基于多步回溯Q(λ)学习的电网多目标最优碳流算法

Multi-objective Optimal Carbon Emission Flow Calculation of Power Grid Based on Multi-step Q(λ)Learning Algorithm

为弥补传统最优潮流计算中缺少对电力网络的碳排放优化控制,文中利用基于潮流计算结果的碳流模型,提出了一种基于半马尔可夫决策过程的Q(λ)学习算法,把潮流问题中的约束、可控变量转换成算法的状态和动作,并以线性加权方式把碳流损耗、网损和电压稳定分量转化为奖励函数,通过不断试错与迭代来动态寻找最优动作策略。将该算法在IEEE 57节点标准算例中进行了验证,取得了良好效果,为解决电网多目标最优碳流问题提供了一种可行、有效的方法。

To make up for the lack of optimal control on carbon emission in the conventional optimization calculation of power flow of the power grid,this paper presents a Q(λ)learning algorithm based on the semi-Markov decision process by the carbon emission flow model based on the results of power flow.This algorithm converts the constraints,controllable variables to status and action of the algorithm,while using a linear weighted approach to convert carbon emission loss,network loss and the stable component of voltage to rewards to dynamically search for the optimal action strategy via continuous fault testing,retrospecting and iteration.Using an IEEE 57-bus system for case study,this method yields good results,providing an effective and feasible way to solve the problem of multi-objective optimal carbon emission flow.