Photovoltaic Charging Station Research Articles

Design and Implementation of Photovoltaic Electric Vehicle Charging Stations: A Case Study at Universidad Tecnológica de Pereira

As the global transportation sector transitions towards sustainability, the need for renewable energy- powered electric vehicle (EV) charging infrastructure becomes increasingly evident. This paper presents a case study on the design and implementation of a photovoltaic electric vehicle charging stations at the Universidad Tecnológica de Pereira (Colombia). The study outlines the design process, technical specifications, and implementation strategies for a photovoltaic (PV) charging station, emphasizing key considerations such as location selection, solar panel configuration, and necessary equipment. Additionally, the paper evaluates the performance and effectiveness of the PV charging station in supplying the demand for EV charging services on campus. This case study provides valuable insights into the feasibility and scalability of PV EV charging infrastructure in educational institutions.

Research on Intelligent Scheduling Strategy for Electric Heavy Trucks Considering Photovoltaic Outputs

Due to the extensive use of fossil fuels, energy conservation and sustainable transportation have become hot topics. Electric vehicles (EVs), renowned for their clean and eco-friendly attributes, have garnered considerable global attention and are progressively being embraced worldwide. However, disorganized EV charging not only reduces charging station efficiency but also threatens power grid stability. In this low-carbon era, photovoltaic storage charging stations offer a solution that accommodates future EV growth. However, due to the significant instability in both the charging load and photovoltaic power generation within charging stations, it is critical to maximize local photovoltaic power consumption and minimize the impact of disorganized EV charging on the power grid. This paper formulates an intelligent scheduling strategy for electric heavy trucks within charging stations based on typical photovoltaic output data. The study focuses on a photovoltaic storage charging station in an industrial zone in Xinjiang. While considering the electricity procurement cost of the charging station, the aim is to minimize fluctuations in the electricity procurement load. A simulation analysis was conducted using MATLAB 2021a software, and the results indicated that, compared to an uncoordinated charging strategy for electric heavy trucks, the proposed strategy reduced electricity procurement costs by CNY 1348.25, decreased load fluctuations by 169.45, and improved the utilization efficiency of photovoltaic energy by 30%. A statistical analysis was also used to support the reduction in electricity procurement costs and load variations. Finally, a sensitivity analysis of the weight factors in the objective function was performed, proving that the proposed strategy effectively reduces electricity procurement costs and improves the utilization efficiency of photovoltaic energy.

Sizing requirements of the photovoltaic charging station for small electrical vehicles

Electric vehicles (EVs) are being introduced in Rwanda and becoming attractive for different reasons. For instance, these types of vehicles can help decrease air pollution and noise emissions. In addition, it presents an alternative to combustion engines, given the increased price of fuel resources in Rwanda and around the world. This paper presents a tool tailored to optimize the design of an electrical charging station serving small-sized electric vehicles, utilizing the algorithm to assist in sizing stand-alone mopped charging stations. The developed tool is based on the toolbox EventSim from MathWorks, which permits the combination of the simulation of discrete events (such as the arrival of customers at the station) with continuous states (such as the simulation of the charging process). The required PV power was estimated by utilizing solar resources, for the location, from renewables. Ninja. The number of customers arriving at the existing oil station is normalized to estimate the energy requirements of the mopped fleet. A Poisson distribution was proposed to model the battery discharge upon arrival, and different related parameters were evaluated through a sensitivity analysis to identify their effects on the performance of photovoltaic charging station. For the testing values, the station parameters were changed by ±25% to determine the impact of key design parameters on station performance, as well as other satisfaction measures such as average waiting time and average queue length. With a 25% increase in photovoltaic panels, the blackout period decreases by 2.12%, while a 25% decrease in photovoltaic panels causes an increase of 2.18% in the blackout period. Utilizing the energy management system (EMS), the waiting time was reduced by 8%.

An urban-scale spatiotemporal optimization of rooftop photovoltaic charging of electric vehicles

Solar photovoltaic (PV) farming is increasingly being used to power electric vehicles (EVs). Although many studies have developed dynamic EV charging prediction and scheduling models, few of them have coupled rooftop PV electricity generation with the spatiotemporal EV charging demands at an urban scale. Thus, this study develops a research framework containing three interconnected modules to investigate the feasibility of EV charging powered by rooftop PVs. The framework is constructed by the statistics of time serial EV charging demands at each station, the planning of rooftop PV installations associated with all charging stations, and the development of a dynamic dispatching algorithm to transmit surplus electricity from one station to another. The algorithm can maximize the overall balance between supply and demand, maximize the total PV electricity generation while minimising the total PV area, minimize the number of PV charging stations used as the suppliers for dynamic dispatch, and minimize the total electricity transmission distance between stations given the same power supply. The experiment utilizes a complete EV charging dataset containing 5574 charging piles with more than 9.7 million records in June and July in Guangzhou, China. The results show that rooftop PVs can supply more than 90% of the charging demand. The results encourage and inspire us to generalize and promote such a solution in other cities. Future work can refine the algorithm by adapting different PV sizes into various charging stations to further improve the electricity generation capability and the dynamic dispatching efficiency.

A Versatile Surrogate Model of the Power Distribution Grid Described by a Large Number of Parameters

This paper aims to present a general-purpose Surrogate Model for the probabilistic analysis of power distribution grids with a large number of input parameters. The distinctive feature of the novel technique is the employment of the partial derivatives of output variables versus input parameters to tame the “curse of dimensionality” problem exhibited by prior surrogate model calculation techniques. The second important feature of the proposed Surrogate Model method is that it does not require any a priori assumption about the nature or statistical distribution of the input parameters. In fact, it can be applied whenever design parameters are deterministic variables as well as when they are uncertain and represented by continuous and/or discrete random variables. Relevant applications presented in the paper refer to the probabilistic analysis of the distribution grid in the presence of a large number of photovoltaic sources and electric vehicle charging stations.

Integrating photovoltaic noise barriers and electric vehicle charging stations for sustainable city transportation

Photovoltaic noise barriers (PVNBs) offer a dual advantage of reducing traffic noise pollution and providing renewable electricity to cities. However, how the effective integration of PVNB-generated power into urban energy networks remains a critical area lacking research. To bridge this gap, this study proposes PVNBs-energy storage (ES)-charging station (CS; PVNBs-ES-CS) strategy. It can facilitate the actual consumption of PVNBs power and the mitigation the burden on the grid posed by electric vehicles (EVs) charging demands. The case study conducted in Guangzhou, China, reveals that PVNBs can support up to 5% of the total power demand for EVCSs. Under the PVNBs power maximization consumption scenario, PVNBs can meet up to 30% of the power demands from 60 EVCSs, with 58% of PVNBs generated power being consumed. In the PVNBs-ES-CS future utilization scenario, up to 30% of the power demand of 125 EVCSs can be met, and 36% of the power of PVNBs can be consumed. The combination of PVNBs and EVCSs offers a practical solution for incorporating renewable energy sources into urban energy networks. This application mode can be applied in various cities with EV demands and PVNB power generation data.

Techno-economic and environmental assessment of renewable energy sources, virtual synchronous generators, and electric vehicle charging stations in microgrids

Optimum design for microgrids that include renewable energy sources (RESs) is a complex process that requires optimization across a wide range of factors, including economic, technological, and environmental effectiveness. The inverter-connected RES units lack the rotational mass required to maintain the system’s inertia and can result in significant frequency instability during disturbances. The battery energy storage system-based virtual synchronous generator (BESS-VSG) is a unique approach to address this challenge since it mimics a conventional synchronous generator (SG) using the inverter regulation concept. Furthermore, given the recent rise in electric vehicles (EVs), EV owners’ charging patterns impact the microgrid’s operational, financial, and technological aspects. Therefore, this research offers a novel Modified Multi-objective Salp Swarm Optimization Algorithm (MMOSSA)-based technique for optimal photovoltaic (PV), wind turbine (WT), BESS-VSG, and EV charging station (EVCS) allocation on microgrids. The objective function is designed to optimize the total net present cost (TNPC), levelized cost of electricity (LCOE), energy loss, frequency deviation, voltage stability indicator (VSI), and carbon emissions. The proposed strategy is evaluated on two real-world grid networks: Masirah Island in Oman and Ankara in Turkey, where wind prospects, solar potential, and EV charging patterns are conflicting. This study investigated PV/BESS-VSG, WT/BESS-VSG, and PV/WT/BESS-VSG configurations regarding the weather and load variability for the two test networks. The obtained results demonstrate that the PV/WT/BESS-VSG is the best choice for both the test networks, with TNPC values of 261030.9 $ and 303840.5 $, respectively, and LCOE values of 0.0383 $/kWh and 0.0469 $/kWh, respectively. The carbon emissions are reduced by 88.8% and 87.3% for the two test networks, respectively. In addition, with the accurate PV/WT/VSG design and appropriate EVCS placements, the microgrids’ renewable fraction, the quantity of sold energy, and technical characteristics improved significantly. Moreover, employing numerical analysis, different loss of power supply probability (LPSP) values, spacing index, and hyper-volume index, the suggested MMOSSA method is compared to five relevant multi-objective optimization strategies. The findings demonstrate that the MMOSSA Pareto fronts offer better solutions across all the assessments.

Technique for the comprehensive characterization of supraharmonic disturbances (9–150 kHz) in the joint time-frequency domain

The inverter-based devices connected to the low-voltage (LV) grid, such as photovoltaic panels (PVs) and electric vehicle charging stations (EVCSs), are known to generate supraharmonic distortions in CISPR Band A (9–150 kHz) that may interfere power line communications (PLC). Current measurement methods were designed to provide results of the disturbances only in the frequency domain. Thus, impulsive signals and fast amplitude variations of the disturbances are lost in the calculation process. This work addresses this issue by developing a statistical study of the distribution of supraharmonic disturbances in the joint time-frequency domain, which allows the clear differentiation of impulsive signals. The results show that the amplitudes of the normalized measurements can be modeled by the t Location-Scale probability density function (PDF), which can be used to set a threshold for the differentiation of the impulsive disturbances. The study proposes a measurement method and a set of metrics for the complete characterization of the disturbances present in the LV grid, by characterizing the noise and distortions as a whole and also impulsive emissions separately. The main contribution of this work is that the measurement technique characterizes the disturbances in the joint time-frequency domain, and it is coherent with methods below 9 kHz. The proposed method is applied to some typical grid recordings containing different types of noise and emissions. The results demonstrate that the method allows the complete characterization of the supraharmonic distortions present in the LV grid and a proper differentiation of the impulsive emissions in the joint time-frequency domain.

Prosumer-Based Optimization of Educational Building Grid Connected with Plug-in Electric Vehicle Integration using Modified Firefly Algorithm

Educational buildings have the potential to support government programs in efforts to reduce carbon emissions. Installing photovoltaics and providing charging stations can reduce the use of fossil fuels and increase the number of electric vehicle users. This paper aims to optimize educational buildings when implementing a prosumer scheme and integrating Plug-in Electric Vehicles (PEV) to meet building electricity demands. Optimization is carried out through two case studies, namely the application of a prosumer scheme with independent photovoltaic generators with and without PEV integration. The optimization process uses the Modified Firefly Algorithm. The results obtained by applying the prosumer scheme to educational buildings, the two case studies can produce LCOE cheaper than just buying electricity from the grid. Optimizing results show that photovoltaic installation and charging stations in educational buildings can be beneficial when implementing a prosumer scheme.

Electric Vehicle Solar Charging Station Siting Study Based on GIS and Multi-Criteria Decision-Making: A Case Study of China

Electric vehicles (EVs) are one of the most practical solutions to the energy issue and environmental pollution. In recent years, EVs have developed rapidly, but are still limited by charging problems. The emergence of photovoltaic charging stations can solve the environmental pollution and charging problems. The location of charging stations is critical in the life cycle of electric vehicles. In this paper, a multiple-criteria decision-making (MCDM) method based on Geographic Information Technology (GIS) for optimal site selection is proposed. First, based on literature reading and expert interviews, a site selection index system was identified, including four aspects with a total of ten sub-criteria. Secondly, a spatial database of relevant evaluation criteria was established using GIS, and preliminary analysis was conducted. Then, the fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory method) is applied for assigning the criteria weights. Then, potential sites are ranked using the fuzzy MULTIMOORA (Multi-Objective Optimization on the basis of Ratio Analysis) method. Then, the model was validated by siting the electric vehicle PV charging stations in Qingdao, and eight stations were identified in the preliminary selection stage, and the most suitable locations were finally selected through the MCDM stage. Finally, the reliability and validity of the model were further verified by comparative analysis and dual sensitivity analysis.

Photovoltaic Charging Dock for Electric Mobility with G2V and V2G Technology

Over the past few decades, consumers have become more and more interested in electric automobiles. As a result, there is an increase in demand for ecologically friendly and more effective charging stations for electric vehicles (EVs). The energy required to charge an EV comes from fossil fuels, despite the fact that EVs are promoted as CO2 emission-free alternative. There have been initiatives to enhance the effectiveness and ecology of solar-powered electric vehicle charging infrastructure. This paper describes the use of a photovoltaic charging station with G2V and V2G solutions for EVs. The proposed model is developed and implemented using MATLAB/Simulink. There are various input parameters and the results are determined.

Photovoltaic power generation and charging load prediction research of integrated photovoltaic storage and charging station

Aiming at the obvious randomness and intermittent problems of photovoltaic power generation output and charging load of photovoltaic storage and charging station, a photovoltaic power generation prediction model based on long short-term memory neural network and a charging load prediction model based on BP neural network are proposed respectively. In the photovoltaic power generation prediction model, the Pearson correlation coefficient is used to analyze the influencing factors of photovoltaic power generation power, and the factors with high correlation are used as the input variables of the long short-term memory neural network model. Then the data is divided into four types of seasons by K-means cluster analysis, and the processed data sets are put into the prediction model for training. In the charging load prediction model, the processed historical load data is put into the time series-based BP neural network for iterative training, meanwhile the weights and thresholds of the model are adaptively optimized. Finally, the simulation results verify the extremely high prediction accuracy of the proposed prediction model. The results also show that compared with PSO-BP, GA-BP neural network, this charging load prediction model has higher precision and stability.

Distribution network planning with distributed photovoltaic and electric vehicle charging stations based on multi-population traction differential evolution algorithm

Currently, in the background of a large number of distributed photovoltaic and electric vehicle charging stations with access to the distribution network, this paper presents a distribution network planning method with distributed photovoltaic and electric vehicle charging stations based on multi-population traction differential evolution algorithm. Firstly, this paper considers the curve characteristics of photovoltaic and load, and uses cluster analysis to determine the planning scenery, so that the planning results are more accurate. Then, the distribution network planning model is constructed from the aspects of the minimum investment cost, operation cost and performance index. Then, to improve the speed and the accuracy of planning results, the multi-population traction differential evolution algorithm is used to solve the planning model. Finally, a 10kV line is used as an example to verify the reliability of the proposed method. Then, the traditional differential evolution algorithm and the other two algorithms are compared and analyzed to verify the effectiveness of the proposed planning method.

The fluvial passenger transport design problem with an electric boat

In this paper, we introduce a new optimization problem called the fluvial passenger transport design problem with an electric boat (FPTDP-EB). This problem is inspired by a future fluvial operation using electric boats (EBs) for passenger transportation between two cities on the banks of the Magdalena River in Colombia. The FPTDP-EB consists of selecting the battery capacity for the EB, the charging infrastructure to install, and the scheduling of each round trip. For the charging infrastructure, we considered the location of the charging stations, their charging powers, and whether they would have photovoltaic (PV) components. For stations with PV components, we also considered the number of PV panels and the capacity of their energy storage systems. For this problem, we considered the stochastic behavior of solar irradiance, the arrival times of candidate passengers for the EB, and power outages for the electric grid. The objective function is to minimize the operation’s investment cost, the maintenance cost of the charging stations and the cost of the energy to be purchased from the grid during a time horizon. We propose a branch-and-bound algorithm with a Monte Carlo simulation to solve the FPTDP-EB. The latter evaluates the feasibility of each solution and estimates the amount of energy to be purchased from the grid during the time horizon. Our method solves the operation design for the Magdalena River scenario in reasonable computation times for a strategic problem. Additionally, we perform some sensitivity analyses to evaluate how certain factors, such as the energy density of batteries, could impact the structure of the solution. Our results show that the use of photovoltaic charging stations helps minimizing the overall cost of the operation, and makes it more resilient towards power outages. Additionally, as the energy density of electric batteries continues improving, the overall cost of these type of operations is expected to decrease significantly.

Techno-economic assessment of photovoltaic-based charging stations for electric vehicles in developing countries

ABSTRACT Many countries are planning their transition to renewable-energy-powered transportation sectors. Among those are developing countries, like Jordan, which face financial burdens in the actual implementation due to their economic status. As the penetration of electric vehicles increases, more green-charging stations will be required. Therefore, to encourage investors to initiate the deployment phase, this work presents a better mapping and comparison method for Photovoltaic (PV) battery charging stations across Jordan. It provides a better visual of the stations’ techno-economic performance, so financiers can use it as a guide to building their future stations, given the economic returns presented, particularly since the designed algorithm considered profit-maximization of the highest priority. All the tested PV-battery configurations; namely, PV-Li-Ion, PV-Zn-Br, PV-Lead-acid, PV-Redox flow, and PV-Li-Ion second life, were feasible, with the southern parts of Jordan being more favorable for installation in terms of the techno-economic parameters. However, a PV-Zn-Br station was found to have the highest annual profit of $7,535 with 2.63 years return of investment and a renewable fraction of 96.7%. Finally, although the obtained results discussed the specifics of Jordan, this work can be extended to any other country planning its transition to a green transportation sector; to improve deployment by attracting investors.

Electric Vehicle Charging Station Based on Photovoltaic Energy with or without the Support of a Fuel Cell–Electrolyzer Unit

The transport sector generates more than 35% of total CO2 emissions. Electric vehicles are the future of transportation systems, and the demand for electric vehicles has grown considerably in the last few years due to government support. Companies worldwide are investing heavily in electric car charging stations based on renewable energy. This research study presents a complete design (including an appropriate energy management strategy) for a photovoltaic energy-based electric vehicle charging station (EVCS) with or without the support of a fuel cell and electrolyzer system. The parameters considered for designing the necessary capacity of the battery pack to support the required load are relative to the location-specific solar radiation (using RETScreen® Clean Energy Management Software, Version 9.0, Government of Canada, Toronto, Canada), the efficiency of the solar panel, the used strategy, etc. The battery capacity in the EVCS design based on a power-following strategy is about 20 times smaller than that resulting in the reference design. Additionally, the cost for an EVCS design based on a power-following strategy is almost half that resulting in the reference design. An analysis of the power-following strategy was carried out according to three EVCS operating scenarios.

Optimal Scheduling Strategy for Photovoltaic Charging Stations Participating in the Electricity Market based on Transactive Energy Mechanism

Regarding the optimization and management of energy in photovoltaic charging stations, existing research mostly focuses on the power grid, with little consideration given to its characteristics as a producer and consumer. This paper proposes an optimized scheduling strategy for photovoltaic charging stations that considers transactive energy mechanisms. Firstly, under the premise of ensuring the safe operation constraints of the distribution network, a distributed transaction model for photovoltaic charging stations was established. Secondly, the optimization model is solved using the Lagrangian decomposition principle and Subgradient method to obtain the scheduling plan of the photovoltaic charging station. The simulation results indicate that the scheduling strategy proposed in this article can promote the on-site consumption of renewable energy and improve the economy of photovoltaic charging stations.

Study of the Energy Efficiency of an Urban E-Bike Charged with a Standalone Photovoltaic Solar Charging Station and its Compliance with the Ecuadorian Grid Code No. ARCERNNR – 002/20

E-bikes are an emerging sustainable means of transportation, if adopted massively, they can help face the challenges of human mobility in urban centers worldwide. In Cuenca, Ecuador, the local government built cycle routes (13.47 km) connecting strategic points to facilitate and encourage sustainable mobility. However, the effective implementation of the electromobility strategies at a large scale entails impacts on the power grid, like the increase in the energy demand and the possible decrease of the energy quality due to the harmonic distortion that characterizes the battery's charging current. This research aims to obtain a primary input to evaluate such impacts through an energy efficiency study of an urban e-bike charged by a standalone solar photovoltaic charging station implemented in the Microgrid Laboratory of Universidad de Cuenca. The methodology includes the experimental characterization of the battery's charging regime, the vehicle's energy efficiency calculation, and the evaluation of its compliance with Ecuadorian grid code No. ARCERNNR – 002/20. Results show that the battery's charger performs a charging regime standardized by German regulations, delivering 92% of charge in 4.82 hours. The e-bike's calculated average energy efficiency is 2.18 kWh/100 miles or 73.77 m/Wh, and a fuel economy of 1545.1 MPGe. Finally, the magnitude of the first four odd harmonic components and the total harmonic distortion of the charging current exceeds the limits established by the grid code in force.

Three Level Bidirectional DC/DC Intelligent Control Technology for Photovoltaic Charging Station of Electric Vehicle Combined With SVPWM Algorithm

Three Level Bidirectional DC/DC Intelligent Control Technology for Photovoltaic Charging Station of Electric Vehicle Combined With SVPWM Algorithm

Multi-area distribution grids optimization using D-FACTS devices by M-PSO algorithm

This paper presents a model for the optimization of a multi-area distribution grid considering the high penetration levels of photovoltaic (PV) resources and electric vehicle charging station (EVCSs) based on distributed flexible AC transmission system (D-FACTS) devices including on-load tap changer (OLTC) transformers and step voltage regulator (SVR) tap control and shunt capacitor (SC) and shunt reactor (ShR) to reduce losses and voltage deviation. The overarching research aim was to avoid the overvoltage caused by power injection by PV sources and the voltage drop caused by charging EVs in distribution grids. The proposed model was designed based on distribution network load flow and was optimized using a modified particle swarm optimization (M-PSO) algorithm. Two different test networks were tested under different scenarios to analyze the proposed model, revealing the efficiency of the proposed model.