An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

Abstract

The rapid increase of the number of electric vehicles (EVs) has posed great challenges to the safe operation of the distribution network. Therefore, this paper proposes an ordered charging scheduling method for EV in the cloud-edge collaborative environment. Firstly, the uncertainty of user load demands, charging station requirements, and renewable outputs are taken into consideration. Correspondingly, the residential distribution points, EV charging stations, and renewable plants are regarded as the edge nodes. Then, the load demands and renewable outputs are predicted by a model combined with the convolutional neural network and deep belief network (CNN-DBN). Secondly, the power supply plans for charging stations are determined at the cloud side aiming at minimizing the operating cost of the distribution network via collecting the forecasting results. Finally, the charging station formulates the personalized charging scheduling strategies according to EV’s charging plans and the charging demands in order to follow the supply plan. The simulation results show that the load peak-to-valley difference and standard deviation of the proposed algorithm are reduced by 30.13% and 16.94%, respectively, compared with the disorderly charging and discharging behavior, which has verified the effectiveness and feasibility of the proposed method.

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

“…A back propagation (BP) neural network is presented to forecast EV charging loads in [91], while a convolutional neural network (CNN) is used to reduce the impact of uncertainty on EV charging demand accuracy in [92]. In [93], a method combining a CNN and a deep belief network (DBN) is presented to describe the uncertainty of EV charging demand. With the wide application of computer technology and social networks, multi-agent technology has been developed.…”

Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network

“…Calculating the charging load of each EV during each period yields the total charging load curve for N EVs in a day, as illustrated in Equation (5).…”

“…The popularity of EVs will also cause a surge in power consumption, placing additional strain on grid operations. The occurrence of EV charging events is related to the driving law of users, and the charging load is subject to random uncertainty [5]. If large numbers of EVs are gathered for charging during peak electricity consumption, the stability of the grid will be compromised, resulting in increased line losses, lower power quality and lower transformer life.…”

On–Off Scheduling for Electric Vehicle Charging in Two-Links Charging Stations Using Binary Optimization Approaches

“…Examples include BLP [37], BQP [38], MILP [48][49][50] and various metaheuristics with integer variables, including genetic algorithms [51], ant colony optimization (ASO) [13], particle swarm optimization [52,53], tabu search [54], memetic algorithm [55], artificial bee colony algorithm [56], binary evolutionary programming [57], and other greedy algorithms [33,58]. Other approaches to scheduling can also be obtained in the literature, for example, agent-based scheduling [59,60], deep reinforcement learning [61], and deep learning [62].…”