Optimal Vehicle-to-Grid Charge Scheduling for Electric Vehicles Based on Dynamic Programming

The rising utilization of electric vehicles (EVs) is exerting additional pressure on the power grid. A feasible option to reduce this difficulty is through the use of bidirectional chargers, which can not only charge EV batteries but also support grid operations. These chargers contain bidirectional converters, consisting of an DC-DC converter and a AC-DC converter, to enable both charging and draining of the battery. By adjusting the working mode of DC-DC converter, its functionality can be altered accordingly. (PHEVs) have the ability to function in “vehicle-to-grid” (V2G) mode, which allows EV batteries and the grid to exchange energy in both directions. The objective of this study is to design a control method that enhances power transfer efficiency for both charging (G2V) and discharging (V2G) processes in electric vehicles. This will help with voltage stabilization, grid balancing, peak load reduction, and improving the overall reliability of power system. The energy saved in EV batteries may be fed back into the grid using V2G technology, which adds reserve power and stabilizes the grid. Additionally, energy transmit from the grid to charge the car battery is made possible by a bidirectional charging circuit. Critical characteristics like grid current, inverter current, and battery performance are assessed in this study using the G2V and V2G frameworks. The bidirectional charger for EVs is modeled and simulated using MATLAB/Simulink, with an emphasis on how it functions in both modes.

Various power system problems and challenges may arise in the future due to the large scale of deployment of electric vehicles (EVs). Hence, the proper placement of EV charging stations (EVCS) effectively mitigates the impact of high EV loads connected to the grid. The research intends to explore and analyze differences between the regulation effectiveness of unidirectional and bidirectional charging technologies by utilizing different comparison evaluation indices. Moreover, considering their penetration level, this study tackles the impact analysis of EV and EVCS integration through time. Specifically, this paper aims to identify the optimal EVCS sites in an IEEE 37-bus test feeder system to minimize power loss brought by EV integration. Through MATLAB R2022b simulation and OpenDSS power flow analysis, the EVCS are optimally located near the supply bus. The findings show a direct relationship between the EV penetration level and system power loss. Due to the EV technology growth, there is an observed voltage profile degradation of up to 1.7094 p.u. The paper also highlights that although EV bidirectional charging technology (BCT) might reduce the load on the grid in the next few years of low penetration compared to unidirectional charging technology (UCT), it will give no significant difference due to the rapid increase of load connected during its high EV penetration.

Overview of improved dynamic programming algorithm for optimizing energy distribution of hybrid electric vehicles

Pricing and free periodic maintenance service decisions for an electric-and-fuel automotive supply chain using the total cost of ownership

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have raised the alarm that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time-consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions. The solutions noted in this white paper require collaborative efforts among utilities, regulators, and EV charging infrastructure developers to streamline the grid connection process for EV charging infrastructure. They are broadly organized into four areas: 1. Increase data access and transparency: Develop automated load service request tools, integrate hosting capacity and load service request analyses, incorporate EV adoption forecasts, and provide transparency on the processing queue. 2. Improve energization processes and timing: Create fast-track options based on prescreening criteria, provide flexibility or phased approvals in the load service request/interconnection process, build internal knowledge within utilities about EV charging technologies, and provide standardized workforce training. 3. Promote economic efficiency: Right size distribution components to accurately reflect the load requirements of EV charging infrastructure, make proactive investments in grid infrastructure based on EV adoption forecasts and growth projections, and consider energy equity and environmental justice factors such as equitable access to EV charging when planning infrastructure. 4. Improve grid reliability and resilience: Use load management/power control systems (PCS) at EV charging stations, adopt and implement harmonized standards for communication protocols and information models between the EV charging and grid control infrastructure, and address cybersecurity considerations by implementing robust security measures and standards for EV charging infrastructure—with particular emphasis on clarifying the security requirements for the interface to the grid. The objective of the solutions proposed in this white paper is to accelerate the timeline and decrease costs associated with connecting EV charging infrastructure to the grid. Electric utilities, utility regulators, EV charging infrastructure developers, and site hosts will first need to understand which solutions are available in their service territory, and if warranted, which combination of solutions would support their specific needs. Through the successful implementations of solutions at scale detailed here, industry will demonstrate a new and innovative ecosystem where timely deployment and energization of EV charging infrastructure with greater grid resiliency and reliability is a reality.

This study aims to determine the influential factors on the market share of electric vehicles through panel data analysis based on time series data from 2011 to 2015 in 31 countries. We selected five significant independent variables that are expected to affect electric vehicle adoption based on literature review. The econometric model in this study suggests that the relative price of electric vehicle compared to internal combustion engine vehicle, driving range, and number of models available in markets are correlated to the market share of electric vehicles. On the other hand, relationship between recharging infrastructure—an important factor for electric vehicle adoption in many studies—and market share of electric vehicles turned out to be insignificant in this study. From a political point of view, we argue that policy makers need to allocate more resources to research and development in order to extend driving range at the early stage of electric vehicle deployment in the markets.

Electric vehicles (EVs) have experienced a rapid growth due to the economic and environmental benefits. However, the substantial charging load brings challenging issues to the power grid. Modern technological advances and the huge number of high-rise buildings have promoted the development of distributed energy resources, such as building integrated/mounted wind turbines. The issue to coordinate EV charging with locally generated wind power of buildings can potentially reduce the impacts of EV charging demand on the power grid. As a result, this paper investigates this important problem and three contributions are made. First, the real-time scheduling of EV charging is addressed in a centralized framework based on the ideas of model predictive control, which incorporates the volatile wind power supply of buildings and the random daily driving cycles of EVs among different buildings. Second, an EV-based decentralized charging algorithm (EBDC) is developed to overcome the difficulties due to: 1) the possible lack of global information regarding the charging requirements of all EVs and 2) the computational burden with the increasing number of EVs. Third, we prove that the EBDC method can converge to the optimal solution of the centralized problem over each planning horizon. Moreover, the performance of the EBDC method is assessed through numeric comparisons with an optimal and two heuristic charging strategies (i.e., myopic and greedy). The results demonstrate that the EBDC method can achieve a satisfactory performance in improving the scalability and the balance between the EV charging demand and wind power supply of buildings. Note to Practitioners —This paper is motivated by the challenging problem due to the substantial charging load of electric vehicles (EVs) on the power grid. Nowadays, modern technological advances and the rapid increase of high-rise buildings have promoted the development of building integrated/mounted wind turbines. As the EVs are usually parked in buildings for a large proportion of time every day, the issue to best utilize locally generated wind power of buildings to suffice EV travelling requirements shows vital significance in reducing their dependence on the power grid. However, there exist two main challenges including: 1) the multiple uncertainties regarding the uncertain wind power generation and the random driving behaviors of EVs and 2) the scalability of the solution method. To tackle the first challenge, the idea of model predictive control is introduced to make charging decisions at each stage based on a short-term prediction of the on-site wind power and the current collection of EVs parked there. To consider the scalability and overcome the lack of global charging information of all EVs in practical deployment, an iterative EV-based decentralized charging algorithm (EBDC) is derived, in which each EV can dynamically update its own charging decisions according to a dynamic charging “price” announced by the buildings. Alternatively, the buildings dynamically adjust the charging “price” to motivate the EVs to get charged during the time periods with sufficient wind power supply. Numeric results demonstrate that the EBDC method is scalable and performs well in improving the balance between the EV charging demand and the wind power supply of buildings.

<div class="section abstract"><div class="htmlview paragraph">The adoption of electric vehicles (EVs) has been announced worldwide with the aim of reducing CO<sub>2</sub> emissions. However, a significant increase in electricity demand by EVs might impact the stable operation of the existing power grid. Meanwhile, EV charging is acceptable to most users if it is completed by the time of the next driving event. From the viewpoint of power grid operators, flexibility for shifting the timing of EV charging would be advantageous, including making effective use of renewable energy.</div><div class="htmlview paragraph">In this work, an EV model and simulation tool were developed to make clear how the total charging demand of all EVs in use will be influenced by future EV specifications (e.g., charge power) and installation of charging infrastructure. Among the most influential factors, EV charging behavior according to use cases and regional characteristics were statistically analyzed based on the real-world usage data of over 14, 000 EVs and incorporated in the simulation tool. Using the resultant statistical model, the Monte Carlo method was applied to conduct a parameter simulation study of continuous EV usage. The impact of EV charging demand on the power grid in a future scenario and also the benefit of controlling EV charging were evaluated quantitatively in the study. Important findings indicated that active smart charging will be necessary rather than passive control based on time-of-use tariffs.</div></div>

While the number of the vehicle actuated with liquid fuels are settled, the count of electric vehicles is increasing. For the present moment most of them are scheduled for daily urban usage. This paper presents an analytical approach for estimation of the impact of electrical vehicle (EV) battery charging on the distribution grid. Based on the EV charge profile, load curve and local distributed generation the grid nodes, the time variation of grid parameters is obtained. A set of typical load profiles of EV charging modes is studied and presented. A software implementation and a 24h case study of low voltage distribution network with EV charging devices is presented in order to illustrate the approach and the impacts of EV charging on the grid. In the current paper an approach using variable nonlinear algebraic equations for dynamic time domain analysis of the charge of the electric vehicles is presented. Based on the results, the challenges due to EV charging in distribution networks including renewable energy sources are discussed. This approach is widely applicable for various EV charging and distributed energy resources studies considering control algorithms, grid stability analysis, smart grid power management and other power system analysis problems.

Optimal deadline scheduling for electric vehicle charging with energy storage and random supply

The powertrain model of the series-parallel plug-in hybrid electric vehicles (PHEVs) is more complicated, compared with series PHEVs and parallel PHEVs. Using the traditional dynamic programming (DP) algorithm or Pontryagin minimum principle (PMP) algorithm to solve the global-optimization-based energy management strategies of the series-parallel PHEVs is not ideal, as the solution time is too long or even impossible to solve. Chief engineers of hybrid system urgently require a handy tool to quickly solve global-optimization-based energy management strategies. Therefore, this paper proposed to use the Radau pseudospectral knotting method (RPKM) to solve the global-optimization-based energy management strategy of the series-parallel PHEVs to improve computational efficiency. Simulation results showed that compared with the DP algorithm, the global-optimization-based energy management strategy based on the RPKM improves the computational efficiency by 1806 times with a relative error of only 0.12%. On this basis, a bi-level nested component-sizing method combining the genetic algorithm and RPKM was developed. By applying the global-optimization-based energy management strategy based on RPKM to the actual development, the feasibility and superiority of RPKM applied to the global-optimization-based energy management strategy of the series-parallel PHEVs were further verified.

In view of the existing problems in DC traction power supply system and electric vehicle (EV) batteries charging system, a DC micro-grid is proposed: EV batteries are connected to DC traction power supply system via off-board bidirectional chargers, forming a ring DC micro-grid in a sub-region, in which EV batteries can be charged by DC traction power supply system and voltage of overhead contact system can be stabilized by the EV batteries charging and discharging system. A method that place ultra-capacitors in parallel with EV batteries is designed to improve system performance. Voltage droop control is adopted by parallel bidirectional chargers, which can achieve doublequadrant operation and avoid the circulating current as well. In the end, simulation results are presented to demonstrate the feasibility of this system.

The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery charging are the main factors affecting the increased adoption of EVs. The installation location of fixed charging stations (FCSs) may not be completely compatible with the changing pattern of EV accumulation. Besides, their power withdrawal location in the network is fixed, and also, the time of receiving the power follows the EVs’ charging demand. The EV charging demand pattern conflicts with the network peak period and causes several technical challenges besides high electricity prices for charging. A mobile battery energy storage (MBES) equipped with charging piles can constitute a mobile charging station (MCS). The MCS has the potential to target the challenges mentioned above through a spatio-temporal transfer in the required energy for EV charging. Accordingly, in this paper, a new method for modeling and optimal management of mobile charging stations in power distribution networks in the presence of fixed stations is presented. The MCS is powered through its internal battery utilizing a self-powered mechanism. Besides, it employs a self-driving mechanism for lowering transportation costs. The MCS battery can receive the required energy at a different time and location regarding EVs accumulation and charging demand pattern. In other words, the mobile station will be charged at the most appropriate location and time by moving between the network buses. The stored energy will then be used to charge the EVs in the fixed stations’ vicinity at peak EV charging periods. In this way, the energy required for EV charging will be stored during off-peak periods, without stress on the network and at the lowest cost. Implementing the proposed method on a test case demonstrates its benefits for both EV owners and network operator.

This study mainly analyses the fuel consumption of plug-in hybrid vehicles during operation. A new control method for automobiles based on energy management strategy and dynamic programming algorithm is proposed. The new method plans and analyses the minimum electricity consumption, and then uses dynamic programming algorithms to analyse this parameter. The research results indicated that the vehicle state was constantly changing with the variation of SOC value during driving. The energy mobilization of the vehicle was more obvious after adding dynamic programming strategy. The efficiency of the vehicle was relatively high in driving state 1, with a minimum value of 70%, which was about 20% higher than in driving state 4. The average fuel consumption in driving state 2 was 1.8L higher than in other driving states. The overall efficiency of automobiles after incorporating dynamic programming was improved, with a shorter time to reach the lowest efficiency point compared with not incorporating dynamic programming algorithms. The highest efficiency value was 7.86% higher than that of not incorporating dynamic programming models. The new control method can reduce energy consumption and improve the energy management and control effect. The study provides a better research direction for energy management and control of hybrid electric vehicles in the future.

Renewable Energy Sources (RESs) are gaining considerable attention to reduce human dependence on fossil fuels and minimize harmful gases in our surroundings. Existing literature on energy trading focused on providing renewable energy to smart homes, smart buildings, and smart offices to fulfill their daily energy demands obtained from RESs. Besides, Electric Vehicles (EVs) use either power grid energy or a battery exchange mechanism to recharge their low EV batteries. The continuous use of power grids to recharge low EV batteries causes a significant load on power grids. Due to this, power grids are inadequate to fulfill the ever-increasing demands of EVs in the future. In this context, we propose a Blockchain Enabled Energy Trading (BEET) framework oriented EV charging. A system architecture of the BEET framework is presented to describe the functioning of each layer and its associated entities. We formulate an optimization problem that maximizes the revenue in the energy trading process using a knapsack optimization. Smart contracts are designed on the consortium blockchain network to sell and buy renewable energy to aggregators and from producers, respectively. Moreover, an EV charging mechanism is designed to intelligently allocate renewable energy to consumers at a low price. A comparative analysis is performed with state-of-the-art works in terms of charging price, revenue, throughput, and latency. The results indicate that the BEET framework outperforms compared to state-of-the-art works to address the renewable energy demand problem to realize E-mobility. It is clarified that the data considered in the experimental analysis were obtained from statistical simulations in realistic E-Mobility environment settings.