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A vehicle-to-grid (V2G) technology enables bidirectional power exchange between electric vehicles (EVs) and the power grid, presenting enhanced grid stability and load management opportunities. This study investigates a comprehensive microgrid system integrating EVs with solar (8 MW), wind (4.5 MW), and diesel generation sources, focusing on peak load reduction and frequency regulation capabilities. Through MATLAB/Simulink simulations using a Phasor model, we analyze five distinct scenarios with varying EV fleet sizes (20–100 vehicles, 40 kW each). Results demonstrate that V2G integration effectively maintains grid frequency within 59.5–60.5 Hz across all test cases, achieving optimal performance using 100 EVs. Through strategic EV discharge scheduling, the system successfully reduces evening peak loads from 3000 to 2200 kW. Economic analysis reveals decreasing payback periods from 5.2 to 2.8 years as fleet size increases, with ROI improving from 12.5 to 23.1%. These findings establish quantitative benchmarks for V2G implementation in microgrids and demonstrate its viability for grid stability enhancement and load management applications. [ABSTRACT FROM AUTHOR]

Energy management systems (EMSs) play a central role in improving the performance of microgrids by ensuring their efficient operation while minimizing operational costs and environmental impacts. This paper presents a comprehensive study of mixed integer linear programming (MILP) based EMSs developed and implemented in MATLAB 2021a using YALMIP software for the energy management of a new positive energy district in the city of Savona, Italy, as part of the Interreg Alpine Space Project ALPGRIDS. The main objective of this research is to optimize the functioning of the microgrid, focusing on cost efficiency and environmental sustainability. In pursuit of this objective, the EMS undergoes comprehensive testing and analysis, replicating actual conditions and addressing the diverse demands of end-users across typical days throughout the year, considering real electricity selling and purchase prices. The EMS also accounts for the reactive power capabilities of the various technologies integrated into the microgrid. The levelized cost of electricity (LCOE) serves as a metric for assessing curtailment costs, while penalties related to reactive power absorption from the distribution network are appraised in alignment with prevailing regulatory guidelines. The case study provides valuable insights into the practical implementation of EMS technology in microgrids and demonstrates its potential for sustainable energy management in complex urban energy districts. In all scenarios, the battery energy storage system (BESS) and combined heat and power (CHP) are pivotal for load satisfaction and microgrid resilience. BESSs balance supply and demand, which are crucial in periods of low renewable energy availability, while the versatile CHP efficiently addresses energy demands, contributing significantly to overall microgrid effectiveness. Their synergy ensures reliable load satisfaction, showcasing the dynamic and adaptive nature of microgrid energy management across diverse scenarios. [ABSTRACT FROM AUTHOR]

This research proposes a unique platform for energy management optimization in smart grids, based on 6G technologies. The proposed platform, applied on a virtual power plant, includes algorithms that take into account different profiles of loads and fairly schedules energy according to loads priorities and compensates for the intermittent nature of renewable energy sources. Moreover, we develop a bidirectional energy transition mechanism towards a fleet of intelligent vehicles by adopting vehicle-to-grid technology and peak clipping. Performance analysis shows that the proposed energy provides fairness to electrical vehicles, satisfies urgent loads, and optimizes smart grids energy. [ABSTRACT FROM AUTHOR]

This research proposes a unique platform for energy management optimization in smart grids, based on 6G technologies. The proposed platform, applied on a virtual power plant, includes algorithms that take into account different profiles of loads and fairly schedules energy according to loads priorities and compensates for the intermittent nature of renewable energy sources. Moreover, we develop a bidirectional energy transition mechanism towards a fleet of intelligent vehicles by adopting vehicle-to-grid technology and peak clipping. Performance analysis shows that the proposed energy provides fairness to electrical vehicles, satisfies urgent loads, and optimizes smart grids energy.

Vehicle-to-Grid (V2G) is the convergence of smart grid, electrical vehicle and information and communication technology. It envisions a system where smart grid and electric vehicles can communicate with each other to provide various services. Researchers are actively trying to solve many of the interesting challenges in this emerging technology. One such challenge is to secure the communications between the entities in V2G environment using various security mechanisms. Recently, Su et al. proposed a scheme to provide authentication between electrical vehicles and charging station. On detailed analysis, the scheme exhibited some vulnerabilities. In our work, cryptanalysis of the protocol by Su et al. to show the scheme is vulnerable to traceability attack, user impersonation attack, insider attack and the protocol fails to support session key establishment is presented. Also, cloud computing is necessary for storing, processing and analyzing large amount of data generated in the V2G environment. Hence, we propose a new communication architecture involving cloud server and an efficient authentication protocol using Elliptic Curve Cryptography (ECC) for the proposed architecture. The security of the proposed protocol is established using the formal method BAN logic. Informal proofs are given to show the resistance of the proposed protocol against many known attacks. Other performance metrics of our protocol are evaluated and compared against many significant protocols of similar architecture. [ABSTRACT FROM AUTHOR]

As an independent unit, microgrid operator (MGO) should respond to the energy demand of customers with an optimal provision cost. Apart from the ability to guarantee the security of microgrid (MG) operation, the MGO should consider the greenhouse emission effects as well. This paper investigates a novel energy management system for an islanded MG. In the proposed strategy, two efficient methodologies are used to maximize the profit and security of the MG: (a) the optimal operation of electric vehicles (EVs) in vehicle‐to‐grid (V2G) mode and (b) the use of demand response (DR) program. Besides, a hierarchical control structure is proposed to manage frequency and voltage uncertainties in a permissible range. Improving the reliability of the MG is another goal of the proposed strategy, which is pursued by the studied controlling framework. The proposed approach is evaluated through a set of simulation and GAMS tests. Results indicate that the frequency deviation of the MG is damped through the proposed management strategy and place the spotlight on the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

This article introduces a unique dynamic balance strategy (DBS) that can overcome many disadvantages such as voltage and frequency deviations, overloading, high infrastructure costs that may occur in the network integration of electric vehicles (EVs). The proposed method is also aimed at keeping the comfort and satisfaction of the EV users at a high level without the obligation of the EV owners to inform the operator of the entry and charge planning. DBS introduces a real-time energy-sharing method that uses both the grid-to-vehicle (G2V) and vehicle-to-vehicle (V2V) topologies simultaneously without knowing the power demand profile in advance. However, from the perspective of smart grid, it is also possible to activate the vehicle-to-grid (V2G) topology in case of frequency or voltage fluctuations in the network with the proposed DBS. The proposed DBS was simulated in real time and phasor analysis for different penetration levels in MATLAB/Simulink program. Considering that EV driver behavior is highly variable and will vary regionally, it is shown that the proposed DBS gives dynamic results against all behavioral situations, varying penetration levels, and all the negativities that may occur in the penetration of EVs are overcome by keeping the satisfaction of EV drivers at a high levels. [ABSTRACT FROM AUTHOR]

The blend of Internet of Things (IoT) and social networking has introduced the emerging notion of social Internet of Things, which is bringing advancements in the operation of concerned industries. There are various prevailing applications of social internet of things; smart grid is one of them. The smart grid is considered as economical robust and intuitive replacement of the conventional grid. However, smart grid experiences two significant challenges, i.e. privacy and security. This article is dedicated to resolve the privacy and security concerns for the vehicle to grid networks to facilitate their large-scale integration with smart grids. As anticipation, a vigorous key agreement protocol is introduced to achieve mutual authentication with an aided feature of user anonymity. Moreover, efficiency in terms of computation, communication and storage needs to be taken care for resource-constrained infrastructure like vehicle to grid network. We have introduced a lightweight key agreement protocol using lightweight cryptographic operations such as exclusive-OR and hash etc. This protocol is validated through a formal security model. An informal security analysis is also elaborated to present the security strength of our protocol against well-known attacks. Furthermore, we have implemented all the cryptographic operations used at trusted agent's side on a desktop system, while the operations used at battery vehicle unit's side are implemented on an Arduino to get the experimental results. In the end, we have presented a performance analysis to compare the performance of our protocol with related ones. This comparison highlights that our protocol is not only lightweight but also efficient in terms of communication and storage cost of related protocols.

Electric vehicles (EVs) have been slowly replacing conventional fuel based vehicles since the last decade. EVs are not only environment-friendly but when used in conjunction with a smart grid, also open up new possibilities and a Vehicle-Smart Grid ecosystem, commonly called V2G can be achieved. This would not only encourage people to switch to environment-friendly EVs or Plug-in Hybrid Electric Vehicles (PHEVs), but also positively aid in load management on the power grid, and present new economic benefits to all the entities involved in such an ecosystem. Nonetheless, privacy and security remain a serious concern of smart grids. The devices used in V2G are tiny, inexpensive, and resource constrained, which renders them susceptible to multiple attacks. Any protocol designed for V2G systems must be secure, lightweight, and must protect the privacy of the vehicle owner. Since EVs and charging stations are generally not guarded by people, physical security is also a must. To tackle these issues, we propose Physical Unclonable Functions (PUF) based Secure User Key-Exchange Authentication (SUKA) protocol for V2G systems. The proposed protocol uses PUFs to achieve a two-step mutual authentication between an EV and the Grid Server. It is lightweight, secure, and privacy preserving. Simulations show that the proposed protocol performs better and provides more security features than state-of-the-art V2G authentication protocols. The security of the proposed protocol is shown using a formal security model and analysis. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a day ahead optimal dispatch method for smart grids including two-axis tracking photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS) and electric vehicles (EV), which serve as additional storage systems in vehicle to grid (V2G) mode. The aim of the day ahead schedule is the minimization of fuel-based energy, imported from the main grid. The feasibility of the proposed method lies on the extensive communication network of the smart grids, including sensors and metering devices, that provide valuable information regarding the production of the distributed energy resources (DER), the energy consumption and the behavior of EV users. The day ahead optimal dispatch method is applied on a smart grid in order to showcase its effectiveness in terms of sustainability, full exploitation of DER production and ability of EVs to act as prosumers.

In this paper, a control method for electric vehicles (EVs) participating in grid Frequency regulation is proposed. Firstly, considering dispatching large-scale electric vehicles, the K-means clustering algorithm is applied to cluster EVs with different battery state of charge and with different average vehicle daily travel miles. Then, for each class of electric vehicle group, a multi-objective optimization model considering reducing power imbalance and feeding the driving power demand for electric vehicles is proposed. Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied to solve the optimization model and obtain the best control parameters for "virtual synchronous machine", which is functioned as the power controller between EVs and the power grid. At last, based on a Monte Carlo sampling, the simulation analysis of 50 EVs with the normal distribution of battery state of charge and average vehicle daily travel miles is carried out by using the proposed method. The results show that the proposed method can effectively classify the electric vehicles with different battery state of charge and different average vehicle daily travel miles. The parameters of the power converter controller for different classes of electric vehicles are optimized considering power grid frequency, their battery state of charge and their average daily travel miles, so as to maintain the balance of power grid frequency, and to meet the power needs of EV daily drive. [ABSTRACT FROM AUTHOR]

Urban complex (UC) is the main place of citizens’ life and work. The construction of an UC often needs to expand the capacity of the power equipment. This paper proposes to use electric vehicles (EVs) in an UC to reduce the power load of the UC during peak periods, so that lower capacity power equipment can be used to reduce the construction costs of the UC and the transformation of electrical facilities. In order to find the relationship between parking and power load in the UC, the UC is decomposed into different functional areas for research. Then, we build a parking information database for clustering and calculation. Divide the load peak into adjustment intervals of equal duration. The EVs parked in the UC for each regulation interval (RI) are grouped according to parking characteristics. Establish an objective function with the minimum load variance during peak hours. The discharge capacity of each group in each RI is obtained and distributed to each EV to realize peak load reduction of UC. Finally, the results of case analysis show that the strategy can reduce the peak load effectively thus save the cost of UC construction.

La modernización de las redes eléctricas es una realidad que genera nuevos desafíos en el diseño, operación y gestión del transformador de distribución, los cuales dependerán, entre otras cosas, de condiciones particulares como la topología de la red y los índices de penetración de tecnologías de movilidad eléctrica y generación distribuida (GD). Este artículo revisa aspectos generales de la bibliografía internacional frente al impacto que puede tener la generación distribuida y los vehículos eléctricos en el transformador en el marco de las Smart Grid . También se presentan las principales estrategias aplicadas para mitigar los posibles impactos negativos.

This paper presents a new multi-function conversion technique for vehicle-to-grid (V2G) applications. The proposed bi-directional charger can achieve three functions, including EV battery charging, grid-connection and reactive compensation, which are keys for energy management of the grid. With the proposed multi-function technology, the bi-directional charger will benefit both the grid and electricity customers. A hybrid regulation of energy bi-directional transfer for V2G systems is proposed in this paper, which consists of the battery-side controller and the grid-side controller. This proposed multi-function conversion technique improves the whole system performance with proportional-resonant (PR) control and achieves reactive power compensation with instantaneous reactive theory and a deadbeat control scheme. Simulation and experimental results demonstrate the validity of this new multi-function technique in a V2G system.

The unpredictability of renewable energy systems can affect the stability of the electricity grid, causing voltage and frequency imbalances. In this work, a suitable methodology based on the peer-to-peer scheme applied to energy communities is developed and implemented in a simulation tool useful for investigating energy management strategies for decision-making aims. The developed model discretizes the energy community and its users into multiple control volumes, taking into account various technologies. It incorporates energy balances for individual users as well as the entire energy community, considering prosumers, consumers, energy storage systems, and electric vehicles. Moreover, the model enables the exploration of different solutions for grid frequency regulation and optimization of distributed energy resources. Additionally, the tool can predict electricity demand one day ahead, facilitating the organization of renewable energy availability and storage systems to minimize grid interactions and flatten electricity demand. The model incorporates different objective functions, including self-consumption, self-sufficiency, and grid-balancing factors, to evaluate the performance of energy communities. To show the capability of the developed model, it will be adopted to optimize the performance of an investigated community. As a result, an increase in renewable energy self-consumption from 59.4 to 83.9 MW h/year is achieved. Furthermore, the objective of grid balancing was achieved by guaranteeing a non-fluctuating load and providing 1.46 and 7.71 MW h/year for upward and downward grid frequency regulation. These findings illustrate the positive impact of energy dispatching management on the integration of renewable energy sources and the importance of further studying this topic to ensure grid stability. [Display omitted] • Development of a flexible mathematical model for energy management in smart grids; • Characterize the role of energy communities in electricity grid balancing; • Implementation of energy-sharing strategies: Peer to Peer and Vehicle to cluster; • Comparison of different optimization problems to ensure electricity grid stability; • Energy management assessment of a smart grid consisting of prosumers, consumers; [ABSTRACT FROM AUTHOR]

In this paper, a control method for electric vehicles (EVs) participating in grid Frequency regulation is proposed. Firstly, considering dispatching large-scale electric vehicles, the K-means clustering algorithm is applied to cluster EVs with different battery state of charge and with different average vehicle daily travel miles. Then, for each class of electric vehicle group, a multi-objective optimization model considering reducing power imbalance and feeding the driving power demand for electric vehicles is proposed. Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied to solve the optimization model and obtain the best control parameters for “virtual synchronous machine”, which is functioned as the power controller between EVs and the power grid. At last, based on a Monte Carlo sampling, the simulation analysis of 50 EVs with the normal distribution of battery state of charge and average vehicle daily travel miles is carried out by using the proposed method. The results show that the proposed method can effectively classify the electric vehicles with different battery state of charge and different average vehicle daily travel miles. The parameters of the power converter controller for different classes of electric vehicles are optimized considering power grid frequency, their battery state of charge and their average daily travel miles, so as to maintain the balance of power grid frequency, and to meet the power needs of EV daily drive.

The battery is a key component in Plug-in Electric Vehicles (PEVs) whose degradation should be considered in vehicle modeling and if the battery pack is to be used in a Vehicle to Grid (V2G) smart grid studies. Several researchers have proposed different methodologies for PEV batteries degradation modeling from various aspects. Most of the battery degradation literature consists of empirical-based studies with results extracted from experimental tests in laboratories. As such, the results have been presented in non-formulated forms and are of less effectiveness for smart grid researchers. Furthermore, the impact of battery degradation in V2G smart grid have not been examined in smart grid studies. This paper reviews and compares different battery technologies focusing on Lithium-ion batteries which dominant in today and future vehicle applications. After that the most prominent degradation models are assessed, the effects of degradation factors on battery performance are examined. The literature shows that the degradation causes can be categorized into two groups namely calendar ageing and cycling ageing. Generally, the calendar ageing is influenced by temperature, time, and state of charge, while the cycling ageing is influenced by cycle number, charge rate and depth of discharge. Finally, in this work a conceptual framework for battery degradation modeling is proposed that can be easily used in smart grid studies, without necessarily requiring a detailed understanding of fundamental electrochemical processes. The proposed framework considers not only the battery degradation modeling, but also that of other related components in a smart grid. [ABSTRACT FROM AUTHOR]

Smart Grid (SG) technology represents an unprecedented opportunity to transfer the energy industry into a new era of reliability, availability, and efficiency that will contribute to our economic and environmental health. On the other hand, the emergence of electric vehicles (EVs) promises to yield multiple benefits to both power and transportation industry sectors, but it is also likely to affect the SG reliability, by consuming massive energy. Nevertheless, the plug-in of EVs at public supply stations must be controlled and scheduled in order to reduce the peak load. This paper considers the problem of plug-in EVs at public supply stations (EVPSS). A new communication architecture for SG and cloud services is introduced. Scheduling algorithms are proposed in order to attribute priority levels and optimize the waiting time to plug-in at each EVPSS. To the best of our knowledge, this is one of the first papers investigating the aforementioned issues using new network architecture for SG based on cloud computing. We evaluate our approach via extensive simulations and compare it with two other recently proposed works, based on real supply energy scenario in Toronto. Simulation results demonstrate the effectiveness of the proposed approach when considering real EVs charging–discharging loads at peak-hours period. [ABSTRACT FROM PUBLISHER]

Renewable energies are a key pillar of power sector decarbonisation. Due to the variability and uncertainty they add however, there is an increased need for energy storage. This adds additional infrastructure costs to a degree that is unviable: for an optimal case of 15 GW of storage by 2030, the cost of storage is circa: £1000/kW. A promising solution to this problem is to use the batteries contained within electric vehicles (EVs) equipped with bi-directional charging systems to facilitate ancillary services such as frequency regulation and load balancing through vehicle to grid (V2G) technologies. Some authors have however dismissed V2G as economically unviable claiming the cost of battery degradation is larger than arbitrage. To thoroughly address the viability of V2G technologies, in this work we develop a comprehensive battery degradation model based on long-term ageing data collected from more than fifty long-term degradation experiments on commercial C 6 /LiNiCoAlO 2 batteries. The comprehensive model accounts for all established modes of degradation including calendar age, capacity throughput, temperature, state of charge, depth of discharge and current rate. The model is validated using six operationally diverse real-world usage cycles and shows an average maximum transient error of 4.6% in capacity loss estimates and 5.1% in resistance rise estimates for over a year of cycling. This validated, comprehensive battery ageing model has been integrated into a smart grid algorithm that is designed to minimise battery degradation. We show that an EV connected to this smart-grid system can accommodate the demand of the power network with an increased share of clean renewable energy, but more profoundly that the smart grid is able to extend the life of the EV battery beyond the case in which there is no V2G. Extensive simulation results indicate that if a daily drive cycle consumes between 21% and 38% state of charge, then discharging 40%–8% of the batteries state of charge to the grid can reduce capacity fade by approximately 6% and power fade by 3% over a three month period. The smart-grid optimisation was used to investigate a case study of the electricity demand for a representative University office building. Results suggest that the smart-grid formulation is able to reduce the EVs' battery pack capacity fade by up to 9.1% and power fade by up to 12.1%. [ABSTRACT FROM AUTHOR]

Vehicle-to-grid and Grid-to-vehicle strategies are often cited as promising to mitigate the intermittency of renewable energy on electric power grids. However, their impact on the vehicle battery degradation has not been investigated in detail. The aim of this work is to understand the impact of bidirectional charging on commercial Li-ion cells used in electric vehicles today. Results show that additional cycling to discharge vehicle batteries to the power grid, even at constant power, is detrimental to cell performance. This additional use of the battery packs could shorten the lifetime for vehicle use to less than five years. By contrast, the impact of delaying the charge in order to reduce the impact on the power grid is found to be negligible at room temperature, but could be significant in warmer climates. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy systems considered as candidate solutions for environmental problems. Solar energy and wind energy are important components of these systems, but both of them cause fluctuations in the grid. Batteries of Electric Vehicles (EVs) can be connected to the grid to create an ancillary resource. These systems known as Vehicle to Grid (V2G) and they could be the most suitable solution to the fluctuation problems of renewable resources. In this paper, economic analysis of V2G is investigated from various perspectives. The findings of this study indicate that battery aging costs of a V2G system can be minimized with an intelligent energy management system, and these systems are both economically and environmentally better alternatives to fossil-fuelled generators. [ABSTRACT FROM AUTHOR]

Copyright of Ciencia e Ingenieria Neogranadina is the property of Ciencia e Ingenieria Neogranadina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Smart Grid construction within conventional network is a step to digitalize and optimize the present grid. However, smart grid itself should be a part of the solution rather than being a new burden. According to this research, Electric Vehicles (EV) in the form of Vehicle-to-Grid (V2G) can play their own role for frequency control in an isolated power system. On the other hand, Microgrids mostly use Renewable Energy Systems (RES) in electrical power production that are naturally fluctuating, in a time there are increasing trends to install the controllable loads such as Heat Pumps (HPs) and V2Gs. On the other hand, operating manners and uncertainties in power systems cause the classic controllers to be unable to provide a proper performance over a wide range of operating conditions. In order to overcome such issues, this paper addresses a control scheme approach using Coefficient Diagram Method (CDM) technique to enhance the system validity and robustness against uncertainties. The proposed scheme suggests a parallel connection of CDM and PI controllers in which the performance has been compared with only conventional controller showing that the proposed scheme has superior validity. Different operation schemes have been simulated showing the flexibility of V2G utilization in the modern power system. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a probabilistic model to reduce the probability of line congestions and voltage violations in a smart grid located in a radial distribution network. Renewable distributed resources and a high penetration of Electric Vehicles (EVs) are considered. The uncertain parameters taken into account are: power demand, power generated by wind and solar photovoltaic generators, and the behavior of the EVs that do not participate in the congestion procedure. A probabilistic power flow based on the point estimate method is firstly used to compute the distribution functions of the line flows and node voltages. Next, a congestion management strategy is proposed in order to keep the line flows and node voltages within the appropriate range at a given confidence level. The strategy is based on the sensitivity distribution factors: Power Transfer Distribution Factors (PTDFs) and Voltage Distribution Factors (VDFs). The control variables to carry out the corrective actions are the active and reactive power injections from a subset of EVs or their charging points. It is assumed that these controlled EVs are able to inject power into the network using Vehicle to Grid (V2G) capabilities. To illustrate the method, a distribution grid based on a modified version of the IEEE-37 Node Test Feeder is tested. [ABSTRACT FROM AUTHOR]

Vehicle-to-grid (V2G) is a technology which can reduce the cost for power distribution network operators by storing electricity in the batteries of electric-drive vehicles and retrieving it when energy demands increase during the course of a day. Participants of V2G are reimbursed for offering their vehicles which can lead to changes in trip schedules when V2G payments are high and travelers are sensitive to the payments. However, prior studies have ignored the effects of V2G on travelers’ schedules. This research gap is addressed with a bi-level V2G market equilibrium model where the lower level model determines the equilibrium activity patterns as a result of upper level pricing and linear approximated AC flow distribution decisions. An algorithm is proposed for the model and illustrated on a simple telecommuting example where travelers can work from home and offer their vehicle charge capacity to the power provider. The model is then applied to the same case study from Lam and Yin (2001) to first replicate the lower level equilibrium problem as a special case when no V2G is present, and then to show the potential effects of the V2G policy to decrease locational marginal prices for a distribution network operator. The proposed algorithm for the V2G policy resulted in a substantial 20% increase in social welfare over the benchmark equilibrium without V2G. [ABSTRACT FROM AUTHOR]

A practical charging strategy for an individual plug-in electric vehicle (PEV) is proposed. A cost function is constructed to reflect bidirectional electricity trade arising from surplus photovoltaic generation under the reward system called feed-in tariffs and time-of-use electricity pricing. It is then extended to include the revenue for providing vehicle-to-grid frequency regulation. In order to take advantages of high performance existing solvers, several mathematical techniques are applied to transform the discrete non-linear cost function to a differentiable continuous function. In the latter part of the paper, simulations are performed to verify and show the performance of the developed model. A quantitatively developed battery wear model is also applied during the simulations to calculate the effective operational cost, and to compare the costs for different control strategies. From the result of case study, the economic feasibility of the frequency regulation under the given circumstance is addressed as well. [ABSTRACT FROM AUTHOR]

This paper investigates the role of integration of distributed storage with high penetration of variable renewable sources in power systems. The paper analyzes the impact of such integrations on the security, emission reduction, and the economic operation of electric power systems. The paper also identifies strategies for a larger penetration of variable generation resources without compromising the power system security. Mixed integer linear programing (MILP) is applied for the optimization of the day-ahead hourly security-constrained unit commitment. The assimilation of EVs (both as a provider and a utilizer of energy), renewable energy sources, and smart grid is regarded as a novel, low-cost, and low-emission solution to the existing challenges of electric power systems including the means of storing large quantities of energy considering variable renewable energy sources and large carbon footprints of conventional thermal units. Numerical studies are conducted in this paper to showcase the potential impacts of EV fleets as battery storage for peak load shaving, minimizing power grid operation costs and hourly wind curtailments, and optimizing the environmental impacts based on hourly commitment and dispatch of thermal generating units. [ABSTRACT FROM AUTHOR]

In this paper, the sustainable day-ahead scheduling of electric power systems with the integration of distributed energy storage devices is investigated. The main objective is to minimize the hourly power system operation cost with a cleaner, socially responsible, and sustainable generation of electricity. Emission constraints are enforced to reduce the carbon footprint of conventional thermal generating units. The stationary electric vehicles (EV) are considered as an example of distributed storage and vehicle to grid (V2G) technology is considered to demonstrate the bilateral role of EV as supplier and consumer of energy. Battery storage can ease the impact of variability of renewable energy sources on power system operations and reduce the impact of thermal generation emission at peak hours. We model the day-ahead scheduling of electric power systems as a mixed-integer linear programing (MILP) problem for solving the hourly security-constraint unit commitment (SCUC). In order to expedite the real-time solution for large-scale power systems, we consider a two-stage model of the hourly SCUC by applying the Benders decomposition (BD). The Benders decomposition would separate the hourly generation unit commitment (UC) in the master problem from the power network security check in subproblem. The subproblem would check dc network security constraints for the given UC solution to determine whether a converged and secure dc power flow can be obtained. If any power network violations arise, corresponding Benders cuts are formed and added to the master problem for solving the next iteration of UC. The iterative process will continue until the network violations are eliminated and a converged hourly solution is found for scheduling the power generating units. Numerical simulations are presented to illustrate the effectiveness of the proposed MILP approach and its potentials as an optimization tool for sustainable operations of electric power grids. [ABSTRACT FROM AUTHOR]

The conventional electricity grids, which are supposed to supply the electricity to the plug-in hybrid electric vehicles (PHEVs), need to adapt to overcome the problems of reliability, security, economics and environmental concerns. Accordingly, electric utilities in several countries are exploring the possibilities of implementing more robust interactive smart grids. This article analyses salient features of a smart grid in the Canadian perspective, and discusses PHEV and its role in the future smart grid. A simulation tool has been used to assess the impacts of charging of PHEV on the future electricity grid of Canada under different penetration rates. The article indicates the associated environmental benefits of PHEV. [ABSTRACT FROM PUBLISHER]

Electric vehicle (EV) charging must be optimised for grid load while guaranteeing that drivers' schedules and range requirements are met. A system encompassing EV owner input via a mobile application, an aggregation middleware, a charge scheduling and vehicle-to-grid (V2G) operation algorithm and a radio-frequency identification reader is proposed. The algorithm's parameters and effectiveness are presented and discussed using simulation results. Simulation results show the algorithm to effectively optimise charging and V2G operation for a given electricity price curve. The proposed system is shown to alleviate grid load during peak hours, take advantage of off-peak charging benefits and generate revenue for the parking garage operator. [ABSTRACT FROM PUBLISHER]

Abstract: The introduction of plug-in hybrid electric vehicles (PHEVs) and electric vehicles (EVs), commonly referred to as plug-in electric vehicles (PEVs), could trigger a stepwise electrification of the whole transportation sector. However, the potential impact of PEV charging on the electric grid is not fully known, yet. This paper presents an iterative approach, which integrates a PEV electricity demand model and a power system simulation to reveal potential bottlenecks in the electric grid caused by PEV energy demand. An agent-based traffic demand model is used to model the electricity demand of each vehicle over the day. An approach based on interconnected multiple energy carrier systems is used as a model for a possible future energy system. Experiments demonstrate that the model is sensitive to policy changes, e.g., changes in electricity price result in modified charging patterns. By implementing an intelligent vehicle charging solution it is demonstrated how new charging schemes can be designed and tested using the proposed framework. [Copyright &y& Elsevier]

Power electronic-based FACTS devices like Unified Power Quality Conditioner (UPQC) can fulfil multiple power quality control objectives such as needs of reactive power compensation, voltage flicker and harmonics current compensation. Although the main disadvantage of UPQCs are their excessive price. Therefore UPQCs are not widely used. In this paper a method proposed to analyze the reactive and active power transaction capabilities of a practical vehicle battery in the Plug-in Hybrid Electric Vehicles (PHEV) in a Vehicles to Grids (V2G) mode of operation, which gives a low-cost solution for designing a virtual UPQC using PHEV charging station. A third order dynamic battery model is used to represent the PHEV and substitute to dc link of UPQC. Simulations have been executed in MATLAB SIMULINK and demonstrated that PHEVs have able to work as a virtual UPQC to improve power quality. [ABSTRACT FROM AUTHOR]

Abstract: A method of dividing complex micro-grid into minor micro-grids and finally to the minimal micro-grid which consists of one AC bus or one DC bus is presented according to classification principles. The protection scheme of minimal micro-grid is discussed and then applied to the whole micro-grid. By analyzing parallel operation mode and islanding operation mode of micro-grid, the protection scheme of each mode is given respectively. [Copyright &y& Elsevier]