考虑车辆路径约束的港口微电网电动转运车充电调度方法OA

Electric transfer vehicles(ETVs),which serve as critical logistics equipment connecting quay areas and yard areas,pose severe challenges to port micro grids in cost reduction and energy consumption minimization due to their uncoordinated charging.To address these challenges,this paper explores the charging flexibility of ETVs and proposes a charging scheduling method for ETVs considering conflict-free path constraints and battery health constraints,with the objective of minimizing task delay penalties and charging costs.Meanwhile,to mitigate battery life degradation caused by frequent and deep charging-discharging cycles,a linear battery healthy operating region is constructed via hyperplane projection and convex approximation techniques,which is compatible with the proposed port ETV charging model without introducing extra computational complexity.Furthermore,to handle the combinatorial decision-making complexity arising from the strong coupling among path,charging,and energy constraints,the port ETV scheduling process is formulated as a multi-agent Markov decision process under a rolling-horizon framework.A Q-learning-based multi-agent reinforcement learning framework is then employed to realize collaborative optimization between vehicle-level decision-making and system-level optimization,thereby effectively reducing the solution scale and improving overall scheduling efficiency.Finally,numerical analyses based on a practical port are carried out to verify the effectiveness and practicability of the proposed method.