摘要
为了解决互联复杂电力系统环境下自动发电协调控制问题,提出了一种多智能体智能发电控制策略。提出了一种具有多步回溯及变学习率的多智能体新算法——"狼爬山"算法。该算法可根据据CPS标准求解各种复杂运行环境下的平均策略。基于混合策略及平均策略,此算法不仅在非马尔可夫环境及大时延系统中具有高度适应性,而且能解决新能源电源接入所带来的互联复杂电力系统环境下自动发电协调控制问题。对标准两区域负荷频率控制电力系统模型及南网模型进行仿真,结果显示该算法能获得最优平均策略,闭环系统性能优异,与已有智能算法相比具有更高的学习能力及快速收敛速率。
This paper proposes a multi-agent（ MA） smart generation control scheme for the coordination of automatic generation control（ AGC） in the power grid with system uncertainties. A novel MA new algorithm,i. e. DWo LF-PHC（ λ） with a multi-step backtracking and a variable learning rate, is developed, which can effectively identify the optimal average policies under various operating conditions by the control performance standard（ CPS）. Based on the mixed strategy and the average policy, the algorithm is highly adaptive in stochastic Non-Markov environments and large time-delay systems and can also achieve AGC coordination in interconnected complex power systems in the presence of increasing penetration of renewable energies. Simulation studies on both a two-area load-frequency control（ LFC） power system and the China Southern Power Grid model have been done respectively. The results show that the algorithm can achieve the optimal average policies,the closed-loop system has excellent properties,and the algorithm has a fast convergence rate and a higher learning ability compared with other existing intelligent methods.
出处
《电工技术学报》
EI
CSCD
北大核心
2015年第23期93-101,共9页
Transactions of China Electrotechnical Society
基金
国家自然科学基金(51177051
51477055)
国家重点基础研究发展(973)计划项目(2013CB228205)
广东省绿色能源技术重点实验室项目(2008A060301002)资助
关键词
智能发电控制
狼爬山
变学习率
平均策略
Smart generation control
DWo LF-PHC（λ）
variable learning rate
average policy
