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Aiming at the problem of source-load uncertainty caused by the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs) into modern power system, a robust optimal operation scheduling algorithm for regional integrated energy systems (RIESs) with such uncertain situations is urgently needed. Based on this background, aiming at the problem of the irregular charging demand of EV, this paper first proposes an EV charging demand model based on the trip chain theory. Secondly, a multi-RIES optimization operation model including a shared energy storage station (SESS) and integrated demand response (IDR) is established. Aiming at the uncertainty problem of renewable energy, this paper transforms this kind of problem into a dynamic robust optimization with time-varying parameters and proposes an improved robust optimization over time (ROOT) algorithm based on the scenario method and establishes an optimal scheduling mode with the minimum daily operation cost of a multi-regional integrated energy system. Finally, the proposed uncertainty analysis method is verified by an example of multi-RIES. The simulation results show that in the case of the improved ROOT proposed in this paper to solve the robust solution of renewable energy, compared with the traditional charging load demand that regards the EVs as a whole, the EV charging load demand based on the trip chain can reduce the cost of EV charging by 3.5% and the operating cost of the multi-RIES by 11.7%. With the increasing number of EVs, the choice of the starting point of the future EV trip chain is more variable, and the choice of charging methods is more abundant. Therefore, modeling the charging demand of EVs under more complex trip chains is the work that needs to be studied in the future. [ABSTRACT FROM AUTHOR]

The need for public fast electric vehicle charging station (FEVCS) infrastructure is growing to meet the zero-emission goals of the transportation sector. However, the large charging demand of the EV fleet may adversely impact the grid's stability and reliability. To improve grid stability and reliability, the development of a DC microgrid (MG) leveraging renewable energy sources to supply the energy demands of FEVCSs is the sustainable solution. Balancing the intermittent EV charging demand and fluctuating renewable energy generation with the stable DC bus voltage of a DC MG is a challenging objective. To address this objective, a piece-wise droop control strategy is proposed in this work. The proposed scheme regulates DC bus voltage and power sharing with droop value updating in a region-based load current distribution. Voltage compensation in individual regions is carried out to further improve the degree of freedom. In this paper, the performance of the proposed strategy is evaluated with the consideration of real-time solar PV dynamics and EV load dynamics. Further, to showcase the effectiveness of the proposed strategy, a comparative analysis with a maximum power point tracking (MPPT) controller against various dynamic EV load scenarios is carried out, and the results are validated through a hardware-in-loop experimental setup. Despite the intermittent source and EV load dynamics, the proposed piece-wise droop control can maintain voltage regulation with less than 1% deviation. [ABSTRACT FROM AUTHOR]

The need to reduce pollution and the shortage of fossil fuels has led to the increased development of hybrid and full electric vehicles. There is also increased development and an increased use of renewable energy resources such as photovoltaic, wind, tidal, etc. These two trends pose serious challenges to the existing grids: a lack of supply power when the demand is high, deficient management of excess power, an increased number off grid faults, grid instabilities and others. One way to increase the penetration of electric vehicles (EV) into the market and to keep the existing grid infrastructure is to combine renewable energy resources with the grid and local battery packs to make EV charging stations. This paper focuses on developing such an EV charging station. The main advantages of the proposed EV charger include the fact that it uses only off-the-shelf inverters, and it is intended to be used in households where the maximum installed power is 3.6 kW to enable fast-charging operation modes or to reduce the costs of energy while charging the EV battery; it can reduce the energy demand from the grid during peak power consumption; it has the potential to lower electrical energy costs; it offers the possibility of vehicle-to-home (V2H) implementation; it is modular (if other technologies become available and more affordable, the consumers can easily update the system, adding more power or adding other types of renewable resources). [ABSTRACT FROM AUTHOR]

The power factor of industrial facilities is typically inductive. The case study in this paper was based on a typical Malaysian 11-kV on-grid industrial system with renewable energy sources and electric vehicle charging station connected. The integration of renewable energy sources reduces energy consumption from the grid; it consecutively reduces greenhouse gas emissions. However, the integration of renewable energy sources such as solar photovoltaic operating at unity power factor results in a reduction of the industry's power factor. According to the Malaysian Distribution Code, the power factor of a medium voltage industrial system should be more than 0.85 lagging. A long-term low power factor will reduce the related electrical equipment lifespan and increase the monthly electricity bills. A classic method to overcome this issue was by installing reactive power compensator devices, such as the synchronous condenser, static VAr compensator and static synchronous compensator. Studies had revealed that solar photovoltaic with appropriate control system design could perform short-term reactive power compensation. The control techniques used are either power factor control, active power control, reactive power control or any combination of them. However, neither the reactive power compensator devices nor the solar photovoltaic with a control system can regulate the industry's power factor to an intended value throughout its operation. Thus, this paper presents a simple, relatively cost-effective design of a master power factor controller that is capable of regulating the industry's power factor to an intended value throughout its operation with a single preset reference. In this research, an industry-grade system comprises an industrial load installed with a power factor-controlled capacitor bank, a power factor-controlled solar photovoltaic system, a bidirectional current-controlled electric vehicle charging system based on CHAdeMO 1.1 standard charging protocol and a master power factor controller was designed using the Matrix Laboratory/Simulink software. This paper has provided simulation results as proof that each of the designed equipment was functioning appropriately. The results also proved that the proposed master power factor controller was capable of regulating the power factor of the industrial system to above 0.85 lagging throughout its operation. [ABSTRACT FROM AUTHOR]

An energy internet is a system that enables energy sharing in a distribution system such as the Internet. It has been attracting a lot attention from both academia and industry. The main purpose of this paper is to develop a model of an electric vehicle (EV) energy network to transmit, distribute, and store energy by EVs from renewable energy sources to places that need the energy. We describe the EV energy internet in detail and then formulate an optimization problem to place charging stations in an EV energy internet. We develop two solutions: one using a greedy heuristic and another based on diffusion. Simulation results using real-world data show that the greedy heuristic requires less charging stations, whereas the diffusion-based algorithm incurs less energy transmission loss. [ABSTRACT FROM PUBLISHER]

The searching and utilization of new energy sources and technologies is a current trend. The effort to increase the share of electricity production from renewable energy sources is characteristic for economically developed countries. The use of accumulation of electrical energy with a large number of decentralized storage units is most preferred, as well as the focus on the production of energy at the point of its consumption. Modern cogeneration units are a good example. This paper describes the accumulation of electrical energy for equalizing the power balance of electric charging stations with high instantaneous power. The possibility of re-utilization of electrical energy from the charged vehicle in the case of lack of electricity in the power grid is solved at the same time. This paper also deals with the selection of appropriate concept of accumulation system and its cooperation with both renewable and distribution networks. Details of the main power components including the results obtained from the system implementation are also described in this paper. [ABSTRACT FROM AUTHOR]

An electric vehicle (EV) battery charging station (EV-BCS) based on a bipolar dc power grid is presented in this paper, which is capable of delivering power to the grid (vehicle-to-grid - V2G mode), and directly exchange power between different EVs connected to the EV-BCS (vehicle-to-vehicle - V2V mode), besides the traditional battery charging operation (grid-to-vehicle - G2V mode). The presented EV-BCS is based on three-level bidirectional buck-boost dc-dc converters and has a modular structure. Simulation results are presented with the aim of validating the aforementioned operation modes, being considered two EVs for simplicity reasons, since it is enough to validate the proposed operation modes. The presented results comprise both balanced and unbalanced operation in terms of power from the EVs viewpoint, with the purpose of considering a real scenario of operation, where a balanced consumption or power injection from the bipolar dc power grid side is always guaranteed. [ABSTRACT FROM AUTHOR]

This paper proposes a Gauss-quadrature-based probabilistic load flow (PLF) keeping in view the uncertainties and correlations in load, renewable generations (wind and PV), and electric vehicle charging. An approach to include uncertainties of voltage-dependent loads has also been proposed. The simulation study has been performed on a modified IEEE-118 bus system. Further, the performance of the proposed method has been compared with that of the benchmark method, i.e., Monte Carlo simulation and a few well-established methods, such as point estimate method, combined cumulant and Gram–Charlier method, and combined cumulant and Gaussian mixture approximation method. The extensive simulation studies and comparison of various results obviates the aptitude of the proposed method for PLF. [ABSTRACT FROM AUTHOR]

In this paper, we consider delay-optimal charging scheduling of the electric vehicles (EVs) at a charging station with multiple charge points. The charging station is equipped with renewable energy generation devices and can also buy energy from power grids. The uncertainty of the arrival of the EV, the intermittence of the renewable energy, and the variation of the grid power price are taken into account and described as independent Markov processes. Meanwhile, the required charging energy for each EV is random. The goal is to minimize the mean waiting time for EVs under the long-term constraint on the cost. We propose queue mapping to convert the EV queue to the charging demand queue, and we prove the equivalence between the minimization of the two queues' average length. Then, we focus on the minimization for the average length of the charging demand queue under the long-term cost constraint. We propose a framework of Markov decision process (MDP) to investigate this constrained stochastic optimization problem. The system state includes the charging demand queue length, the charging demand arrival, the energy level in the storage battery of the renewable energy, the renewable energy arrival, and the grid power price. Additionally, the number of charging demands and the allocated energy from the storage battery compose the 2-D policy. We derive two necessary conditions of the optimal policy. Moreover, we discuss the reduction of the 2-D policy to be the number of charging demands only. We give the sets of system states for which charging no demand and charging as many demands as possible are optimal, respectively. Finally, we investigate the proposed policies numerically. [ABSTRACT FROM PUBLISHER]

Integrating renewable energy sources (RES) and the transition of the transport sector to electric vehicles (EV) is a necessary step in reducing global greenhouse gas emissions (GHG). The integration of RES and EV charging stations in the power grid and the optimal scheduling of device operation are problems addressed by the research community. This paper proposes the concept of an energy community (EC) with three business buildings and a local electricity market (LEM) to ensure optimal grid operation and the lowest individual prosumer costs. Furthermore, a charging model for EVs in the building parking lot was introduced to increase overall social welfare. The proposed optimization model is developed as a stochastic two-stage mixed integer nonlinear programming (MINLP) that considers the realization of uncertain input data in the second stage, the virtual cost of battery degradation, and the functional dependencies of variables. Several case studies were conducted to evaluate the proposed model’s advantages correctly. The results show a possible reduction in the total daily cost, a reduction of the total daily imported electricity from the grid and a reduction of the total daily exported electricity to the grid up to 100%, depending on the case. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations' energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station's performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station's performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system's simulation results are provided and scrutinized to confirm the concept's suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations. [ABSTRACT FROM AUTHOR]

Severe pollution induced by traditional fossil fuels arouses great attention on the usage of plug-in electric vehicles (PEVs) and renewable energy. However, large-scale penetration of PEVs combined with other kinds of appliances tends to cause excessive or even disastrous burden on the power grid, especially during peak hours. This paper focuses on the scheduling of PEVs charging process among different charging stations and each station can be supplied by both renewable energy generators and a distribution network. The distribution network also powers some uncontrollable loads. In order to minimize the on-grid energy cost with local renewable energy and non-ideal storage while avoiding the overload risk of the distribution network, an online algorithm consisting of scheduling the charging of PEVs and energy management of charging stations is developed based on Lyapunov optimization and Lagrange dual decomposition techniques. The algorithm can satisfy the random charging requests from PEVs with provable performance. Simulation results with real data demonstrate that the proposed algorithm can decrease the time-average cost of stations while avoiding overload in the distribution network in the presence of random uncontrollable loads. [ABSTRACT FROM PUBLISHER]

Electric vehicles (EVs) are pivotal in the progression toward sustainable transportation. However, a growing body of recent studies has challenged the often-assumed notion that electric vehicles are often considered to be net-zero emissions. These studies suggest that electric vehicles could potentially generate substantial emissions due to their reliance on fossil fuels to generate electricity, especially when receiving fast charging services. This phenomenon has been mitigated with the use of renewable energy sources in the power system. However, due to the intermittent nature of renewable energy sources, additional requirements have arisen. To address this situation, this paper presents an interdisciplinary study of collaborative fast-charging stations (FCS) and distributed renewable energy planning based on the interactions between traffic and power networks in order to increase the adoption rate of electric vehicles and reduce emissions caused by traffic and power systems. The locations and sizing of fast-charging stations, distributed photovoltaic generation, and renewable energy power supply for electric vehicles are determined in the proposed strategy through multi-objective integer programming. The proposed planning strategy considers heterogeneous electric vehicle driving range constraints, the operation security of the power distribution system, the reliability and expansion of the current power distribution, as well as different types of air pollutant classifications resulting from the charging behaviors. To guarantee the feasibility and accuracy of the planning results, an interactive algorithm is applied to solve the multi-objective integer model. Several cases are conducted to validate the proposed approach. The results demonstrate that the proposed planning strategy has good performance in the planning of fast-charging stations and distributed renewable energy integration. • A joint planning approach is proposed. • Optimize the locations and sizing of the fast-charging stations and distributed renewable energy. • Quantify different types of air pollutant emissions during charging services to promote the adoption of EVs and renewable energy. • An integer multi-objective model with an interactive algorithm is proposed. • The results demonstrate that the proposed method confers significant advantages to the application of EVs and renewable energy. [ABSTRACT FROM AUTHOR]

Recently, Electric Vehicles (EV) have been providing fast response and substantial progress in the power generation model. Further, EVs are exploited as adaptable Energy Storage Systems (ESSs) and show a promising performance in ancillary service markets to increase the demand of Smart Grid (SG) integration. The expansion of Vehicle-to-Grid concept has created an extra power source when renewable energy sources are not available. Yet, numerous operational problems still are required to be considered for EV implementation to turn out to be extensive. Even the development of Photo-Voltaic (PV) technology creates a problem in SGs when used for EV charging. Because of this, the Energy Management System (EMS) is required to handle charging requirements and deal with the intermittent generation. Here, in this research, an Improved Honey Badger algorithm (IHBA) is proposed for integrating SGs with EV parking lot, solar panels, and dynamic loads at the Point of Common Coupling (PCC). The proposed IHBA uses a dynamic programming method to optimize the charging Grid-to-Vehicle (G2V) or discharging Vehicle-to-Grid (V2G) profiles of the EVs using the forecasts of PV generation. This algorithm considers user preferences while also lowering reliance on the grid and maximizing SG effectiveness. The study's findings show that the Honey Badger method is efficient in resolving issues involving large search spaces. The developed method is used to optimize charging and discharging of EV which is tested in MATLAB to obtain a stable load profile. From the evaluation of obtained results, it is evident that the IHBA controller outperforms the WOA and EHO controllers in terms of total harmonic distortion voltage (3.12%), power loss (0.197 kW) and efficiency (98.47%). [ABSTRACT FROM AUTHOR]

In China, the power sector is currently the largest carbon emitter and the transportation sector is the fastest-growing carbon emitter. This paper proposes a model of solar-powered charging stations for electric vehicles to mitigate problems encountered in China's renewable energy utilization processes and to cope with the increasing power demand by electric vehicles for the near future. This study applies the proposed model to Shenzhen City to verify its technical and economic feasibility. Modeling results showed that the total net present value of a photovoltaic power charging station that meets the daily electricity demand of 4500 kWh is $3,579,236 and that the cost of energy of the combined energy system is $0.098/kWh. In addition, the photovoltaic powered electric vehicle model has pollutant reduction potentials of 99.8%, 99.7% and 100% for carbon dioxide, sulfur dioxide, and nitrogen oxides, respectively, compared with a traditional gasoline-fueled car. Sensitivity analysis results indicated that interest rate has a relatively strong influence on COE (Cost of Energy). An increase in the interest rate from 0% to 6% increases COE from $0.027/kWh to $0.097/kWh. This analysis also suggests that carbon pricing promotes renewable energy only when the price of carbon is above $20/t. [ABSTRACT FROM AUTHOR]

This paper presents an energy management strategy for a car park with charging stations for electric vehicles. The study focuses on the supervision strategy with a fuzzy logic controller. The power comes from renewable energy sources (wind turbine, photovoltaic panel) and main grid. The algorithm objective is to distribute the grid power and the renewable power according to the electrical load demand of the parking. The main goal of the design is to obtain maximum utilization of the renewable source energy. Simulation results show that the fuzzy logic is a suitable energy management control strategy. The use of renewable energy reduces the impact on the power grid caused by increasingly high number of electric vehicles deployed worldwide. [ABSTRACT FROM AUTHOR]

The problem of supraharmonics (SH) in a microgrid (MG) system connected to an electric vehicle (EV) charging station is discussed in this work. SH, or high-frequency harmonics which occur beyond the typical harmonic spectrum, can cause problems with power quality (PQ) and equipment failure. A multilevel converter (MLC)-based solution is proposed together with a frequency domain analysis method to lessen this issue. Using frequency domain methods like the fast fourier transform (FFT), the suggested method precisely measures and examines the SH content in the MG and EV charging station. Based on the findings, an MLC control method that actively lowers the SH is created. The efficiency of the suggested strategy in lowering SH levels and ensuring PQ is assessed through experimental validation. The development of SH analysis and mitigation techniques as a result of this research makes it easier to safely and effectively integrate the infrastructure for EVs with renewable energy sources (RES). [ABSTRACT FROM AUTHOR]

Electric vehicles represent an innovation in mobility that can help significantly reduce greenhouse emissions and mitigate climate change. However, replacing internal combustion with electric vehicles is not enough. This replacement needs to be complemented with a change in the energy mix of individual countries towards renewable energy sources and efficient use of electricity generated as a secondary product. Recuperative braking energy from trains can serve as one source of such secondary energy. Following an analysis of recuperative energy generated and analysis of charging requirements of individual electric vehicles, the paper proposes a model of a charging site near train stations. Using this energy to charge electric vehicles helps to reduce energy consumption from the electricity grid and thus reduce carbon emissions. Compared to other articles, the proposed model ensures the efficient use of recuperative braking energy from trains by using the variable charging power function; thereby, the installation of additional battery storage is eliminated. Our model results show that the benefits of a car park with a reservation system near train stations increase the car park efficiency, provide a sufficient number of private charging points, contribute to efficient use of recovered energy, and reduce carbon emissions. [ABSTRACT FROM AUTHOR]

Achieving the goal of sustainable development is dependent on the widespread integration of renewable energy sources, energy storage systems (ESSs), and electric vehicles (EVs). However, a continuous increase in the penetration of such elements would bring more complexities to the distribution network. Accordingly, this paper presents a unified planning model comprising renewable energy-based distributed generation (DG), ESS, and electric vehicle charging stations (EVCSs). In this regard, a Latin Hypercube Sampling method is utilized to generate multi-scenario for describing the uncertainty of renewable energy and load demand. The stochastic EV charging behaviors are represented by various probability density functions (PDF). In addition, an exploitable capacity loss of ESS and EV batteries is calculated by the battery degradation model based on the depth of discharge (DOD). Furthermore, the battery degradation cost is incorporated into the objective of the planning model to identify the optimal decision for candidate assets. A piecewise linearization approach is introduced to convert the problem into a mix-integer linear programming (MILP) model. Numerical results demonstrate that the exploitable capacity loss of batteries plays a key role in asset planning and provides potential contributions to the optimal decisions of the distribution network. In the meantime, by considering battery degradation in the optimization model, the sustainability and lifetime of the battery can be preserved. • Uncertainties include the renewable energy, load demand, and behaviors of electric vehicles are considered. • Renewable energy-based DG, ESS, and EVCS are involved in the unified planning model. • The degradation model is proposed to analyze the exploitable capacity loss of ESS and EV battery. • The enhancement of investment decision while maintaining the lifetime of battery is achieved. [ABSTRACT FROM AUTHOR]

This manuscript proposes a new hybrid system to analyze the impact of the electric-vehicle-charging-station with the combination of renewable energy and grid-connected system. The proposed method is the implementation of Forensic-Based Investigation (FBI) and Artificial Transgender Longicorn Algorithm (ATLA) called FBI–ATLA method. The objective of the proposed system is to provide the computational complexity, reduce the cost, improve efficiency and enhance the network power. Here, two sorts of charging stations are deemed regarding with power supply capabilities. While type 1 charging stations utilize the power grid, renewable energy sources and vehicle-to-grid technology, type 2 charging stations have swappable batteries. The charging station may be met by four dissimilar power sources: (i) power grid (PG); (ii) batteries stored in charging stations; (iii) renewable energy sources (e.g., solar-energy); (iv) the energy discharged from electric vehicles (EV) to grid via vehicle-to-grid (V2G) connection. The implementation of the FBI–ATLA introduced through the MATLAB platform, and the performance of proposed method is compared with existing methods. In ANN, PSO, GA and proposed technique, the values of effectiveness during 100, 200, 500 and 1000 is 99.0037%, 99.2356%, 99.8363% and 99.9373%. Finally, the result shows that the proposed system is proficient than other existing techniques. This manuscript proposes a new hybrid system to analyze the impact of the electric-vehicle-charging-station (EVCS) with the combination of renewable energy and grid-connected system. The proposed method is the implementation of Forensic-Based Investigation (FBI) and Artificial Transgender Longicorn Algorithm (ATLA) called FBI–ATLA method. The objective of the proposed system is to provide the computational complexity, reduce the cost, improve efficiency and enhance the network power. [ABSTRACT FROM AUTHOR]

The usage of electric vehicles (EV) has been increasing over the last few years due to a rise in fossil fuel prices and the rate of increasing carbon dioxide (CO2) emissions. EV-charging stations are powered by existing utility power grid systems, increasing the stress on the utility grid and the load demand at the distribution side. DC grid-based EV charging is more efficient than AC distribution because of its higher reliability, power conversion efficiency, simple interfacing with renewable energy sources (RESs), and integration of energy storage units (ESU). RES-generated power storage in local ESU is an alternative solution for managing the utility grid demand. In addition, to maintain the EV charging demand at the microgrid levels, energy management and control strategies must carefully power the EV battery charging unit. In addition, charging stations require dedicated converter topologies, control strategies, and need to follow set levels and standards. Based on EV, ESU, and RES accessibility, different types of microgrid architecture and control strategies are used to ensure optimum operation at the EV-charging point. Based on the above said merits, this review paper presents different RES-connected architecture and control strategies used in EV-charging stations. It highlights the importance of different charging station architectures with current power converter topologies proposed in the literature. In addition, a comparison of microgrid-based charging station architecture with its energy management, control strategies, and charging converter controls are also presented. The different levels and types of charging stations used for EV charging, in addition to controls and connectors used, are also discussed. An experiment-based energy management strategy was developed to control power flow among the available sources and charging terminals for the effective utilization of generated renewable power. The main motive of the EMS and its control is to maximize the usage of RES consumption. This review also provides the challenges and opportunities in EV-charging, and parameters in selecting appropriate charging stations. [ABSTRACT FROM AUTHOR]

In order to reduce the environmental hazards from non-renewable energy consumption and promote sustainable social development, renewable electric energy has been developed vigorously in countries around the world as the best strategical choice of cleaner energy development. Especially in transportation sector, large quantities of electric vehicles (EVs) are considered the most promising alternative to alleviate the problems of energy crisis, environment pollution and climate warming. However, with the rapid growth of EVs, much more small-scale fast charging stations (FCSs) have limited distribution capacity to satisfy every charging demand simultaneously and are under big pressure to ensure good service capability. Based on current conditions, this paper proposes a hierarchical power control strategy (HPCS) that considers EVs demand, FCS service capability, and FCS daily load fluctuation as a whole with weighted dynamic priority (WDP) and fluctuation smoothing index (FSI). The assumed system model is based on each EV's unique demand and FCS constraints. The simulation results show that the optimal WDP is sufficiently elastic to satisfy EV's demand urgency and fast charging station operator (FCSO)'s income simultaneously. Moreover, FSI is illustrated concretely to decrease daily load fluctuation and peak load value, which is useful to reduce overall power loss. As a result, the HPCS can be realized on a 32-bit processor platform in a FCS to gain an optimal balance between EVs demands and FCSO's profits. [ABSTRACT FROM AUTHOR]

The electric vehicle (EV) can be utilized as a dynamically configurable dispersed energy storage in the vehicle-to-grid (V2G) and vehicle-to-building (V2B) operation mode to balance the energy demand between buildings and EVs. This paper proposes a mixed integer linear programming based collaborative decision model to study the energy sharing between a building and an EV charging station (CS). The building has its distributed generator, and electric and thermal energy storage, and the CS has its own renewable energy source. To model the V2G/V2B integration, we introduce three sets of decision variables to represent the energy exchange among building, CS and power grid. A set of parameters are introduced to model the driver behaviors, such as initial and desired state of charge level of EV battery, and available hours of EV, and sixteen different building categories (e.g., office, restaurant, hotel, warehouse, etc.) are studied. The impacts of driver behaviors and building categories on the economic performance of V2G/V2B integration are characterized and analyzed. The results from this research can recommend best V2G/V2B integration considering various driver behaviors and building categories which can provide valuable insight for smart community design. [ABSTRACT FROM AUTHOR]

The use of Electric Vehicle (EV) to the transport section is increasing and replacing the conventional fossil fuel-based vehicles. Still, EV has not received success due to some limitations such as cost of the vehicle, battery capacity, and availability of charging station. The availability of charging station depends on its geographical location. At the same time, location of the electrical network affects the energy loss and voltage deviation. Therefore, the test system considered here is a road network of the urban area overlapped with a 33-bus radial network. Allocation of EV charging stations and photovoltaic energy resources as renewable distributed generation was attempted simultaneously using 2-layer optimization. Differential Evolution and Harris Hawks Optimization (HHO) are the two tools that were used to solve the problem and the final results were validated using eight other established optimization techniques. 2m point estimation method was employed to deal with uncertainties in EV and PV. Monte-Carlo simulation was applied to cross verify the performance. The land cost and customer accessibility to charging stations were taken into account to allocate it to proper places. The entire study was performed based on the 24-hour dynamically varying EV flows and PV outputs. [ABSTRACT FROM AUTHOR]

Inhabited islands depend primarily on fossil fuels for electricity generation and they also present frequently a vehicle fleet, which result in a significant environmental problem. To address this, several governments are investing in the integration of Renewable Energy Sources (RESs) and Electric Vehicles (EVs), but the combined integration of them creates challenges to the operation of these isolated grid systems. Thus, the aim of this paper is to propose an Electric Vehicle charging strategy considering high penetration of RES. The methodology proposes taxing CO 2 emissions based on high pricing when the electricity is mostly generated by fossil fuels, and low pricing when there is a RES power excess. The Smart charging methodology for EV optimizes the total costs. Nine scenarios with different installed capacity of solar and wind power generation are evaluated and compared to cases of uncoordinated charging. The methodology was simulated in the Galapagos Islands, which is an archipelago of Ecuador, and recognized by the United Nations Educational, Scientific and Cultural Organization (UNESCO) as both a World Heritage site and a biosphere reserve. Simulations results demonstrate that the EV aggregator could reduce costs: 7.9% for a case of 5 MW installed capacity (wind and PV each), and 7% for a case of 10 MW installed (wind and PV each). Moreover, the use of excess of RES power for EV charging will considerably reduce CO 2 emissions. [ABSTRACT FROM AUTHOR]

Nowadays, there is a great development in electric vehicle production and utilization. It has no pollution, high efficiency, low noise, and low maintenance. However, the charging stations, required to charge the electric vehicle batteries, impose high energy demand on the utility grid. One way to overcome the stress on the grid is the utilization of renewable energy sources such as photovoltaic energy. The utilization of standalone charging stations represents good support to the utility grid. Nevertheless, the electrical design of these systems has different techniques and is sometimes complex. This paper introduces a new simple analysis and design of a standalone charging station powered by photovoltaic energy. Simple closed-form design equations are derived, for all the system components. Case-study design calculations are presented for the proposed charging station. Then, the system is modeled and simulated using Matlab/Simulink platform. Furthermore, an experimental setup is built to verify the system physically. The experimental and simulation results of the proposed system are matched with the design calculations. The results show that the charging process of the electric vehicle battery is precisely steady for all the PV insolation disturbances. In addition, the charging/discharging of the energy storage battery responds perfectly to store and compensate for PV energy variations. [ABSTRACT FROM AUTHOR]

Worldwide, the hosting capacity of renewable energy sources (RES) is remarkably expanded in distribution systems. One of the most auspicious RES is wind turbine systems (WTSs), which can improve the performance of distribution systems. In turn, the integration of high WTS penetrations can also deviate the system operation away from the standard condition. To tackle this issue, we propose a method for enhancing the hosting capacity of multiple WTSs considering their intermittent generations in distribution systems. The proposed method considers the operation of the on-load tap changer (OLTC), allowing to solve voltage problems efficiently. Especially, the proposed method optimises the charging/discharging power of electric vehicles (EVs), which can contribute positively to regulating WTS intermittent generation. Additionally, the reactive power support of WTSs, complying with the IEEE 1547:2018 standard, is incorporated in the planning model of WTSs. To solve such an optimisation problem, a bi-level optimisation algorithm is developed based on the gravitational search algorithm. Comprehensive simulation results are performed on the 69-bus distribution feeder interconnected to four EV stations. Based on the results, the proposed approach can efficiently enhance/increase the hosting capacity of WTSs in distribution systems, thanks to the consideration of OLTC, reactive power support of WTSs and EVs. [ABSTRACT FROM AUTHOR]

As we move towards electrification of transportation, there is a need is a need to replace gas stations with Electric Vehicle (EV) charging stations at equally convenient locations and look at various energy storage methods onboard an electric vehicle. There are various charging methods which have been discussed in literature. This paper discusses the common methods of charging and discusses the various technologies that will have tremendous impact in the future on energy storage technology on autonomous electric vehicle.

The rapid spread of renewable energy resources has increased need for demand flexibility as one of the solutions to power system imbalance. However, to properly estimate the demand flexibility, demand characteristics must be analyzed first and the corresponding flexibility measures must be validated. Thus, in this study, a novel approach is proposed to evaluate the demand flexibility provided by Electric Vehicle Chargers (EVC) in the residential sector based upon a new process of electric charging demand characteristic data analysis. The proposed model estimates the frequency, consistency, and operation scores of the flexible demand resource (FDR) during identified ramp-up/down intervals presented in our previous work. The scores are included in the components that calculate the flexibility score referring that the closer it is to 1, the higher utilization as an FDR. A case study was conducted by considering EV user segmentation based on their demand characteristic analysis. The results confirm that flexibility scores of segmented EVC groups are about 0.0273 in ramp-up and ramp-down intervals. Based on the experimental results, the flexibility score can be utilized for multi-dimensional analysis and verification in perspectives of seasonality, participation time interval, customer group classification, and evaluation. Thus, the proposed method can be used as an indicator to determine how a segmented EVC group is adequate to participate as an FDR while suggesting meaningful implications through EVC demand data analysis. [ABSTRACT FROM AUTHOR]

In the context of the increased acceptance and usage of electric vehicles (EVs), vehicle-to-building (V2B) has proven to be a new and promising use case. Although this topic is already being discussed in literature, there is still a lack of experience on how such a system, of allowing bidirectional power flows between an EV and building, will work in a residential environment. The challenge is to optimize the interplay of electrical load, photovoltaic (PV) generation, EV, and optionally a home energy storage system (HES). In total, fourteen different scenarios are explored for a German household. A two-step approach is used, which combines a computationally efficient linear optimizer with a detailed modelling of the non-linear effects on the battery. The change in battery degradation, storage system efficiency, and operating expenses (OPEX) as a result of different, unidirectional and bidirectional, EV charging schemes is examined for both an EV battery and a HES. The simulations show that optimizing unidirectional charging can improve the OPEX by 15%. The addition of V2B leads to a further 11% cost reduction, however, this corresponds with a 12% decrease in EV battery lifetime. Techno-economic analysis reveals that the V2B charging solution with no HES leads to strong self-consumption improvements (EUR 1381 savings over ten years), whereas, this charging scheme would not be justified for a residential prosumer with a HES (only EUR 160 savings). [ABSTRACT FROM AUTHOR]