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中文摘要：
结合电网能流和碳排放流的传输特性,建立了电网最优碳-能复合流的数学模型,并提出了基于群智能的多步回溯Q（λ）强化学习算法,有效解决了电网碳-能复合流的动态优化问题。其中以线性加权的方式把电网网损、碳流损耗和电压稳定设计为奖励函数,通过引入粒子群的多主体计算,每个主体都有各自的Q值矩阵进行寻优迭代。IEEE118节点仿真结果表明：较传统Q（λ）算法本文所提出算法能在保证较好全局寻优能力的同时,收敛速度至少能提高10倍以上,为解决实际大规模复杂电网的碳-能复合流在线滚动优化提供了一种快速、有效的方法。
英文摘要：
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- caUy. Simulation in an IEEE ll8-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.
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