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

Chen Jing-hua; Liang Li-li; Ding Lin-jun; Zhou Jun; Liu Guo-xiang

doi:10.12052/gdutxb.160087

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

更新时间：2026-05-27

- Unit Commitment Optimization Based on Chance-constrained Programming in Wind Power Integrated System
- Vol. 34, Issue 1, Pages: 50-54,64(2017)
- 作者机构：
- 作者简介：
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- DOI：10.12052/gdutxb.160087
  CLC： TM731
- Online First：09 January 2017，
  
  Published：2017
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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.

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references

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