基于多智能体相关均衡算法的自动发电控制  

Automatic Generation Control for Interconnected Power Grids Based on Multi-agent Correlated Equilibrium Learning System

作　　者：王怀智[1] 余涛[1] 唐捷[2] 

机构地区：[1]华南理工大学电力学院,广东省广州市510640 [2]广东电网公司韶关供电局,广东省韶关市512026

出　　处：《中国电机工程学报》2014年第4期620-627,共8页Proceedings of the CSEE

基　　金：国家高技术研究发展计划(863计划)(2012AA050209);国家自然科学基金项目(51177051);中国南方电网科技项目资助~~

摘　　要：提出了一种分散式多智能体均衡算法(decentralized correlated equilibrium Q(?),DCEQ(λ))以解决新能源接入所带来的强随机环境下的互联电网自动发电控制。该算法以相关均衡概率选择机制平衡利用与探索,是一种典型的试错寻优且与模型无关的智能算法。在综合考虑分散式多智能体均衡算法在自动发电控制(automatic generation control,AGC)系统设计适用性的基础上,改进了多智能体算法的奖励函数;以区域控制偏差(area control error,ACE)实时绝对值赋予公平系数的方法设计了均衡选择函数;在分析了3种常用资格迹算法特点的基础上,融入了SARSA(λ)资格迹以有效解决火电机组等大延时环节所带来的时间信度分配问题。IEEE标准两区域频率响应模型与南方电网模型仿真研究表明,所提出的DCEQ(λ)控制器相对于单智能体Q(λ)控制器具有更好的控制性能,在控制过程中能有效消除ACE与控制性能标准(control performance standard,CPS)中的实时毛刺,显著提高互联电力系统的稳定性与鲁棒性。This paper proposed a multi-agent decentralized correlated equilibrium Q（λ） （DCEQ（λ）） learning algorithm to tackle automatic generation control （AGC） under strong random gird environment considering emerging renewable energy sources. This algorithm does not need to consider the tradeoffs between exploitation and exploration, it also does not need any knowledge of the system model and uses the trial and error methods to find the most desired policy. After the adaptive problem of this algorithm in AGC fields had been figured out, an improved reward function and an equilibrium selected function integrated with fair factor were proposed. Three kinds of eligibility traces were also analyzed and SARSA（λ） was introduced in this algorithm to reassign the delayed reward appropriately due to the long time-delay control link such as AGC thermal plants. Simulation tests on a two-area load frequency control （LFC） power system model and China Southern Power Grid demonstrated that DCEQ（λ） controller has better control performance than Q（λ） controller, and can effectively smooth the instantaneous value of automatic generation control （ACE） and control performance standard （CPS）, and thus improve the stability and robustness of interconnected power systems.

关 键 词：智能体 自动发电控制 控制性能标准 相关均衡 强化学习 随机最优控制 资格迹 

分 类 号：TM73[电气工程—电力系统及自动化]