基于强化学习方法的风储合作决策Optimal Decision-Making in the Cooperation of Wind Power and Energy Storage Based on Reinforcement Learning Algorithm
刘国静;韩学山;王尚;杨明;王明强;
摘要(Abstract):
在风储配置给定前提下,研究风电与储能系统如何有机合作的问题。核心在于风电与储能组成混合系统参与电力交易,通过合作提升其市场竞争的能力。针对现有研究的不足,在具有过程化样本的前提下,引入强化学习算法。所建立的控制器具备在线学习能力,在学习过程中不断以混合系统收益为反馈信息逐步具备对储能系统充/放电功率、购买备用容量的决策能力。伴随学习时间的累积,将渐进趋于最佳策略,减轻电网调控负担的同时,提高风储合作效率。
关键词(KeyWords): 风电;储能系统;强化学习理论;Q学习算法
基金项目(Foundation): 国家自然科学基金项目(51477091,51177091);; 国家重点基础研究发展计划项目(973计划)(2013CB228205)~~
作者(Author): 刘国静;韩学山;王尚;杨明;王明强;
Email:
DOI: 10.13335/j.1000-3673.pst.2016.09.021
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