Multinodes interval electric vehicle day-ahead charging load forecasting based on joint adversarial generation. (December 2022) - Huang, Nantian; He, Qingkui; Qi, Jiajin; Hu, Qiankun; Wang, Rijun; Cai, Guowei; Yang, Dazhi
Highlights: To describe the relationship between the EV users' charging behavior, the analysis is carried out from the two dimensions of temporal and spatial. Comprehensively analyze the daytime similarity of the multinode joint charging scenario on the forecast day and the historical day (put the charging load data of each node on the same date in 32 rows in turn, and the charging load matrix is called the multinode joint charging scenario) and the similarity between the multinode charging loads in the scenario (i.e., the correlation between the charging loads in each row in the charging load matrix). To deal with the strong spatial–temporal uncertainty of EV charging load and mining the potential spatial–temporal distribution of charging load, a data-driven scenario analysis method is proposed. This method uses the WGAN-GP model for scenario generation, which avoids assuming that the charging load data of different charging stations must obey a single probability distribution. To more effectively forecast the spatial–temporal distribution of charging load in space, a new multinode charging load interval-forecasting method is proposed considering the spatial correlation of charging load. It avoids the problem that the total charging load obtained by each node forecasting method is much higher than the actual demand. Abstract: The spatial–temporal distribution of electric vehicle (EV) charging load has strong randomness and is affected by battery capacity and user behavior. In addition, the multinode charging load in the distribution network has differential correlations. A multinode charging load joint adversarial generation interval-forecasting method considering the spatial correlation of the charging load between nodes is proposed to effectively forecast the spatial–temporal distribution of EV charging load. First, the multinode joint charging scenario is constructed. Under the spatial charging load matrix, the spatial–temporal correlation between multinode charging loads in the joint charging scenario of the forecast day and the historical day is analyzed. According to the strong-correlation historical-day multinode joint charging scenario of the forecasting day, the original multinode multiple-correlation-day joint charging scenario set, describing the charging behavior of multinode EVs, is determined. Second, a Wasserstein generative adversarial network with a gradient penalty is used to characterize the strong randomness of the spatial–temporal distribution of the charging load. A large number of joint charging scenarios with similar probability distributions but different timing distributions from the original scenario set are generated to obtain the potential spatial–temporal distribution of the multinode joint charging load. Then, based on the weighted two-dimensional correlation coefficient, the strong-correlation joint scenario set on the day to be forecast is selected from the generated multinode multiple-correlation-day joint charging scenario set. Finally, according to the strong-correlation joint scenario set on the day to be forecast, the interval-forecasting conclusion of the multinode EV charging load is obtained. To verify the effectiveness of the new multinode charging load interval-forecasting method, the simulation experiment uses the measured charging load data of 32 charging stations in a region of Zhejiang Province. The comparative experiment demonstrated that the proposed method has more-refined intervals and higher coverage than state-of-the-art interval forecasting models. Among the evaluation indexes of the charging load forecasting results of the new method, the PICP value is higher than 90.4%, and the PINAW and MAPE values are lower than 32.1% and 17.7%, respectively. The new method overcomes the limitation that the total charging load obtained by each node's charging load forecasting method is much higher than the actual demand.