针对电动汽车集群入网进行无序充电会加剧峰谷差进而造成“峰上加峰”的问题,提出一种考虑新能源消纳的电动汽车分层式有序充电控制策略。首先,基于长短时间记忆神经网络理论,建立电动汽车充电负荷预测模型;其次,建立风光发电系统的数学模型;最后,提出分层式有序充电控制策略,上层主控制系统制定电动汽车充电功率指导曲线,下层次级控制系统合理安排电动汽车充电计划,利用概率转移矩阵算法获得最佳充电控制策略。仿真结果表明,所提出的有序充电控制策略能够有效降低电网负荷峰谷差。
Abstract
Electric vehicle cluster connected to the grid for disorderly charging will increase the peak-valley difference and cause the problem of ‘peak on peak’. To solve this problem, a hierarchical control method for orderly charging of electric vehicles which considers new energy accommodation is proposed. Firstly, based on the theory of long short-term memory neural networks, the charging load forecasting model for electric vehicles is established; Secondly, the mathematical models of wind and photovoltaic power generation are established; Finally, the hierarchical orderly charging control strategy is proposed. The upper master control system formulates the electric vehicle charging power guidance curve. And the lower secondary control system reasonably arranges the electric vehicle charging plan and uses the probability transfer matrix algorithm to obtain the optimal charging control strategy. The simulation results show that the proposed orderly charging control strategy can effectively reduce the peak-valley difference of grid load.
关键词
电动汽车 /
神经网络 /
新能源 /
有序充电 /
概率转移矩阵
Key words
electric vehicle /
neural networks /
renewable energy /
orderly charging /
probability transfer matrix
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基金
中央高校基本科研业务费专项资金（2023JC002）; 国家自然科学基金（61873091）
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