基于供需兩側協同優化的電動汽車V2G充放電負荷時空分布預測研究

彭偉倫　馬力　劉琦穎　于洋

【摘要】為準確預測電動汽車的V2G充放電負荷，以調節電網負荷峰谷差，保證供電穩定性，提出了一種基于供需兩側協同優化的電動汽車V2G充放電負荷時空分布預測方法。構建供需兩側協同優化目標模型，利用鯨魚優化算法迭代求解，得出最優充放電負荷曲線，據此明確最優充放電時段。采集不同空間區域最優充放電時段內的充放電負荷影響指標，并以此為輸入，構建基于多元線性回歸的預測模型，實現電動汽車V2G充放電負荷時空分布預測。試驗結果表明，采用所提出的方法得到的負荷預測模型具有較大的決定系數，表明該方法的預測結果更接近實際負荷，具有較高的預測準確性。

主題詞：協同優化 電動汽車 V2G充放電負荷 時空分布預測

中圖分類號：TP225.66? ?文獻標志碼：A? ?DOI： 10.19620/j.cnki.1000-3703.20230832

Research on Prediction of Time and Space Distribution of V2G Charge and Discharge Load of Electric Vehicle Based on Collaborative Optimization of Supply and Demand Side

【Abstract】In order to accurately predict the V2G charging and discharging load of electric vehicles， so as to regulate the peak to valley difference of power grid load and ensure power supply stability， this paper proposed a spatiotemporal distribution prediction method for V2G charging and discharging loads of electric vehicles based on collaborative optimization of supply and demand sides. A collaborative optimization objective model for both supply and demand sides was built， the Whale Optimization Algorithm was used for iterative solution to obtain the optimal charging and discharging load curve， and the optimal charging and discharging period was determined. The influencing indicators of charging and discharging loads within the optimal time periods in different spatial regions were collected， serving as inputs for constructing a prediction model based on multiple linear regression， thus achieving the prediction of spatial-temporal distribution of electric vehicle V2G charging and discharging loads. The experimental results show that the load prediction model obtained with the proposed method has a relatively large coefficient of determination， indicating that the prediction results of this research method are closer to the actual load， and have high prediction accuracy.

Key words： Collaborative optimization， Electric vehicle， V2G charging and discharging load， Time-space distribution prediction

1 前言

在車輛與電網互動（Vehicle to Grid，V2G）場景中，電動汽車的充、放電過程分別從電網獲取電能和向電網釋放電能，將給電網調度帶來巨大壓力。因此，需要準確預測電動汽車的V2G充放電負荷，據此制定合理有序的充電策略，從而有效調節充放電功率和負荷峰谷差，實現電網供電與電動汽車負荷之間的供需互補，優化電網調度管理[1]。

在上述背景下，很多學者進行了相關研究。張琳娟等[2]首先分析了電動汽車充放電行為的影響因素，建立了城市路網拓撲結構，利用蒙特卡洛方法實現了充電負荷時空分布預測。該方法可準確預測充電負荷的連續變化，但需要大量的數據作為支撐，因此預測精度并不穩定。……