Penetration Of Electric Vehicles Research Articles

Electrifying the Road to Net-Zero: Implications of Electric Vehicles and Carbon Emission Coefficient Factors in European Power Systems

The global trend is shifting towards adopting low-carbon transportation solutions, with electrification emerging as a prominent approach. The effectiveness of this transition in mitigating climate change hinges significantly on the source of electricity used for charging electric vehicles. This study focuses on four European Union countries: Switzerland, Italy, Germany, and Poland, each characterized by varying levels of carbon emissions from their power systems. Assumptions are made for the short-term (10%), medium-term (30%), and long-term (60%) penetration of electric vehicles, aligning with the 2050 net zero emissions targets. The study investigates the impact of these penetration levels on energy demand, exploring scenarios ranging from 100% renewable source-generated electricity to 100% coal-generated electricity for EV charging. Finally, utilizing PSS®E 35.5 simulation software, the study assesses the implications of the electric vehicles’ load on medium-voltage transmission lines. The findings highlight the substantial influence of electrifying the transport sector on both environmental sustainability and the power system infrastructure, underscoring the critical role of regional energy mixes and the power system carbon emissions coefficient factor. Regions with lower carbon emission coefficient factors witness significant benefits even with a modest transition to electric vehicles, whereas regions with high carbon emission coefficient factors experience minimal impact despite large-scale EV adoption. Additionally, densely populated urban areas may encounter challenges related to transmission line congestion to meet the growing demand for electric vehicle charging.

Fast penetration of electric vehicles in China cannot achieve steep cuts in air emissions from road transport without synchronized renewable electricity expansion

Adopting electric vehicles (EVs) is considered to be a critical strategy to achieve China's goals of reducing air pollutant emissions and reaching carbon neutrality. However, the effects of different EV and renewable electricity (RE) expansion scenarios on air pollutants and carbon emissions from China's road transport have rarely been comprehensively assessed. This study developed a highly disaggregated provincial level inventory for CO, NOX, PM2.5, PM10, SO2, and CO2 emissions from China's road transport in 2020, using a bottom-up approach that considered crucial technical factors such as vehicle type, fuel type and vehicle age. This inventory was then used as a basis to analyse future emissions trajectories up to 2050 under various provincial and regional level EV and electricity development pathways. The results suggest that without changing the current electricity generation mix, rapidly growing EVs could aggravate air pollution in China by increasing NOX, PM2.5, PM10, and SO2 emissions. The Chinese government's current targets for EV market shares can help achieve peak road transport CO2 emissions around 2030. However, if RE expansion is slow, then the EV targets would achieve only limited CO2, NOX, and CO emission reduction while increasing SO2, PM2.5, and PM10 emissions.

Understanding the Economics of Aged Traction Batteries: Market Value and Dynamics

The growing demand and market penetration of electric vehicles (EVs) have led to an expansion in the size of the market for used EVs, accompanied by a continuous increase in the return rate of aging battery systems. Consequently, a second-hand market for aged battery systems, known as second-life batteries, is slowly emerging. Understanding this market is crucial for enabling a functioning circular economy for batteries. This paper analyzes the market mechanisms influencing price formation for used goods, drawing parallels to the largest second-hand market, the used car market, and applies them to the second-life battery market. By examining these mechanisms, insights are provided into the dynamics of the second-life battery market, facilitating the development of strategies to optimize resource utilization and sustainability in the EV industry. Finally, the second-life battery price index is introduced, increasing the transparency of prices for lithium-ion batteries and the circular economy.

Feasibility Study of Solar-Powered Electric Vehicle Charging Infrastructure at Selected Petrol Stations in Malaysia

The transition from the conventional internal combustion engine (ICE) to electric vehicles (EV) are expected to result in higher total electricity demand in the country. With the introduction of more EVs into the market, the need for charging stations will grow, subsequently increasing power consumption. This research study aims to assess the potential of solar energy production at petrol stations in central region of Peninsular Malaysia and assess the feasibility of solar-powered petrol stations in meeting the energy demands of electric vehicles. 10 samples of existing petrol stations are selected to install a solar-powered DC charging station as a case study. To study their energy consumption, 90kW DC charger was evaluated. The design of the PV systems for the petrol stations was done by using the PVsyst simulation software. As a result, it is found that 90% of the designed PV systems are able to generate enough energy to meet the estimated annual energy consumption of the 90kW DC charging system. The results also showed that the cost to install the solar-powered charging system for both power ratings can be recouped in less than 2 years. Overall, this research study offers valuable insights for addressing the rising energy consumption resulting from the increased penetration of electric vehicles in the future. Additionally, the implementation of solar-powered charging infrastructure aligns with the country’s renewable energy objectives and efforts to reduce greenhouse gas emissions. However, the assessment of EV penetration and the adoption of charging infrastructure was conducted based on government targets rather than relying on current data regarding the actual numbers and trends of EVs and chargers in Malaysia. The availability of accurate and up-to-date data on the number of EVs, their battery characteristics, chargers, and user charging behaviors in Malaysia would contribute to further enhancing this research work.

Charging Ahead- Addressing Key Barriers to Electric Vehicle Market Penetration in India

The transition to electric vehicles (EVs) represents a pivotal step towards sustainable transportation, particularly in the context of India's burgeoning population and rapid urbanization. Despite significant potential, the widespread adoption of EVs in India is hampered by various barriers, including infrastructure limitations, policy gaps, high upfront costs, and consumer perceptions. This research paper aims to analyze these key barriers and propose strategies to accelerate the market penetration of EVs in India. Through a comprehensive literature review, policy analysis, and examination of case studies, this paper identifies the challenges hindering EV adoption and offers recommendations for addressing them. Proposed strategies include infrastructure development, policy interventions, consumer awareness initiatives, and technological innovations. By implementing these recommendations, India can overcome barriers to EV adoption and realize the environmental and economic benefits associated with clean mobility.

Coordinated optimal operation of integrated electrical and transportation network considering source-load uncertainties in severe weather scenarios

The large-scale penetration of electric vehicles and the emerging dynamic wireless charging technology strengthen the coupling between distribution networks and transportation networks. However, the randomness of travels in the transportation network, the fluctuation of the output of clean energy such as wind power and photovoltaic, and the sudden change of weather will have a significant impact on the safe and stable operation of the two networks. In this context, based on the background of wireless charging technology, a cooperative optimal operation scheduling model of electric transportation integrated network is proposed, which considers the uncertainty of traffic demand and photovoltaic prediction error, and regards it as the uncertainty factor of load side and source side of power grid. The Information gap decision theory (IGDT) is introduced to model the bilateral uncertainty of the network, and the corresponding risk avoidance model and risk speculation model are obtained. Furthermore, considering the effect of inclement weather scenarios on the uncertain factors of road traffic and charging facilities, a modified integrated network model of electric traffic under inclement weather scenarios is proposed. Finally, a case study is carried out on a test system consisting of a 12-node transportation network and a 20-node power distribution network. The results show that the information gap decision theory can effectively deal with the changes of dual networks under multiple uncertain scenarios, form a risk decision-making model, and provide theoretical support for different decision-making methods.

Integration of Electric Vehicles and Renewable Energy in Indonesia’s Electrical Grid

As the global transition toward sustainable energy gains momentum, integrating electric vehicles (EVs), energy storage, and renewable energy sources has become a pivotal strategy. This paper analyses the interplay between EVs, energy storage, and renewable energy integration with Indonesia’s grid as a test case. A comprehensive energy system modeling approach using PLEXOS is presented, using historical data on electricity generation, hourly demand, and renewable energy, and multiple scenarios of charging patterns and EV adoption. Through a series of scenarios, we evaluate the impact of different charging strategies and EV penetration levels on generation capacity, battery storage requirements, total system cost, renewable energy penetration, and emissions reduction. The findings reveal that optimized charging patterns and higher EV adoption rates, compared to no EVs adoption, led to substantial improvements in renewable energy utilization (+4%), emissions reduction (−12.8%), and overall system cost (−9%). While EVs contribute to reduced emissions compared to conventional vehicles, non-optimized charging behavior may lead to higher total emissions when compared to scenarios without EVs. The research also found the potential of vehicle to grid (V2G) to reduce the need for battery storage compared to zero EV (−84%), to reduce emissions significantly (−23.7%), and boost penetration of renewable energy (+10%). This research offers valuable insights for policymakers, energy planners, and stakeholders seeking to leverage the synergies between EVs and renewable energy integration to pursue a sustainable energy future for Indonesia.

Targeted information intervention among consumer clusters for electric vehicle penetration – A case study of Punjab, India

Low success rates of existing EV adoption policies in India highlight the need for targeted policies to boost EV adoption. The current study employs a single-arm information intervention to analyse the impact of information provision on electric vehicle (EV) adoption among consumer clusters in a developing country context. Using Gaussian Mixed Modelling clustering, the sample was grouped into four segments based on three psychological latent variables. Clusters with greater pro-environmental values had higher pre-intervention positive purchase intention, signifying that policies aimed at promoting EVs among environmentally conscious consumers have higher chances of success. The intervention showed significant desirable changes in every cluster. Results indicate that such campaigns would achieve higher success in consumer clusters with low eco-friendliness. Appropriate measures for each cluster are suggested. The characteristics of consumer clusters with higher pre-intervention purchase intention could serve as guidelines for businesses and governments while formulating new and updating their existing EV policies.

Optimal Allocation of Fast Charging Stations on Real Power Transmission Network with Penetration of Renewable Energy Plant

Because of their stochastic nature, the high penetration of electric vehicles (EVs) places demands on the power system that may strain network reliability. Along with increasing network voltage deviations, this can also lower the quality of the power provided. By placing EV fast charging stations (FCSs) in strategic grid locations, this issue can be resolved. Thus, this work suggests a new methodology incorporating an effective and straightforward Red-Tailed Hawk Algorithm (RTH) to identify the optimal locations and capacities for FCSs in a real Aljouf Transmission Network located in northern Saudi Arabia. Using a fitness function, this work’s objective is to minimize voltage violations over a 24 h period. The merits of the suggested RTH are its high convergence rate and ability to eschew local solutions. The results obtained via the suggested RTH are contrasted with those of other approaches such as the use of a Kepler optimization algorithm (KOA), gold rush optimizer (GRO), grey wolf optimizer (GWO), and spider wasp optimizer (SWO). Annual substation demand, solar irradiance, and photovoltaic (PV) temperature datasets are utilized in this study to describe the demand as well as the generation profiles in the proposed real network. A principal component analysis (PCA) is employed to reduce the complexity of each dataset and to prepare them for the k-means algorithm. Then, k-means clustering is used to partition each dataset into k distinct clusters evaluated using internal and external validity indices. The values of these indices are weighted to select the best number of clusters. Moreover, a Monte Carlo simulation (MCS) is applied to probabilistically determine the daily profile of each data set. According to the obtained results, the proposed RTH outperformed the others, achieving the lowest fitness value of 0.134346 pu, while the GRO came in second place with a voltage deviation of 0.135646 pu. Conversely, the KOA was the worst method, achieving a fitness value of 0.148358 pu. The outcomes attained validate the suggested approach’s competency in integrating FCSs into a real transmission grid by selecting their best locations and sizes.

Towards sustainable urban living: A holistic energy strategy for electric vehicle and heat pump adoption in residential communities

Electric vehicles (EVs) and heat pumps (HPs) are key in reducing carbon emissions from transportation and domestic heating, yet their adoption may increase peak load demands on electrical networks. One of the aims of this research is to assess the potential impact of uncontrolled EV charging on community-scale distribution networks, exploring how this could stress the existing electrical infrastructure. It also explores the role of EVs in Vehicle-to-Grid (V2G) and smart charging applications, aiming to enhance community distribution systems. The study investigates the maximum stabilisation level achievable under various scenarios, highlighting the importance of smart energy management in integrating renewable energy and addressing uncertainties in the modelling process. Additionally, this study discusses the proposed systems' scalability, consumer behaviours' impact on the suggested energy solutions, and the potential implications of recent technological advancements for simulated communities. The research employs a sophisticated, integrative approach, combining stochastic methods with several robust energy software. Key findings suggest that uncontrolled EV charging can lead to grid capacity issues at high EV penetration levels, particularly during colder months. While smart charging and V2G technologies can moderate peak loads in many scenarios, achieving 100 % sustainable technology integration requires enhanced energy management or increased network capacity, especially in winter. Wind and solar power integration demonstrates strategic complementarity, particularly in winter, enhancing the reliability and stability of the community grid. It is also observed that peak solar generation hours misalign with the community's highest demand times, posing challenges for solar energy utilisation in EV charging in residential-based areas.

Multi-objectives transmission expansion planning considering energy storage systems and high penetration of renewables and electric vehicles under uncertain conditions

Transmission expansion planning (TEP) integrating electric vehicles (EVs) and renewable energy sources (RESs) is pivotal for the transition toward cleaner and more sustainable energy systems. One of the biggest challenges in TEP with EVs and RESs is the uncertainty inherent in their behaviors. Managing this uncertainty is critical for ensuring the grid's reliability and resilience, and for facilitating a seamless transition to EVs. A potent method for addressing uncertainties in power systems is the application of a scenario-based approach. However, the efficiency of this approach is contingent upon an increase in the number of representative scenarios, which, in turn, escalates computational time, making solving the TEP problem a crucial task. To tackle these challenges, this paper proposes a multi-objective resilience model for TEP, to advance towards more sustainable energy systems. The model seeks to strike a balance between economic, environmental, and technical considerations by minimizing planning costs, carbon dioxide emissions, and enhancing the voltage profile. Furthermore, the paper proposes a cascaded intelligent strategy to handle uncertainties and reduce computational complexity by reducing the number of representative scenarios without compromising system reliability. The adaptive neuro-inference system is designed to manage long-term uncertainties, while two fuzzy systems are developed to address short-term uncertainties. The problem is formulated as a complex non-linear multi-objective optimization problem. To address this, a hybrid approach combining the mountain gazelle optimizer and the multi-objective salp swarm optimizer is developed to solve the problem. The efficacy of the proposed method is validated on two separate test systems, demonstrating its superiority in maintaining system reliability while reducing computing time by at least 92 % in comparison with the considered traditional methods. The results also highlight the superior performance of the proposed hybrid algorithm compared to existing meta-heuristic algorithms in solving the TEP.

MAGE: Microgrids with Advanced Grid Efficiency through Battery-Aware EV management

Higher penetration of Electric Vehicles (EVs) yields a positive impact by curbing pollution. However, this rapid proliferation of EVs presents several challenges to the efficient management of EVs and the existing grid infrastructure due to the very high energy requirement of newly adopted EVs, which can also act as energy resources. This work delves into the problem of intelligent charging–discharging of EVs in a single offline microgrid framework, which can partially cater to the energy needs of other EVs (V2V, vehicle-to-vehicle), grid (V2G, vehicle-to-grid), building (V2B, vehicle-to-building), etc. This approach also preserves the battery’s health by limiting the number of charging–discharging cycles. Hence, a battery degradation-aware energy scheduler named MAGE (Microgrids with Advanced Grid Efficiency through Battery-Aware EV Management) is formulated to minimize the microgrid’s operational cost (MOC). The MOC is comprised of — the energy procurement cost, the cost of surplus energy sales, and penalties for unsatisfied demands. A formulation based on Mixed-Integer Quadratic Programming is proposed to ascertain an optimal solution. Heuristic algorithms are designed to identify a reasonable solution quickly. A comprehensive experimental analysis of our proposed approach exhibits its potential in optimizing MOC and ensures grid efficiency by reducing the peak load and fattening the load curve.

Online Optimization of Vehicle-to-Grid Scheduling to Mitigate Battery Aging

The penetration of electric vehicles (EVs) in vehicle-to-grid (V2G) interaction can effectively assist the grid in achieving frequency regulation and peak load balancing. However, the customer perceives that participating in V2G services would result in the additional cycling of the battery and the accelerated aging of the EVs’ power battery, which has become a major obstacle to the widespread adoption of V2G services. Most existing methods require long-term cycling data and battery parameters to quantify battery aging, which is not suitable for the V2G scenario with large-scale and short-time intervals. Consequently, the real-time and accurate quantification of battery aging for optimization remains a challenge. This study proposes a charging scheduling method for EVs that can accurately and online quantify battery aging. Firstly, V2G scheduling is formulated as an optimization problem by defining an online sliding window to collect real-time vehicle information on the grid, enabling online optimization. Secondly, battery aging is more accurately quantified by proposing a novel amplitude-based rain-flow cycle counting (MRCC) method, which utilizes the charging information of the battery within a shorter time period. Lastly, an intelligent optimization algorithm is employed to optimize the charging and discharging power of EVs, aiming to minimize grid fluctuations and battery aging. The proposed method is validated using a V2G scenario with 50 EVs with randomly generated behaviors, and the results demonstrate that the proposed online scheduling method not only reduces the EFCC of the battery by 8.4%, but also achieves results close to global optimization.

Balancing Awareness Fast Charging Control for Lithium-Ion Battery Pack Using Deep Reinforcement Learning

Minimizing charging time without damaging the batteries is significantly crucial for the large-scale penetration of electric vehicles. However, charging inconsistency caused by inevitable manufacture and usage inconsistencies can lead to lower efficiency, capacity, and shorter durability due to the “cask effect”. This goal can be achieved by solving a series of constrained optimization problems with the model-based framework. Nevertheless, the high computational complexity, identifiability, and observability issues still limit their fidelity and robustness. To overcome these limitations and provide end-to-end learning strategies, this paper proposes a balancing-aware fast-charging control framework based on deep reinforcement learning. In particular, a cell-to-pack equalization topology is first introduced to dispatch energy among in-pack cells. Then, the balancing awareness fast charging problem is formulated as a multiobjective optimization problem by considering charging time, consistency, and over-voltage safety constraints. Thirdly, a deep reinforcement learning framework using a deep Q-network is established to find the optimal policy. By using the generalization of a neural network, the learned policies can be transferred to real-time charging and balancing control, thus improving the applicability of the proposed strategy. Finally, numerous comparative simulations and experimental results illustrate its effectiveness and superiority in terms of charging rapidity and balancing.

Design of a Method for Analyzing Power Systems: Mathematical Models of Electric Vehicles and Distributed Generators

Electric vehicles (EVs) have significantly increased in recent years due to a growing awareness of the effects of polluting gases produced by combustion engines. The use of electric vehicles produces no emissions, making them a much more environmentally friendly alternative to traditional combustion engines. In addition, the price of EVs has decreased significantly in recent years, making them more affordable for consumers. The use of distributed generation (DG), an alternative source of energy for power systems, should also be considered. This study proposes a methodology for incorporating DGs and EVs into existing power systems. Power can be generated by DG directly where needed, while excess energy can be stored by EVs. Existing power systems can be made more efficient and cost-effective by combining these technologies. The methodology was applied to a 6-node IEEE system and a 33-node IEEE distribution system. It was found that DG can reduce network losses and voltage drops associated with EV penetration and that DG improves voltage drops.

Climate change adaptation and mitigation potential of EVs in Tokyo metropolitan area

Penetration of low heat-emitting electric vehicles (EVs) in urban areas is expected to have a positive effect on climate change adaptation by improving the thermal environment, and indirect mitigation by reducing building CO2 emissions. To assess these effects, a case study was conducted in Tokyo using an urban canopy model. We quantified the impact and characterize its spatio-temporal structure, through a comparative evaluation with other forms of e-mobility (hybrid electric vehicles and fuel cell vehicles) and established UHI measures (ground greening and cool roofs). EVs showed the largest effect among all e-mobilities, both in absolute temperature reduction (ΔT) and cooling efficiency (ΔT per reduced heat), owing to the positive feedback on atmospheric stability. The ΔT caused by EV is more pronounced in the morning and evening hours, and in urban centers. On the other hand, the ΔT due to UHI measures peaks at midday and is more pronounced in suburban areas. In other words, they complement each other spatially and temporally. The peak ΔT caused by the EVs exceeded that of the UHI measures by approximately 40% of the total area. The contribution of indirect CO2 mitigation is negligible in the life-cycle CO2 emissions of EVs.

Enhanced EV charging algorithm considering data-driven workplace chargers categorization with multiple vehicle types

The increasing penetration of Electric Vehicles (EVs) presents significant challenges in integrating EV chargers. To address this, precise smart EV charging strategies are imperative to prevent a surge in peak power demand and ensure seamless charger integration. In this article, a smart EV charging pool algorithm employing optimal control is proposed. The main objective is to minimize the charge point operator’s cost while maximizing its EV chargers’ flexibility. The algorithm adeptly manages the charger pilot signal standard and accommodates the non-ideal behavior of EV batteries across various vehicle types. It ensures the fulfillment of vehicle owners’ preferences regarding the departure state of charge. Additionally, we develop a data-driven characterization of EV workplace chargers, considering power levels and estimated battery capacities. A novel methodology for computing the EV battery’s arrival state of charge is also introduced. The efficacy of the EV charging algorithm is evaluated through multiple simulation campaigns, ranging from individual charger responses to comprehensive charging pool analyses. Simulation results are compared with those of a typical minimum-time strategy, revealing cost reductions and significant power savings based on the flexibility of EV chargers. This novel algorithm emerges as a valuable tool for accurately managing the power demanded by an EV charging station, offering flexible services to the electrical grid.

Fast Frequency Response for Future Low-Carbon Indian Power System: Challenges and Solutions

India has transitioned from being an energy-deficient nation to an energy-surplus nation. As of July 2023, India’s power system has seen a rapid transformation, with renewable energy, including large hydro, accounting for 40% of the total installed capacity at 177 GW. The ongoing energy transition has not only changed the country’s energy mix but also brought new challenges for system operators. The primary challenge is frequency instability due to the reduction of system inertia and governor-based primary frequency response (PFR) availability by large-scale integration of uncertain renewable energy resources (RES) such as wind and solar photovoltaic generators. Furthermore, the absence of frequency control ancillary services market, inadequate penetration of electric vehicles (EVs) and uncertainty of RES generation enhance the complexity of the frequency response services in the Indian power system. In this context, this paper presents a comprehensive review of the frequency response challenges of the future low-carbon Indian power system and underlines the new potential fast frequency response (FFR) services from advanced energy storage and renewable generation technologies. Such FFR services can be swiftly deployed in low-inertia grids through the integration of energy storage systems and inverter-based resources in a shorter span. Modeling of FFR services in security constraint unit commitment (SCUC) can enhance grid resiliency and facilitate high penetration of RES in the Indian power system without loss of frequency stability.

EV Integration-Oriented DC Conversion of AC Low-Voltage Distribution Networks and the Associated Adaptive Control Strategy

Driven by carbon-neutral targets and transportation electrification, the widespread use of electric vehicles (EVs) has become an irreversible trend. However, low-voltage distribution networks (LVDNs) can face several challenges under the high-penetration EV integration, including overloads, voltage violations and unbalance issues. In this paper, an EV integration-oriented DC conversion scheme and the associated adaptive control method for LVDNs is proposed, aiming at releasing more capacity for EVs and collaborative improvement of the above issues caused by EV charging. Based on analyzing the influence of different charging piles connected to the AC and DC LVDNs, a DC conversion scheme for three-phase four-wire LVDNs under high-penetration EVs is proposed. The voltage source converter (VSC) control strategies aimed at alleviating the overload of the DTs, voltage violations, and three-phase unbalance are designed separately based on the modified three-phase four-wire voltage sensitivity. Then, a coordinated adaptive control strategy of on-load tap changer and VSCs is proposed considering the simultaneous occurrence of multiple power quality issues in hybrid AC/DC LVDNs. Case study verifies the effectiveness of the proposed DC conversion and adaptive control methods, by which the maximum EV penetration of the hybrid AC/DC LVDN is increased from 85% to 215% compared with the AC LVDN.

ADMM-Based Hierarchical Single-Loop Framework for EV Charging Scheduling Considering Power Flow Constraints

This paper presents a three-layer hierarchical distributed framework for optimal electric vehicle charging scheduling (EVCS). The proposed hierarchical EVCS structure includes a distribution system operator (DSO) at the top layer, electric vehicle aggregators (EVAs) at the middle layer, and electric vehicles (EVs) charging stations at the bottom layer. A single-loop iterative algorithm is developed to solve the EVCS problem by combining the alternating direction method of multipliers (ADMM) and the distribution line power flow model (DistFlow). Using the single-loop structure, the primal variables of all agents are updated simultaneously at every iteration resulting in a reduced number of iterations and faster convergence. The developed framework is employed to provide charging cost minimization at the EV charging stations level, peak load shaving at the EVAs level, and voltage regulation at the DSO level. In order to further improve the performance of the optimization framework, a neural network-based load forecasting model is implemented to include the uncertainties related to non-EV residential load demand. The efficiency and the optimality of the proposed EVCS framework are evaluated through numerical simulations, conducted for a modified IEEE 13 bus test feeder with different EV penetration levels.