基于MC-BiLSTM-BDA预测的混合需求响应电动汽车充电策略

钟婷; 王爱娟; 丁雪

doi:doi:10.19912/j.0254-0096.tynxb.2024-1472

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基于MC-BiLSTM-BDA预测的混合需求响应电动汽车充电策略

更新时间：2026-02-06

- 基于MC-BiLSTM-BDA预测的混合需求响应电动汽车充电策略
- 基于MC-BiLSTM-BDA预测的混合需求响应电动汽车充电策略
- 太阳能学报 2026年47卷第1期页码：747-755
- 作者机构：
- 作者简介：
- 基金信息：
  
  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)
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1472
  中图分类号： TM73
- 网络首发：2026-02-05，
  
  纸质出版：2026-02-05
- 稿件说明：
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钟婷, 王爱娟, 丁雪. 基于MC-BiLSTM-BDA预测的混合需求响应电动汽车充电策略[J]. 太阳能学报, 2026,47(1):747-755.

钟婷, 王爱娟, 丁雪. 基于MC-BiLSTM-BDA预测的混合需求响应电动汽车充电策略[J]. 2026, 47(1): 747-755.
钟婷, 王爱娟, 丁雪. 基于MC-BiLSTM-BDA预测的混合需求响应电动汽车充电策略[J]. 太阳能学报, 2026,47(1):747-755. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1472.

钟婷, 王爱娟, 丁雪. 基于MC-BiLSTM-BDA预测的混合需求响应电动汽车充电策略[J]. 2026, 47(1): 747-755. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1472.

摘要

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

Keywords

references

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