Plug-in Vehicles Research Articles

Probabilistic operational costs assessment of combined PV–PEV connections in LV distribution networks

Decarbonization stimulates the increased penetration of photovoltaic (PV) systems and plug-in electric vehicles (PEV) in low-voltage (LV) distribution networks. However, the individual or combined PV and PEV massive connections may incur significant economic costs for distribution utilities due to technical issues caused by these connections. This paper proposes a probabilistic approach for operational costs assessment of combined PV–PEV penetration in LV distribution networks catering to the uncertainties associated with PV, PEV and households. In addition, to identify the relationships between the technical issues with each other and the increased combined PV and PEV penetration levels, a correlation analysis is developed to help utility engineers to estimate the operational costs of combined PV and PEV connections. The proposed methodology is applied to a real Brazilian LV distribution network showing that the costs of real power losses decrease in reduced penetration levels of combined PV–PEV. However, they increase under high combined penetration levels representing financial losses to utilities. Also, operational costs related to steady-state voltage level compliance indicators increase with PV connection, and the combined PV–PEV connections have beneficial results, reducing these costs. Further, the proposed methodology can be used by utility engineers responsible for studies of LV distribution network reinforcement actions. • Proposed methodology converts technical parameters into operational costs. • Operational costs in utility’s point-of-view due to combined PV–PEV connection. • Proposed approach considering stochastic uncertainties of PV, PEV and households. • Identifies high-risk consumer units, allowing LV network reinforcements. • Proposed methodology uses technical correlation analysis and Monte Carlo Simulation.

Joint Mobility and Vehicle-to-Grid Coordination in Rebalancing Shared Mobility-on-Demand Systems

Vehicle-to-Grid (V2G) technology enables plug-in electric vehicles (PEVs) to act as controllable loads and distributed energy resources for power systems. However, these resources, under the context of Mobility-on-Demand (MoD) market, have yet to be fully exploited for the grid services. This paper investigates how providing energy service from the shared PEVs affects both the power and transportation systems. We consider a shared MoD platform where PEVs can choose to provide either the V2G service or the traveling service when they are rebalanced to future high demand areas. In this market, the power and transportation system operators determine appropriate rewards to attract the PEV drivers. On the other hand, these drivers choose between energy and mobility services to maximize their aggregate benefits. In particular, we analytically model drivers’ preferences for discharging and traveling services through carefully selected utility functions. Moreover, a distributed algorithm is developed to manage the interactions between PEVs and the grid operation. We show that PEV drivers can be encouraged to achieve a desired energy and mobility pattern by optimizing their individual utility. Numerical results demonstrate that integrating energy service into the ride-sourcing market can benefit both PEV drivers and the power system.

Active power management of grid-connected PV-PEV using a Hybrid GRFO-ITSA technique

In this manuscript proposed a hybrid Garra Rufa Fish Optimization (GRFO) and Improved Tunicate Swarm Algorithm (ITSA) for improving the power quality of the integrated Photovoltaic (PV) and Plug-in Electric Vehicle (PEV) in Smart Grid (SG) system. The GRFO-ITSA approach is hybrid wrapper of GRFO and ITSA. Commonly it is named as GRFO-ITSA approach. The grid-connected PV-PEV, active power management is performed by the proposed approach. The proposed GRFO approach is used to determine the individual harmonic components and to reduce the recompense currents applied to PVs via PEV converters. The load flow control is performed by ITSA approach, which controls the power among the PVs, and PEVs. Additionally, it satisfies the power demand, and voltage variation. The proposed approach is also to analyze the mutual properties of PVs as well as PEVs on the feeder and transmitting loads, voltage outlines, harmonic alterations of an urban electric power distribution system. Also, the performance of the GRFO-ITSA is implemented on MATLAB site as well as associated with several existing approaches. The GRFO-ITSA have improved the power quality and compensate the harmonics and reactive power of the system. The optimal outcome is obtained by GRFO-ITSA with less computation time.

A Robust Renewable Energy Source-oriented Strategy for Smart Charging of Plug-in Electric Vehicles Considering Diverse Uncertainty Resources

Nowadays, the notion of plug-in electric vehicle (PEV) as a valuable tool of energy management has been extensively employed in smart distribution grids. The main advantage of clean energy as well as elastic behaviour of operation in both electrical load/generation modes can sufficiently justify the utilization of such emerging technology. Moreover, the specific capability of renewable energy sources (RESs) in terms of contribution in PEV smart charging/discharging scheme would cause to remarkable techno-economic benefits in smart grids. However, the load demand, RES generation and also the electrical energy price encounter with uncertainty in practice required to be properly handled. Hence, a non-deterministic optimization model based on information gap decision theory (IGDT) is proposed in this paper to specify a robust PEV smart charging pattern. To solve the multi-objective proposed IGDT-based PEV smart charging (IGDT-PSC) model, the multi-objective version of particle swarm optimization (MOPSO) is utilized to define a set of Pareto optimal solutions. Furthermore, the final solution among the Pareto solutions is selected by means of a linear fuzzy satisfaction rule. The simulation results for a test smart microgrid comprising a PEV, a set of RES units and a load demand verify the effectiveness of the proposed IGDT-PSC model.

A Dynamic Optimal Scheduling Strategy for Multi-Charging Scenarios of Plug-in-Electric Vehicles Over a Smart Grid

Green transportation has become our top priority due to the depletion of the earth’s natural resources and rising pollutant emission levels. Plug-in electric vehicles (PEVs) are seen as a solution to the problem because they are more cost and environment friendly. Due to rapid industrialization and government incentives for zero-emission transportation, a significant challenge is also constituted in power grids by the self-interested nature of PEVs, with the asymmetry of information between the charging power demand and supply sides. In this paper, we propose an optimal strategy in industrial energy management system, based on evolutionary computing, to characterize different charging situations. The proposed approach considers stochastic, off-peak, peak, and electric power research institute charging scenarios for attaining the vehicle-to-grid capacity in terms of optimal cost and demand. An extensive scheduling of charging cases is studied in order to avoid power outages or scenarios in which there is a significant supply-demand mismatch. Furthermore, the proposed scheme model also reduces the greenhouse gases emission from generation side to build a sustainable generation infrastructure, which maximizes the utility of fuel-based energy production in the presence of certain nonlinear constraints. The simulation analysis demonstrates that PEVs can be charged and discharged in a systematic manner. The participation of transferable load through the proposed methodology can significantly reduce the economic costs, pollutant impacts, efficiency, and security of power grid operation.

Hierarchical High-Resolution Load Forecasting for Electric Vehicle Charging: A Deep Learning Approach

The mass roll-out of electric vehicles substantially contributes to reducing fossil fuel consumption and environmental emissions. Meanwhile, the large fluctuation, strong uncertainty, and multiple coupled impact factors of plug-in electric vehicle charging significantly challenge the existing load forecasting approaches. Unlike the conventional load, the fluctuation of plug-in electric vehicle charging is particularly significant at the very-short-term minute-level time scale given the large charging power and stochastic human behaviors. In this article, a novel historical-data-based high-resolution plug-in electric vehicle load forecasting model structure is established, accommodating the various factors that affect the charging load. Moreover, an enhanced attention-based long short-term memory deep learning approach is proposed for solving the intractable forecasting problem, where a feature upscaling and downscaling algorithm is proposed for the hierarchical high-resolution data processing. Numerical results on real-world data of a charging station in Shenzhen show that the proposed model structure and algorithm demonstrate well performance in short-term and very-short-term hierarchical high-resolution plug-in electric vehicles charging load forecasting problems.

Techniques for compensation of unbalanced conditions in LV distribution networks with integrated renewable generation: An overview

An increasing number of single-phase loads and renewable energy resources (RESs), such as single-phase rooftop PV units, are unevenly distributed in low voltage (LV) distribution networks. This exacerbates unbalanced conditions in the network which in turn adversely affects the power quality, stability and reliability. This paper presents a comprehensive review of the state-of-the-art techniques for alleviation of the unbalanced conditions in both three-wire and four-wire LV three-phase distribution networks. Technical issues arising from the unbalanced conditions are highlighted. Several definitions of the unbalance indicators and standards that are widely used for unbalance analysis are also given. Furthermore, recent unbalance compensation techniques based on three devices; namely, power electronic converters, electric vehicles (EVs) and energy storage systems (ESSs) are critically reviewed. It is found that the power electronic converters with suitable control strategies and management of EV chargers and plug-in electric vehicles (PEVs) have been intensively studied so far. However, based on the literature survey, there is currently no work on applying cutting-edge methods, such as the distributed cooperative control strategy and multi-agent deep reinforcement learning (MADRL), to utilize distributed single-phase ESSs to mitigate the unbalanced conditions. Therefore, key challenges, possible unbalance mitigation techniques and future research directions are recommended and discussed. • Techniques for alleviating unbalanced conditions in LV systems are reviewed. • Three devices with recent techniques for compensation are presented. • Indicators and standards for analysis of unbalanced conditions are provided. • 1-phase ESSs is recommended with new strategies for future research directions.

Introductory Analysis of Spark Ignition Engine and Extensively Implemented Electric Alternatives

Energy crises and climate change lead to restricted standards of emission and energy consumption in regular road vehicles. Combustion method, ignition time, and injection time are engine development priorities. Optimal injection and ignition timing at 1600 rpm can save 10% on brake fuel. Perfect injection and ignition timing reduce heat loss and emissions. Various approaches to reducing transportation emissions and energy were implemented. Plug-in electric vehicles and hydrogen fuel cell vehicles (HFCV) were eliminated because of the lack of charging and refilling facilities. Mild hybrid energy recovery devices are considered straightforward and effective. Because of its effectiveness and longevity, Kinetic Energy Recovery System (KERS) is the most popular hybrid system in road automobiles. Future road energy recovery systems may include heat energy recovery methods and solar panels. However, electric vehicles with regenerative technologies own the highest energy efficiency among all the proposed solutions with the advantage of no pollution during the operation.

Performance assessment of a distribution system by simultaneous optimal positioning of electric vehicle charging stations and distributed generators

Extensive penetration of Renewable Energy based Distributed Generator (REDG) units in a Distribution System necessitates its efficient and optimal applicability. Plug-in Electric Vehicles (PEV) is currently gaining importance steadily in a distribution system due to its environmental compatibility. However, suitable positioning of PEV is a mandate as it increases the overall demand of the system and has appreciable impact on the system's technical performance in terms of losses, voltage profile, penetration level, to name a few. This paper proposes a simple apparent power loss driven technique of Electric Vehicle Charging Station (EVCS) placement along with a REDG unit in a distribution system taking time-driven load models into consideration. The initial State of Charge of the PEV is reformulated taking traffic and weather conditions, proportional distance covered by the electric vehicle and non-availability of EVCS into consideration using Dynamic Fault Tree Analysis and Bayesian optimization techniques. An optimum combination of different sets of PEV with proportional distance coverage is considered. Results are validated on the basis of computation of three REDG assessment indices in comparison with an arbitrary slot of positioning of the above elements.

INSIGHTS INTO ELECTRIC VEHICLE MARKET GROWTH IN SOUTH AFRICA: A SYSTEM DYNAMICS APPROACH

The international share of plug-in electric vehicle sales was 8.6% in 2021, compared with South Africa’s 0.1%. Increasing the sale of these vehicles in South Africa is directly tied to the continued existence of South Africa’s vehicle manufacturing industry. To address this challenge, businesses and governments need to understand better the factors influencing electric vehicle adoption. This study identified significant factors that influence and are influenced by electric vehicle adoption. By reviewing the current literature, numerous factors were identified and mapped in a causal loop diagram with an adapted system dynamics approach. The impact of taxation on the cost of electric vehicles and the low availability of electricity negatively affects both consumer confidence in owning an electric vehicle and local manufacturing capacity. This model serves as a useful tool to piece together how various factors in South Africa affect and are affected by electric vehicle adoption.

A Soft-Switched Single-Stage Single-Phase PFC Converter for Bidirectional Plug-In EV Charger

This paper proposes a novel zero-current assisted soft-switching bidirectional single-stage single-phase isolated power factor correction (PFC) based converter for plug-in EV charger. An isolated topology consists of a current-fed full-bridge converter with bidirectional switches on the ac-side that is connected with a full-bridge converter on the dc-side of a high-frequency transformer (HFT). HFT offers galvanic isolation for safety and high power density, and it is the most desired specification for a battery charger. The converter enables zero current switching (ZCS) of ac-side switches and zero current turn-ON for dc-side switches without any active clamp circuit or passive snubbers, which significantly reduced switching losses, footprints, and cost. The proposed modulation strategy and control technique are presented for promising the soft-switching operation of all semiconductor devices throughout the range for all modes with bidirectional power flow capability. These merits make the converter more feasible, robust, and reliable with high power density for an EV application. A comprehensive study of proposed steady-state operation, design, and simulation results is reported. A proof-of-concept hardware laboratory prototype rated at 1.5 kW is established and tested to validate the claims and proposed converter performance.

Charging Coordination of Plug-in Electric Vehicles Considering Machine Learning Based State-of-Charge Prediction for Congestion Management in Distribution System

This article proposes a novel strategy for congestion mitigation in a distribution system by charging coordination strategy of plug-in electric vehicle (PEV) considering grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. The G2V mode of a large figure of PEVs creates a congestion scenario in the distribution system. Therefore, a coordinated charging strategy has been considered in this work to mitigate distribution system congestion. Moreover, a precise estimation and prediction of PEVs state-of-charge (SOC) is necessary while formulating PEVs coordinated strategy. The study of the work is two folded. First, for the first time, a combination of machine learning approach like gradient boosting method-Bayesian optimization (GBM-BO) is considered in prediction of PEVs SOC at the finishing of trip. Second, a coordinated charging scheme is established based on particle swarm optimization (PSO) and firefly algorithm (FA) using the PEVs SOC at the finishing of trip. The charging coordination strategy is analyzed on the 38-bus radial distribution system integrated with solar powered charging-cum parking lot (SPCPL). The machine learning based prediction results reveal the significant reduction in errors between predicted and calculated values. The results further reveal the reduction in PEVs charging cost and congestion scenario while considering SPCPL in the distribution system.

Experimental and numerical investigation on shell and coil storage unit with biodegradable PCM for modular thermal battery applications

Thermal energy storage (TES) in automotive applications is currently growing in importance. TES can visibly reduce primary energy consumptions, decrease CO2 emission, and improve thermal comfort in electric as well as hybrid vehicles. However, to meet the new ambitious target (15% reduction of CO2 emissions in the new cars until 2025) it is required to use plug-in electric vehicles. For this reason, this paper focuses on the optimization of key coil designing parameters. For the reference geometry, both experimental and CFD results have been presented. The optimization of coil geometrical parameters has been carried out based on numerical modelling. The prototype of TES is proposed as a honeycomb battery of individual modules. The results show that increasing the diameter and pitch of the coil decreases the melting and solidification time by 13.2% and 11.8% respectively for chosen geometry. CFD calculation has also been made for TES with a hexagonal geometry. The best results were obtained for the TES with a cylindrical shell. However, the results for the TES with hexagonal shell were, very similar (the difference was less than 1% of the share of the liquid phase). It is seen that presented coil optimization is also a good fit for TES with hexagonal shell geometry.

Network-Constrained Peer-to-Peer energy trading for multiple microgrids considering zoning pricing

For multiple microgrids (MMGs) with different zone consumption characteristics in active distribution network (ADN), it is a challenge to combine technical issue about grid operation security and economic issue about energy trading revenue together. To tackle this challenge, a network-constrained peer-to-peer (P2P) energy trading method considering differentiated zoning pricing is proposed for MMGs. The proposed method consists of three parts: 1) a leader–follower energy trading framework considering different consumption characteristics is developed based on Stackelberg game theory with a unique Stackelberg equilibrium (SE); 2) the utility function of distribution network operator (DNO) is conducted by considering different zones buying and selling prices, in order to minimize the operation costs, and to deal with technical issue by reshaping the power flow and on-load tap changer (OLTC) of transformers for power loss reduction and voltage regulation; 3) the utility function of prosumers within MGs is conducted based on differentiated zone prices to maximize their profit by managing the demand response (DR) behavior of shiftable load, battery storage (BS), and plugin electric vehicle (PEV) in charging station (CS). Numerical results for 3-MGs system, modified by IEEE 33-bus distribution network, have verified the effectiveness of the proposed network-constrained and zone pricing leader–follower P2P energy trading method.

Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System

As plug-in electric vehicles (PEVs) become more and more popular, there is a growing interest in the management of their charging power. Many models exist nowadays to manage the charging of plug-in electric vehicles, and it is important that these models are implemented in a better way. This paper investigates a price-driven charging management model in which all plug-in electric vehicles are informed of the charging strategies of neighboring plug-in electric vehicles and adjust their own strategies to minimize the cost, while an aggregator determines the unit price based on overall electricity consumption to coordinate the charging strategies of the plug-in electric vehicles. In this article, we used an asynchronous distributed generalized Nash game algorithm to investigate a charging management model for plug-in electric vehicles in a smart charging station (SCS). In a charging management model, we need to consider constraints on the charge and discharge rates of plug-in electric vehicles, the battery capacity, the amount of charge per plug-in electric vehicle, and the maximum electrical load that the whole system can allow. Meeting the constraints of plug-in electric vehicles and smart charging stations, the model coordinates the charging strategy of each plug-in electric vehicle to ultimately reduce the cost of smart charging stations, which is the cost that the smart charging station should pay to the higher-level power supply facility. To the best of our knowledge, this algorithm used in this paper has not been used to solve this model, and it has better performance than the generalized Nash equilibria (GNE) seeking algorithm originally used for this model, which is called a fast alternating direction multiplier method (Fast-ADMM). In the simulation results, the asynchronous algorithm we used showed a correlation error of 0.0076 at the 713th iteration, compared to 0.0087 for the synchronous algorithm used for comparison, and the cost of the smart charging station was reduced to USD 4800.951 after coordination using the asynchronous algorithm, which was also satisfactory. We used an asynchronous algorithm to better implement a plug-in electric vehicle charging management model; this also demonstrates the potential advantages of using an asynchronous algorithm for solving the charging management model for plug-in electric vehicles.

A stochastic optimal scheduling of distributed energy resources with electric vehicles based on microgrid considering electricity price

With the expanding demand for electrical energy across the world and the growing concern about environmental matters resulting from the widespread use of fossil fuels in the power system, it is necessary to find suitable alternatives to resolve the problem. In this regard, Renewable Energy Sources (RES) that produce almost no pollution have become the preferred means of supplying the global energy demand. In this paper, a novel approach is proposed for formulating the problem and reducing reliability costs to achieve the minimum total cost of the grid. Concurrently, the transportation system has been replacing conventional vehicles with Electric Vehicles (EVs), where Plug-in Electric Vehicles (PEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) have got the public's attention. Thanks to the Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies, it is technically possible and economically justified to plug in such vehicles to receive/inject power from/to the grid. Alternatively, an emerging concept in electric power systems, i.e. Microgrid (MG), was developed and employed to level up the RESs’ penetration rate and maximize the capabilities of EVs using the smart infrastructure. Indeed, the V2G option is utilized for mitigating operating costs in order to host PEVs in the network. Therefore, optimally scheduling the MG would be of utmost importance. Thus, an efficient day-ahead stochastic operation framework has been developed in this research work for an MG equipped with renewable energy-powered Distributed Generation (DG) units and EVs. The mentioned stochastic programming framework is based on the Unscented Transform (UT). It is worth mentioning that the problem has been formulated as a stochastic programming problem with the objective of optimizing the total operating cost. The studied problem is then tackled using a biomimicry well-known approach called the “Converged Barnacles Mating Optimizer (CBMO) algorithm” and the results derived from simulating the problem would be compared to the ones achieved by some prominent algorithms.

Representative Driving Cycles for Trinidad and Tobago with Slope Profiles for Electric Vehicles

The rise of plug-in battery electric vehicles presents an excellent opportunity for small island developing states such as those of the Caribbean. The vehicles offer improved energy efficiency, enhanced energy security, and reduced foreign exchange expenditure. Incentivizing the uptake of electric vehicles seems to be an obvious policy decision, but there could be unintended consequences. Real-world driving cycles are essential for validating electric-vehicle energy and economic benefits using simulation studies. In drive-cycle methodologies, the microtrip method is the preferred choice for countrywide representation and is ideal for fuel consumption estimations. This paper reports on the first such real-world driving cycle for the Caribbean, specifically Trinidad and Tobago, which is developed using a microtrip construction method with a novel approach to establishing a slope profile. Highway and suburban segments show some distinct characteristics, including relatively high average accelerations and decelerations in the highway and suburban cycles and low average speeds of 40.2 km/h (highway) and 21.74 km/h (suburban). The driving cycle is unique but reasonable when compared with others derived in emerging economies.

A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective

Today’s plug-in electric vehicle (PEV) technology is one of the important ways to address the dependence of fossil energy and greenhouse gas emissions. With the explosive growth of the number of PEVs worldwide, surging PEVs charging has offered new opportunities and challenges for large amounts of existing buildings where more and more charging infrastructures have been added. By means of microgrid technology, energy-flexible buildings are nowadays integrated with multiple distributed energy sources, energy storages and various types of consumers, allowing the possibility of providing measures to mitigate the growing impact of surging PEVs charging demand and accommodate more PEVs in buildings. Collaborative management between PEVs and energy-flexible buildings is attracting more attention of energy professionals and provides incentives to make more efforts in developing more extensive and optimal control methods for the integrated building energy systems. However, there is little review focusing on the impact analysis and co-management technologies on PEVs charging in buildings. This paper firstly provides an overview of the literatures related to the impact of PEV charging on the building power demand profile and the associated distribution transformer performance as well as the key impact factors. Additionally, a comprehensive review and analysis are conducted on the state-of-the-art researches with regard to the co-management of PEVs and energy-flexible buildings with various energy flexibility sources for the co-benefits of the integrated building energy system. The further efforts needed for developing more applicable co-management methods for facilitating surging penetration of PEVs in buildings are also addressed.

Plug-in electric vehicle load modeling for charging scheduling strategies in microgrids

Utilization of plug-in electric vehicle (PEV) load models can improve the performance of smart charging strategies, which increase the reliability of the grid by harnessing the flexibility of PEV loads. This paper presents a method for utilizing personal PEV load models in real-time stochastic charging control with single and finite system-time horizons. First, the drivers’ load models are found with Kernel Density Estimation (KDE). Second, a single system-time horizon coordinated charging control algorithm is devised to ensure each PEV is charged at least a critical amount given a feasible set of optimization constraints. The coordinated charging algorithm tackles the NP-hardness of single-deadline charging scheduling problems efficiently with a sorting algorithm utilizing the stochastic PEV load models. Third, we extend the single system-time horizon coordinated charging control algorithm to a scheduling algorithm considering a finite system-time horizon. This approach utilizes the stochastic PEV load models in a model predictive control based approach to decrease the complexity of stochastic online charging scheduling problem into a deterministic case. The scheduling algorithm makes assumptions about the future arrivals to the charging station, unlike the classical online EV charging scheduling algorithms, which optimize the load demand revealed at the current time but underestimate the load demand revealed in the future. Our findings suggest the individual load models complement smart charging algorithms’ decision process by improving the fairness of charging time allocation and extending the degree of knowledge of future random data for the scheduling algorithm.

Where should electric vehicle users without home charging charge their vehicle?

• Curbside charging stations are still highly costly compared to other options. • The need for curbside charging is often overestimated in literature. • Charging possibilities at workplace and shopping facilities should be incentivized. An often-heard argument against using plug-in electric vehicles (PEV) by urban dwellers is the missing curbside charging option. Some users are even expecting these charging stations to deliver cheap electricity. However, from an economic perspective several arguments speak against this hope and, furthermore, the need of curbside chargers is often exaggerated in literature. Consequently, we shed more light into these two hypotheses (H1. curbside charging is very costly and H2. there is hardly any need for curbside charging) and base our arguments on current and representative mobility data from Germany. Policymakers may focus stronger on charging offers at workplace or shopping facilities for achieving an efficient allocation of charging infrastructure.