Scheduling Of Electric Vehicles Research Articles

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.

Bi-directional Optimal Scheduling of Electric Vehicles Based on V2G Technology

Bi-directional Optimal Scheduling of Electric Vehicles Based on V2G Technology

A Model for Scheduling of Electric Vehicles Charging in a Distribution Network using Multi-agent Model

A Model for Scheduling of Electric Vehicles Charging in a Distribution Network using Multi-agent Model

An Improved Bilevel Algorithm Based on Ant Colony Optimization and Adaptive Large Neighborhood Search for Routing and Charging Scheduling of Electric Vehicles

An Improved Bilevel Algorithm Based on Ant Colony Optimization and Adaptive Large Neighborhood Search for Routing and Charging Scheduling of Electric Vehicles

Exploring Symmetry-Induced Divergence in Decentralized Electric Vehicle Scheduling

Due to the low computational efficiency and high communication pressure caused by centralized control, decentralized coordination of electric vehicles (EVs) has become a research hotspot in recent years. However, the integration of numerous EVs with homogeneous parameters brings unprecedented challenges for the optimal scheduling of the power system. To explore symmetry-induced divergence in decentralized electric vehicle scheduling, the established centralized optimization scheduling model of electric vehicles is decomposed into a distributed optimization model by Lagrange relaxation, achieving parallel control of each EV aggregator (EVA). To solving the oscillation caused by parameter symmetry in the process of optimization, we propose a convergence acceleration algorithm based on a perturbation function, which is a random distribution function used to break parameter symmetry within an acceptable calculation error range. On the premise of sensitivity analysis, a closed form of the perturbation limit is theoretically derived. Case studies based on different scales of users validate the effectiveness and computational efficiency of the proposed method.

Coordinated Cyber Security Enhancement for Grid-Transportation Systems With Social Engagement

Effective coordination of energy and transportation systems is fundamental and vital to smart city management. Yet, the deeply intertwined information and communication technologies have exposed smart city management to malicious cyberattacks vastly. This study develops a two-stage mitigation strategy for integrated transportation-energy systems (TESs) by coordinating energy and transportation systems to safeguard against false data injection attacks (FDIAs). Our strategy targets social engagements by electric vehicle (EV) users who can be incentivized to alter their regular EV usage patterns for supporting the energy system via vehicle-to-grid actions. We consider a TES that contains a multi-energy system comprised of power and gas systems as well as an urban transportation system. Integrated system operators can take emergency measures to coordinate important system assets (e.g., traditional generators, renewable generators, EV fleets) in a sensible and intelligent way, and thereby mitigate the consequences of FDIAs. Specifically, we take a two-stage distributionally robust approach to cope with uncertain FDIAs, with which a moment-based ambiguity set is employed to yield the worst-case uncertainty distribution. We leverage constraint generation-based relaxation to solve the problem iteratively. To demonstrate our strategy's value and practical utilities, we use a coupled urban TES to conduct case studies. According to the evaluation results, the optimized EV scheduling and energy dispatch scheme produced by the proposed strategy can contain cyberattacks’ impacts and enhance cyber resilience in smart city management more effectively.

Smart Decentralized Electric Vehicle Aggregators for Optimal Dispatch Technologies

The number of electric vehicles (EVs) is growing exponentially, which presents the power grid with new challenges to turn their reliance to renewable energy sources (RESs). Coordination between the available generations from RESs and the charging time should be managed to optimally utilize the available generation from RESs. The dispatch scheduling of EVs can significantly reduce the impact of these challenges on power systems. Three different technologies can be used to manage the dispatch of EV batteries which are unregulated charging (UC), unidirectional grid-to-vehicle (G2V), and bidirectional vehicle-to-grid (V2G) technologies. This study aims to address the primary reason for EV owners’ disbelief in the accuracy of battery wear models, which is impeding their involvement in V2G technology. This paper introduces a novel accurate EV battery wear model considering the instantaneous change in the operation of the EV battery. Moreover, an effective musical chairs algorithm (MCA) is used to reduce everyday expenses and increase revenue for V2G technologies in a short convergence time with accurate determination of optimal power dispatch scheduling. The results obtained from these three strategies are compared and discussed. The salient result from this comparison is that V2G technology increases wear and reduces the battery lifespan in comparison with the UC and G2V. The yearly expenses of G2V are reduced by 33% compared to the one associated with the UC. Moreover, the use of V2G technology provides each EV owner with USD 3244.4 net yearly profit after covering the charging and wear costs. The superior results extracted from the proposed model showed the supremacy of V2G usage, which is advantageous for both EV owners and the power grid.

Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach

The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the fluctuation of electricity prices. Moreover, other issues that raise concern among EV drivers are the long waiting time and the inability to charge the battery to the desired State of Charge (SOC). In order to alleviate the range of anxiety of users, we perform a Deep Reinforcement Learning (DRL) approach that provides the optimal charging time slots for EV based on the Photovoltaic power prices, the current EV SOC, the charging connector type, and the history of load demand profiles collected in different locations. Our implemented approach maximizes the EV profit while giving a margin of liberty to the EV drivers to select the preferred CS and the best charging time (i.e., morning, afternoon, evening, or night). The results analysis proves the effectiveness of the DRL model in minimizing the charging costs of the EV up to 60%, providing a full charging experience to the EV with a lower waiting time of less than or equal to 30 min.