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Nowadays business has shifted from product-centric to customer-centric due to the industrial revolution. As a result, the customer's problem and experience need to be studied and inspected thoroughly to find solutions. Charging stations implementing different charging technologies are reinforced in various topographical locations. Varied charging methods, including AC slow charging, AC fast charging, DC level 1 charging, DC level 2 charging, wireless charging is implemented so far to reduce the charging time. Various business models are established and reported by researchers to support the EV ecosystem. This paper presents one such business model for an electric vehicle (EV) charging system. Design Thinking is one of the problem-solving approaches discussed here. How to tackle the charging issues by implementing different technologies is also discussed. Different phases of Design Thinking are used to find the possible solution to the problems faced by customers. Different groups of customers' problems and how the proposed solution can fulfil a major group of customers' needs are discussed here. [ABSTRACT FROM AUTHOR]

In the current scenario, the impact of the automobile sector on greenhouse gas emissions becomes inevitable. Considering the case, automobile manufacturers pay attention to commercializing Electric vehicles (EVs) in a wide range. The effective functioning of the electrified vehicle system is highly determined by its charging infrastructure. This paper presents a comparative analysis of wired and wireless charging methods, followed by an overview of the evolution of the Wireless Power Transfer (WPT) system. WPT system's performance is flexible, safe, reliable, and cost-effective operation retains its essentiality in the research area for further development in contactless charging methods. In recent years, EV battery charging is dominated by the attractive concept of wireless power transfer with the capability of automatic power transmission without human assistance. Planar pad structures such as polarized and non-polarized pads and compensation networks like SS, SP, PS, and PP are used in the inductive power transfer system to achieve high power transmission. This literature review briefly compares different charging pads and various compensation techniques used in WPT. The prominent role and essentiality of different stages of power converters in the WPT system are highlighted in detail. The effect of EV charging on the grid, the bidirectional capability of EVs, and the strategy of charge management are mentioned. The paper particularly brings out the essentiality of integrating Renewable Energy Sources (RES) with EVs in concern with environmental health. At last, the short notes on the status of the WPT system in the Indian scenario, and the future challenges like safety, health management, Foreign Object Detection (FOD), and economic criteria in the progress of wireless EV charging systems are also addressed. [ABSTRACT FROM AUTHOR]

The inductive charging stations of the future should be able to charge any car, regardless of the type of coil configuration attached to it. In this study the performance of different configurations of coils is investigated. Where the principle of interoperability is studied between the most common types of coils in the IPT system (rectangular and double-D) recommended by Society of Automotive Engineering (SAE) J2954. This interoperability analysis is done at a power level of 11.1 kVA and Z3-class ground clearance using the two types of coils on the side of the car, while double D (DD) is used as a general transmitter in one case, and in the other case, a rectangle (R) is used as a general transmitter. Four 3D finite-element models (FEMs), along with compensation circuits, are built to perform the interoperability tests. The circuit models are utilized to determine the frequency of operation and the parameters of compensation circuit that realize the rated power at highest efficiency. The concept of interoperability has been verified through multiple performance indicators, namely coupling coefficient, transmitted power, and transmission efficiency. These indicators were calculated at ideal alignment and the different cases of misalignment, whether lateral or angular. The findings prove that the two different types of receivers (R and DD) can work efficiently and smoothly with the general transmitter, whether DD pad or rectangular pad are used. At the same time, the nominal power can be transmitted at high efficiency, above the limits permitted by SAE j2954 (>85 % at alignment conditions and >80 % at misalignment conditions). [ABSTRACT FROM AUTHOR]

The Proton Exchange Membrane Fuel Cells (PEMFCs) are one of the most effective and optimistic renewable energy source and they are extensively used in automotive applications. In the past, many researchers focused on solving the issues of extracting maximum power from fuel cell, controlling the speed and reducing the torque ripple of Brushless DC (BLDC) motor for fuel cell based Electric Vehicle (EV) systems. However, it is challenging to fine-tuning the gain parameters in the existing works MPPT approach and extracting maximum amount of energy. Additionally, it has limitations like unregulated voltage, problems of large overshoot, slow tracking speed, output power fluctuation, computational complexity and intricate modeling. Thus, the proposed work aims to create a revolutionary methodology called Unified Firefly Ersatz Neural Network (UFENN) - Maximum Power Point Tracking (MPPT). The UFENN is a kind of optimization-based machine learning technique that was created for efficiently optimizing the parameters to extract the maximum energy from the fuel cells. Furthermore, in order to control the output voltage with the least amount of power loss, an Interleaved SEPIC converter is also used in this work. During performance analysis, an extensive simulation results have been taken for validating the results of the proposed scheme by using various evaluation indicators. • Maximum power extraction from PEMFC. • An improved optimization based AI mechanism is developed for MPPT controlling. • The output voltage is highly boosted and regulated with the use of interleaved SEPIC converter. • An extensive simulation is carried out to assess the performance of the proposed model for electric vehicle application. • Enhancement of quality in electric power at EV systems. [ABSTRACT FROM AUTHOR]

The world's demand for fossil fuels has recently increased significantly for both transportation and electric power generating sectors. Using these resources not only results in high costs and depletion of them but also increases greenhouse gas emissions and pollution. The electric vehicle (EV) market is growing globally, aiming to face climate change due to greenhouse gas (GHG) emissions and to reduce reliance on fossil fuels. However, the long charging time of EVs and the shortage of charging outlets limits the global adoption of EVs, especially on highways where the problem of accessibility to the electricity distribution grid appears. These issues can be faced by the good planning of charging infrastructure. However, this planning is a multidisciplinary field that includes electricity generation, transportation networks, EVs' characteristics, and driver behavior. A methodology to provide the optimal locations and sizing of electric vehicle charging stations with their own electricity generation and storage using photovoltaic (PV) and energy storage systems on highways considering different factors is proposed in this paper. This paper takes a section of the western desert highway in Egypt connecting Assiut and Cairo cities as a case study. Four scenarios are proposed for the design of EV charging stations' locations and sizing which are centralized charging stations, two-way charging stations, utilizing oil stations' locations, and distributed fixed sizing charging points with a comparison between them. The work also discusses the potential effects of highway slope, wind speed, and number of passengers on the location problem. The results can be used to optimize the design of EV charging stations along highways for a completely sustainable system. [Display omitted] • Full Model of EVs, highway, and traffic flow using Monte Carlo simulation. • Highway EV charging stations optimal allocation methodology using MILP. • Estimating number of chargers for minimizing the waiting time at EV station. • Optimal PV and ESS sizing for standalone EV charging stations. • Different planning scenarios and effects of all parameters are considered. [ABSTRACT FROM AUTHOR]

Accurately estimating the charging behaviours of electric vehicles (EVs) is crucial for various applications, such as charging station planning and grid impact estimation. However, the analysis of EV charging behaviours using real-world data remains limited due to (confidential) data availability constraints. Furthermore, while existing modelling studies have demonstrated EVs as effective tools for electricity balancing and delivery between locations, their potential remains unexplored empirically. This study aims to bridge the research gap by studying EV charging behaviours and their capacity for electricity balancing and delivery. Using data from 179,665 real-world charging sessions in Sweden, we employed statistical and clustering analysis to scrutinize charging behaviours comprehensively. Synthetic weekly charging load profiles are generated for both residential areas and workplaces, considering varying charging power levels, which can be used as inputs for large-scale EV charging load modelling. Furthermore, performance indicators are proposed to quantify the potential of EVs for electricity balancing and delivery. Results show that EVs exhibit significant potential for electricity balancing (up to 51.5 kWh daily). Many EV owners underutilize their EV battery capacity, providing an opportunity for active electricity delivery across locations. This study can help understand EV charging behaviours and recognize their significant potentials for electricity regulation and integrating more renewables in the future power system. [ABSTRACT FROM AUTHOR]

The heat pump air conditioning (HPAC) systems in electric vehicles (EVs) consume a significant amount of electric power to manage the thermal requirements of various subsystems, particularly in hot and cold weather conditions. A comprehensive review of previous studies was conducted to assess the current status of technical challenges concerning HPAC systems for EVs, with a specific focus on vapor compression cycle systems. The review specifically addresses technical issues related to heat pump systems operating with refrigerants R-1234yf, R-290, and R-744. Furthermore, challenges in HPAC operation are comprehensively discussed, including frost formation, defrosting methods, start-up procedures, load management (pull-down or heat-up), lubricant oil effects, refrigerant charge imbalance, virtual prototyping, limitations of artificial neural network models, and control strategies. It is noted that the subcritical system using R-1234yf requires improvements for heating operations, while the transcritical system using R-744 needs enhancements in cooling performance. Additionally, the review suggests that further attention and exploration are needed in hybrid systems and multi-objective comprehensive control strategies for the vehicle thermal management system. The review provides valuable insights into the current state and future research directions in HPAC systems for EVs. • State-of-the-art heat pump air-conditioning (HPAC) systems for EVs were comprehensively reviewed • Topics include various aspects of HPAC system design and their operation challenges • This review provides insights into the current state and future directions of EV ACHP systems. [ABSTRACT FROM AUTHOR]

Electrified vehicle batteries primarily use lithium-ion batteries. During vehicle operation, the charge and discharge cycles cause battery aging, leading to reduced battery lifespan. This issue is particularly pronounced in nickel-rich nickel cobalt manganese (NCM) cathodes. In addition, when the battery operates at low temperatures, increased internal resistance further reduces its lifespan. The electrification of internal combustion engine vehicles is underway. However, realistically it is not feasible. Therefore, research on hybrid electric vehicles that utilize both engines and motors is necessary. hybrid electric vehicles (HEVs) utilize various motor control strategies according to the state of charge (SoC) and accelerator pedals, which are influenced by various factors including driving patterns and routes. In this study, the energy efficiencies of hybrid and electric vehicles was compared under real-world driving conditions. In the eco driving mode, which is characterized by frequent motor operation, energy-consumption efficiency levels similar to those of electric vehicles were observed. However, during real-world road tests at ambient temperatures, the energy consumption efficiency of electric vehicles deteriorated compared to that of HEVs owing to internal battery resistance. Therefore, in energy efficiency evaluation studies, it is essential to validate the characteristics that reflect the various influencing factors of real-world driving environments. • SoC control strategies for HEVs and EVs were compared based on energy consumption. • The energy consumption of HEVs varies depending on the contribution of the engine. • HEVs and EVs consume energy at the same level. • Unlike EVs, HEVs are less affected by external temperatures. [ABSTRACT FROM AUTHOR]

Electric vehicle charging stations (EVCS) that are based on DC microgrids are presented in this research. The system comprises a solar photovoltaic system (SPVS), storage battery (SB), electric vehicle (EV) and grid. The adaptive interaction artificial neural network (AI-ANN)-based vehicle to grid (V2G) and grid to vehicle (G2V) power management controller (PMC) is suggested for DC microgrid based EVCS. This EVCS is suitable for the residential building and offices where EV may be parked. This EVCS provides the facility to manage the power of the building in addition to charge the EVIt has two different modes of operation. The first mode uses the EV as a power source. In the second mode, the EV functions as a load. This controller is developed to acquire electrical power from the solar photovoltaic system (SPVS), storage battery, EV and grid respectively. If the solar photovoltaic system (SPVS) and storage battery power are insufficient to meet the demand, power is extracted from electric vehicle (V2G). If the solar photovoltaic system (SPVS), storage battery and EV are not sufficient to meet up demand, then deficit power is obtained from the grid (G2V). ANN based power management controller (PMC) Also provides a consistent DC bus voltage and reduces overshoot from 9.6 % to 0 %., settling time from 1.18 sec. to 0.52 sec. and rise time from 0.27 sec. to 0.25 sec. of DC bus voltage compared to conventional controller. The suggested power management controller tested for two different modes i.e., V2G and G2V using MATLAB Simulink software. [ABSTRACT FROM AUTHOR]

Foreign object detection (FOD) is crucial to preventing wireless electric vehicle charging (WEVC) systems from thermal risk caused by intruded metals in the charging area. In practice, the spatial misalignment between EV and ground-assembled power transmitter is evitable. Unfortunately, according to our quantitative analysis based on conventional voltage-difference-based method, a misalignment of merely tens of mm has been enough to severely deteriorate the FOD accuracy. To combat this misalignment effect, a FOD strategy based on passive sensing coils utilizing voltage vector decomposition (VVD) is proposed in this paper. The proposed strategy can be invariant to misalignment in an automatic manner, during the whole charging process, and regardless of the resonance state of charging circuit. To facilitate VVD implementation, a method called inductance-misalignment mapping (IMM) is devised to obtain the spatial misalignment of EV in real time, and it can be executed by reusing the sensing coils without introducing extra cost. The effectiveness of optimized sensing coils with the proposed FOD method is validated successfully using a commercialized 3-kW prototype working from light load to rated load. • A foreign object detection (FOD) strategy based on sensing coils utilizing voltage vector decomposition (VVD) is proposed to combat misalignment effect that deteriorates FOD accuracy. • A method called inductance-misalignment mapping (IMM) is devised to obtain the value of spatial misalignment of EV in real time by reusing the sensing coils without introducing extra cost. • The proposed VVD and IMM methods are applicable to the wireless charging systems working from light-load to heavy-load condition. [ABSTRACT FROM AUTHOR]

[Display omitted] • Modeling of optimal operation in an unbalanced MG including CASE and EVs. • Reformulating the nonlinear and non-convex day-ahead optimal operation problem of unbalanced MG to a linear and convex problem via SDP and hence, guaranteeing the global optimum. • Considering shiftable load ToU as DRPs. • Applying the IGDT method with risk averse strategy in order to involve uncertainties in unbalanced MG. This paper proposes a mathematical optimization approach based on semidefinite programing (SDP) for optimal operation of an unbalanced microgrid (MG) equipped with renewable energy resources (RERs) as well as energy storage systems (ESSs) including compressed air energy storage (CAES) and electric vehicles (EVs). The SDP converts nonlinear and non-convex models proposed for day-ahead optimal operation into a convex approximation, which is easily implemented by commercial software. An optimization problem is formulated such that it minimizes the costs related to operation and environmental effects limited to several technical limitations. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in MG. This paper presents a stochastic optimal operation model based on Information Gap Decision Theory (IGDT) with risk averse strategy to overcome this information gap and to help Microgrid Operator (MGO). As demand response programs (DRPs), the shiftable load and time of use (ToU) are considered for enhancing the flexibility of MG. Employing the presented model in the 21-bus test system shows that CAES and EVs, as novel ESSs, have good capability to reduce the costs pertaining to operation and emissions in the day-ahead optimal operation. [ABSTRACT FROM AUTHOR]

This study presents a novel Vehicle-to-Grid (V2G) integration strategy. By utilizing the energy stored in electric vehicles (EVs) to inject power into the grid optimally during peak-load/high-tariff periods. This V2G strategy has two main objectives: enhancement of the system performance and offering financial profit to EV owners. The V2G strategy optimizes the energy exchange between EVs and the grid based on load leveling methodology through intelligent scheduling of charging/discharging times to alleviate grid stress during peak demand, leading to enhanced system performance. Also, it enables EV owners to sell their excess energy to the utility at favorable prices, which not only helps EV owners offset their charging costs but also turns their EVs into profitable assets. The contribution over recent similar studies is targeting multiple objectives simultaneously, besides employing a comprehensive set of factors, involving all relevant participants, and rigorous load modelling. The effectiveness of this strategy is demonstrated through comprehensive simulations with two different charging scenarios applied to two networks, RBTS-bus2 and ShC-D8. The results show the enhancement of system performance, as evidenced by improvements in voltage profiles and load curves, besides reductions in losses, operational costs, and feeder and transformer loading. Furthermore, the proposed approach maximizes the profit of EV owners. • This paper presents a novel V2G strategy for distribution systems. • The proposed methodology can enhance electric vehicles profitability. • Real measurement data are incorporated in the model. • Different types of nonlinearity and uncertainty are added to the model. • The results of proposed model are compared with other recent models. [ABSTRACT FROM AUTHOR]

The recent upsurge in electric vehicle (EV) adoption has led to greener mobility but has also broadened the attack surface due to the increased interconnection between the entities like EV, EV charger, grid etc. We show in this paper that among these entities, the EV charger provides a possible attack surface through the available communication network. Adversaries at a minimum can disrupt the vehicular charging process known as denial of charging (DoC) attack. This attack is demonstrated on the real hardware setup of an EV charging, where we have considered the Bharat EV DC charging standard (BEVC-DC001) adopted by India which uses the controller area network (CAN) bus to communicate between EV charger and EV. The DoC attack can have significant consequences both on the electrical grid as well as individuals. The EV chargers (with connected EV) collectively serve as a large load demand, whose sudden inaccessibility would disrupt the supply–demand balance, triggering over frequency relays to either cause local or national blackout. Such a scenario is presented in this work on a microgrid (MG), in a real-time OPAL-RT environment. Not only can this attack lead to major transportation related problems but would also disrupt medical and emergency services. [ABSTRACT FROM AUTHOR]

This paper presents a new and effective approach for optimally allocating Electric Vehicle (EV) stations in a distribution network by integrating electrical and road constraints. The proposed methodology, a two-stage optimization technology, addresses the challenges posed by the increasing energy demand caused by the adoption of EVs. In the first stage, the stochastic demand load of EVs is simulated considering the probability and hierarchic clustering of EV load based on the travelling distance of users. This stage focuses on emulating the uncertainty of EV user travel patterns, time of arrival and departure, and their impact on the EV load profile. In the second stage, a multi-objective problem is formulated to minimize power losses, voltage deviation, and enhance system reliability. To achieve this, charging station zones are determined by clustering the transportation network into groups, and statistical simulations of EV user behaviour are conducted numerous times based on probability. To obtain the optimal solution for the investigated system, which consists of a 25-node transportation network interconnected with the IEEE 69 bus distribution network, the Galaxy Gravity Optimization technique is applied. The results obtained from this study illustrate how the placement of charging stations influences reliability indices, power loss, and voltage deviation. [ABSTRACT FROM AUTHOR]

Smart grids can use energy stored in electric vehicles (EVs) through vehicle-to-grid (V2G) technology. When the mobility of electric vehicles is combined with V2G technology, electric vehicles can form an energy transmission network called the EV energy network. The EV energy network relies heavily on renewable energy, but due to the unstable nature of renewable energy, sometimes the energy supply is insufficient. If no adjustment is made for energy shortage, the EV energy network will soon collapse. This paper studies how to make the EV energy network operate normally as long as possible when the energy shortage comes. In order to ensure the survivability of the system in the event of energy shortage, a global optimization algorithm is proposed. New terms and criteria are defined for this scenario to evaluate algorithms' performance. The global optimization algorithm is simulated on real bus line data from Manhattan and the Pioneer Valley Transportation Authority (PVTA). Simulation results show that the algorithm is effective. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are experiencing substantial investment and widespread acceptance. However, successful penetration of the global market is contingent upon the development of a strategic plan for the efficient allocation of EVs to optimal charging stations (CSs). This study combines several optimization models to systematically assign EVs to the optimal charging stations, with the goal of maximizing trading energy while simultaneously minimizing total response time. Factors taken into consideration include traveling distance, charging (V2G), and discharging (G2V) energy trading, total response time, and energy prices. The efficacy of the combined models is validated using GAMS and BARON solvers, with a focus on EV satisfaction factor, updated energy and response time, number of served EVs, and alleviation of range anxiety. The proposed models demonstrate 85% satisfaction factor for the majority of charging requests, reaching almost 99% for discharging requests. These results surpass those of contemporary models, underscoring the heightened effectiveness of the proposed approach. [Display omitted] • Optimal EV allocation balances energy trading and response time. • Comprehensive model considers mobility, V2G, G2V, response, and prices. • GAMS simulations show consistently high EV satisfaction levels. • Superior outcomes compared to COP and EVaaS models in satisfaction. • Future work targets an emergency model for EVs with low energy levels. [ABSTRACT FROM AUTHOR]

The growing deployment of new flexible resources, renewable energy resources (RES), and Electric Vehicles (EV) in the distribution system necessitates new methods to manage the distribution system operation optimally. In this regard, our paper, by deploying the concept of Virtual Power Plants (VPPs) as the aggregation of multiple agents and local power markets that are known as important tools for future power systems presents a management framework for the distribution systems with high penetration of EVs. To this end, the interaction of the DSO and VPPs is studied based on their strategic behaviour through the local active and reactive power markets. This way, a bilevel optimization approach is proposed where the DSO aims to minimize its operational cost by setting the operation point of its own facilities and determining the hourly active and reactive power prices for VPPs considering the distribution system congestion in the upper level. At the lower level, VPPs try to minimize their cost by scheduling their assets based on the local active and reactive power prices set by the DSO. The results show how nodal pricing in local markets could improve the distribution system operation. In addition, it is indicated that Reactive Power Support (RPS) from VPP-owned EVPLs can decrease the VPPs' cost by gaining profit in the reactive power market and facilitating their participation in the active power market. • Distribution system management considering the strategic behaviour of the VPPs. • Bilevel optimization for local active and reactive market. • Optimal dispatching strategy of the DSO and VPPs considering EV parking lot. • Assessing the potential of EV parking lot to provide reactive power and flexibility. • Managing the challenges of high EV deployment and assessing their potential benefits. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) possess untapped potential as mobile power banks for actively delivering electricity between different energy communities, known as Community-to-Vehicle-to-Community (C2V2C) service. While C2V2C represents an effective means of inter-community electricity sharing, limited research explores EVs' role in electricity delivery between locations. Suitable control approaches of EV charging for the C2V2C service are lacking, and it is unclear how robust the C2V2C service is and how its performance is affected by different factors. This paper aims to bridge these research gaps by developing an advanced control of EV smart charging/discharging to facilitate the C2V2C service. By comparing the power balance in the EVs' current-connecting and next-destination communities, the advanced control derives a target state-of-charge for the EVs in the current-connecting community, which can optimize the electricity delivery between the two communities. Then, the robustness of the C2V2C service is analyzed by evaluating its performances under different scenarios. Major factors like community combinations, renewable energy system (RES) configurations, EV battery capacity and numbers are examined for their impacts on C2V2C performance. The findings demonstrate that the C2V2C service significantly enhances energy balance across diverse community combinations, particularly in workplaces with substantial RES capacity. A large EV battery capacity is beneficial for performance improvements, but the impact diminishes at higher values due to limited surplus renewables availability. The increasing EV number enhances both electricity delivery capability and utilization of self-produced renewables. This study validated the effectiveness of the C2V2C service and provides valuable insights into optimizing its application across different scenarios. • Propose Community-to-Vehicle-to-Community (C2V2C) for inter-community energy sharing. • Study mechanisms of electricity delivery and sharing via EV over-charging/discharging. • Develop an advanced control to optimize EVs' electricity delivery and sharing. • Analyze robustness of the C2V2C service under different scenarios. • Reveal how building-/RES-/EV-related factors affect the C2V2C service performance. [ABSTRACT FROM AUTHOR]

Lithium-ion cell usage is increasing worldwide due to its high energy density, low self-discharge rate and good life span. The temperature history of the cell impacts the life and safety; hence it is important to understand the impact of cooling design and cooling parameters for optimum cooling system performance. In the present study, a mathematical model for transient heat conduction with internal heat generation is developed in MATLAB. Capability of the present model, for air as coolant is validated with a wide range of available experimental results. Subsequently, the model was used to study the response of discharge rate (1–5C), coolant inlet velocities (1–5 ms-1) and coolant inlet temperature (15–35 °C). To improve the cooling characteristics, the analysis is extended to converging fluid channels, which enhances heat removal capability of coolant. A detailed analysis suggests that operating a cell at 3C discharge rate with a coolant velocity of 3 ms-1 and 25 °C is an ideal trade-off between heat removal and good battery thermal management system. Converging fluid domain along flow direction has been found to provide uniform temperature (Δ T = T max - T min) and low T max for optimum cell performance and life. Further, the model is used to analyze the cooling requirement for drive cycle based performance of cell and monthly average temperature variation over a year in a typical Indian city. The proposed methodology can be applied to obtain a safe and cost-effective thermal management system for air-cooled battery pack design. • Development of 2D electro-thermal numerical model for prismatic cell using MATLAB. • Propose and design of a converging fluid channel for efficient thermal management. • Estimation of current profile based on drive cycle and its impacts on cell temperature. • Effect of monthly average temperature on performance of thermal management system. [ABSTRACT FROM AUTHOR]

In this paper, a robust Internet-of-Thing (IoT) enabled Smart Energy Management Device (SEMD) is developed for Distributed Energy Resources (DERs) equipped energy consumers. The SEMD communicates with the existing metering infrastructure through serial communication using the Modbus RTU protocol and the RS 485 communication standard. The SEMD's energy management framework comprises three sequentially operated software modules, viz., data preprocessing, forecasting, and an optimization module, which aims to minimize the energy costs of end consumers. The software and hardware architecture of the developed SEMD is robust enough to fit real-time implementation and makes it user-friendly. The effectiveness of the SEMD has been tested on an experimental test rig at the Indian Institute of Technology Gandhinagar. The demonstration test rig comprises two DERs equipped energy consumers (viz. Energy consumer-A & B). Energy consumer-A comprises a roof-top solar PV system, Battery Energy Storage System (BESS), and conventional flexible/non-flexible household appliances. The Energy consumer-B consists of Vehicle-to-Grid (V2G) enabled Electric Vehicle (EV), lighting, and pump loads. The experimental results obtained in this work demonstrate that using SEMD satisfies all technical constraints and benefits consumers in reducing bill payments. Thus, SEMD is a critical enabler for DER-equipped energy consumers to participate actively in various demand-side energy management programs. • Robust software and hardware architecture of SEMD. • For fetching real-time data, a reliable IoT based Network. • Day-ahead optimal energy management algorithm development. • A PoC development at an educational institute campus. [ABSTRACT FROM AUTHOR]

Managing uncertainty is key to enhancing robustness in microgrids. This study focuses on the uncertainties in aggregated electric vehicles (EVs) and establishes a two-layer model predictive control (MPC) strategy for charging EVs with a microgrid. The uncertainty in sampling results of EV connection times to the microgrid and the uncertainty in sampling results of state of charge (SOC) of each EV at the time of the initial connection are both taken into account through multi-uncertainty sampling. In order of consider the impact of extreme scenarios, as feedback from arriving EVs are combined with multi-uncertainty sampling for higher accuracy of sampling results during rolling optimization. Simulation results show that, compared to conventional strategies, the proposed strategy has better performance in reducing forecasting error and regulating the charging and discharging of aggregated EVs with uncertainties. • A model predictive control approach is proposed for coordination with wind energy. • The uncertainty of sampling results of aggregated electric vehicles is handled. • The impact of extreme scenarios of aggregated electric vehicles is considered. • Feedback from aggregated electric vehicles is used for higher forecasting accuracy. [ABSTRACT FROM AUTHOR]

The potential of e-mobility to participate in intermittency flattening ancillary services (AS) to support solar photo-voltaic (PV) grid integration is analysed in this paper. First, the capability of Plug-in Electric Vehicles (PEVs) to act as Energy Storage Systems (ESS) based on daily energy storage potential (ESP) with and without vehicle-to-grid (V2G) services is studied. Mobility behaviour and penetration levels of various categories of vehicles-cars, vans, trucks, buses and motorcycles in the island are modeled using traffic data from Menorca island. Next, ancillary service (AS) potential of 1000 PEVs to support solar PV grid integration is analysed. A crucial parameter of PEVs to offer uni-V2G services under aggregator to participate in hour-ahead AS market, termed achievable power capacity (APC), is explored. Results showed that, for bidirectional V2G services in summer, ESP can reach up to 100 MWh for minimum (P1) and 375 MWh for maximum (P6) penetration levels considered. The regulation potential for every 1000 private PEVs aggregated to a maximum of 4.6 MW and reserve potential to a maximum of 5.25 MW under coordinated charging. This reveals the great potential of PEVs in offering AS in small touristic islands. • Assessed the potential of e-mobility to support RE integration in Menorca island. • Explored the potential of Plug-in Electric Vehicles (PEVs) to act as a temporary storage medium. • Determined the Achievable Power Capacity (APC) of the PEVs to participate in V2G with the help of Flexibility factor (FF). • A uni-V2G intermittency flattening service examined in Mao Municipality area. • Discussed the challenges in estimating the V2G potential of PEVs to offer V2G services. [ABSTRACT FROM AUTHOR]

• Control design and optimization of an on-grid PV system with EV charging station. • Controller's parameters design and optimization using analytical technique. • Controller's parameters design and optimization using Salp Swarm Algorithm (SSA). • Modeling a hypothetical EV charging station containing 20 ac L2 chargers. • Fast and stable response for a fast variation of irradiance level. • Fast and stable response for load pulsation due to EV charging. • Small steady state error and THD at different generation and load conditions. Integrating photovoltaic (PV)-based power systems with electric vehicle (EV) charging loads makes perfect sense due to the similarities between them in terms of power form, interface, and locations. However, this integration involves a high level of uncertainty, due to the random nature of solar generation and EV charging, which requires a careful control design and optimization to keep the system stable. This paper presents an optimization algorithm to find the best combination of the control parameters of a voltage source inverter that integrates a PV power system with an EV charging station through a common grid-connected ac-bus. The controller parameters are optimized using Salp Swarm Algorithm to minimize the fluctuation in the dc-bus voltage through balancing the active power-flow and the injected harmonics to the grid. The controller performance under different severe disturbances from both generation and load sides is investigated and compared with an optimum control design using system transfer functions and particle swarm optimization. A hypothetical level 2 ac charging station for EVs is modeled considering different operating conditions and utilized to test the controller along with real-world irradiance profiles. The proposed controller is validated using simulation and processor-in-the-loop platforms. The outcomes show that the proposed control design is able to reduce the fluctuation of the dc-bus voltage by around 50%, the total harmonic distortion of voltage by 40% and current by 64% compared to the analytical-based design, which makes the system compatible with the requirements defined by the IEEE 1547 international standard. [ABSTRACT FROM AUTHOR]

• A multi-objective model is proposed, which contains both the cost and carbon emission objectives. The sizing of the grid-connected converter is included. The carbon emission of the power from the grid is considered. The model can comprehensively describe the requirements of LVDC systems in the economy and environmental protection. • This paper considers the effect of EVs on the sizing of LVDC systems. Both the charging and discharging of EVs are fully used to reach better sizing and operation for the system. • A minimum fuzzy carbon emission method (MFCEM) is given to transform the multi-objective into a single objective. Compared with the linear weighting method, MFCEM is a useful method for finding a point on the Pareto front that balances cost and carbon emissions. Grid-connected low-voltage direct current (LVDC) systems offer a promising solution for achieving low-carbon buildings. However, a new challenge arises in sizing grid-connected LVDC systems, which is to fully utilize distributed renewable energy, energy storage, and load demand response to minimize both the economy and carbon emissions. To address this gap, this paper studies the sizing of grid-connected LVDC systems, considering the grid-connected converter, photovoltaic (PV), battery, and demand response of electric vehicles (EVs). Firstly, two objective functions are proposed, including the economy and carbon emissions. Secondly, constraints for sizing and operation are presented, with particular consideration given to the discharging of EVs and the corresponding cost of cycle-life loss. Finally, a minimum fuzzy carbon emission method (MFCEM) is proposed to solve the model, which can adaptively and objectively transform the multi-objective into a single objective. According to the case study, the proposed model comprehensively considers the cost and carbon emissions of grid-connected LVDC systems, and its feasibility and validity are verified. Compared with the linear weighting method, MFCEM is a useful method for finding a point on the Pareto front that balances cost and carbon emissions. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are considered a promising solution for reducing emissions in urban transportation and addressing energy crises. Several countries, including China, have implemented direct financial subsidies to encourage the adoption of EVs. However, there is a lack of comprehensive evaluation regarding the effectiveness of these subsidies and their interaction with non-financial incentive policies. Additionally, the design of subsidy policies can be endogenous, potentially leading to biased estimations of their effects on EV penetration. To address these issues, this study utilizes a "quasi-natural experiment" approach, taking advantage of the gradual reduction of nationwide EV subsidies by the Chinese government due to financial constraints, rather than market conditions, thus mitigating the endogeneity problem. The study collects panel data from 224 prefecture-level cities in China and employs a modified panel data fixed effects model to quantify the impact of financial subsidies on EV sales and market shares. The analysis focuses on subsidy variations during six periods. The main empirical findings are as follows: (1) 1 % increase in purchase subsidies leads to a 1.36 % increase in EV sales and a 2.31 % increase in EV market share. (2) Non-financial policies, such as parking benefits, promotional goals, and restrictions on the number of vehicles, also contribute to boosting EV sales. (3) The effects of purchase subsidies vary across cities with different socio-economic conditions. (4) Some non-financial policies are found to enhance the positive effects of subsidy policies. [Display omitted] • We empirically examine the impact of the purchase subsidy on EV promotion in China. • A subsidy decline due to government financial burden is used as a quasi-natural experiment. • Purchase subsidy and some non-financial policies are found to promote EVs penetration. • Heterogeneous effects of purchase subsidy on different kinds of Chinese cities are verified. • Some non-financial policies are found to enhance the positive effect of the subsidy policy. [ABSTRACT FROM AUTHOR]

Because of the energy crisis and environmental challenges, it is important to establish a new smart city model to offer some effective solutions. Electric vehicles are one of the primary components that may help to reduce environmental issues and intervene in the energy recovery and management process. On-board the vehicle, many methods can be used to manage the energetic flux. Hybrid arrangements are one of the strongest ways to merge two options. Arrangements can incorporate more than two separate energy sources, with the vehicle using one or both of them. Renewable energy advances these systems and provides new potential for the widespread use of hybrid and pure electric vehicles. The dynamic nature of the field, which includes varied technologies critical for renewable energy integration, justifies energy storage and recovery strategies. Its significance arises from its role in leading future research, informing policy decisions, and encouraging sustainability in energy practices across multiple industries. This work contributes to the development of robust and efficient energy infrastructures by addressing existing difficulties and optimizing energy systems. Generally, we will look at some existing energy storage methods that provide needed energy in electric vehicles. Some vehicles already employ these conventional technologies, so we will present some industry examples. And discuss the alternatives generated from these conventional techniques based on the most recent research on this subject. Energy recovery systems are also being considered as a supply system to increase range and support energy storage systems in a variety of configurations. • Electric mobility in smart cities: infrastructure, efficiency, and optimization. • EV hybrid energy storage & recovery: overcoming challenges and expanding research. • Hybrid storage alternatives extend range and boost ultra-low emissions. • Hybrid storage alternatives address energy recovery issues. • Mechanical & electrical energy recovery innovation. [ABSTRACT FROM AUTHOR]

• A probabilistic framework is proposed to quantify the service losses in road-power infrastructures due to distruptions. • The integrated road-power infrastructure is populated by both electric and internal combustion vehicles. • Graph theory and finite state machines are used to model the road-power infrastructure and the vehicles. • FSMs enable the performance assessment of single vehicles as well as fleets of vehicles. • The realistic case study shows that applicability of the proposed framework of simulation and analysis. Electric Vehicles (EVs) are key contributors to the reduction of CO2 emissions. However, reliance on EVs must come with the guarantee that the integrated road-power infrastructure is capable of providing adequate mobility serviceability, even in case of disruption due to accidents or disturbances due to traffic jams. In this paper, we propose a probabilistic scenario analysis framework to quantify service losses in terms of delays that vehicles (both EVs and Internal Combustion Vehicles (ICVs)) may incur due to different car accident scenarios. The framework is based on modelling the System of Systems (SoS) comprised by road network, electric power system and vehicles, with graph theory and Finite State Machines (FSMs), respectively, and then embedding the model within a probabilistic scenario analysis, wherein meaningful disruption scenarios are sampled, service losses are measured (specifically as the ratio between the increase in travel time spent along the origin-destination routes on the road network following a disruption, and the corresponding travel time in nominal traffic conditions), and the economic losses and transport reliability of the infrastructure are assessed. To exemplify the application of the framework, we consider a benchmark road-power infrastructure in New York state travelled by a mixed fleet of EVs and ICVs, with different EVs penetration levels and under car accidental scenarios of different magnitudes. By using the insightful graphical representation of the results in terms of traffic volume across different road sections, the framework allows comparing alternative road-power infrastructure designs (e.g., critical roads, optimal gas and charging station locations, power network structure and topology, ...) with respect to travel times, economic service losses and transport reliability considering different nominal and disruption scenarios under different EVs penetration levels service. [ABSTRACT FROM AUTHOR]

• Various optimization techniques in AGC are critically reviewed. • Role of demand side management in AGC is also addressed. • Impact of electric vehicle integration to the grid is studied. Load and generation must be balanced for the system to be stable, which implies frequency deviation must be minimal. Whenever load perturbation takes place in the network, frequency diverges from its nominal value and unplanned exchange of power leads to unfortunate consequences. This will cause the mal-operation of electrical equipment such as change in speed, low efficiency, vibrations, harmonics, inaccuracy etc. Automatic Generation Control (AGC) provides a signal to alter the actual output power of multiple generators within a certain range based on changes in system frequency. For this purpose, several control techniques were employed in the system. This paper focuses on various approaches in the domain of automatic generation control for microgrid based hybrid power system. AI based techniques shows progressive results as compared to other classical and cascaded approaches. Machine learning and deep learning based controlling schemes has also been studied and study shows that these techniques provides loftier performance in comparison of other AI methods due to the availability of large number of trained data sets of controlled variable. Further massive data of controlled variables can be used to restore the system after cyber-attack. It is also addressed how to integrate electric vehicles with the power grid for Load Frequency Control. At the end of this article, some research gap such as role of demand response, protection and control issues, resilient controllers for cyber threats etc. are also addressed which will be useful for researcher working in this area. [ABSTRACT FROM AUTHOR]

As the cost of solar photovoltaic (PV) systems decreases and incentives such as feed-in tariffs (FiTs) are offered, solar-PV homes are becoming popular. Furthermore, solar-PV homes integrated with hybrid or electric vehicles (EVs) are emerging as a paradigm for future homes. Given the fact that there exists a considerable price difference between grid electricity supply and FiTs, decision making of energy storage using batteries becomes an imperative topic. This research proposes an innovative and generic framework for the decision making of energy storage using batteries based on Smart Meter data, which incorporates the actual energy generation and consumption patterns of solar-PV homes. The proposed energy storage decision is based on a developed economic model, with the consideration of the electricity price from the grid, the FiTs, and the storage cost using batteries (i.e. the average price per kWh of battery capital cost and maintenance cost). Moreover, an intelligent algorithm is developed to calculate the electricity quantities, i.e. electricity supplied from the grid, fed into the grid and stored in a battery of a given capacity, based on the monitored data obtained from a Smart Meter over a year. The results reveal that at the present utility prices of $0.3/kWh and a feed-in-tariff of $0.10/kWh in the studied region, energy storage with a battery cost of $0.2/kWh or more is excessive and not economically feasible. However, with the increasing cost of electricity in recent years and constant changes in battery price, the outcomes could quickly reverse. This research contributes an innovative framework for battery storage decision-making of solar-PV homes, based on economic analysis and Smart Meter data. [ABSTRACT FROM AUTHOR]

This paper addresses the management of a Fuel Cell (FC) – Supercapacitor (SC) hybrid power source for Electric Vehicle (EV) applications. The FC presents the main energy source and it is sustained with SCs energy storages in order to increase the FC source lifespan by mitigating harmful current transients. For this aim, the reported work proposes a Grey Wolf Optimizer (GWO) for an efficient power management of the studied hybrid power system. The key idea of the proposed approach is to incorporate the benefit of the GWO in terms of fast optimization and convergence accuracy, in order to achieve efficient energy sources exploitation and provide the desired driving performances. Simulations and experimental results verify the validity of the proposed management algorithm. • An advanced power management strategy. • Grey Wolf. • Electric Vehicle Application. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are currently used in electric vehicles (EVs) with many advantages; however, batteries contribute significantly to the life cycle environmental impacts of these vehicles. This article presents a comparative life cycle assessment of two types of batteries – lithium manganese oxide (LiMn 2 O 4) and lithium ion phosphate (LiFePO 4) – frequently used in EVs, addressing real-life operational conditions and battery capacity fade. The influence of the location of battery manufacturing and vehicle charging (electricity mix) was also assessed. A life cycle model of batteries in an EV with a service life of 200 000 km (functional unit) was implemented to assess Primary Fossil Energy, Global Warming, Acidification, and Eutrophication impacts. Moreover, the environmental performance of EVs considering both battery types was evaluated. The LiFePO 4 battery had better operational performance than the LiMn 2 O 4 one and fewer batteries were needed over the EV service life. Conversely, the life cycle impacts of the LiFePO 4 batteries were higher than the LiMn 2 O 4 ones mainly due to higher manufacturing impacts. In countries with a mix based on renewable electricity, the battery production is the most important contributor to impacts, while in countries with electricity mostly from fossil sources, the use phase is the most important. For both battery types and most impact categories, the environmental impacts of EVs were highly dependent on the usage scenario and the electricity mix used for charging. • We use LCA and capacity fade models to assess environmental impacts of EV batteries. • LiMn 2 O 4 and LiFePO 4 batteries are compared under different EV usage scenarios. • More LiMn 2 O 4 than LiFePO 4 batteries are needed for an EV service life of 200 000 km. • The LiMn 2 O 4 battery has lower LC impacts than the LiFePO 4 in all EV usage scenarios. • Usage conditions and electricity mix are critical for the EV environmental performance. [ABSTRACT FROM AUTHOR]

Highlights • The lack of controllability of the PI FOC for PMSM at high speeds has been analysed. • An adaptive second order controller has been designed in z domain. • An accurate model of the EV propulsion system has been designed. • A numerical methodology for the maximum operating stable frequency has been detailed. • Numerical results illustrate the superior performance of the proposed algorithm. Abstract Permanent magnet synchronous machines (PMSM) are widely used in the automotive industry for electric vehicle (EV) and hybrid electric vehicle (HEV) propulsion systems, where the trend is to achieve high mechanical speeds. High speeds inevitably imply high current electrical frequencies, which can lead to a lack of controllability when using field oriented control (FOC) due to sampling period constraints. In this work, a comprehensive discrete-time model is fully developed to assess the stability issues in the widely used FOC. A speed-adaptive control structure that overcomes these stability problems and extends the speed operation range of the PMSM is presented. Also, a numerical methodology from which the maximum operating stable frequency can be computed in advance of any experimentation, is developed. All contributions are accompanied and supported by numerical results obtained from an accurate MATLAB/Simulink model. [ABSTRACT FROM AUTHOR]

Management for the temperature increase of lithium-ion batteries in electric vehicles (EV) is required for safety and energy efficiency. As a cooling medium for lithium-ion batteries, one suitable candidate is trimethylolethane (TME) hydrate, which may be utilized as a phase change material (PCM). This hydrate has a large dissociation heat and will form/dissociate at approximately 30 °C under atmospheric pressure conditions. A cooling system which uses TME hydrate can have a high performance with regards to the efficiency and the stability of heat transfer process. However, in previous studies, the equilibrium temperature and the dissociation heat of TME hydrate have been reported only for a TME mass fraction of 0.625. Since TME hydrate forms in an aqueous solution, it is necessary to know how these thermophysical properties change for a range of TME mass fractions. Therefore, in this study, the equilibrium temperatures and the dissociation heats were measured respectively by the visual observation method and differential scanning calorimetry measurements for TME mass fraction range from 0.20 to 0.80. The highest equilibrium temperature was 29.6 °C at the mass fraction of 0.60. For the cooling system, the largest dissociation heat was 190.1 kJ kg−1, also at the mass fraction of 0.60. Image 1 • Proposed new hydrate-based technology of cooling Li-ion battery for electric vehicle. • Investigated thermophysical properties of TME hydrate as phase change material. • Revealed the relationship between the mass fraction and TME thermophysical properties. • Observed the saturation of TME aqueous solution over the mass fraction range of 0.65. • The optional mass fraction of TME hydrate for stably cooling Li-ion battery was 0.60. [ABSTRACT FROM AUTHOR]

Abstract One of the most important challenges to mitigate global climate change is to move towards sustainable mobility. In this line, electric vehicles are currently an efficient and environmentally friendly means of transport. The objective of this research is to analyze the incidence of implementing fast charging stations for electric vehicles in the power distribution system of a Latin American intermediate city. The paper covers social, geographic, and technical aspects to determine the minimum infrastructure needed for the selected case study. The analysis is carried out by computational tools in order to model a 50 kW fast charging station with an AC/DC and DC/DC power converter. The inclusion of this type of stations in one of the feeders of the power distribution system of the city of Cuenca, Ecuador, is studied. After the analysis, it can be concluded that the impact of the inclusion of fast charging stations in the distribution system of the study area is reduced in terms of harmonic distortion and energy capacity. This low technical impact can lead to economic and environmental benefits for the city. This study helps to establish the necessary procedure to determine the fast charging infrastructure in urban centers and verify its impact on the power distribution network. Highlights • The number of EV fast charging stations needed in an intermediate city is determined. • The impact of EV chargers on the network is low considering voltage and energy quality. • With a penetration of 10% of EV, the city´s power system has no operational problems. • The inclusion of EV reduce an average of 10 kg CO2/day per vehicle in the study area. [ABSTRACT FROM AUTHOR]

Abstract Electric vehicles (EVs) have recently been significantly developed in terms of both performance and drive range. There already are various models commercially available, and the number of EVs on road increases rapidly. Although most existing EVs are charged by electric cables, companies like Tesla, BMW and Nissan have started to develop wireless charged EVs that don't require bulky cables. Rather than physical cable connection, the wireless (inductive) link effectively avoids sparking over plugging/unplugging. Furthermore, wireless charging opens new possibilities for dynamic charging – charging while driving. Once realised, EVs will no longer be limited by their electric drive range and the requirement for battery capacity will be greatly reduced. This has been prioritised and promoted worldwide, particularly in UK, Germany and Korea. This paper presents a thorough literature review on the wireless charging technology for EVs. The key technical components of wireless charging are summarised and compared, such as compensation topologies, coil design and communication. To enhance the charging power, an innovative approach towards the use of superconducting material in coil designs is investigated and their potential impact on wireless charging is discussed. In addition, health and safety concerns about wireless charging are addressed, as well as their relevant standards. Economically, the costs of a wide range of wireless charging systems has also been summarised and compared. Highlights • The paper timely presents a thorough review on wireless charging technology for electric vehicles. • Following the previous review papers, the paper includes the most recent developments. • The paper shows future directions for wireless charging from both commercial and research perspective. • The paper has reviewed the health and safety aspect of wireless charging technology, as well as relevant standards published recently. • The paper covers the economic aspect of wireless charging systems, and presents a detailed comparison on cost. [ABSTRACT FROM AUTHOR]

Abstract The Battery Swapping Station (BSS) is emerging as a viable means for fast energy refill of Electric Vehicles (EVs), in addition to many potential benefits in providing energy and ancillary services to the distribution grid. This paper focuses on developing a mathematical model for uncertainty-constrained BSS optimal operation that not only covers the random customer demands of fully charged batteries, but also leverages the available batteries to reduce its operation cost through demand shifting and energy sellback. The battery degradation is further modeled and formulated to ensure a practical solution. Numerical simulations on a test BSS demonstrate the effectiveness of the proposed model and show its viability in achieving the predefined objectives as discussed in this paper. Highlights • Developed a mathematical model for uncertainty-constrained BSS optimal operation. • Proposed random customer demands, demand shifting and energy sellback. • Developed the costs of power exchange with the utility grid and battery degradation. • Proposed robust optimization for capturing uncertainties in demand and electricity price. • Proposed mixed integer linear programming for problem modeling and formulation. [ABSTRACT FROM AUTHOR]

Abstract In this work, an advanced range estimation method based on experimental test data including environmental factors and dynamic vehicle parameters with driver and road type predictions is proposed for electric vehicles. The focus point of the given study is to predict remaining range in general, at first start-up, without knowing future driving profile, in terms of giving an idea of how much distance can be travelled with the remaining amount of energy. Road type and driver profile are estimated by utilizing decision tree method and periodogram of jerk trace, respectively. Based on the preliminary results, utilized decision tree algorithm classifies the road type with an accuracy over 98%. Vehicle range is estimated online by using machine learning algorithm based on experimental train data sets where chassis dynamometer tests were conducted by performing specific driving cycle in various conditions. For a real life verification, the vehicle is driven for a 50.4 km distance in a road having mostly urban driving characteristics. The results of real life measurements show that the proposed method predicts range with a low margin of error and estimates final remaining capacity 11.3% better than rated one. Highlights • An ANN is developed for range estimation method based on experimental test data. • Decision tree is utilized for road type detection. • The proposed model verification is performed in a real-life with a BEV. • Driver aggressiveness is leveled by periodogram of jerk trace. • The ANN model outperforms the rated range model especially for unknown destinations. [ABSTRACT FROM AUTHOR]

Highlights • The paper presents an overview of the main challenges that affect EU power systems due to the penetration of cleaner and distributed resources. • The first part of the paper describes the changes that are occurring in the power system and market sector. • The second part of the paper presents the challenges in the Eu-wide planning process. Abstract In the past, much of Europe's electricity grid network has been designed in consideration of the locations of conventional generation plants. However, a large share of today's renewables production – notably variable wind and solar – does not correspond to this grid architecture. Interconnectors, in addition to internal infrastructure, are key to creating new electricity corridors to connect areas of surplus to areas of scarcity. In this context, in 2014 the European Council, in recognizing that a fundamental role of transmission infrastructure is to enable the integration of areas of high renewable energy potential with main consumption areas, endorsed the proposal by the European Commission to extend the current 10% electricity interconnection target to 15%. In this way, more available interconnection capacity would enable the grid to accommodate such increasing levels of variable renewable generation in a secure and cost-effective way. The previously described changes in interconnected system operating conditions, resulting in potential occurrences of unforeseen serious disturbances – most notably the well-known system split observed in the continental synchronous area on 04 November 2006 – have led to create the first Regional Security Coordination Initiatives (RSCIs now RSCs) and establishment of Coreso and TSC back in 2008. These entities have allowed TSOs to further coordinate not only system operations but also network planning, system adequacy analysis, market setups, etc. However, given the challenges faced by the electricity industry in Europe, one may wonder whether the pace of developments in regulation and market design, system operations and system planning can keep up with the pace of change in the electricity system. In response to this concern, the present paper focuses on a central question: how the electricity system that is today primarily organized in a centralized, top-down manner will be reshuffled in the future? The structure of the paper is as follow. The authors begin by describing the changes that are occurring in the power system and market sector, together with their drivers and underpinning regulation. Then the paper presents the challenges in the Eu-wide planning process, starting from TYNDP and PCI and then proceeding also to technical aspects of cost-benefit analysis and interconnection targets. [ABSTRACT FROM AUTHOR]

Abstract Electrifying urban transportation through the adoption of Electric Vehicles (EVs) has great potential to mitigate two global challenges, namely climate change and energy scarcity, and also to improve local air quality and further benefit human health. This paper was focused on the six typical factors potentially influencing the purchase behaviour of EVs in Beijing, China, namely vehicle price, vehicle usage, social influence, environmental awareness, purchase-related policies and usage-related policies. Specifically, this study used the data collected in a paper-based questionnaire survey in Beijing from September 2015 to March 2016, covering all of the 16 administrative regions, and tried to quantify the relative importance of the six factors, based on their weights (scores) given by participants. Furthermore, Multinomial Logit (MNL) models and Moran's I (a measure of global spatial autocorrelation) were used to analyse the weights of each factor from statistical and spatial perspectives, respectively. The results suggest that 1) vehicle price and usage tend to be more influential among the six factors, accounting for 32.3% and 28.1% of the importance; 2) Apart from the weight of social influence, the weights of the other five factors are closely associated with socio-demographic characteristics, such as individual income and the level of education; 3) people having similar attitudes towards vehicle usage (Moran's I = 0.10) and purchase restriction (Moran's I = 0.14) tend to live close to each other. This paper concludes with a discussion on applying the empirical findings in policy making and modelling of EV purchase behaviour. [ABSTRACT FROM AUTHOR]

Abstract The flexibility of a net zero energy/emission building (NZEB) in the cooling dominated area is quantitatively investigated in this research. Flexibility sources include the renewable energy, i.e. building integrated photovoltaics (BIPVs) and wind turbine, and thermal energy storages, i.e. air handling unit cooling storage tank (ACST) and domestic hot water tank (DHWT). Parametric analysis has been conducted based on all proposed flexibility indicators with respect to the renewable energy capacity, the volume of both the ACST and the DHWT, and the set point temperature for recharging the DHWT. From our results, to meet the annual energy balance, this NZEB should be equipped with BIPVs and an 8 kW wind turbine, while these BIPVs should be installed on four walls and the roof (totally 306 m2 BIPVs). Considering the grid feed-in tariff in Hong Kong, the NZEB will get the net annual operational income at 646.3 HK$/m2.a. Flexibility factors indicate the flexibility of the energy storage systems for storing the surplus renewable energy (REe) and fulfilling the building demand. During the charging process, the increase of the REe capacity shows positive impact on flexibility factors of both the ACST and the DHWT. In the case when BIPVs are on four walls, with respect to the increase of the rated wind turbine capacity from 0 to 8 kW, the flexibility factor of the ACST increases from 0.49 to 0.58 during the charging process, whereas it decreases from -0.51 to -0.59 during the discharging process. Meanwhile, the flexibility factor of the DHWT increases from 0.32 to 0.57. Moreover, with respect to the increase of the ACST volume from 0.5 m3 to 2.5 m3, the flexibility factor increases from -0.23 to 0.17 considering both the charging and the discharging processes. With respect to the increase of the DHWT volume from 1 m3 to 3 m3, the flexibility factor increases from 0.32 to 0.74. With respect to the increase of the set point temperature for recharging the DHWT from 65 ºC to 100 ºC, the flexibility factor increases from 0.32 to 0.82. Moreover, in comparison with the nighttime interaction between the building and the vehicle, the daytime interaction will reduce the reliance on the grid for charging the EV. Moreover, the energy interaction between the building and the vehicle will be enhanced. [ABSTRACT FROM AUTHOR]

Abstract With the increasing penetration of electric vehicles (EVs) in distribution network (DN), it becomes crucial for distribution system operators (DSO) to determine how many EVs can be integrated into one node of DN. This paper presents a probabilistic method to evaluate distribution network EV hosting capacity based on Combined Cumulants and Gram-Charlier Expansion. First, the mathematical model of EV charging demand is established, and the Cumulants of EV and power load are derived based on Monte Carlo simulation. Then, stochastic power flow based on combined Cumulants and Gram–Charlier series expansion is used to analyze the DN EV hosting capacity. Finally, the methods are tested on IEEE 33-node DN, and the simulation results prove their validity and rapidity. [ABSTRACT FROM AUTHOR]

Highlights • A price-responsive early charging control (PRECC) is proposed, which uses an adaptive speedup factor to tackle the dynamic problem caused by the uncertainty of EV behavior. • PRECC considers price preference in early charging to further narrow the gap between online and offline charging optimizations. Besides, it is not limited by EV states, therefore effectively coping with base load variations. • No iterative optimization is required to obtain the control factor, thus PRECC comes with low computational cost. This is very meaningful for reducing the overall costs of charging coordination. Abstract The uncertainty of electric vehicle (EV) behavior is deemed as a major challenge in online charging scheduling. It may lead to charging congestion to compromise the whole benefits of EV owners and aggregators. Early charging is the most efficient way to tackle the dynamic problem. However, it is very challenging for early charging to achieve the adaptive control and minimize electricity bill. In this paper, a price-responsive early charging adaptive control (PRECC) is proposed. The speedup factor is designed as a subtotal of charging demand categorized by electricity price, and it can be determined with only one offline charging optimization through a data-mining method. Due to the strong correlation with electricity price, PRECC can help online scheduling algorithms minimize early charging cost. Since it is not limited by the states of EVs, it can rapidly respond to the variations of base load and electricity price. Besides, with the independent design, it can well match online scheduling algorithms. Computer simulations are made to verify the proposed control. The results show that PRECC can improve the optimality of online scheduling by an average of 5.4%. Compared with the traditional early charging strategies, it has obvious advantages in terms of optimality, power capacity utilization, and profitability. [ABSTRACT FROM AUTHOR]

Highlights • The study focusses on an EV charging station design problem. • EV charging station operation is modelled in detail. • EV power demand is represented by an Erlang B queuing model. • The EV operations include the purchase and sale of energy in the electricity market. • This design problem includes a wide variety of energy sources. • A genetic algorithm was adapted to deal with the complexity of the design problem. Abstract The development of electric vehicles (EVs) depends on several factors: the EV's acquisition price, autonomy, the charging process and the charging infrastructure. This paper is focused on the last factor: the design of an EV fast-charging station. In order to improve the profitability of the fast-charging stations and to decrease the high energy demanded from the grid, the station includes renewable generation (wind and photovoltaic) and a storage system. Unlike other papers, this one uses a detailed model of the charging process that considers the arrival time and state of charge of electric vehicles. First, the Monte Carlo method is used to model the EV demand and the renewable generation. Later, a genetic algorithm (GA) optimizes the installation and operation of the EV fast-charging station. It finds the optimal solution that maximizes the profit measured by its net present value (NPV). Several cases are studied to analyse the influence of renewable energies and storage systems. The obtained results show that a mix of renewable energies and storage systems attains the best cost efficient solution. [ABSTRACT FROM AUTHOR]

Silicon nanowires (SiNWs) and silicon nanotubes (SiNTs), with high specific capacity and low volume expansion during charging and discharging processes, have been recognized among the most promising anode materials for use in next-generation lithium ion batteries (LIBs). However, the life cycle impacts of SiNW-and SiNT-based LIBs are of grave concern due to the heavy use of toxic chemicals and potential nanoparticle emissions during their manufacturing processes. In this study, life cycle assessment (LCA) method is adopted to evaluate the environmental impacts of two battery packs, which are configured with the same capacity of 63.5 kWh to power a mid-size electric vehicle (EV). Both battery packs are based on a lithium nickel manganese cobalt oxide (LiNi 1/3 Mn 1/3 Co 1/3 O 2 , NMC) cathode, but one uses a SiNW anode, whereas the other uses a SiNT anode (NMC-SiNW and NMC-SiNT). Following a cradle-to-gate approach, the analysis is conducted based on LCA databases and literature. Results indicate that to obtain the same capacity, the NMC-SiNW LIB battery is with higher material consumptions and environmental impacts than the NMC-SiNT LIB battery. This study provides insights into life cycle impacts of SiNW-and SiNT-based LIBs and can be useful for understanding and improving the environmental performance of nanostructured Si-based LIBs for next-generation EV applications. [ABSTRACT FROM AUTHOR]

Aggregators act as a mediator between electric vehicles (EVs) and distribution network operator (DNO), providing mutual advantages for both. To achieve these advantages, the optimal operation of distribution network and EV aggregators (EVAs) should be coordinated. This paper aims at establishing a decentralized robust model to minimize the total cost of the system by operating the smart distribution network (SDN) and EVAs in a coordinative manner. To tackle the enforced operating uncertainties associated with wind generation and wholesale market price, an adaptive robust optimization (RO) approach is utilized enabling DNO to adjust different conservation levels throughout the operating horizon. In order to preserve the independency of EVAs and reduce the computational burden, the RO based model is solved using a decentralized algorithm which is developed based on the alternating direction method of multipliers (ADMM). Accordingly, the operating problems of DNO and EVAs are coordinated and optimized, independently. The effectiveness of proposed model is demonstrated using a modified 33-bus smart distribution network with multiple EVAs. [ABSTRACT FROM AUTHOR]

The deployment of electric vehicles (EVs) is growing significantly in recent years. The increasing EV charging loads pose great stress on power grids in Sweden, as many existing power grids are not designed to host such large shares of new electric loads. Hence, studies investigating the impact of EV charging are needed. This study conducts a case study based on an existing Swedish residential power grid using real-life EV charging data to estimate the local grid hosting capacity (HC) for EVs. A combined time-series and stochastic HC assessment method is used with voltage deviation, cable loading and transformer loading as the performance indices. Uncertainty in EV charging locations and individual charging behaviour have been considered via Monte Carlo simulations. The power grid HC is analysed and compared under three charging strategies and four EV penetration levels. Study results show that a charging strategy based on low electricity prices gave lower HC due to simultaneous EV loads compared to the other two strategies: charging directly after plugging in the EV and an even charging load through the plug-in session. This implies the need for coordinated charging controls of EV fleets or diversified power tariffs to balance power on a large scale. • Analyze EV charging behaviors based on real data collected from a large number of chargers. • Conduct Monte Carlo simulation to model the stochastic charging behavior of EVs. • Use a real residential distribution network as case studies for evaluating hosting capacity. • Investigate the impacts of EV charging forms on the hosting capacity of a real power grid. • Study how EV penetration levels affect the hosting capacity of a real power grid. [ABSTRACT FROM AUTHOR]

• Proposing optimal management of a local operator under the concept of P2P trading. • Assessing the performance of the local operator in different uncertainty attitudes. • Several sources of uncertainty considered such as load, price, and PV generation. • Risk-averse decision-maker tends to purchase from the grid to reduce potential risks. • Risk-seeking decision-maker targeting demand supply through the P2P trading scheme. Small-scale smart microgrid is prone to economic consideration of adequate transactive energy sharing and reliable certified power pool hub. Moreover, direct integration of efficient with renewable energy providers in presence of storage such as electric vehicle (EV) aggregation improves the performance of energy systems and guarantees secure trade among the consumers. However, unified modeling of the energy community framework faces the challenge of load management. In this paper, an optimal risk management procedure is proposed for renewable-based prosumers such as photovoltaic (PV), and EV to maximize the horizon of uncertainty parameter. The energy demand in peer-to-peer (P2P) household energy sharing is considered as the uncertain parameter. To this end, in order to study the behavior of a risk-averse and a risk-seeker decision-maker, an information-gap decision theory (IGDT) probability model is applied. In order to demonstrate the performance of the proposed approach, the model is implemented in a test microgrid and the simulation results are presented and discussed. [ABSTRACT FROM AUTHOR]

Electric vehicles are affected by various factors such as the ambient temperature, traffic conditions, driver behavior, vehicle weight, and route characteristics. This study evaluated the energy efficiency of an electric SUV with regenerative braking system under real-world driving conditions. Data were collected using a controller area network while driving on the same route at each regenerative braking stage. Chassis dynamometer tests were performed to verify battery consumption during acceleration and regenerative braking. From the real-world driving test, it was determined that as the regenerative braking stage increased, the battery consumption (excluding regenerative braking) and energy recovered. However, the net battery consumption decreased. In addition, as the speed increased, the energy consumption increased in the order of urban, rural, and motorway sections owing to the air resistance and rolling resistance. Although the energy efficiency tended to increase with the regenerative braking stage, we observed that the real-world driving environment also had an impact. Therefore, in energy efficiency evaluation research, it is essential to analyze the results that reflect the various influencing factors in real-world driving environments and to verify the characteristics of each regenerative braking stage through chassis dynamometer tests. • Real-world testing of regenerative braking stages shows improved energy efficiency in electric vehicles. • Higher regenerative braking stages lead to greater energy recovery and reduced net energy consumption. • Energy efficiency varies with driving conditions, such as speed and traffic volume. • Further research is needed to quantify the effects of various parameters on EV energy efficiency. [ABSTRACT FROM AUTHOR]