09

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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.

关键词: 节能减排, 机组组合优化, 混沌文化粒子群优化算法, 机会约束规划

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.

Key words: energy conservation, unit commitment optimization, chaos cultural particle swarm optimization, chance-constrained programming

中图分类号: 

  • TM731

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