Participation Of Electric Vehicles Research Articles

Fault prevention strategy for coupled networks of power grids and transportation with the participation of electric vehicles

The increasing number of electric vehicles (EVs) that are connected to the power grid has raised concerns about the security of both transportation and power grids. This paper presents a fault prevention strategy that involves the use of social networks to improve the performance of electric vehicle charging stations. Firstly, the goal of this strategy is to improve the electric vehicle charging station’s performance by developing an evolutionary game model that takes into account the various factors that affect the electric vehicle’s response and travel time. Secondly, a number of charging station sample data is generated and analyzed using a convolutional neural framework. The capacity of the stations is then calculated by taking into account the different incentive measures offered by the power grid.. Furthermore, an optimal power flow model considering fault risk constraints and charging and discharging capacity constraints is established in the power grid or power grid subsystem to adjust the generator output and charging station power to realize the safe dispatching of the power grid. The simulation example shows that this method can fully consider the influence of traffic networks in the participation of EVs in power grid fault prevention, which is more reasonable.

A comprehensive framework for predicting electric vehicle's participation in ancillary service markets

AbstractElectric vehicles (EVs) have significant potential to offer unused capacity in ancillary service markets, providing unique opportunities for market operators to utilise these resources. EVs have a rapid response and high availability, making them a good fit for the frequency containment reserve (FCR) market. However, EV aggregators (EVAGs) must aggregate capacity blocks due to the limited capacity of individual EVs. An application of a supervised machine learning method named XGBoost is suggested to help EVAGs predict the amount of EV participation in the FCR market. The objective is to forecast yearly involvement using data from only a single week, using the game theory method SHapley Additive exPlanations (SHAP) to minimise extra data. The proposed strategy helps aggregators and uses feature engineering to select EVs with high potential to boost revenue. The proposed framework is effective in predicting EV performance in the DK‐2 market, as shown by multiple analyses.

Electric vehicle participation for optimized load frequency control in a multi-area restructured power system

ABSTRACT Electric vehicles (EVs) can be employed intelligently in Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) modes for Load Frequency Control (LFC) in the power grid. Due to the rapid variation of charging or discharging power, EVs can perform frequency regulation tasks and serve as distributed energy storage devices. In this paper, a linearized model of an interconnected multi-area power system is formulated in the restructured environment to include Electric Vehicle Aggregators (EVA). The DISCO Participation Matrix (DPM) is used to model Poolco and bilateral contracts in restructured multi-area power systems along with the participation of EVs. The faster dynamic response of EVs can effectively control the deviations in area frequencies imposed by the DISCOs load demand. The gains of the integral controller employed in each area are tuned by Real Coded Genetic Algorithm (RGA) by minimizing the integral square error function for area frequency deviations. The efficacy of power support from the EVs for load frequency control is validated by simulating different Poolco and bilateral transactions. It is shown that the EVs have a significant impact on obtaining optimal transient response for area frequencies, tie-line powers and power generation of conventional thermal generating units for any type of transaction in a restructured environment.

An Incentive Mechanism for Electric Vehicles Participation in Security Constrained Unit Commitment of Renewable-Integrated Smart Grids

An Incentive Mechanism for Electric Vehicles Participation in Security Constrained Unit Commitment of Renewable-Integrated Smart Grids

Research on the Influence of Electric Vehicle Integration in Island Microgrid, Vietnam

Vietnam's economy is developing strongly, and the demand for energy use will increase rapidly. The development of smart grids contributes significantly to the transition and sustainable development of energy from renewable energy sources to improve the quality of the national power supply and promote the sustainable use of electricity economically and efficiently. Thus, this is highly beneficial in reducing carbon emissions and other types of pollution. Besides, electrification in the transportation industry is developing rapidly, such as Electric Vehicles (EVs) and Metros in recent years. Integrating electric vehicles into the grid will enable two-way energy exchange, reactive power compensation and load balancing. However, the number of EVs participating in charging at a time will cause some conflicts, such as voltage and power loss at the nodes. Therefore, the balancing problem between load demand and generation source is a difficult task in planning operations. This paper presents a method to optimize island Microgrid (MG) operation with the participation of electric vehicles based on renewable energy sources. Optimization techniques in intelligent resource forecasting and management algorithms are built in MATLAB to achieve different requirements. The proposed Microgrid manages energy efficiency that adapts to the variability of Renewable Energy with improved efficiency.

Source-Load Coordinated Low-Carbon Economic Dispatch of Microgrid including Electric Vehicles

As the global warming crisis becomes increasingly serious, sustainable dispatch strategies that can reduce CO2 emissions are gradually developed. Aiming at the problems of poor synergy between carbon capture systems (CCS) and P2G as well as the potential of the source-load interaction of microgrids with electric vehicles for carbon reduction that needs to be explored, this paper proposes a source-load coordinated low-carbon economic dispatch strategy for microgrids, including electric vehicles. Firstly, considering the low-carbon operation characteristics of CCS and P2G, a comprehensively flexible and cooperative operation mode for CCS and P2G is constructed. Secondly, based on the carbon reduction potential of demand response on the energy consumption side, a demand response optimal scheduling model considering the participation of electric vehicles in the microgrid is established. Finally, based on the complementary characteristics of low-carbon resources on both sides of the microgrid, a source-load coordinated low-carbon economic dispatch strategy for the microgrid is proposed. The results show that the strategy proposed in this paper can fully use the energy time-shift advantage of CCS and P2G and can combine EVs and other load-side resources to flexibly participate in demand-side response, which effectively realizes source-load synergy and improves the low carbon and economy of the microgrid.

Optimal Energy Scheduling in Smart Buildings with Electric Vehicle and Demand Response

In a number of countries, smart microgrid (SMG) technology is being exploited in the energy system infrastructure along with other generators such as electric and storage systems. In the framework of SMGs, communication links between generation and demand sides have been established to optimise energy consumption by means of economic signals. The framework of energy management for Smart Home appliances is presented in the present paper, taking account of appliance and electric vehicle participation. The approach to the energy schduling at day-ahead is defined as three strategies, such as optimum use of existing appliances, optimal production of electricity resources and optimal discharge and charging for electrical vehicles. Reducing energy generation costs and minimizing emissions of air pollutants are key objectives. Two case studies with the aim of reducing costs and emissions while maximizing system flexibility are taken into account to demonstrate preference and viability of home energy scheduling.

Simultaneous AC/DC distribution for the sustainable operation of smart building clusters in hybrid urban microgrids

Hybrid AC/DC urban microgrids (HUMG) have emerged as a candidate solution to reliably, efficiently, and economically meet the increasing consumer electric demand and to ensure the quality of delivered power. Smart buildings (SB) are prevalent prosumers in HUMGs. To effectively utilise renewable resources in SBs, it is imperative to have assistance from battery energy storage systems (BESSs). Also, uncertainty in generation and demand necessitates real-time ancillary power support for SBs. Smart building interconnection forming SB clusters is an emerging solution for providing ancillary power support. Existing SB interconnection topologies require multiple power conversion stages, additional infrastructure, and underutilise the existing AC grid infrastructure. Most of these SB interconnections are not sustainable due to higher losses from more power conversion stages, increased cost, and excessive dependence on BESSs. A composite transmission system-based ancillary power dispatching architecture (CTS-APDA) for the sustainable operation of SB clusters is proposed in this paper. A multi-agent system based hierarchical control and decision logic in the CTS-APDA ensures the optimal ustilisation of battery resources. The operation of the proposed CTS-APDA and its efficacy as a fast-acting ancillary support is validated through extensive case studies. The proposed CTS-APDA will foster participation of electric vehicles in the transactive energy market of emerging SBs.

Decentralized Control Strategy for Participation of Electric Vehicles in Distribution Voltage Control

With the accelerating transportation electrification, increasing numbers of electric vehicles (EVs) are connected to distribution networks via electric vehicle charging stations (EVCSs). Due to the uncertain charging behaviors of EV users, concentrated high-power EV charging during peak hours can result in significant undervoltage issues, affecting the regular operation of distribution networks and having detrimental effects on regular EV charging. Conventional EV charging scheduling methods are conducted in a centralized way, requiring extensive investment in communication infrastructure and intensive computing power. Accordingly, this paper proposes a novel decentralized voltage control scheme with the participation of EVs and EVCSs. The proposed control scheme first divides the distribution network into clusters based on a modified modularity index. Then, it applies the rolling optimization-based control of the EVs and EVCSs within each cluster to solve the regional voltage problems. The optimization window length is self-adapted based on the monitored states of the grid-connected EVs and EVCSs. Case studies show that the proposed decentralized voltage control strategy could effectively address the undervoltage violation issues and alleviate the pressure on peak shaving during peak hours, while satisfying the charging requirements of EV users.

Day-ahead energy management in smart homes with demand response and electric vehicle participation

Day-ahead energy management in smart homes with demand response and electric vehicle participation

A multi-stage sequential load restoration method for under-frequency load shedding with electric vehicles participation

As a low-cost and high-efficiency distributed energy storage resource, electric vehicles (EVs) play an increasingly prominent role in power system regulation. Since EVs are the regulation resources on the demand side, EVs should return to the initial states as soon as possible after completing the grid regulation task to avoid affecting the charging demand. Although EVs can provide a more flexible response to under-frequency load shedding (UFLS) than load shedding, the disorderly recharging of EVs during the load restoration stage may cause a significant second disturbance to the system frequency after UFLS. To ensure frequency stability and simultaneously restore the power supply, a multi-stage sequential load restoration method for UFLS with EVs participation is proposed to coordinate the priority and sequential restoration between the shedding load and recharging power of EVs. In the proposed method, the restoration willingness of EVs is quantified by the membership function, and the EV recharging optimization model with the objective function of maximizing the user’s restoration willingness satisfaction is established based on a two-stage robust under-frequency load shedding framework. Simulation results show that the proposed method can ensure system frequency stability and complete the load restoration quickly, effectively avoiding the secondary frequency drop caused by the disorderly recharging of EVs.

Coordinated Participation of Electric Vehicles and Generating Units in Primary Frequency Control in the Presence of Renewables

With the integration of renewables into the grid, the ability to perform primary frequency control (PFC) by the conventional generating units decreases in terms of frequency quality and stability. Also, the wear and tear of the generating units responsible for the PFC increases. In this article, a hierarchical PFC scheme is proposed that combines the coordinated actions of hydro-generation units and electric vehicles (EVs) such that the stability is maintained for large and small disturbances, while at the same time, frequency quality is improved and wear and tear of the generating units is reduced. The control parameters of the proposed scheme are tuned by solving the proposed optimization problem with proper objective functions and constraints that are representative of frequency stability and quality, wear and tear of the generating units, and comfort of EVs owners. The scheme is complemented by a new combination of frequency filters for EVs and generating units to more effectively reduce the effect of small frequency fluctuations on the wear and tear of generating units. Comparative simulation studies show the effectiveness of the proposed method in ameliorating the problems raised by the integration of renewables.

Day-Ahead Optimal Bidding for a Retailer With Flexible Participation of Electric Vehicles

The existing bidding models for retailers managing electric vehicles (EVs) in distribution-level day-ahead (DA) electricity markets have not fully addressed EVs’ temporal distribution, charging and discharging management, or bidding curves. To address these challenges, this paper proposes a DA optimal bidding model for a retailer with flexible participation of EVs. First, the investigating period for DA optimal bidding is modeled to cover the connecting periods of all EVs that may potentially impact the DA power demand prediction. Then, incentive mechanisms for charging and discharging are proposed to enable retailers to manage EVs under uncertain market prices and EV parameters while also considering the battery degradation cost and the preferences of EV users based on a social survey. The optimal charging model based on incentive mechanisms for charging-discharging helps EVs minimize their energy purchase costs under different price scenarios. Based on the minimum EV energy purchase cost, the optimal bidding model of a retailer aims to achieve the maximum bidding profit considering the conditional value at risk (CVaR), with the step bidding curves and the temporal differences for different time intervals considered. Simulations validate the proposed model under flexible incentive mechanism for charging and incentive mechanism for discharging participation of EVs.

Electric Vehicle Participation in Regional Grid Demand Response: Potential Analysis Model and Architecture Planning

When a large-scale random charging load is connected to the regional power grid, it can negatively affect the safe and stable operation of the power grid. Therefore, we need to study its charging load and response potential in advance so that electric vehicles can interact well with the grid after being connected to the regional grid. Firstly, after analyzing the influencing factors of regional electric vehicle ownership, an electric vehicle ownership prediction model based on the sparrow search algorithm-improved BP neural network (SSA-BPNN) is established. On this basis, an electric vehicle charging load prediction model is established based on the sparrow search algorithm-improved BP neural network and Monte Carlo algorithm (SSA-BPNN-MC). Secondly, the charging behavior of different types of electric vehicles is analyzed and modeled, and the data from a certain area are taken as an example for the prediction. Then, according to the load forecasting results, the potential of electric vehicles participating in demand response in the region in the future is deeply analyzed using the scenario analysis method. Finally, with the aim of resolving the problems of massive multi-source heterogeneous data processing and the management of electric vehicles participating in the regional power grid demand response, a basic framework of electric vehicles participating in the regional power grid demand response is developed, which provides effective support for promoting electric vehicles to participate in regional grid demand response.

The participation of electric vehicles in a peer-to-peer energy-backed token market

This paper designs a peer-to-peer (P2P) energy-backed token market for prosumers under blockchain technology. The prosumers in the presented market are classified into buyers and sellers based on their net generation. Another set of prosumers is electric vehicle (EV) owners that participate in the proposed energy token market via the storage capacity of their EVs. Using smart contracts, all market players can engage in bilateral (P2P) transactions of energy-backed tokens with other prosumers at the agreed price and with their existing market via real-time pricing. Furthermore, the buyers can participate in DR programs as virtual generators without the interference of a third party and shift their load based on their preferences to the hours with an abundant local generation or low market price. To avoid inflation and “false welfare” in the energy-backed token market, this paper applies a demurrage mechanism to lower the redemption value of energy tokens. With this mechanism in place, the buyers and sellers try to minimize the time interval between token purchase and consumption and between token generation and selling. A decentralized alternating direction method of multipliers (ADMM) scheme is developed to clear the proposed fully-decentralized energy-backed token market in the presence of EVs and the demurrage mechanism. This approach protects the players’ privacy while ensuring solution feasibility. The simulation results suggest that the presence of EVs enhances the freedom of the prosumers in choosing their energy peers and increases their total welfare by 20 percent.

Participation of Electric Vehicles in a Delay-Dependent Stability Analysis of LFC Considering Demand Response Control

Today, time-varying delays may result from a communication network’s engagement in frequency control services. These delays have an impact on the effectiveness of the load frequency control (LFC) system, which can occasionally lead to power system instability. The electric vehicle (EV) can be used as a beneficial source for the LFC system with the development of demand-side response due to its vehicle-to-grid capacity. Although demand response control has certain advantages for the power system, communication networks used in LFC systems result in time delays that reduce the stability of the LFC schemes. A stability study of an LFC system, comprising an EV aggregator with two additive time-varying delays, is demonstrated in this work. An enhanced Lyapunov–Krasovskii functional (LKF), which incorporates time-varying delays using the linear matrix inequality approach, is used to perform a delay-dependent stability analysis of the LFC to determine the stability zone and criterion. In conclusion, the efficiency of the proposed stability criterion is validated by making use of the thorough simulation findings.

Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach

Renewable -based microgrid (MG) is recognized as an eco-friendly solution in the development of renewable energy (RE). Moreover, the MG energy management with high RE penetration faces complicate uncertainties due to the inaccuracy of predictions. Besides, the growing participation of electric vehicles (EVs) makes the traditional model-based methods even more infeasible. Considering uncertainties associated with RE, EVs, and electricity price, a model-free deep reinforcement learning (DRL), namely twin delayed deep deterministic policy gradient (TD3), is employed to develop an optimized control strategy to minimize operating costs and satisfy charging expectations. The energy management problem is first formulated as a Markov decision process. Then, TD3 solely relies on the limited observation to find the optimal continuous control strategy. The proposed method flexibly adjusts the operating and charging strategies of components according to RE output and electricity price. Its real-time optimized performance on three consecutive days along with electricity price is evaluated, indicating its practical potential for future application. Additionally, comparison results demonstrate that the proposed method reduces the total costs up to 15.27% and 4.24%, respectively, compared to traditional optimization and other DRL methods, which illustrates the superiority of the TD3 method on optimizing total costs of the considered MG.

Vehicle to everything in the power grid (V2eG): A review on the participation of electric vehicles in power grid economic dispatch

AbstractThe increasing popularity of electric vehicles (EVs) and the enhanced energy storage capability of batteries have made EVs adjustable resources in economic dispatching for power grids. The guidance and control of discharging EVs have become issues with ever‐increasing concerns, and the EVs can be discharged to other entities in the grid, which is called vehicle to everything in the power grid (V2eG) technology. When V2eG technologies become widespread, EVs could be widely distributed, fully controllable, and marketable power sources. As ideal mobile power sources, EVs are geographically available all the time. In this paper, the application process of V2eG technology is first introduced, and the current research status of V2eG technology is discussed. The modelling technology involved in V2eG technology is next analysed and discussed in detail. Then, the applications of V2eG technology in economic dispatch are summarized. Furthermore, research on communication technology for V2eG technology is investigated. In addition, the policies and electricity market access criteria related to V2eG are discussed, and the corresponding trading mechanisms are analysed. Finally, the related V2eG technologies are summarized, and the future development direction of V2eG technology is prospected.

Uncertainty Modeling for Participation of Electric Vehicles in Collaborative Energy Consumption

This paper proposes an accurate and efficient probabilistic method for modeling the nonlinear and complex uncertainty effects and mainly focuses on the Electric Vehicle (EV) uncertainty in Peer-to-Peer (P2P) trading. The proposed method captures the uncertainty of the input parameters with a low computational burden and regardless of the probability density function (PDF) shape. To this end, for each uncertain parameter, multitude of random vectors with the specification of corresponding uncertain parameters are generated and a fuzzy membership function is then assigned to each vector. Since the most probable samples occur repeatedly, they are recognized by the superposition of the generated fuzzy membership functions. The simulation results on various case studies indicate the high accuracy of the proposed method in comparison with Monte-Carlo simulation (MCs), Unscented Transformation (UT), and Point Estimate Method (PEM). It also scales down the computational burden compared to MCs. Also, a real-world case study is employed to examine the ability of the method in capturing the uncertainty of EVs’ arrival and departure time. The studies on this case reveal that involving EVs in P2P trading augments the amount of energy traded within the prosumers.

An Assessment Method for the Impact of Electric Vehicle Participation in V2G on the Voltage Quality of the Distribution Network

In order to further evaluate the impact of vehicle-to-grid (V2G) on the distribution network, this paper studies a method to assess the influence of electric vehicles participating in charge and discharge on the voltage quality of the distribution network. First, considering the state of charge of the EV, the participation of the owner and other factors, the charging and discharging model is built. Then, the probabilistic power flow calculation based on Latin hypercube sampling is used to obtain the probability distribution of the voltage amplitude of the charge and discharge load connected to the distribution network, and finally the evaluation index is established to quantify and calculate the voltage quality of the distribution network participating in the V2G process of electric vehicles. Simulation results show that the evaluation method has the advantage of fast calculation speed while ensuring known accuracy, introduces the probability distribution of expected value and variance quantification of voltage amplitude, more intuitively understands the degree of influence on voltage quality before and after V2G, and can effectively assess the impact of electric vehicles accessing the distribution network in V2G mode on the power quality of low-voltage residential areas and industrial and commercial areas, and this evaluation method can provide useful reference for the formulation of future V2G control strategies and the planning of future urban power grids.