| 引用本文: | 张孝顺,余涛.互联电网自动发电控制功率分配的改进逼近于理想解的排序--Q多目标优化算法[J].控制理论与应用,2015,32(4):497~503.[点击复制] |
| ZHANG Xiao-shun,YU Tao.Stochastic optimal generation command dispatch of interconnected power grids based on improved multi-objective technique for order preference similar to an ideal solution–Q algorithm[J].Control Theory and Technology,2015,32(4):497~503.[点击复制] |
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| 互联电网自动发电控制功率分配的改进逼近于理想解的排序--Q多目标优化算法 |
| Stochastic optimal generation command dispatch of interconnected power grids based on improved multi-objective technique for order preference similar to an ideal solution–Q algorithm |
| 摘要点击 2496 全文点击 1370 投稿时间：2014-07-11 修订日期：2015-01-06 |
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| DOI编号 10.7641/CTA.2015.40649 |
| 2015,32(4):497-503 |
| 中文关键词 改进TOPSIS--Q 强化学习 多目标优化 AGC功率分配 |
| 英文关键词 improved TOPSIS–Q reinforcement learning multi-objective optimization AGC generation command dispatch |
| 基金项目 国家重点基础研究发展计划(“973”计划)项目(2013CB228205), 国家自然科学基金项目(51177051, 51477055), 中国南方电网科技项目资助. |
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| 中文摘要 |
| 本文提出了一种多目标决策与强化学习相结合的改进的逼近于理想解的排序(technique for order preference similar to an ideal solution, TOPSIS)--Q算法, 有效解决 了自动发电控制(automatic generation control, AGC)总功率 指令分配到风电、水电、火电等各类AGC机组的动态随机多目标优化问题. 算法采用3个不同的目标$Q$值矩阵进行迭代更新, 然后 利用改进TOPSIS方法对$Q$值矩阵进行多目标决策处理, 客观地给出各目标的动态最优权重系数, 从而得到各状态--动作对的综合评价判据. IEEE标准两区域模型仿真研究验证了改进TOPSIS--Q算法在AGC机组功率多目标动态优化分配过程应用的可行性和有效性, 在复杂 随机扰动的环境中提高系统CPS性能的同时, 有效降低了AGC机组调节成本和碳排放. |
| 英文摘要 |
| This paper proposes an improved technique for order preference similar to an ideal solution (TOPSIS)–Q learning approach to solve the dynamic optimization of generation command dispatch (GCD) for automatic generation control (AGC) in a multi-energy power system. Three optimization objectives are simultaneously optimized by three different Q-value matrixes. Then dynamic optimal weight of each objective is calculated by improved TOPSIS method such that the evaluation criterion of each state-action is obtained. Case studies are carried out to evaluate the optimization performance of the proposed algorithm in the two-area load frequency control (LFC) power system model. Simulation results indicate that the proposed method is feasible and effective for dynamic optimization of GCD problem, and can reduce the regulating cost and carbon emissions while improving the control performance of AGC systems in the complex stochastic power system. |