提出一种基于MC-BiLSTM-BDA预测的混合需求响应有序充电优化策略。首先,结合马尔科夫链（MC）、双向长短期记忆网络（BiLSTM）和双向注意力机制（BDA）,构建MC-BDA-BiLSTM充电负荷预测模型。其次,依据K均值模糊聚类算法划分的峰、平、谷充电时段,提出基于价格-激励混合的需求响应（PrIncHDR）策略,该策略是在价格需求响应的基础上引入价格补偿激励机制以避免峰谷倒置和促进供需平衡。基于此策略,构建包含可再生能源利用率、用户收益及光伏充电站（PVCS）的电动汽车有序充电优化模型,并采用NSGA-Ⅱ算法进行求解。实验结果表明,MC-BDA-BiLSTM模型的负荷预测精度优于传统BiLSTM模型,决定系数提高11.93%,均方根误差和平均绝对误差分别降低35.06%和37.41%。实验证明：所提的基于MC-BiLSTM-BDA预测的混合需求响应有序充电优化策略可有效降低峰谷差,增加可再生能源消纳,可实现用户和PVCS的双赢。
Abstract
A hybrid demand response orderly charging optimization strategy based on MC-BiLSTM-BDA prediction is proposed. Firstly, a charging load prediction model, named MC-BDA-BiLSTM, is constructed by integrating Markov chains （MC）, Bi-directional long short-term memory （BiLSTM） network, and Bi-directional Attention （BDA）. Secondly, according to the peak, flat, and valley charging periods classified by the K-means fuzzy clustering algorithm, a price-incentive hybrid demand response （PrIncHDR） strategy is presented, which introduces price compensation incentive mechanism on the basis of price based demand response to avoid peak-valley inversion and promote supply and demand balance. Based on this strategy, an EV orderly charging optimization model is constructes, which includes renewable energy utilization rate, user benefits and photovoltaic charging station （PVCS） benefits, and is solved by the NSGA-II algorithm. The experimental results show that compared with BiLSTM model, the MC-BiLSTM-BDA model increases the determination coefficient of the charging load prediction by 11.93%, and reduces the root-mean-square error by 35.06% and the mean absolute error by 37.41%, respectively. The proposed charging optimization strategy based on price-incentive hybrid demand response effectively reduces peak-valley differences, enhances renewable energy consumption, and achieves a win-win for users and photovoltaic charging stations.
关键词
电动汽车 /
预测 /
马尔科夫链 /
双向长短期记忆网络 /
双向注意力机制 /
混合需求响应 /
有序充电优化策略
Key words
electric vehicle /
prediction /
Markov chains /
bi-directional long-short-term memory network /
Bi-directional Attention /
hybrid demand response /
orderly charging optimization strategy
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基金
国家自然科学基金（62103070); 重庆市教育科学技术研究项目（KJZD-K202301103); 重庆市自然科学基金面上项目（CSTB2023NSCQ-MSX0539）; 重庆理工大学研究生创新项目（gzlcx20243189)
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