Scheduling Of Electric Vehicles Research Articles

Electric vehicle charging scheduling control strategy for the large-scale scenario with non-cooperative game-based multi-agent reinforcement learning

With the popularity of electric vehicles (EVs), electric vehicle charging scheduling control in the complex urban environment has become a hot research issue, especially the use of multi-agent reinforcement learning (MARL) to solve game problems in the process of EV scheduling. In a complex and unstable environment involving high-speed dynamic changes of vehicles, the traditional methods have poor learning effects. In this paper, a multi-agent charge scheduling control framework with the cooperative vehicle infrastructure system (CVIS) is proposed to solve the game problems between electric vehicle charging stations (EVCSs) and EVs. To achieve effective control of electric vehicle charging stations in large-scale road networks, a new multi-agent A2C algorithm based on a non-cooperative game (NCG-MA2C) is proposed. In the proposed NCG-MA2C algorithm, the state representation is based on the K-nearest neighbor multi-head attention mechanism, the action definition is based on the proposed adaptive service price adjustment strategy, and spatio-temporal discount joint reward stable learning convergence. The results show that the proposed algorithm has good performance in increasing the efficiency of EVCSs while reducing EV charging cost compared with the fixed strategy, the greedy strategy, the independent Q learning (IQL) algorithm, the multi-agent Q-learning algorithm (MAIQL), and the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The NCG-MA2C algorithm has strong extensibility and validity in the complex urban environment.

Predictive control and coordination for energy community flexibility with electric vehicles, heat pumps and thermal energy storage

Electrification of private transportation and residential heating is a potential action to decrease significantly carbon emissions. However, the lack of coordination of such emerging loads can increase stress on the power distribution grid. To this end, this paper proposes a two-stage energy management strategy, to enhance the flexibility of energy communities. First, an optimization algorithm-based model predictive control (MPC) is developed for home energy management systems (HEMS) to schedule electric vehicles (EVs) for charging and discharging in cooperation with heat pump and thermal energy storages. The objective is to reduce electricity bills and EV battery degradation costs while maintaining thermal demand. Second, the predictive coordination based on the nonlinear AC optimal power flow approach is applied to enhance the grid flexibility under a community energy management system scheme. To validate the performance of the proposed method, we develop a co-simulation framework by implementing the optimization algorithm and simulation scenarios in a Python Environment and modelling the electricity grid in the PowerFactory-DIgSILENT, which are linked together. Extensive numerical simulations are carried out under the active demand response program-based hourly electricity pricing and different weather data in Sweden. The results show that the proposed method can reduce operating costs and enhance the energy flexibility of the energy community. Moreover, it can decrease the stress on the community power grid and prevent load-shedding events compared to the non-predictive coordinated method.

Optimal EV scheduling and voltage security via an online bi-layer steady-state assessment method considering uncertainties

Steady-state voltage security region (SVSR) is an important criterion that can intuitively and effectively evaluate the power system's operation security and overall safety margin. However, there is no literature on its application to electric vehicle (EV) proliferation. Since EV introduces dynamism and uncertainties to the grid, most existing indices cannot assess the grid accurately. Given this, we propose a load margin index (LMI) based on SVSR considering uncertainties. This is used to assess the impact of EV proliferation via a bi-layer optimal EV scheduling model. The upper layer of the model solves a dispatching plan for EV aggregators (EVA) by maximizing the LMI and minimizing the deviation between the upper and lower layer power schedules. The lower layer models each EV's charging/discharging strategy by minimizing the EV's operating cost, the EV user's target residual energy, and the deviation between the lower and upper layer schedules. We also employ a real-time scheduling plan to further adjust the day-ahead scheduling due to forecast errors. A case study based on the IEEE-33 node distribution network verifies the practicability and effectiveness of the proposed method.

Hierarchical Transactive Energy Scheduling of Electric Vehicle Charging Stations in Constrained Power Distribution and Transportation Networks

There is a growing opportunity to explore transactive energy potentials among electric vehicle charging station (EVCS) systems as the ongoing trend is toward the deployment of more distributed energy resources such as solar PV and energy storage units at EVCS premises. In this paper, we propose a multi-agent hierarchical framework for energy scheduling and trading of EVCSs considering the mobility constraints in the transportation network (TN) and the operational constraints in the power distribution network (PDN). Modeled as independent profit-driven entities, each EVCS optimally schedules its operation based on a multi-period traffic assignment problem (TAP) solved by the traffic operator (TO) agent. A modified single-sided auction mechanism with limited shared information is used to clear the electricity market based on submitted EVCS bids and offers. The resulting trading operations are shared with the PDN operator to guarantee a reliable network operation. A trading adjustment signal is sent back to market participants (i.e., EVCSs) in case of any PDN violations. We use a realistic three-phase unbalanced representation formulated as a MISOCP problem to model the PDN operation. A four-stage solution method is proposed to solve the proposed EVCS energy scheduling and trading problem. Numerical simulations performed on a modified IEEE 33-bus test system and 12-node benchmark TN prove the effectiveness of the proposed multi-agent-based hierarchical transactive market model and its solution approach for EVCSs.

Joint scheduling optimization of a microgrid with integration of renewable energy sources and electric vehicles considering energy and reserve minimization

AbstractTo lower operational costs as well as emissions when wind and solar resources are available in a microgrid (MG), this study discusses the scheduling of electric vehicles (EVs) and responsive demands simultaneously. To mitigate the effects associated with undispatchable energy sources such as wind and solar, the proposed system makes use of EVs for peak shaving and load curve changes, while responsive demands provide the reserves required to do so. In addition, a two‐stage model is provided to evaluate MG's planned running costs (energy and reserve). Costs related to generating and reserving electricity are minimized in Stage 1, while costs related to adjusting unit scheduling to account for fluctuations in wind and photovoltaic output are minimized in Stage 2. Converged barnacles mating optimizer (CBMO) is a highly effective and powerful optimization tool that is used to handle the resultant objective optimization issue. An MG consisting of multiple dispersed generations is used to implement the proposed model. It is worth mentioning that three scenarios have been defined to analyze the impact of joint scheduling of EVs and controllable loads on the MG's day‐ahead operation. The three cost terms, that is, the generation cost, the reserve cost, and the startup cost of units in this scenario, are derived as $745.6913, $10.5278, and $6.35, respectively, remarkably less than the values reported in Scenarios 1 and 2. In Scenario 1, the CBMO algorithm yielded a lower MG operational cost than methods by a margin of 843.2 $/day. Costs per day of operation in Scenario 2 are derived to be $819.3 using the CBMO technique, whereas in Scenario 3, they are determined to be $743.1.

Optimal control study of home energy management with cooperative dispatch of electric vehicles and energy storage devices

In order to solve the problem that the charging and discharging characteristics of electric vehicles (EVs) are not fully utilized by household users with distributed photovoltaic (DPV), which leads to low photovoltaic utilization efficiency and poor household electricity cost and economy, this paper proposes an optimal control strategy for household energy management based on cooperative scheduling of EVs and energy storage devices. In this paper, we firstly classify the household electric devices based on the load characteristics, and then formulate the control strategies of electric vehicles and energy storage devices based on the relationship between the total load power and the PV output, and propose the objectives of household side, environment and grid side based on this, and solve the multi-objective problem by using the improved gray wolf optimization (GWO) algorithm based on Tent mapping chaos optimization. The​ effectiveness of the model and algorithm is verified by simulating and analyzing a household customer under real-time electricity price. The results show that the system operating conditions can be improved to achieve the expected goals of minimizing household electricity cost and maximizing PV consumption under the conditions of energy management optimal control.

Optimal Scheduling of Electric Vehicles Charging and Discharging Strategy Based on Real‐Time Price Considering Multi‐party Interest

In order to mitigate the negative impact brought by the large‐scale grid connection of EVs, it is highly significant to study the optimal charging scheduling strategy of EVs. In this paper, a multiobjective optimization model of ordered charging and discharging with the participation of distribution network operator (DNO), charging station operator (CSO) and EV users is established, which maximize the interest of three parties. At the same time, the real‐time price (RTP) based on the change in load rate is designed and compared with the time‐of‐use (TOU) price model to analyze the impact of both on the interests of all parties. At last, the sparrow search algorithm (SSA) is introduced and three improvements are made. The results demonstrate that different charging modes of EVs and charging price models will affect the balance of interests of the three parties, while the improved SSA performs better under different users' response rates. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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.

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.

Resilience-Oriented Scheduling of Shared Autonomous Electric Vehicles: A Cooperation Framework for Electrical Distribution Networks and Transportation Sector

As autonomous electric vehicles and car-sharing services are becoming more popular, the contribution of shared autonomous electric vehicles (SAEVs) to the future of urban transportation is getting more achievable. Like conventional electric vehicles, SAEVs can provide power grids with ancillary services. This article proposes a new scheduling scheme for SAEV fleets within a cooperative plan to let power distribution networks benefit from the energy storage of vehicle batteries in recovering critical loads after a predictable extreme event. According to a long-term contract, the detailed request of the distribution system operator (DSO), together with desired constraints and perquisites, is sent to the SAEVs aggregator (SA) prior to the landfall of a predictable extreme event. Afterward, SA runs a targeted algorithm to schedule trip assignments and charging cycles of SAEVs so that the required constraints of DSO are satisfied. The SAEV participants will continue carrying passengers within the scheduled time horizon in addition to delivering energy to the distribution network at the scheduling deadline declared by DSO. This deadline is the time instant when the capacity of the SAEV fleet may be no more applicable to enhance the system preparedness against the approaching event. Numerical results illustrated that the proposed scheme helps improve the power grid resilience by delivering 2396.1 kWh of energy to the distribution network in addition to increasing the total income of each participant SAEV by about 130%. Thus, it is implied that the proposed method offers a win-win situation for both entities.

Management of Fluctuating Output Power of Renewable Energy Resources by Optimum Charge/Discharge Scheduling of Electric Vehicles in Smart Parking Lots

امروزه آلودگی‌های زیست محیطی و کاهش منابع تجدیدناپذیر از جمله سوخت‌های فسیلی، موجب شده است تا تولیدات پراکنده برق گسترش قابل توجهی یابد. از طرفی با گسترش روز افزون خودروهای الکتریکی، بررسی حضور خودروها بسیار با اهمیت است. زیرا حضور بدون برنامه خودروهای الکتریکی و شارژ همزمان آن‌ها تأثیرات مخربی بر پارامتر‌های شبکه توزیع برق خواهد داشت. لذا با مدیریت مناسب شارژ و دشارژ خودروهای الکتریکی در پارکینگ‌های هوشمند می‌توان از خودروهای الکتریکی در راستای بهبود پارامترهای شبکه و همچنین گسترش حضور منابع انرژی تجدیدپذیر استفاده نمود. از همین رو در این مقاله از پارکینگ‌های هوشمند خودروهای الکتریکی برای کاهش نوسانات توان تولیدی یک مجموعه منابع انرژی تجدیدپذیر استفاده می‌شود. در این راستا دو هدف اصلی این مقاله عبارت است از پاسخگویی به توان مبادله‌ای مورد نیاز برای کاهش نوسانات توان تولیدی منابع انرژی تجدیدپذیر و رسیدن سطح شارژ خودروها به سطح مناسب. این مدل پیشنهادی به صورت برنامه‌ریزی خطی آمیخته با اعداد صحیح مدل‌سازی شده و با استفاده از نرم‌افزار متلب حل می‌شود. نتایج شبیه‌سازی بر روی داده‌های پارکینگ شهر نمونه تهران نشان می‌دهند که مدیریت بهینه و مناسب خودروها علاوه بر آنکه سبب می‌شود خودروها در هنگام خروج به سطح شارژ مطلوب خود برسند، سبب کاهش قابل توجهی در تعداد باتری‌های مورد نیاز و در نتیجه هزینه بالای خرید و نصب باتری‌ها می­گردد.

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.

Evaluation of Distinct EV Scheduling at Residential Charging Points in an Unbalanced Power Distribution System

Electric vehicles (EVs) have significant potential and sustainability in the future transportation sector due to their less pollution and low operating cost. The uncoordinated charging of these EVs may push the power delivery system towards lower performance and even at the risk of damaging the power system equipment. The proper scheduling and coordination of EVs with the system may lessen the negative impact. In this paper, a multi-objective scheduling approach for individual candidate-based EVs is proposed to relieve the distress in power delivery in the best possible way. Minimization of average demand deviation and system unbalance reduction are selected as the objectives to restore the power delivery performance. The proposed approach has been verified on a realistic three-phase power distribution infrastructure. A simple linear regression model is employed to predict the near future number of EVs in the test region. An optimizitaion algorithm based on particle swarm optimization (PSO) technique is operated to find the finest solution satisfying EV charging constraints. The stochastic nature of load demands at the node points of the test network is analyzed with the help of a probabilistic demand model and it is further incorporated into the optimization framework. It is possible to reduce the system’s average demand deviation and unbalance level by 53.74% and 51.36%, respectively, with the proposed EV scheduling approach. A comparative study with the existing methods further claims the efficacy of the proposed approach. HIGHLIGHTS Framework for home charging schedule of distinct EVs Compliance with multi-objective power supply efficiency Probabilistic modeling of load demand from real collected data Optimization-driven solution for a test case in Mysore city in India

Metaheuristic for the integrated electric vehicle and crew scheduling problem

Encouraged by international efforts to reduce greenhouse gases and local emissions, many public transport operators are converting their fleets to battery-powered electric buses. Public transport operators can choose between different electric bus concepts, with the total cost of ownership being the most important decision criterion. The associated strategic decisions regarding charging strategy, vehicle concept, and charging infrastructure have a significant impact on the operational planning of the electric buses.Motivated by this, this paper aims to analyze the interactions between electrification and operational planning, especially vehicle scheduling and crew scheduling. This allows us to make a more comprehensive comparison of different electrification concepts. Prior work has addressed the impact of electrification on vehicle scheduling but has neglected the interactions with crew scheduling. Crew scheduling dominates operational costs and planning for many public transport operators and must therefore be considered in all strategic decisions. For this reason, in this work we focused on integrated electric vehicle and crew scheduling problem. This allows us to calculate the total cost of ownership of different electric bus concepts under better representation of local conditions. We deal with the electric vehicle and crew scheduling problem with a metaheuristic based on Adaptive Large Neighborhood Search. We tested the developed methodology for a real-world bus route. Our results indicate that the constraints for crew scheduling significantly impact the total cost of ownership and the required number of vehicles of the different electrification concepts. Our case study suggests that the choice of the most cost-effective concept depends significantly on crew scheduling constraints. These findings imply that crew scheduling constraints should be considered as part of the local framework for bus fleet electrification.

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.

Optimal scheduling and techno-economic analysis of electric vehicles by implementing solar-based grid-tied charging station

The integration of Electric Vehicles (EVs) and Photovoltaics (PVs) into the electric power distribution system is rising worldwide due the environmental concerns and the rapid depletion of fossil fuels. However, the simultaneous integration of EVs and PVs in the power system poses challenges of uncertain power penetration, charging, and dynamic loading conditions, which requires a suitable control design and scheduling optimization to keep the modified electrics system stable. This paper proposes a solar-based grid-tied charging station (SGTCS) that optimizes EV charging by enabling the scheduling technique resulting in maximum utilization of PV power. The proposed model also identifies and considers seasonal variation effects of power generation and EV charging connectivity. The SGTCS is implemented using HOMER Grid, and a case study of a practical locality in Islamabad was investigated; the conducted detailed analysis comprises the annual cost estimation, energy costs, EV scheduling, main grid power usage reduction, and the impact of environmental changes. Examining the integration of small and big EVs in the grid-tied solar-integrated charging system, a brief scheduling strategy of 250 sessions per day is analyzed. The resultant techno-economic implications are presented, depicting the proposed study's relevance and applicability.

Hierarchical User-Driven Trajectory Planning and Charging Scheduling of Autonomous Electric Vehicles

Autonomous electric vehicles (A-EVs), regarded as one of the innovations to accelerate transportation electrification, have sparked a flurry of interest in trajectory planning and charging scheduling. In this regard, this work employs mobile edge computing (MEC) to design a decentralized hierarchical algorithm for finding an optimal path to the nearby A-EV parking lots (PLs), selecting the best PL, and executing an optimal charging scheduling. The proposed model makes use of unmanned aerial vehicles (UAVs) to assist edge servers in trajectory planning by surveying road traffic flow in real time. Furthermore, the target PLs are selected using a user-driven multiobjective problem to minimize the cost and waiting time of A-EVs. To tackle the complexity of the optimization problem, a greedy-based algorithm has been developed. Finally, charging/discharging power is scheduled using a local optimizer based on the PLs’ real-time loads, which minimizes the deviation of the charging/discharging power from the average load. The obtained results show that the proposed model can handle charging/discharging requests of on-move A-EVs and bring fiscal and nonfiscal benefits for A-EVs and the power grid, respectively. Moreover, it observed that user satisfaction in terms of traveling time and traveling distance is increased by using the edge-UAV model.

Optimal Charging Scheduling of Electric Vehicles: The Co-Charging Case

A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">co-charging</i> parking system is a charging system in which the chargers are automated, i.e., they can automatically connect to or disconnect from vehicles presented in the parking. This system makes it possible to have less chargers than the number of parking places. This paper studies an optimal scheduling problem aiming at charging all vehicles to required battery level while minimizing the total charging cost. We formulate this scheduling problem as a non-linear optimal control problem with mixed integer constraints and mixed discrete and continuous time. The problem is solved using a Branch and Bound Strategy coupled with Pontryagin Maximum Principle (PMP) and Interior Point Methods (IPMs) for optimal control. The presented method is tested on numerous configurations and shows promising results both in term of precision and in term of convergence rate.

Hierarchical Optimization for User-Satisfaction-Driven Electric Vehicles Charging Coordination in Integrated MV/LV Networks

The growing uptake of electric vehicles (EVs) will likely require management schemes to enable their connection into distribution systems. While most of the existing approaches are developed from the operator’s perspective considering EV aggregated demands at the medium-voltage (MV) level, individual users’ comfort and the particularities associated with low-voltage (LV) networks need to be considered to holistically assess the EV effects in an integrated MV/LV network. This article proposes a two-level hierarchical optimization framework for the EV charging coordination (EVCC) that maximizes users’ satisfaction, while avoiding operational grid issues in the whole distribution system. The framework is tailored for unbalanced distribution systems with high penetration of EVs and introduces a novel index to measure charging priority-based EV user satisfaction. To reduce the computational burden, the EVCC problem is disaggregated into an upper level for MV network operation, and a lower level for LV network and individual EV scheduling, using mixed-integer linear programming models. This framework is later embedded in a dynamic scheduling approach that copes with unexpected EV arrivals. Benefits (increased overall user satisfaction and reduced strain over distribution assets) are demonstrated via case studies in a 459-node three-phase network in which solutions were achieved under a 60-s threshold.

Fast frequency response constrained electric vehicle scheduling for low inertia power systems

Reduction in System Inertia (SI) and Primary Frequency Response (PFR) driven by the large-scale integration of uncertain and variable Renewable Energy (RE) sources disturbs the post-fault frequency dynamics. This necessitates the identification of potential solutions that can restrict high Rate of Change of Frequency (RoCoF). Electric vehicles (EVs), due to their fast response capability and instantaneous power delivery, are a viable source for the Fast Frequency Response (FFR) service. Potential assessment of EVs for FFR service is required to restrict high RoCoF and maintain secure and reliable system operation in real-time. The main contribution of this work is to assess the potential of EVs for FFR support through both Grid-to-vehicle (G2V) and Vehicle-to-grid (V2G) modes. In this work challenges like EVs aggregation, related netload uncertainty characterization, and post-fault frequency dynamics are modelled in a stochastic security-constrained scheduling framework. Further, a case study is carried out to investigate the role of EVs and their techno-economic impact on the overall system: (a) only in dumb charging mode and (b) participating in FFR service by scheduling aggregated EVs for both G2V and V2G with different level of RE integration. Numerical results indicate that the aggregated EVs scheduling can provide FFR support of around 71%, by simultaneously reducing system operating cost by 62% and 87% reduction in RE curtailment.