Plug-in Vehicles Research Articles

Evaluation of the time-of-use tariff responsiveness for plug-in electric vehicle home charging in Malaysia

Evaluation of the time-of-use tariff responsiveness for plug-in electric vehicle home charging in Malaysia

A TOPSIS based multi-objective optimal deployment of solar PV and BESS units in power distribution system electric vehicles load demand

The growing concerns regarding the depletion of fossil fuels, CO2 emissions, and the effects of climate change prompt the usage of plug-in electric vehicles (PHEVs) all over the world in a big way. The increased electrical demand brought on by the charging of electric vehicles puts a burden on the distribution network parameters like energy loss, voltage profile and thermal limits. Recently, renewable energy-based distribution generation (RDGs) units are firmly integrated with the transmission and distribution system networks to lower the carbon footprint generated due to conventional thermal power plants. In addition, Battery Energy Storage Systems (BESS) are used to enhance grid operation and lessen the consequences of the high intermittency nature of RDGs power. In this work, two charging methods of PHEVs are considered: charging electric vehicles at home during night-time and charging electric vehicles at public fast charging stations (PFCS). The uncertain nature of arrival time and trip distance of PHEVs are addressed using probability density functions (PDFs). The 33-bus test system consists of commercial, industrial and residential buses is taken to implement the proposed methodology. In this work, 500 PHEVs are taken into consideration. The aforementioned charging methods produce a 24-h electric demand for PHEVs, which is then placed on the corresponding distribution system buses. The effect of PHEVs on technical distribution system metrics, including voltage profile and energy loss, is investigated. To improve the above metrics, optimal planning of inverter-based non-dispatchable PV units and dispatchable PV-BESS units in the distribution network by the inclusion of PHEVs electric load demand is addressed. The Pareto-based meta-heuristic multi-objective chaotic velocity-based butterfly optimization method (MOCVBOA) is chosen for optimization of desired objectives. The results of the MOCVBOA optimization algorithm are compared with those of the other optimization algorithms, NSGA-II & MOBOA, frequently described in the literature to assess its effectiveness.

Peer-to-Peer Transactive Energy Trading of Smart Homes/Buildings Contributed by A Cloud Energy Storage System

This paper presents a model for transactive energy management within microgrids (MGs) that include smart homes and buildings. The model focuses on peer-to-peer (P2P) transactive energy management among these homes, establishing a collaborative use of a cloud energy storage system (CESS) to reduce daily energy costs for both smart homes and MGs. This research assesses how smart homes and buildings can effectively utilize CESS while implementing P2P transactive energy management. Additionally, it explores the potential of a solar rooftop parking lot facility that offers charging and discharging services for plug-in electric vehicles (PEVs) within the MG. Controllable and non-controllable appliances, along with air conditioning (AC) systems, are managed by a home energy management (HEM) system to optimize energy interactions within daily scheduling. A linear mathematical framework is developed across three scenarios and solved using General Algebraic Modeling System (GAMS 24.1.2) software for optimization. The developed model investigates the operational impacts and optimization opportunities of CESS within smart homes and MGs. It also develops a transactive energy framework in a P2P energy trading market embedded with CESS and analyzes the cost-effectiveness and arbitrage driven by CESS integration. The results of the comparative analysis reveal that integrating CESS within the P2P transactive framework not only opens up further technical opportunities but also significantly reduces MG energy costs from $55.01 to $48.64, achieving an 11.57% improvement. Results are further discussed.

Demand management of plug-in electric vehicle charging station considering bidirectional power flow using deep reinforcement learning

The recent development in plug-in electric vehicle technology has increased its popularity. Plug-in electric vehicles are widely used for their environment-friendly nature and they contribute to the reduction in global warming. With the increasing number of plug-in electric vehicles, charging coordination becomes essential for managing the charging station demand. The random charging behaviour of plug-in electric vehicles makes it a difficult task. This paper proposes a novel demand management strategy of charging stations. The proposed strategy supports the grid and reduces its burden during peak load. Deep-Q network based deep reinforcement learning is used in the proposed strategy. It is a value based deep reinforcement learning algorithm that approximates the Q value function using deep neural network. The deep reinforcement schedules the charging and discharging of plug-in electric vehicles to optimize the cost and manage the charging station load. Deep reinforcement learning enhances charging coordination by dynamically optimizing charging schedules according to real-time conditions and user preferences, thereby increasing efficiency and better integration with the grid. The reward function in deep reinforcement learning is designed based on the power price and demand of the charging station. A discount factor is introduced based on the service time to make the charging coordination efficient. A case study using dynamic pricing is carried out to validate the proposed strategy. The results prove that the proposed strategy optimizes charging and discharging costs and manages charging station demand efficiently. It is also observed that the proposed strategy is fast and incurs less computational cost.

Multi-objective optimization of power networks integrating electric vehicles and wind energy

In the ever-evolving landscape of power networks, the integration of diverse sources, including electric vehicles (EVs) and renewable energies like wind power, has gained prominence. With the rapid proliferation of plug-in electric vehicles (PEVs), their optimal utilization hinges on reconciling conflicting and adaptable targets, including facilitating vehicle-to-grid (V2 G) connectivity or harmonizing with the broader energy ecosystem. Simultaneously, the inexorable integration of wind resources into power networks underscores the critical need for multi-purpose planning to optimize production and reduce costs. This study tackles this multifaceted challenge, incorporating the presence of EVs and a probabilistic wind resource model. Addressing the complexity of the issue, we devise a multi-purpose method grounded in collective competition, effectively reducing computational complexity and creatively enhancing the model's performance with a Pareto front optimality point. To discern the ideal response, fuzzy theory is employed. The suggested pattern is rigorously tested on two well-established IEEE power networks (30- and 118-bus) in diverse scenarios featuring windmills and PEV producers, with outcomes showcasing the remarkable excellence of our multi-purpose framework in addressing this intricate issue while accommodating uncertainty.

Analyzing the influence of electric vehicle charging scheduling on distribution transformer lifespan

The ongoing growth in global electricity demand and rapid proliferation of electric vehicles (EVs) are posing challenges to the operation of the low voltage power networks and their components. This study aims to analyze the impacts of peak-load demand on the distribution transformers, considering mild and extreme combinations of electric energy demand and localized charging of plug-in EVs under their different penetration levels, hypothesizing that shifting the demand for charging throughout the day can reduce the aging of distribution transformers. The proposed method employs a fuzzy-logic-based tool, which incorporates the influence of ambient temperature, higher harmonics, reactive power compensation, reverse power flows, and overload. Three different charging/discharging modes have been considered: demand charging, off-peak charging, and synergy of off-peak charging and vehicle-to-house technology. The results indicate that off-peak charging emerges as 2.1 times more effective than demand charging in terms of the transformer's loss of life, particularly for ultimate EV penetration of 100 %. It is demonstrated that the utilization of charging scheduling for EVs should be prioritized to defer costly system upgrades and reinforcements, particularly when EV penetration exceeds 50 %.

Demand management and pricing in renewable integrated plug-in electric vehicle charging station using reinforcement learning

ABSTRACT The increasing adaption of Plug-in Electric Vehicles (PEVs) has increased the power demand for charging. Renewable generation can significantly help to meet this demand. The uncertainty of renewable generation and randomness in PEV charging makes the demand management of charging stations challenging. To address these challenges, this study proposes a novel demand management pricing strategy for renewable integrated charging stations. The proposed strategy uses reinforcement learning for charging coordination of PEVs. The renewable generation is estimated using weather data and accordingly PEVs are scheduled for demand management. The strategy decides the PEV power price based on real-time price, renewable tariff and PEV load. The strategy uses the bidirectional power flow with battery degradation cost. The distribution transformer operating cost and loading constraints are included to resemble the real-time environment. The PEV randomness and uncertainty of renewable generations are incorporated in the study. The results of the numerical case study show that the proposed strategy can manage the charging station load efficiently. The proposed strategy has optimized the cost of charging, discharging and charging station profit and increased the service capability of the charging station.

Fuel constrained sustainable water-gas-heat-power generation expansion planning of isolated microgrid

On account of slowly reducing of fossil-fuel, profitable utilization of available fossil-fuel to produce electric power is a major concern for electric power producers. Here, short-period fuel constrained power, heat, natural gas and clean water augmentation planning (FCPHGWAP) of isolated microgrid considering carbon capture is presented. FCPHGWAP finds out the most economical and consistent augmentation plan to meet the prophesied power, heat, natural gas and clean water demand over a short-term time horizon whilst satisfying several technical as well as societal constraints. An archetypical system having extant diesel generators (DGs), PVT panel, wind turbine generator (WG), micro-cogeneration unit (MCU), battery energy storage system (BESS), plug-in electric vehicles (PEVs), thermal energy storage system, natural gas storage unit, carbon capture unit (CCU), electrolyzer, hydrogen storage unit and aspirant PVT panel, WGs, MCUs and PEVs is taken into consideration. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and grey wolf optimization are used to solve this FCPHGWAP problem. It is observed that net present value having fuel constraints is less than that of without fuel constraints.

Cybersecurity Certification Requirements for Distributed Energy Resources: A Survey of SunSpec Alliance Standards

This survey paper explores the cybersecurity certification requirements defined by the SunSpec Alliance for Distributed Energy Resource (DER) devices, focusing on aspects such as software updates, device communications, authentication mechanisms, device security, logging, and test procedures. The SunSpec cybersecurity standards mandate support for remote and automated software updates, secure communication protocols, stringent authentication practices, and robust logging mechanisms to ensure operational integrity. Furthermore, the paper discusses the implementation of the SAE J3072 standard using the IEEE 2030.5 protocol, emphasizing the secure interactions between electric vehicle supply equipment (EVSE) and plug-in electric vehicles (PEVs) for functionalities like vehicle-to-grid (V2G) capabilities. This research also examines the SunSpec Modbus standard, which enhances the interoperability among DER system components, facilitating compliance with grid interconnection standards. This paper also analyzes the existing SunSpec Device Information Models, which standardize data exchange formats for DER systems across communication interfaces. Finally, this paper concludes with a detailed discussion of the energy storage cybersecurity specification and the blockchain cybersecurity requirements as proposed by SunSpec Alliance.

Impacts of Plug-in Electric Vehicles and Renewable Energy Source on Unit Commitment Problem using Cat Mouse Based Optimization A lgorithm

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer

Optimal power quality enhancement of photovoltaic and plug-in electric vehicle for smart grid using HBA-GJO technique

Optimal power quality enhancement of photovoltaic and plug-in electric vehicle for smart grid using HBA-GJO technique

Photo-voltaic system with battery employing parallel converters operation for charging stations and distributed loads amidst anomalous grid

In this work, solar photovoltaic array-based generation unit is connected to non-ideal distribution network and powering plug-in electric vehicle (PEV) charging infrastructure and other local loads connected to AC common bus while ensuring reliable operation of parallel connected voltage source converters under dynamics. A photovoltaic array connected via voltage source inverter, which feeds power to loads and battery is used to act as power management entity and provides reactive power and harmonic power support via converter. In context of widespread incorporation of PEVs and renewable energy sources (RES) into grid, inherent intermittency and dynamic behavior pose significant challenges in establishing a resilient and flexible control framework. Consequently, a solution is proposed that employs dual low pass filter integrated along an adaptive noise elimination filtering algorithm, serving as a primary processing stage. Additionally, integrator-based frequency locked loop control strategy is implemented in conjunction to mitigate power quality concerns, regulate voltage levels, and correct power factor discrepancies. Furthermore, system ensures continuous operation, providing uninterrupted power to local loads even during fluctuating conditions while simultaneously functioning as a seamless PEV charging station. Additionally, a comparative analysis of controller performance is summarized in a tabular format, alongside a power quality assessment. The analysis includes the evaluation of total harmonic distortion (THD) for both grid currents and grid voltages, which are measured at 2.36 % and 3.60 %, respectively. These values are well within the limits established by IEEE 519 standard, demonstrating compliance with the required power quality criteria. This RES based charging structure topology is tested using a hardware prototype under dynamics and impactful performance is observed through obtained results.

A dynamic pricing strategy and charging coordination of PEV in a renewable-grid integrated charging station

The increasing penetration of Plug-in Electric Vehicles (PEV) in the transportation system has increased the burden on the power system. This has made peak load demand management a challenging task for the power grid. To address this issue, a novel dynamic demand response pricing strategy in a grid-renewable generation integrated charging station environment is proposed in this paper. Renewable energy sources reduce the cost of generation and grid integration makes the system reliable. The proposed strategy models a Stackelberg game to provide dynamic prices for charging, discharging and grid power supplied for charging stations. Uncertainty and economics of renewable generation are considered for effective analysis and evaluation of the feasibility of the proposed strategy. The study considers the bidirectional flow of power and the battery degradation cost. Charging coordination is performed to optimize the cost of charging and discharging and support the grid in peak load demand management. Random charging behaviour and other parameters of PEVs are simulated using a random distribution function to resemble the real-time environment. A numerical case study validates that the proposed strategy has optimized the cost of charging and discharging and the serving capabilities of the charging station are enhanced with existing infrastructure.

A decentralized energy management scheme for a DC microgrid with correlated uncertainties and integrated demand response

Secure operation of a DC microgrid with high penetration of renewables and electric vehicle load is challenging. This paper proposes a decentralized energy management scheme for a grid-connected renewable integrated community DC microgrid considering the water-energy nexus. Uncertainties of renewable generation, plug-in electric vehicle load, electricity demand, electricity price in the day-ahead wholesale market, ambient temperature, and water demand are modelled using a probabilistic approach. Correlation between the input random variables is modelled using Copula theory. Flexibilities on the consumer side across multiple entities (temperature-dependent loads, electric vehicles, and water supply system storage) are coordinated to form an “integrated demand response entity”, which is further coordinated with the DC microgrid operator side flexibility (electrochemical storage) to support system operation. A distributed dynamic pricing scheme is used to implement the integrated demand response program. The objectives of the energy management scheme are to minimize the DC microgrid operator’s operating cost and consumers’ electricity cost. The decentralized algorithm is solved by the “alternating direction method of multipliers”. Simulation studies on a six-bus DC microgrid test system demonstrate that the proposed strategy reduces the operating cost of the DC microgrid operator by ∼19.28% and the electricity cost of the consumers by ∼13.82%.

Identifying mass market adoption in the transition to electric vehicles

Abstract When plug-in electric vehicles (PEVs)—plug-in hybrids and battery electric vehicles—reach mass market adoption, their growth may be self-sustaining and their replacement of fossil-fuel vehicles nearly certain. Understanding this milestone is important, because afterward research and policy interventions that support PEVs will be less necessary. In this paper we outline how to identify mass market adoption and show that the PEV market has not reached this point based on two essential criteria: achieving adoption (not sales) of about 16% and the resolution of early market issues (any deficiencies affecting vehicles, infrastructure, and the surrounding ecosystem). The fact that neither of these criteria have been met highlights the early stage of the PEV market and means PEVs will require policy support throughout this decade.

A novel approach for optimal deployment of plug-in electric vehicles with integrated renewable energy sources

A novel approach for optimal deployment of plug-in electric vehicles with integrated renewable energy sources

Techno-economic analysis of stand-alone hybrid PV-hydrogen-based plug-in electric vehicle charging station

The increase in the feasibility of hydrogen-based generation makes it a promising addition to the realm of renewable energies that are being employed to address the issue of electric vehicle charging. This paper presents technical and an economical approach to evaluate a newer off-grid hybrid PV-hydrogen energy-based recharging station in the city of Jamshoro, Pakistan to meet the everyday charging needs of plug-in electric vehicles. The concept is designed and simulated by employing HOMER software. Hybrid PV-hydrogen and PV-hydrogen-battery are the two different scenarios that are carried out and compared based on their both technical as well as financial standpoints. The simulation results are evident that the hybrid PV- hydrogen-battery energy system has much more financial and economic benefits as compared with the PV-hydrogen energy system. Moreover, it is also seen that costs of energy from earlier from hybrid PV-hydrogen-battery is more appealing i.e. 0.358 $/kWh, from 0.412 $/kWh cost of energy from hybrid PV-hydrogen. The power produced by the hybrid PV- hydrogen - battery energy for the daily load demand of 1700 kWh /day, consists of two powers produced independently by the PV and fuel cells of 87.4 % and 12.6 %, respectively.

Integration of the wind and solar power for the dynamic economic emission dispatch with the charging and discharging of plug-in electric vehicles

Wind and solar power are incorporated into the dynamic economic emission dispatch with the plug-in electric vehicles (DEED-PEV) in this paper, seeking to reduce the generation cost and pollutant emission significantly. A Laplacian non-dominated sorting genetic algorithm-II (LNSGA-II) and a constraint handling method (CHM) are proposed for the DEED-PEV. The LNSGA-II applies the Laplace distribution to both the crossover and mutation. The Laplace-based crossover makes each individual carry out unidirectional search towards its partner and enhances the information exchange between each pair of individuals. The Laplace-based mutation makes each individual carry out bi-directional search surrounding itself and adjust the searching range adaptively. Therefore, the LNSGA-II is able to explore and exploit the decision space of the DEED-PEV sufficiently, contributing to the reductions of the generation cost and pollutant emission in the objective space. For the variables of every individual, the CHM limits them within the feasible operating zones. The CHM also limits them within the dynamic boundaries. It further adjusts them according to the power balances. The power generation constraints and up/down ramp rate constraints can be always satisfied while the elimination of the total violation of the other constraints can be expedited. Therefore, the CHM can transform infeasible individuals to feasible ones rapidly. Four strategies are investigated for six PEV charging/discharging scenarios. The first three strategies consider no more than one kind of renewable energy generation while the fourth strategy involves both the wind power and solar power. Experimental results suggest that the fourth strategy is more economical and environment-friendly than the other three strategies. Furthermore, the LNSGA-II outperforms the other three competitor algorithms for six DEED-PEV problems in the light of hypervolume, coverage rate and spacing.

Investigating the potential of a battery swapping method at refuel stations for electric vehicle: A case study of INDIA

Electric vehicles (EVs) are introduced to mitigate environmental problems and develop sustainable modes of transport across the globe. Researchers often report that the non-availability of charging infrastructure is the primary concern for EV adoption. The traditional plug-in EV charging method has a higher waiting time, discouraging users from adopting EVs. Many countries, including India, are promoting an alternative battery swapping method (BSM) for EV charging to reduce the waiting time. But the user preference and willingness to adopt BSM remain unexplored, especially in developing countries. The existing refuel stations are a potential location for developing charging infrastructure that provides the EV charging service and helps create awareness among users because of their wide visibility. Thus, this study aims to bridge the research gap by exploring the influential factors, user preferences, and willingness to use the BSM at refuel stations. For this purpose, 1013 samples were collected from road users who visited 51 refueling stations using the random sampling method in the twin cities of Ahmedabad and Gandhinagar. An integrated partial least squares-structural equation modelling with the artificial neural network method was adopted in this study. The study’s findings reveal that the development of public EV charging facilities at refuel stations significantly impacts the user’s willingness to shift to EVs. The convenience and cost-related motivating factors like a lesser waiting time compared to the plug-in charging method, reduced range anxiety, no concern about battery usage, reduced initial purchase cost of an EV, and comparatively lower maintenance costs significantly motivates user’s willingness to adopt BSM. Similarly, battery-related demotivating factors like the non-reliable range of the swapped battery, non-standard battery design in terms of type, size, capacity, and brand across the country, additional cost for leasing or renting the battery, and chances that EV batteries might be replaced with fake batteries concern BSM adoption.

Eco-environmental regret-aware optimization of networked multi-energy microgrids with fully carbon elimination and electric vehicles' promotion

With the escalating concern of global warming propelled by the rise in Earth's temperature, the need for effective CO2 management has become crucial. This paper presents an innovative CO2 elimination approach, wherein a multiple integrated system of energies (MISEs) incorporating sustainable resources, including renewable resources (RENs), plug-in electric vehicles (PEVs), and demand response programs, is optimized. The proposed carbon elimination framework begins by modeling the onsite carbon capturing and recycling within each MISE. To effectively utilize the onsite carbon recycling facilities and achieve carbon neutrality, the proposed model also incorporates carbon transfer capability between MISEs, thereby enhancing the efficiency of overall carbon recycling. Furthermore, a stochastic p-robust optimization technique is proposed to effectively manage uncertainties by combining the advantages of stochastic programming and robust optimization. This uncertainty modeling approach promotes greater utilization of sustainable resources like PEVs and RENs due to their lower operational regrets from economic and environmental perspectives. Based on the simulation results, implementing the p-robust-based regret assessment technique led to the total operation cost increasing by only 2.75 %, while achieving a significant 44.5 % reduction in maximum relative regret. These results underscore the effectiveness of the proposed framework in enhancing both the economic and environmental performance of MISEs.