Scheduling Of Electric Vehicles Research Articles

A resilience-oriented pre-positioning approach for electric vehicle routing and scheduling in coupled energy and transport sectors

High-impact low-probability (HILP) events have occurred more frequently than before and caused severe damage to conventional power systems. Energy hubs (EHs) consist of various distributed energy resources (DERs) for energy generation, conversion and storage across different sectors. The high energy integration of EH systems makes them more robust under extreme events than traditional power systems. In addition, the large penetration of electric vehicles (EVs) has been witnessed in modern energy systems due to their significant benefits in accelerating transport electrification and reducing carbon emissions. Due to the characteristics of mobility and flexibility, EVs can be utilised as mobile energy resources in coupled energy and transport sectors and shift energy between different regions via effective routing and scheduling behaviours. In this context, this paper proposes a resilient-oriented pre-positioning approach for the routing and scheduling of multiple EVs in a coupled energy-transport system, where the energy system includes multiple EHs across both electricity and heat sectors. To simulate real-world scenarios, uncertainties associated with renewable generation, load profiles, and EV commuting time as well as contingencies related to component failures are incorporated into the proposed pre-positioning approach via stochastic programming. Extensive case studies are carried out based on an EH system including three EHs and five EVs, which illustrate that EVs can coordinate with static DERs in the system and perform energy shifting between different EHs via appropriate routing and scheduling behaviours. Additionally, results demonstrate that the proposed pre-positioning approach can achieve higher resilience level and ensure the supply continuity of critical loads.

Dynamic pricing strategy for efficient electric vehicle charging and discharging in microgrids using multi-objective jaya algorithm

The rising demand for electric vehicle (EV) charging is spurring their increased integration into microgrids. With significant advancements, EVs have become widely adopted and integrated into various settings for charging/discharging. EVs integrated with the microgrids possess the capability to serve as variable loads and the various energy suppliers present it as a dual opportunity. However, a primary challenge in EV deployment lies in efficiently managing charging stations (CSs) to minimize waiting times for users and reduce charging costs for EV owners. In addressing these challenges require consideration of dynamic pricing mechanisms and the diverse characteristics of EVs to achieve optimal scheduling. A novel approach that combines dynamic pricing strategies with optimized scheduling for effective EV charging operations using multi-objective Jaya algorithm. To evaluate its performance, we conducted a numerical experiment using real-time data and the Nissan Leaf model EV. The results demonstrate that our multi-objective Jaya-based approach outperforms existing methods by achieving a remarkable cost saving rate of 16.013% and an average profit of ₹ 243.6331 per kilowatt-hour with a network convergence time of 112 s. Also, our proposed algorithm provides a correlation between minimized EV charging costs and maximized EV aggregator profits. These findings validate the effectiveness and practical applicability of our proposed EV scheduling algorithm in real-world scenarios.

Dynamic scheduling of electricity demand for decentralized EV charging systems

The rapid growth of electric vehicles (EVs) has brought forth new challenges to the power grid. Further, the simultaneous charging of EVs could lead to peak demand, potentially causing overloading, voltage swings, and other grid-related problems. To address these issues and lower the high energy costs faced by EV owners and grid operators, EV charging must be optimized. The study proposes an innovative strategy that utilizes decentralized charging systems to lessen the impact of EVs on the grid. A decentralized EV scheduling strategy offers scalability. Thus, making it suitable for a large EV population and it remains resilient to the dynamic arrivals of the EVs. The approach aims to balance the load on the grid and improve the effectiveness of charging operations. To achieve this, a convex optimization problem has been developed to effectively regulate the charging procedure, taking into account the distinct attributes of each EV. The mechanism operates by dividing time into several intervals. Each electric vehicle in the system autonomously adjusts its charging rate during the assigned time slots, with the goal of minimizing individual charging expenses. Moreover, the system demonstrates flexibility in deciding when to charge and discharge, allowing prioritization based on individual EV battery levels and power grid conditions. As a result, the cost analysis was conducted using the number of EVs and the average group size. A comparison of computational times between centralized and decentralized systems was undertaken to demonstrate the efficacy of the system.

Electric Vehicle Cluster Scheduling Model for Distribution Systems Considering Reactive-Power Compensation of Charging Piles

With the increasing number of electric vehicles (EVs), their uncoordinated charging poses a great challenge to the safe operation of the power grid. In addition, traditional individual-EV scheduling models may be difficult to solve due to the increasing number of constraints. Therefore, this paper proposes a cluster-based EV scheduling model. Firstly, electric vehicle clusters (EVCs) are formed based on the charging and discharging preferences of EV users and the expected time for EVs to leave. Secondly, the EVC energy and power boundary aggregation method based on the Minkowski addition algorithm is proposed. Finally, for the sake of reducing user charging cost and distribution network energy loss, and smoothing the daily load curve, an EVC scheduling model for EV participation in grid auxiliary services is proposed. The optimization model includes the reactive-power compensation of EV charging piles. The simulation results show that the proposed EVC scheduling model can greatly reduce the solution time compared to traditional individual-EV scheduling model. The model has high potential to be applied to large-scale EV scheduling. The reactive-power compensation provided by EV charging piles improves the voltage quality of the grid and enables more EVs to be connected to the grid.

Optimizing routing and scheduling of shared autonomous electric taxis considering capacity constrained parking facilities

This paper focuses on routing and scheduling of autonomous electric vehicles to provide reservation-based shared ride services, while a set of parking facilities with limited capacity are used for vehicle intermittent charging. A mixed-integer linear program model is formulated in the form of a vehicle routing problem with satellite facilities (VRPSF), subject to a series of additional time and capacity-related constraints. The objective of the model is to minimize the total operating costs of the system, including those related to vehicle miles traveled and the deployed vehicle fleet size. The number of vehicles inside each parking facility is tracked so as to ensure that the capacity is never exceeded throughout the service horizon. A customized solution method based on an adaptive large neighborhood search algorithm with an explicit treatment of parking facility choices is developed. A series of numerical experiments, consisting of both hypothetical examples and a real-world case study in Hangzhou, China, have been conducted to evaluate the effectiveness and applicability of the proposed model and algorithm. The results demonstrate that ride-sharing services and parking facilities have the potential to significantly reduce the total vehicle energy consumption and operating costs for a shared autonomous electric taxi (SAET) operator in practical scenarios.

Integrating Life Cycle Principles in Home Energy Management Systems: Optimal Load PV–Battery–Electric Vehicle Scheduling

Integrating Life Cycle Principles in Home Energy Management Systems: Optimal Load PV–Battery–Electric Vehicle Scheduling

Peak shaving potential and its economic feasibility analysis of V2B mode

Electric vehicles (EVs) as mobile energy-storage devices improve the grid's ability to absorb renewable energy while reducing peak-to-valley load differences. With a focus on smoothing the load curve, this study investigates the peak shaving potential and its economic feasibility analysis of V2B mode. First, based on the virtual microgrid system established in this paper, a Monte Carlo method was used to simulate the driving, charging, and discharging processes of EVs. Second, the effects of the number of charging stations, battery capacity of EVs, and photovoltaic (PV) penetration rate on the peak shaving results were evaluated separately by using the standard deviation of daily loads. Third, the V2B peak shaving strategy and cost composition were explored considering the battery aging costs. Moreover, the economic feasibility and factors influencing the proposed strategy were analyzed. The results indicate that 1) V2B effectively transfers peak power loads between 08:00 h And 20:00 h To low-demand periods at night; 2) as the deployment ratio of charging station increases, the level of load fluctuation decreases significantly. When EV battery capacity is at or above 60 kW h, charging station deployment ratios of 0.43, 0.42, and 0.47 are required for saturated load smoothing in winter, transition period, and summer, respectively; 3) the PV penetration rate for maximizing the collaborative peak shaving capability of V2B and PV power is 30%–40 % and 4) reducing battery costs or extending battery life has the most significant effect on V2B power costs, followed by charging station costs. This study provides a theoretical basis for determining the economic feasibility of charging station planning and provides technical guidance for the rational scheduling of EVs and their friendly interaction with the power grid.

Hierarchical co-optimization of EV scheduling considering customer and system in distribution networks

The global surge in the adoption of electric vehicles (EV) has affected distribution networks, with uncoordinated charging increasing peak load and loss. EV scheduling has the potential to become a business that utilizes vehicle-to-grid (V2G) technology to mitigate system impacts while providing financial incentives to customers. Efficient EV scheduling is a multi-objective optimization problem that simultaneously considers network and customer perspectives for co-optimization. This study introduces a hierarchical EV charging management platform (EVMP) with day-ahead and real-time stages that satisfy customer preferences while ensuring efficient system operation. In this process, in order to solve the computational issues of the optimal power flow (OPF) problem, including integer variables, the computational burden was alleviated by removing integer variables from the OPF problem of the day-ahead stage, and EV dynamics were effectively handled using the rolling-horizon framework in the real-time stage. This study focuses on the relationships between the objective functions at each stage and utilizes the augmented ɛ-constraint method (AUGMECON) to obtain a Pareto front that assists in stakeholder decision making. The Pareto front is constructed by considering stakeholders from both two- and three-way perspectives, allowing the system operator to select a solution from the Pareto front for decision making.

A novel navigation and charging strategy for electric vehicles based on customer classification in power-traffic network

With electric vehicles getting increasingly popular, there has been a lot of interest in encouraging future electric vehicle development in terms of greater charging efficiency, greater benefits to the environment, and greater reliability. One of the most prominent research is on the scheduling of electric vehicle charging and navigation. High-quality navigation and charging scheduling strategy must depend on the state of the coupled network between the transportation and power networks, while the outcomes of scheduling can also have a significant impact on these networks. In this paper, we propose a novel dynamic navigation and charging strategy that fully considers the coupled traffic and power distribution network. Firstly, electric vehicles with similar travel and charging requirements are classified. Secondly, an elastic scheme including fixed route navigation and flexible charging scheduling at charging stations is provided. We also propose an information integration framework for the implementation of electric vehicle routing and charging scheduling, information interaction between sectors including the status of heterogeneous electric vehicles, day-ahead power scheduling, and base load demands. The novel strategy aims at optimal electric vehicle navigation and charging at the system level achieving a lower overall travel cost, charging cost, carbon emission, and load variance of the power distribution network. Classification algorithms, stochastic dynamic programming, and queuing theory are used for mathematical modeling. Numerical results demonstrate the effectiveness of the proposed novel strategy for the navigation and charging scheduling of electric vehicles.

Fleet scheduling for electric towing of aircraft under limited airport energy capacity

Taxiing aircraft using electric vehicles is seen as an effective solution to meet aviation targets of climate neutrality. However, making the transition to electric taxiing operations is expected to significantly increase the electricity demand at airports. In this paper we propose a mixed-integer linear program to schedule electric vehicles for aircraft towing and battery charging, while considering a limit for the supply of energy. The objective of the schedule is to maximize emissions savings. For computational tractability, we develop an Adaptive Large Neighbourhood Search which makes use of multiple local search heuristics to identify scheduling solutions. For daily scheduling with a small fleet size, the developed heuristic achieves solutions with an average 4% gap to the best linear programming solution. The results show that charging the vehicles during daytime is essential to maximize saved emissions: removing charging opportunities for a few hours during the day reduces the performance by an average of 6.4%. In addition, it is found that fast charging leads to low vehicle downtime, unless the battery size exceeds 750kWh, when charging rates over 150kW become unnecessary. Overall, our model provides support for infrastructure planning of airports during the transition to aircraft electric taxiing.

Scheduling of Electric Vehicle’s Power in V2G and G2V Modes Using an Improved Charge–Discharge Opportunity-Based Approach

With rise in the popularity of Electric Vehicles (EVs), challenges related to its charging infrastructure are also soaring. In the upcoming smart grid scenario with significant renewable energy sources and distributed generation, EVs have been identified as a key element for grid stability and its optimal operation, primarily due to energy storage capacity. With a large number of EVs in parking, it has huge potential with optimal scheduling. However, EV owners should have the motivation to participate in Vehicle-to-Grid (V2G). This paper proposes a scheduling mechanism using a novel Forward-Backward energy allocation, based on an improved charge-discharge opportunity approach. The proposed method (PM) addresses the scheduling of power flow in EVs to operate in V2G and Grid-to-Vehicle (G2V) modes considering EV owners’ preferences, optimal cost, and low battery degradation. Implementation of the PM is facile and flexible with the vehicle’s time of arrival and departure. A 40 kWh and 60 kWh battery capacity EVs with multiple real-life scenarios have been considered in this study to demonstrate the effectiveness of the PM. The performance and features of the PM have been compared with existing methods to distinguish its benefits.

A price-regulated scheduling of electric vehicle with grid supporting photovoltaic and battery storage

With solar photovoltaic (PV) sources integrated into microgrids and electric vehicle (EV) charging stations, users have a variety of charging mode options to meet their needs. However, overloading of the distribution transformer, higher peak demand, and voltage instability are all potential drawbacks associated with EV charging loads. Microgrid optimisation becomes more difficult when dealing with intermittent PV generation and uncertain EV charging and discharging profiles. A Smart Home Energy Management System (SHEMS) is proposed for efficient power flow from PV sources, battery energy storage system (BESS), and an EV to maximise economic advantages for a consumer using the Artificial Hummingbird Algorithm (AHBA)-based optimisation approach. This approach considers the EV's unpredictable energy consumption, variations in residential load demand and seasonal variations in the solar insolation profile throughout the year. Consequentially, potential EV requirements such as initial and predetermined state of charge (SOC) arrangements and arrival and departure hours are also considered. Three energy management scenarios satisfying the allowable loading limit of the distribution transformer are examined, namely power transfers from home to electric vehicle (H2EV), EV to home (EV2H), and EV to grid (EV2G). The reduction in yearly operating costs (YOC) of 49.5 %, 53.2 %, and 70 % was achieved in H2EV, EV2H, and EV2G modes, respectively. This demonstrates that an effective EV and BESS management strategy can reduce the annual operating costs of a residential consumer by up to 70 % while simultaneously achieving peak load shaving.

Data-driven-based distributionally robust optimization approach for a virtual power plant considering the responsiveness of electric vehicles and Ladder-type carbon trading

To encourage the utilization of decentralized renewable energy systems, a data-driven-based distributionally robust optimization (DRO) model is proposed for a virtual power plant (VPP) considering the responsiveness of electric vehicles (EVs) and a ladder-type carbon trading mechanism (LT-CTM). An EV virtual energy storage (EV-VES) calculation model for dispatchable power is formulated to facilitate the centralized scheduling of EVs, considering responsiveness and spatial–temporal distribution characteristics. Additionally, multiple kinds of energy storage systems and flexible loads are considered in the VPP. Furthermore, to handle multiple uncertainties for both the renewable energy and load, Latin hypercube sampling is applied to generate the renewable energy scenarios, and K-means clustering is used to obtain typical scenes. Then the DRO model is used to solve the dispatch problem. An LT-CTM is leveraged to reduce carbon emissions. The column and constraint generation algorithm is then employed to identify an optimal operational benefit solution. Finally, a multi-parameter coupling analysis of ladder-type carbon trading is used for the environmental protection and economy of the VPP. The results confirm the efficacy of the proposed day-ahead scheduling model, offering a theoretical reference for operators when making scheduling decisions.

A three-stage optimization of charging scheduling of electric vehicles considering electricity price and user selection

A three-stage optimization of charging scheduling of electric vehicles considering electricity price and user selection

Coordinated integration of wind energy in microgrids: A dual strategy approach leveraging dynamic thermal line rating and electric vehicle scheduling

As the world shifts towards sustainable energy, integrating wind energy as a key renewable resource is crucial, despite the challenge of its intermittent output. This paper proposes an integrated approach to enhance wind energy utilization within microgrids, introducing a novel synergy of Dynamic Thermal Line Rating (DTLR) and Electric Vehicle (EV) scheduling. This synergy dynamically adjusts power transmission capacities and harnesses EVs as mobile energy storage units. Notably, case study simulations reveal that compared to other approaches, our proposed solution reduces total operational costs by 24.6% (STLR only) and 11.6% (STLR+EV), and increases wind power absorption by 41.7% (STLR only) and 12.1% (STLR+EV). This marks a significant stride towards sustainable microgrid operation amidst renewable energy integration challenges.

A real-time energy flow management of a grid-connected renewable energy sources-based EV charging station

ABSTRACT As the sales of electric vehicles (EVs) increase in India, there will be an increase in the number of charging stations in the future. Meanwhile, the demand for extra power from the grid and intermittent power from renewable energy presents significant challenges for charging infrastructure. This paper presents a real-time energy flow management strategy (EFMS) designed for an EV charging station (EVCS) that utilises power from renewable energy sources (RES), an energy storage battery unit (ESBU), and the grid. The primary objective is to increase the utilisation of renewable energy and minimise the quantity of electricity bought during the peak period of the grid by implementing direct electric vehicle load control. EVCS has been designed to charge up to 80 EVs using five DC chargers. The EFMS aims to enhance the scheduling of EV charging by considering grid time-of-use (ToU) pricing and efficient usage of ESBU in terms of battery degradation effect and maximising the use of RES. A multi-objective optimisation algorithm has been developed for EV scheduling. The baseline uncoordinated charge method is contrasted with the proposed approach. The EFMS effectiveness is tested in MATLAB, and the subsequent analysis and results are drawn and discussed.

Bayesian deep neural network for low-carbon scheduling strategy planning of V2G

Abstract With the increasing adoption of electric vehicles, the Vehicle-to-Grid (V2G) model has become crucial in integrating renewable energy generation. However, challenges exist in developing grid scheduling strategies that are tailored to different regions, as well as in quantifying the economic benefits and carbon emissions associated with such scheduling. To address these issues, our study proposed a novel V2G low-carbon scheduling strategy planning method based on Bayesian neural networks. Initially, we established a stochastic V2G model that incorporated grid and electric vehicle scheduling, along with a mathematical model that captured the random behavior of EV users, enabling us to capture the essential characteristics of scheduling planning. Subsequently, we employed an enhanced Bayesian deep neural network to learn and assimilate these scheduling planning characteristics, allowing for the provision of a grid scheduling strategy that ranks economic benefits based on weighted priorities. Furthermore, we conducted simulation experiments within the coverage area of the Internet of Things in Energy (IoTE) to gather scheduling characteristics. The experimental results demonstrated that our method outperformed other deep learning models in terms of voltage amplitude stability during the grid scheduling process, showcasing superior robustness and accuracy. Moreover, we evaluated the economic benefits of the scheduling model and compared it with the original V2G scheduling model. The findings revealed that our model exhibited higher economic benefits and lower carbon emissions. Considering the future challenges of low-carbon urban planning, our method holds significant potential in terms of grid scheduling economic benefits and carbon emission indicators.

An advantageous charging/discharging scheduling of electric vehicles in a PV energy enhanced power distribution grid

Electric vehicles (EVs) are going to overrule the transportation sector due to their pollution-free technology and low running costs. However, charging the EVs causes significant power demand and stress on the power delivery network. The challenge can be tackled well when charging and discharging scheduling are coordinated with intelligent EV routing. In this work, two-stage charging and discharging scheduling are proposed. In the first stage, a time scheduling algorithm is structured to identify EV charging/discharging slots at different hours, and at a later stage, the slots are optimally distributed among different charging stations. Routing of the EVs towards the EVCSs has been designed to enhance the useful participation of the EVs in the charging and discharging program. In this regard, a possible number of EVs in the test region has been forecasted with a regression model. The adequacy of the combined charging-discharging and location scheduling model is tested on a typical PV-enhanced 28-bus Indian distribution network. Three case studies containing three sub-cases in each have been performed incorporating the choice of the EV owners towards charging and discharging in different time slots in a day. The case studies have resulted in a peak-to-average ratio (PAR) of 1.151,0, 1.165,0, 1.196,8, 1.165,0, 1.180,9, 1.196,8, 1.196,8, 1.196,8 and 1.196,8 for the 24-h demand pattern in Case-1a, Case-1b, Case-1c, Case-2a, Case-2b, Case-2c, Case-3a, Case-3b and Case-1c respectively in comparison to a PAR of 1.2 for the 24-h demand in base case.

Learning-Based Real-Time Aggregate Flexibility Provision and Scheduling of Electric Vehicles

Learning-Based Real-Time Aggregate Flexibility Provision and Scheduling of Electric Vehicles

Optimal Spatial–Temporal Scheduling for EVs With Stochastic Behaviors and Possible Inauthentic Information

This paper studies the spatial-temporal scheduling for electric vehicles with stochastic charging behaviors. A new spatial-temporal scheduling model is proposed to achieve both the spatial coordination and temporal coordination for EV users and is modeled as a non-convex optimization problem which can be solved by the coordinate descent method. Further, a new billing method of charging cost is proposed based on the Vickrey-Clarke-Groves to avoid possible inauthentic information from users. Specially, the individual rationality and incentive compatibility of the method are theoretically proved. Moreover, a stochastic optimization problem is proposed to describe the stochastic charging behavior of users, which can fit the real application better than the deterministic one. Consequently, an efficient charging strategy is obtained via chance constraint method. Simulation results and comparison studies show the effectiveness of both the model and the billing method.