Adaptive Multiagent Model Based on Reinforcement Learning for Distributed Generation Systems

被引：0
|
作者：
Divenyi, Daniel [1 ]
Dan, Andras [1 ]
机构：
[1] Budapest Univ Technol & Econ, Dept Elect Power Engn, Budapest, Hungary
来源：
2012 23RD INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATIONS (DEXA) | 2012年
关键词：
component; distributed generation; multiagent modeling; state-based method; strategies; reinforcement learning;
D O I：
10.1109/DEXA.2012.31
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Distributed generation have been widely spread in the last decades raising a lot of questions regarding the safe and high-quality operation of the power systems. The investigation of these questions requires a proper model considering the different technical, economical and legal aspects. Our research is aimed at develop a multiagent system where rational agents control each distributed generation unit. Based on intelligent agent-program the agents are able to optimize their operations taking several viewpoints into account: fulfilling the contractual obligations, considering the technical constraints and maximizing the realized profit in a continuously varying market environment. This paper describes a simple reinforcement learning method that results in an adaptive agent-program. The agents are informed about their realized profits and they apply this information to evaluate their former decisions and adjust the parameters of own agent-program. The verification of the model proved that the developed agent-program provides acceptable results compared to the real productions.
引用
收藏
页码：303 / 307
页数：5
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