Schedulable capacity forecasting for electric vehicles based on big data analysis

Abstract

Fast and accurate forecasting of schedulable capacity of electric vehicles (EVs) plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems. Traditional methods are insufficient to deal with large-scale actual schedulable capacity data. This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales. The time scale of these data analysis comprises the real time of one minute, ultra-short-term of one hour and one-day-ahead scale of 24 hours. The predicted results for different time scales can be used for various ancillary services. The proposed algorithm is validated using operation data of 521 EVs in the field. The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm, the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment.

Uncertain flexoffers, a scalable, uncertainty-aware model for energy flexibility

“…The experiment generates flexibility according to the chosen model and, in succession, perform aggregation of 𝑁 𝐵 batteries, optimization for profit, and disaggregation. Since bids for flexibility need to be done at most one hour before the deadline, in order to reduce errors [4], and flexibility has to be modeled before it can be aggregated, we consider 30 minutes as the time limit for considering an approach feasible. Figure 4 shows that it is possible to aggregate 3000 UFOs for 𝑇 = 24, and 750 UFOs for 𝑇 = 96.…”

Bi-level decomposition algorithm of real-time AGC command for large-scale electric vehicles in frequency regulation

“…Considering the temporal-spatial dual uncertainty, reference [5] evaluates their dispatchable ability by Monte Carlo method using the random travel chain. Reference [6] uses a large amount of history data to predict the dispatchable ability of EVs through machine learning. However, the accuracy of prediction will be limited by the amount of historical data.…”

Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN

“…In recent years, mainly two approaches have been utilized to forecast the future amounts of products (such as EVs and their charging loads [18,19]), i.e., statistical and machine learning methods. For the former, time series analysis [20], grey theory [21], autoregressive integral moving average model [22], exponential smoothing method [23], Monte Carlo simulation method [24], and Kalman filtering [25] are widely applied.…”