Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches

Abstract

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.

Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

“…However, these traditional methods are difficult for quantifying the external factors that affect the charging load of PEVs, and it is impossible to establish a deterministic model. In our previous study [43], the deep learning method is used for hourly level PEV load forecasting and obtained well performance. However, the minute level super-short-term forecasting is more challenging.…”

Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine

“…Electric load forecasting (LF) is an indispensable task for electric utilities in the long term [1]. As a fundamental business problem, LF is associated with decision-making processes in power system plannings, operations, energy trading and so forth [2,3]. Prediction of future load demand is a tendency forecasting subject based on actual load data.…”

Cost Consensus Algorithm Applications for EV Charging Station Participating in AGC of Interconnected Power Grid

“…Particularly, charging stations are always equipped with a large number of batteries, which means they possess high frequency regulation capacity through making full use of retired batteries [20]. Consequently, the charging stations can be regarded as independent EV agents to participate in the frequency regulation service [21], which is crucial for power quality adjustment [22], while the power quality parameters can be estimated by the curve fitting algorithm [23]. When multiple charging stations participate in the secondary power system frequency regulation, there are typically two critical steps: (a) assigning the total power command ∆P EV of EV centralized control center to each charging station and (b) assigning the power command of the charging station to each battery in the station.…”

“…Hence, this paper applies the consensus algorithm to the AGC power allocation for each EV charging station. In order to avoid battery over-charging or over-discharging and to prolong the lifespan of the battery, a proportional allocation method based on SOC [21] is adopted to solve the AGC power redistribution. Finally, the model of the Hainan power grid in south China is utilized to evaluate the specific performance of the proposed method.…”