电测与仪表2017，Vol.54Issue(1)：1-7,7.
基于群智能强化学习的电网最优碳-能复合流算法
Multi-objective optimal carbon-energy combined-flow algorithm of power grid based on swarm intelligence reinforcement learning
摘要
Abstract
Considering the transmission characteristic of carbon emission flow and power flow in power grid , this paper proposes the mathematical model of optimal carbon-energy combined-flow of power grid .Furthermore , this paper a-dopts a PSO-Q(λ) learning algorithm for optimal carbon-energy combined-flow.The carbon emission loss, active power loss and voltage stability are chosen as the optimization objectives on linear weighted way .The algorithm intro-duces multi-agent particle swarm computation , converts the load sections and controllable variables to status and ac-tion, and searches for the optimal action strategy via continuous fault testing , action correction and iteration dynami-cally.Simulation in an IEEE 118-bus system indicates that the PSO-Q(λ) learning algorithm, which improves the convergence speed and maintain the abilities of seeking the global excellent result , providing a feasible and effective way to carbon-energy combined-flow on-line receding horizon optimization in a complex power grid .关键词
Q(λ)算法/群智能/最优碳-能复合流/强化学习Key words
Q(λ)learning/swarm intelligence/optimal carbon-energy combined-flow/reinforcement learning分类
信息技术与安全科学引用本文复制引用
郭乐欣,张孝顺,谭敏,余涛..基于群智能强化学习的电网最优碳-能复合流算法[J].电测与仪表,2017,54(1):1-7,7.基金项目
基国家重点基础研究发展计划(973计划)(2013CB228205) （973计划）
国家自然科学基金项目(51177051 （）
51477055) （）
