Electric Vehicle Charging Scheduling Research Articles

A Gaussian random walk salp swarm algorithm for optimal dynamic charging of electric vehicles

Salp swarm algorithm (SSA) is one of the recently developed meta-heuristic optimization algorithms. Since SSA outperforms other swarm-based algorithms, it has recently been employed in various applications, including feature selection, neural network training and renewable energy systems. In this paper, an improved salp swarm algorithm based on a Gaussian random walk is proposed, which enhances the algorithm’s performance particularly for multidimensional constrained global optimization problems. The integration of a Gaussian random walk into the algorithm balances between its exploration and exploitation capabilities. Furthermore, the proposed algorithm introduces a new re-dispersion strategy in the case of stagnation at local optimum points, which considerably enhances exploration. The performance of the proposed algorithm is evaluated using a set of twenty-three benchmark test functions and is compared to the performance of prevalent metaheuristic algorithms. Statistical analysis is performed using Wilcoxon signed-rank test, and the results reveal considerable improvement over the competing algorithms. Then, 21 real-world optimization problems are used to further evaluate the efficacy of the proposed algorithm. The winners of the CEC2020 Competition on Real-World Single Objective Constrained Optimization, SASS, sCMAgES, EnMODE, and COLSHADE algorithms, are used as four comparable algorithms in the real-world optimization problems. The convergence curves and simulations provide very competitive performance compared to the comparative algorithms. The proposed algorithm is used to address one of the most challenging real-world constrained problems in power system applications, namely, determining the optimal charging schedule for electric vehicles at charging stations. The results reveal that the proposed algorithm outperforms other existing algorithms in terms of increasing the charging revenues and achieving maximum power grid stability.

Learning and optimizing charging behavior at PEV charging stations: Randomized pricing experiments, and joint power and price optimization

In this paper, we introduce, implement, and assess a framework for jointly optimizing the pricing policy and the charging schedule of electric vehicles (EVs) by learning and shaping human behavior with pricing. The proposed methodology uses time-based pricing to incentivize user behavior at charging stations towards actions that achieve the station operator's objectives. The optimization framework incorporates endogenous human behaviors by explicitly accounting for the willingness to delay charging, as well as the plug-in duration of each session, as a function of the hourly prices. The approach also addresses the issue of overstay at EV charging stations by casting the problem as a trade-off between occupying resources and giving more flexibility to the station operator. We discuss the design and analysis of the behavioral experiments used to model the charging behavior of participants at the charging stations, and demonstrate the effectiveness of those learned behavioral models in an optimization framework aimed at minimizing the total cost of providing the charging service without sacrificing the user experience. Our simulations show that our framework increases total revenue, reduces utility cost, and increases net profit for the station operator, while maintaining a high level of service and consumer utility.

Electric Vehicle Charge scheduling approach based on Smart Decision Hunting optimization

Electric Vehicle Charge scheduling approach based on Smart Decision Hunting optimization

Fair and Efficient Electric Vehicle Charging Scheduling Optimization Considering the Maximum Individual Waiting Time and Operating Cost

The shortage of charging piles and the soaring power demand of charging stations will lead to long waiting times for electric vehicles (EVs) that need to accomplish charging tasks. Most charging scheduling methods pay more attention to the overall waiting time, and they usually do not consider the time-aware fairness among EV users. Moreover, it has become important to schedule EVs effectively to minimize the operating cost of the station. This paper considers the trade-off between the time-aware fairness and overall waiting time of EVs to optimize the individual waiting time. A novel metric, the time-aware fairness index (TAFI), is proposed to evaluate the fairness performance for competing EVs. Therefore, a bi-objective charging and discharging scheduling problem is formulated. A novel online scheduling algorithm based on the dynamic schedulable time of EVs and the energy demand fluctuation of the station is proposed to obtain an optimal scheduling solution. Experimental results indicate that our method effectively reduces the total waiting time while minimizing the maximum individual waiting time and reducing the operating cost of the station. Moveover, the TAFI results confirm that the desired fairness behavior occurs.

Deep learning framework for day-ahead optimal charging scheduling of electric vehicles in parking lot

The increase of electric vehicles (EVs) in the power system, in addition to environmental benefits, also has economic advantages. Charging parking lots can perform better economically through aggregators the benefits of participating in energy markets and developing different charging programs. The main challenge for participating in the electricity market for parking lots is the uncertainties in the behavior of EV owners and the day-ahead market (DAM) price. Therefore, this paper proposes a deep learning-based framework for day-ahead optimal charging scheduling, which is in three stages. In the first stage, the parameters of charging sessions are modeled using the proposed Copula generative adversarial network (CopulaGAN). In the second stage, the DAM price is forecasted using the time series multi-input nonlinear autoregressive neural network for 24 h ahead. In addition, in the proposed framework, the balancing energy market (BEM) is used to keep the charging power balance on the implementation day of the optimal charging program. In the third stage, optimal day-ahead EV scheduling is performed. The simulation is carried out using Grey Wolf Optimization (GWO) based on the Caltech charging sessions dataset and the Germany energy market dataset. The simulation results show that using the proposed deep learning framework reduces the charging cost for the parking lots compared to the fixed price approaches, the use of the BEM, the time of use (TOU) program, and using normal distribution modeling about 19.94 €, 159.97 €, 14.27 €, and 114.03 € respectively.

Computational Challenges and Approaches for Electric Vehicles

Researchers worldwide have become increasingly interested in developing computational approaches to handle challenges facing electric vehicles (EVs) in recent years. This article examines the challenges and future potential of computational approaches for problems such as EV routing, EV charging scheduling (EVCS), EV charging station (CS) placement, CS sizing, and energy or load management. In addition, a summary of the fundamental mathematical models employed to solve the EV computational problems is presented. We cover recent work on computational solutions for various EV problems utilizing single and coupled mathematical models. Finally, we also examine potential research avenues that researchers could pursue to realize the objective of environment-friendly transportation and smart grids (SGs).

Demand Response Approach for Coordinated Scheduling of EV Charging in a Micro-Grid

Compared to traditional fuel vehicles, electric vehicles (EVs) can use energy efficiently without polluting the environment, which is why environmental experts are pushing the switch to EVs. When a large number of EVs are connected to the present power grid, negative impacts to the power system dynamics may happen. Implantation of demand response (DR) programming may avoid unplanned demand peaks, and support grid voltage. The objective of this article is to minimize the cost of charging the EVs while also utilizing their battery storage capacities to support grid voltage. Price-based DR programming is used to control the EVs’ charging. A study of an 8-bus distribution system with three EVs connected during charging/discharging periods, and different scenarios are simulated and compared with the grid response without any EV. New distribution tariffs have been developed to encourage small electricity users toward peak load restriction. The proposed scheduling method is valuable for the utility to estimate the capability of different control strategies for EV charging. It can also be extended to more complicated systems with large number of EVs and hence applied to a real network. MATLAB environment is used to test the suggested control scheme’s robustness and efficacy.

Logistics Pure Electric Vehicle Routing Based on GA-PSO Algorithm

Abstract:Based on current energy storage technologies such as batteries and fuel cells, the inherent battery capacity of electric vehicles puts constraints on their driving range and requires charging in the process of completing driving tasks. In this paper, with the current practical application in logistics industry as the background, from electric vehicle charging scheduling and path planning, a hybrid algorithm combining genetic-particle swarm algorithm is proposed to plan the best driving route for a group of electric logistics vehicles with vehicle load, vehicle battery life, charging facility location and customer time window as constraints and the total cost as the objective function. Based on the single distribution center, a more complex multi-distribution center electric vehicle path planning problem is considered. In this paper, multiple sets of Solomon VRPTW data sets are selected to test the prepared algorithm, and the results show that the algorithm can effectively plan the best distribution scheme.

Electric vehicle charging scheduling considering infrastructure constraints

The impacts of large-scale electric vehicles (EVs) charging on the power grid and the lack of charging infrastructure may directly hinder the promotion of EVs. With the limited number of charging piles and maximum instantaneous power at the charging station, how to effectively charge scheduling for EVs and reduce the charging cost for users becomes an important issue. To address this problem, we propose an EV charging scheduling strategy in response to time-of-use price. Here, the least cost of charging is set as the objective function and the limitations of charging piles number and instantaneous power of the stations are constraints. EV charging behavior characteristic is simulated using the Monte Carlo method based on 876,012 sets of historical charging data. Then, after solving the optimization problem by the adaptive genetic algorithm, each EV is assigned a specific charging pile that can meet its charging demand. The experimental results show that the proposed method can achieve better results than the comparative methods while ensuring the safe operation of charging stations. The effect of peak and valley reduction on the grid side is also realized.

Study on orderly charging strategy of EV with load forecasting

The development and popularization of electric vehicles (EVs) is of great significance to environmental protection, energy saving and emission reduction. With the wide popularization of EV, the EV's disorderly charging brings the security hidden trouble to the grid. Firstly, according to the safe operation of power grid and the charging requirements of EVs, an optimal scheduling model based on grid loss is established, then, the optimal scheduling model is transformed by second-order cone relaxation technology. Secondly, because the orderly charging schedule of EV is based on accurate charging load forecasting, this paper based on LSTM-XGBoost dynamic combination forecasting, the dynamic combination model of LSTM and XGBoost is optimized by using Bayesian optimization method, and more accurate charging load forecasting results are obtained. Finally, the accuracy of the prediction method and the effectiveness of the optimal scheduling strategy are verified by the charging data of the EV in the actual area.

A Multi-Objective Optimization Framework for Electric Vehicle Charge Scheduling With Adaptable Charging Ports

A Multi-Objective Optimization Framework for Electric Vehicle Charge Scheduling With Adaptable Charging Ports

A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management

The transition from internal combustion engines to electric vehicles (EVs) has received significant attention and investment due to its potential in reducing greenhouse gas emissions. The integration of EVs into electric and transport systems presents both benefits and challenges in energy management. The scheduling of EV charging can alleviate congestion in the electric system and reduce waiting times for EV owners. The use of renewable energy sources (RESs) for EV charging and supporting the grid can help mitigate the uncertainty of these energy resources. Vehicle-to-grid (V2G) technology can be used as an alternative approach in the event of sudden high consumption of the grid. Additionally, cost minimization through large-scale coordinated planning is crucial for the future of e-mobility systems. This review paper focuses on the latest trends considering the various approaches and features in coordinated EV scheduling, as well as the influence of different stakeholders, categorized as single- and multiple-charging stations (CS) and aggregator levels. By implementing coordinated EV scheduling, various methods are presented to better manage the needs and satisfaction of EV owners as well as the profit of CS and the market trends of e-mobility systems. In this regard, EV charging strategies considering V2G, uncertainty evaluation of parameters, coordinated charging management, congestion of CSs and electrical lines, route mapping, and technical and economic aspects of the system hierarchy, including consumers, CSs and aggregators, are reviewed and discussed.

Electric Vehicle Charging Scheduling Problem: Heuristics and Metaheuristic Approaches

Electric Vehicle Charging Scheduling Problem: Heuristics and Metaheuristic Approaches

A new decentralized architecture and a fully parallel distributed algorithm for charge scheduling of plug-in electric vehicles

In this paper, a distributed charge scheduling problem for plug-in electric vehicles (PEVs) is considered. It is shown that the problem of charge scheduling of PEVs in the centralized framework can also be recast into a Resource Allocation problem. A differentiation from existing literature is that this paper presents a new configuration of network architecture connecting charging station to PEVs for which a discrete-time fully parallel distributed primal-dual algorithm with fixed step size is proposed. In the proposed configuration, the existence of aggregator is removed, and the interactions are allowed among individual PEVs as well as between PEVs and the smart charging station (SCS) while private information of each PEV and the SCS is preserved. The heterogeneity in PEV charge specifications is considered in the formulated problem. The distributed algorithm can converge to an optimal solution of the problem for any convex cost functions of PEVs/utility with arbitrary initialization over any non-switching connected undirected networks without solving a sub-problem at each time step. Performance of the algorithm does not depend on the structure of the graph for convergence. Finally, a numerical example on load regulation is provided to demonstrate the results.

Engineering IoT-Based Open MAS for Large-Scale V2G/G2V

In this paper, we aimed to demonstrate how to engineer Internet of Things (IoT)-based open multiagent systems (MASs). Specifically, we put forward an IoT/MAS architectural framework, along with a case study within the important and challenging-to-engineer vehicle-to-grid (V2G) and grid-to-vehicle (G2V) energy transfer problem domain. The proposed solution addresses the important non-functional requirement of scalability. To this end, we employed an open multiagent systems architecture, arranging agents as modular microservices that were interconnected via a multi-protocol Internet of Things platform. Our approach allows agents to view, offer, interconnect, and re-use their various strategies, mechanisms, or other algorithms as modular smart grid services, thus enabling their seamless integration into our MAS architecture, and enabling the solution of the challenging V2G/G2V problem. At the same time, our IoT-based implementation offers both direct applicability in real-world settings and advanced analytics capabilities via enabling digital twin models for smart grid ecosystems. We have described our MAS/IoT-based architecture in detail; validated its applicability via simulation experiments involving large numbers of heterogeneous agents, operating and interacting towards effective V2G/G2V; and studied the performance of various electric vehicle charging scheduling and V2G/G2V-incentivising electricity pricing algorithms. To engineer our solution, we used ASEME, a state-of-the-art methodology for multiagent systems using the Internet of Things. Our solution can be employed for the implementation of real-world prototypes to deliver large-scale V2G/G2V services, as well as for the testing of various schemes in simulation mode.

A Two-Stage Multi-Agent EV Charging Coordination Scheme for Maximizing Grid Performance and Customer Satisfaction

Advancements in technology and awareness of energy conservation and environmental protection have increased the adoption rate of electric vehicles (EVs). The rapidly increasing adoption of EVs may affect grid operation adversely. However, the increased integration of EVs, if managed appropriately, can positively impact the performance of the electrical network in terms of power losses, voltage deviations and transformer overloads. This paper presents a two-stage multi-agent-based scheme for the coordinated charging scheduling of EVs. The first stage uses particle swarm optimization (PSO) at the distribution network operator (DNO) level to determine the optimal power allocation among the participating EV aggregator agents to minimize power losses and voltage deviations, whereas the second stage at the EV aggregator agents level employs a genetic algorithm (GA) to align the charging activities to achieve customers' charging satisfaction in terms of minimum charging cost and waiting time. The proposed method is implemented on the IEEE-33 bus network connected with low-voltage nodes. The coordinated charging plan is executed with the time of use (ToU) and real-time pricing (RTP) schemes, considering EVs' random arrival and departure with two penetration levels. The simulations show promising results in terms of network performance and overall customer charging satisfaction.

Preemptive scheduling of EV charging for providing demand response services

We develop a new algorithm for scheduling the charging process of a large number of electric vehicles (EVs) over a finite horizon. We assume that EVs arrive at the charging stations with different charge levels and different flexibility windows. The arrival process is assumed to have a known distribution and that the charging process of EVs can be preemptive. We pose the scheduling problem as a dynamic program with constraints. We show that the resulting formulation leads to a monotone dynamic program with Lipschitz continuous value functions that are robust against perturbation of system parameters. We propose a simulation based fitted value iteration algorithm to determine the value function approximately, and derive the sample complexity for computing the approximately optimal solution.

Hybrid optimization enabled multi‐aggregator‐based charge scheduling of electric vehicle in internet of electric vehicles

SummaryIn modern days, electric vehicles are quickly industrialized as well as their penetration is also increased highly, which brings more challenges for the power system. The electric vehicle charge scheduling process is vital to encourage the daily usage of the electric vehicle. However, irregular charging methods for electric vehicles may disturb voltage security areas because of their stochastic characteristics. Moreover, an electric vehicle requires recurrent charging owing to its constrained battery capacity, but it is a time‐consuming process. In this article, an effective charge scheduling model is devised using the fractional social sea lion optimization (Fr‐SSLO) algorithm. At first, IoEV network is simulated along with charge station and electric vehicle location. Furthermore, multi aggregator‐based charge scheduling is done for increasing the profit and amount of scheduled electric vehicles. Then, routing is performed based on developed Fr‐SSLO algorithm. Moreover, several fitness measures, including distance, energy and variable energy purchase are included. Here, the devised Fr‐SSLO model is designed by integrating fractional calculus (FC) and sea lion optimization (SLnO) technique along with SOA. After the completion of routing process, charge scheduling is performed based on developed Fr‐SSLO approach. Moreover, various fitness functions are also considered for computing better performance.

Stacking regression technology with event profile for electric vehicle fast charging behavior prediction

Large-scale deployment of electric vehicles (EVs) poses a huge challenge to the operation of the distribution network. As a possible mobile energy carrier, the interaction between EVs and distribution networks can provide some opportunities for power operation. Where to charge and how to charge have become an important research topic in EV charging scheduling. Previous studies mainly focused on slow-charging behavior analysis rather than fast-charging behavior. Here, we provide an in-depth understanding of EV user fast-charging behavior in public stations based on more than 220,000 real-world charging records with the Variational-Bayesian Gaussian-mixture model. Characteristics related to charging energy and charging duration are mainly considered in the cluster model, especially dwelling duration after charging is taken into account to better support the decision of charging recommendation strategy and charging power allocation. Inspired by the future application scenario of the charging behavior cluster of previous studies, we propose a charging behavior prediction framework considering behavior catalogues with stacking regression technology. The results show that the proposed framework improves the prediction accuracy of charging behavior and can effectively evaluate the priority of charging behavior.

Deep learning-based power prediction aware charge scheduling approach in cloud based electric vehicular network

Electric vehicles (EVs) are considered emerging popularity due to their cost-saving and eco-friendliness nature. The charging of EVs with elevated efficiency is a major issue for users as the hardware facilities, like charging stations are limited. Thus, the EV charging scheduling model is required to discover the charging schedule for each EV. An optimization-driven framework for EV charge scheduling in a Vehicular ad hoc network (VANET) topology is presented in this paper. First, the network simulation with the charge station (CS) and the EV locations are performed. The Deep Maxout network (DMN) is employed for predicting the power. To charge at the charging station, the vehicles are scheduled based on various parametric factors. The proposed Fractional Feedback Tree Algorithm (FFTA) is used to schedule charges in the EV network. The Feedback Artificial Tree (FAT) algorithm and fractional calculus (FC) are combined to create the proposed FFTA. Additionally, a new model of the fitness function is created taking into account variables like the average waiting time, the distance, the predicted power, and the quantity of EVs that are requested to be charged. With the shortest distance of 11.00 km, the highest power of 8.17 J, the shortest average waiting time of 0.47 s, and less number of EVs charged at 4.4, the proposed FFTA offered improved performance.