面向低碳与经济性协同优化的光储充一体化平台调度策略综述

A Review of Dispatching Strategies for Photovoltaic-Storage-Charging Integrated Platforms towards Low-Carbon and Economic Synergistic Optimization

摘要: 为了平衡“双碳”目标下光储充一体化平台联通交通网与配电网时低碳性与经济性之间的矛盾，本文系统总结面向低碳–经济协同优化的调度策略。调度架构从集中式向边云协同演变；协同建模包括权重系数法、约束转化法和帕累托优化法；针对光伏与充电负荷不确定性，通过随机规划、鲁棒优化、分布鲁棒优化和模型预测控制可兼顾保守性、计算效率；平台参与绿电交易、碳普惠、CCER等碳市场，结合需求响应与主从博弈实现用户交互。结果表明，目前的研究中存在多时间尺度协同、碳流追踪精度、博弈模型现实性和算法可解释性不足的问题。未来应重点发展数字孪生虚实协同、可解释人工智能、源–网–车–储–碳多维协同和信息物理安全防御。

Abstract: To balance the contradiction between low-carbon performance and economic efficiency of the photovoltaic-storage-charging integrated platform connecting the transportation network and distribution grid under the “dual carbon” goals, this paper systematically reviews dispatching strategies for low-carbon and economic synergistic optimization. The dispatching architecture evolves from centralized to edge-cloud collaboration; synergistic modeling includes weight coefficient method, constraint transformation method, and Pareto optimization method. To address uncertainties in photovoltaic output and charging load, stochastic programming, robust optimization, distributionally robust optimization, and model predictive control are employed, balancing conservatism and computational efficiency. The platform participates in carbon markets such as green power trading, carbon inclusion, and CCER, combined with demand response and Stackelberg game for user interaction. Results indicate that existing studies suffer from insufficient multi-timescale coordination, low carbon flow tracking accuracy, unrealistic game models, and weak algorithm interpretability. Future research should focus on digital twin-based virtual-real collaboration, explainable artificial intelligence, source-grid-vehicle-storage-carbon multi-dimensional synergy, and cyber-physical security defense.