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A data-driven multi-objective optimization strategy for coordinated EV charging based on user review mining
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DOI：10.1016/j.rtbm.2026.101684.png)
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针对无序充电引起的电网负荷波动及用户满意度降低问题，本文提出一种数据-模型双驱动的协同充电优化框架，整合需求感知、优化求解和决策评估。采用BERTopic主题模型从用户评论中提取“排队时间”和“充电成本”作为优化目标，结合电网侧负荷标准差构建多目标模型。对MOGNDO算法进行拟对立学习与精英策略增强，并与NSGA-II和NSGA-III进行性能比较。最后采用熵权法结合CODAS进行决策。结果表明，改进的MOGNDO在收敛速度和支配关系上均优于NSGA-II和NSGA-III，对后两者的支配比例分别为0.269±0.044和0.353±0.075。三种算法选出的综合最优解在三个目标上均优于无序充电。本研究为充电运营商在电网友好性、用户体验和成本控制之间寻求平衡提供了灵活的决策支持。
Keyword:
coordinated EV charging
user review mining
multi-objective optimization
grid load fluctuation
BERTopic topic modeling
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