Massive Electric Vehicles Research Articles

Load redistribution attack for power systems with high penetration of EVs

This paper provides insights for cyber defenders of power systems with high penetration of electric vehicles (EVs) by proposing a novel consecutive attack model based on load redistribution for price control in both transmission networks (TNs) and distribution networks (DNs). The target of the attack is to induce massive EV users in DNs to charge simultaneously and cause a spike of loads in the TN at peak hours. The problem is formulated as a multi-slot bi-level optimization problem, where the upper level describes the hacker behavior and attacking constraints. The lower level explains the TN operator and DN operators’ behaviors and the relationship between the local marginal price and retail charging price. The bi-level problem is converted into an equivalent single-level mix-integer linear problem and is solved based on greedy algorithm. Simulations on IEEE 30-bus system prove the effectiveness of the attack strategy.

Feature-enhanced deep learning method for electric vehicle charging demand probabilistic forecasting of charging station

The fast proliferation of electric vehicles (EVs) profoundly affects the stable operation of the power system. Since EV charging demand is affected by users volatile behaviors and many other environmental and social factors, their uncertainty is quite pronounced. Traditional deterministic forecasting methods which provide little information on the uncertainty may not be informative to the decision-making of power system with massive EVs. This paper proposes a novel feature-enhanced deep learning method (FEDM) for EV charging demand probabilistic forecasting of individual station, in which a two-stage feature selection model is constructed to introduce the Pearson correlation coefficient between charging demand and external features as a prior knowledge to reweight the initial weights. The performance is improved compared to which is without feature-enhanced. In the comparison with eight benchmarks, FEDM shows superiority in both deterministic and probabilistic evaluation metrics. With high computational efficiency, FEDM has low dependence on the historical data stock, which can still achieve good performance in the case of low data volume. It provides a fast and accurate forecasting scheme for the demand forecasting of individual stations under the limitation of low data stock.

Scenario Planning of Pertamina Patra Niaga Business Strategy to Address the Growth of the Indonesian Electric Vehicle Market

PT Pertamina Patra Niaga is facing an era of energy transition, and one of the biggest phenomena is the adoption of electric vehicles (EVs). Massive EV adoption will impact Patra Niaga’s future business, and they must prepare a strategy to deal with these changes. This research aims to determine the factors that influence EV adoption in Indonesia and propose strategies that need to be carried out by Patra Niaga. The literature review shows that internal and external factors are very influential in determining Patra Niaga’s strategy. The research design is qualitative, with data obtained from primary sources from interviews and secondary data sources, including industry documents, reports, and journals. Using the scenario planning method with the PESTEL approach, the paper identifies four possible scenarios for the future of EV adoption in Indonesia. The possible scenarios are green tech frontier, policy-driven change, market-led innovation, and stagnant development. Environment analysis conducted both external analysis and internal analysis. The research uses Porter’s Five Forces to identify rivalry among the industry players for external analysis. Meanwhile, the internal analysis uses the use VRIO Framework to identify the competitive advantage of the company and the strength and weakness analysis to determine the opportunities and threats for the company. Finally, the research uses the BCG Matrix to develop a strategy for each scenario. Based on the BCG Matrix, the proposed strategies are to invest more in the EV infrastructure where the scenario is green tech frontier, to invest or divest where the scenario is policy-driven, to keep the EV infrastructure business running where the scenario is market-led innovation, and to s liquidated where the scenario is stagnant development. The chosen strategy is suggested to be implemented based on the probability of occurrence for each of the scenarios within a timeframe from the year 2024 to 2035 by involving various divisions in the company, including marketing, business development, and project leader of the EV division.

Development of an efficient vehicle-to-grid method for massive electric vehicle aggregation

The growing adoption of renewable energy and electric vehicles (EVs) has contributed to environmental sustainability; nevertheless, integration of these products into the power grid has become complex owing to their unpredictable nature and variable energy demands. A significant challenge lies in the realization of large-scale, coordinated control of EVs to serve as an alternative to traditional energy storage systems. This challenge is underscored by the complexity of optimization in large-scale cooperative control problems and the difficulty in reducing such problems to an easily manageable and practical level in real-world application. In response to these challenges, a practical mechanism for the integration of EVs into a vehicle-to-grid concept is proposed in this study. In this approach, the constraints involved in merging multiple EVs into a fictitious clustered energy storage unit, which are often neglected, are given renewed focus. An iterative multi-stage optimization method is introduced that includes an EV aggregation clustering model, multi-tier optimization model, and recursive framework. Here, marked efficacy for larger EV fleets is demonstrated for this method, providing optimal charging and discharging schedules for each vehicle while a high degree of precision is maintained. With the proposed technique, validated through numerous case studies using historical data, the global optimum solution is largely approximated, with a marginal deviation of 4 %. In addition, robustness of the model is demonstrated under varying pricing scenarios, with a computation time that increases in a linear manner, rather than exponentially, as the number of EVs increases. Meanwhile, compared with conventional methods, the technique proposed in this study has the capacity to fulfill the charging demands of all users with reduced charging expense, demonstrating the high precision and efficacy of the technique.

(Invited) Novel Materials for Future Li-Ion Batteries

Advancements in the capabilities of lithium-ion batteries have slowed down in the last decade. As conventional electrode materials approach their theoretical limits, substantial gains in battery energy density only come as a trade-off in safety or performance. This talk will discuss how replacing conventional active and inactive materials will significantly improve performance, drastically reduce the cost of Li-ion batteries, and pave the way for renewable energy technologies. For example, Sila's innovative drop-in-replacement nanocomposite silicon-based anode powder offers over five times higher gravimetric capacity than graphite, permits both fast charging and over 20% more energy density today over state-of-the-art lithium-ion, and enables radical product innovation - all without compromising performance. Most importantly, this material has been shipping for over a year and is scaling up for massive electric vehicle (EV) use by mid-decade.With Sila’s industrialized and scaled scientific innovation, wearables, portable electronics, and EV manufacturers can create breakthrough products today that will minimize our environmental impact tomorrow. This talk will also provide multiple examples of lightweight conversion-type cathodes, flexible ceramic separators, and other technologies that may become instrumental for the transition to a clean and energy-sustainable economy.

(Invited) Delineating the Intricacies Involved in the Synthesis of Cobalt-Free High-Nickel Layered Oxide Cathodes for Lithium-Ion Batteries

Improving the energy density and cost of lithium-ion batteries (LIBs) requires a rational compositional design of layered oxide LiNi1-x-yMnxCoyO2 (NMC) cathodes. The prevailing trend is to increase the nickel content in the cathode to maximize energy density, but high-nickel NMC generally suffers from surface and bulk instabilities that shorten battery life. Dopants, such as cobalt, can help mitigate these issues, but its supply shortage issue prohibits its usage especially in the massive electric vehicle market. Our recent finding suggests that a combination of manganese and aluminum in LiNi0.9Mn0.05Al0.05O2 (NMA-90) promotes attractive energy density, cycle life, rate capability and thermal stability, proving that cobalt can be eliminated in high-nickel cathodes. Post-mortem analysis further reveals that the Mn-Al combination provides synergistic improvements in structural and surface stabilities compared to LiNi0.9Mn0.05Co0.05O2 (NMC-90) and LiNi0.9Co0.05Al0.05O2 (NCA-90). A thorough understanding of the relationship between the synthesis condition and optimal materials properties of NMA-90 is necessary to enable its large-scale deployment. We will present findings from the calcination and coprecipitation study of several cobalt-free cathodes to elucidate this relationship. It is revealed that the cycle stability of LiNi0.9Mn0.1O2 (NM-90) is not dependent on Li/Ni mixing or primary particle size, but is dependent on the calcination temperature. Incorporating Al into NM-90 during coprecipitation reactions cause high internal porosity in the hydroxide precursor, which impacts the final morphology of the calcined cathodes and thus their electrochemical stability. The ongoing work in our lab provides insight on the synthesis sensitivity of NMA and strategizes methods to control its electrochemical properties.

Electric Vehicles as Electric Energy Storage for a Zero Emission Grid

Electric Vehicles, especially electric vehicles with bidirectional Vehicle-to-Grid (V2G) connectivity, can serve as electric energy storage assets. Following the dramatic increase in electric vehicle registrations in 2022 there can be little doubt that electric vehicles will become a major portion of the entire light duty personal transportation fleet. This paper endeavours to project the growing electric energy storage capability of this asset. It shows that the storage available from the EV fleet may grow to match the daily output of projected solar PV generation in the 2025-30 time period, and greatly exceed the requirements of the EV fleet itself. This presents the possibility of a massive renewable generation capability, levelled and controlled by an even more massive electric vehicle fleet energy storage capability which can offset its intermittent and uncontrolled nature, as a future zero emission energy grid.

Hybrid energy management on electric vehicles for power grids with renewables system

An all including control strategy for massive extension decided power foundations in which the stored singular edges are exhausting and the system components don't swing convincingly inside a short period of time stretch is readied. It is suggested to use a two-stage energy management system (EMS) for power networks with massive electric vehicle (EV) consolidation and safe for the biome energy resources. For EVs, the module and battery exchanging modes are the only ones used to determine the best functioning reasons for charging stations and battery exchanging stations (BSS). A chance-grateful upgrade plan that can reasonably obtain the system and the gauge faintness serves to illustrate the proposed stochastic model judicious control (SMPC) issue at this level. The substituting bearing approach for multipliers (ADMM), a spread guess, is used to breathe the progression count when speaking similarly. The causing stage is focused on organising the EV charging tools to continuously follow the primary stage plans, i.e., the goal working centres, and addressing the accusing strains experienced by EV customers via devices for the Advanced Metering Infrastructure (AMI).

A lightweight privacy preserving scheme of charging and discharging for electric vehicles based on consortium blockchain in charging service company

Charging service companies (CSC) provide charging and discharging services for electric vehicles (EV) by managing numerous charging piles (CP). At that time, the privacy preservation of EV users has become a problem that must be solved. The lightweight solution has a broad applicated prospect because of its excellent efficiency and security. Simultaneously, the centralized storage of massive EVs’ real-time power information (RPI) may cause some risks, such as data tampering and single point of failure. Therefore, a lightweight privacy preserving scheme for EVs based on consortium blockchain (CB) in CSC is proposed to solve the problems mentioned above. The scheme has the following novelties and contributions: (1) a pseudo identity algorithm is designed to realize EV users to charge, discharge, and enjoy other services with pseudo identity, which can protect EV users’ privacy and security; (2) a certificateless aggregation signcryption algorithm is designed to realize lightweight RPI secure communication: in this algorithm, the function of information signature and encryption in one operation is realized, and the operation steps are simplified; bilinear pairing and exponential operation are not used to improve the efficiency of aggregate signcryption; batch aggregation and verification are designed to ensure the EV’s RPI can be stored in the CB securely and efficiently; (3) the CB technology is adopted to solve the problems of data tampering and single point of failure of the central organization. Moreover, the consensus mechanism in the CB is designed in this scheme to further improve the scheme’s efficiency. Finally, theoretical analysis proves that the scheme can guarantee users’ privacy. Experiments show that for 800 EV users, the time overhead of our scheme is only 1969.5 s, which is lower than other existing schemes.

Dynamic Bayesian network-based disassembly sequencing optimization for electric vehicle battery

The sharply increasing end-of-life (EOF) battery volume in the global complex energy market has created significant challenges for its recycling and reuse, to reduce environmental pollution and resource waste, and efforts have been focused on the disassembly process considering the uncertainty of electric vehicle (EV) battery pack categories and quality. Compared with traditional disassembly, the EV battery disassembly process needs to consider more uncertainty factors for each EOF battery pack to represent its disassembly structure, which significantly reduces disassembly production efficiency. Even though sequence optimization methods for the disassembly process have been developed to solve these problems, there are still two important challenges that remain: uncertain disassembly structure representation and optimal disassembly sequence selection. To address these challenges, this paper proposes a dynamic disassembly Bayesian network approach based on an EV battery disassembly graph model. This method offers dynamic process optimization to manufacturers to deduce the optimal disassembly sequences using the forward–backward algorithm and the Viterbi decoding algorithm. To validate the proposed method, an EOF battery is used to demonstrate the disassembly sequence selection, which indicates the possibility of massive EV battery disassembly prediction.

Distributed Hierarchical Coordination of Networked Charging Stations Based on Peer-to-Peer Trading and EV Charging Flexibility Quantification

Power systems are facing the challenges of integrating massive electric vehicles (EVs) and lacking operational flexibility. An optimal and efficient coordination strategy is needed to coordinate the operations among EV charging stations (CSs) and the distribution system operator (DSO) and to make full use of the EVs’ charging flexibility for improving the operational flexibility of power systems. To this end, this paper proposes a CSs-DSO distributed coordination strategy. The proposed strategy integrates a supply-curve-based quantification method to quantify the EVs’ flexibility contribution and a Nash bargaining game-theory-based peer-to-peer (P2P) transactions model for maximizing the profits of entities participating in the coordination. To tackle the CSs-DSO coordination problem that has a bi-level-parallel coupling structure, an analytical target cascading (ATC) and alternating direction method of multipliers (ADMM) jointly-based distributed solution approach is proposed. First, the CSs-DSO bi-level model is decoupled by ATC with tie-line power being the only exchanged information; then, the model is further decomposed for each CS by ADMM for protecting the CSs’ privacy and autonomy. The effect of coordination and the computational efficiency of the proposed strategy is verified by numerical simulations on a modified IEEE 33-bus distribution system connecting multiple CSs.

A new model of resilient LV network for massive EV charging and distributed generation

One of the main challenges facing distribution network operators today is the expected increase in electric vehicles (EVs). The technical challenges related to the market penetration of EVs in Low Voltage Distribution Networks (commonly known as LV networks) are addressed in this paper. It has been widely acknowledged that EVs increase the network loading level, leading to a reduced system reliability performance (due to interruptions, voltage variations...). In this paper, the authors share innovative approaches using LV panel/feeder monitoring and automation, as well as OLTC transformers and House Connection Box solutions. These solutions are proposed in order to reinforce the reliability of the grid and to boost the maximum EV and local renewable resource penetration in the distribution networks. This will help to extract maximum benefits from EVs, minimize the associated impact on the distribution network and create a real resilient LV network.

Data-Driven Study of Low Voltage Distribution Grid Behaviour With Increasing Electric Vehicle Penetration

In this paper, the impact of Electric Vehicle (EV) uncontrolled charging with four levels of EV penetration in overall 21 real low voltage distribution grids in two seasons are analysed. The employed real grid data is provided by distribution system operators from three European countries: Austria, Germany and the Netherlands. At least six grids in each country were considered and they are categorised into three types, namely rural grids, suburban grids and urban grids. The EV charging data used in this study is based on real measurements or surveys. The seasonal and the weekday-weekend factors are also considered in the EV charging impact research. Three key congestion indicators, the transformer loading, line loading and node voltage as well as several other evaluation indexes are studied. The results reveal that the majority of the simulated grids had no or minor moments of mild overloading while the rest grids had critical issues. Among all the grids, suburban grids are most vulnerable to massive EV integration. Out of the evaluated grids, those who are located in Germany have the highest redundancy for high EV penetration accommodation. Overall, the impact of uncontrolled EV charging depends on the combination of EV charging demand as well as the grid inherent features.

Integrating vehicle-to-grid contract design with power dispatching optimisation: managerial insights, and carbon footprints mitigation

Towards booms on massive electric vehicle adoptions, vehicle-to-grid service creates significantly social-environmental benefits on acting as a flexible power resource alternative for stabilising the violent fluctuations and overload in the power grid. However, the customer's acceptance and unstable external environment degrade these positive influences on embracing V2G operations. Motivated by these practical challenges, we attempt to design a V2G power dispatching plan integrated with a revenue-sharing contract to coordinate the multi-parties interests in the power supply chain and further improve V2G service adoption. Specifically, we focus on an urban microgrid system, in which the aggregator integrates the supply chain coordination conditions of a revenue sharing contract into a V2G power dispatching plan. The nonlinear terms generated by the supply chain coordination conditions are approximated into a tractable convex form. Furthermore, by solving instances generated from real-world and synthetic data from CAR2GO in Amsterdam, the Netherlands, we demonstrate several implications: (1) the proposed contract integrated policy is proved cost-saving for the aggregator operation, revenue-improving for the EV user, and carbon footprint mitigation for the society; (2) our proposed policy facilitates the aggregator to balance the trade-off between revenue sharing ratio and operation cost; (3) based on our contract integrated policy, we also explore the influences of service rate.

Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends

The topic of microgrids (MGs) is a fast-growing and very promising field of research in terms of energy production quality, pollution reduction and sustainable development. Moreover, MGs are, above all, designed to considerably improve the autonomy, sustainability, and reliability of future electrical distribution grid. At the same time, aspects of MGs energy management, taking into consideration distribution generation systems, energy storage devices, electric vehicles, and consumption components have been widely investigated. Besides, grid architectures including DC, AC, or hybrid power generation systems, energy dispatching problems modelling, operating modes (islanded or grid connected), MGs sizing, simulations and problems solving optimization approaches, and other aspects, have been raised as topics of great interest for both electrical and computer sciences research communities. Furthermore, the United Nations Framework Convention on Climate Change and government policies and incentives have paved the way to massive electric vehicle (EV) deployment. Hence, several research studies have been conducted to investigate the integration of EVs in national power grid and future MGs. Specifically, EV charging stations’ bi-directional power flow control and energy management have been considerably explored. These issues index challenging research topics, which are in most cases still under progress. This paper gives an overview of MGs technology advancement in recent decades, taking into consideration distributed energy generation (DER), energy storage systems (ESS), EVs, and loads. It reviews the main MGs architecture, operating modes, sizing and energy management systems (EMS) and EVs integration.

Revisiting polyanionic LiFePO4 battery material for electric vehicles

Although oxide cathodes have been widely used in these Li-ion batteries, these cathodes suffer from instability of the oxygen close-packed structure. In contrast, polyanionic phosphates such as LiFePO4 have incredible lattice stability and safety features owing to the strong covalent bond of P–O, which constrains the oxygen atoms and minimizes the defects of the oxygen site, resulting in stable frameworks. In addition, the presence of the strong P–O covalent bond stabilizes the anti-bonding transitional metal redox couple through an M–O–P inductive effect to generate a relatively high potential. Hence, polyanionic LiFePO4 has been an ideal choice of cathode materials for batteries deployed in electric vehicles. In this review, we revisit the basic features and development of LiFePO4, as an attempt to speeding its future deployment in massive electric vehicles.

THREE-LEVEL METHODOLOGY FOR SECURE AND EFFICIENT GRID INTEGRATION OF ELECTRIC VEHICLE

The secure integration of electric vehicle (EV) plays a key role in the energy transition through a resilient and decarbonised economy. However, a massive EV penetration means a rise in electricity demand with negative consequences to the distribution systems (voltage drops, branches congestion, etc) if the charging infrastructure is not cybersecure and does not perform smart charging mechanisms. Furthermore, these new infrastructures and their operating procedures provide new chances to cyberattacks to be performed, aimed at either exploiting those grid vulnerabilities or acquiring some user’s private information. Therefore, to ease the secure integration of EV charging infrastructures in the future network, this paper presents a three-level actuation methodology for charging infrastructures, which includes active management of EV supply equipment (EVSE) to allow dynamic control of charges, installation of ancillary protection systems, planning of EVSE’s location within the distribution system and cybersecure management of the whole infrastructure. The presented methodology is based on a thorough analysis of the possible cyberattacks that may occur during the transactions of the charging process, as well as tests carried out on a real pilot, which demonstrate the possible impacts that an uncontrolled charging of the EV can have on the distribution network, thus identifying the vulnerabilities of the distribution network. Keywords: Smart Grid, electrification, electric vehicle, charging station, Charge point operator, cybersecurity, smart charging.

Demand response to improve the shared electric vehicle planning: Managerial insights, sustainable benefits

Massive adoption of shared electric mobility benefits people’s daily commute and environment but creates overload issues into the power grid, then further cause challenges to charging service operations and power management. Previous research always focuses on single optimization process on shared vehicle planning, rather than the combination of demand management into day-ahead planning operations. To this end, we attempt to propose a mixed integer programming model integrating demand response operations to further explore the impacts of demand response on shared electric vehicle planning operations. We first model a two-stages model integrating charging facility location in the first stage and vehicle relocation in the second stage. Moreover, both supply-side and demand-side uncertainties are considered and approximated into tractable form by applying sample average approximation and distributional robust set featuring the entropy knowledge and electric vehicle’s multi-level charging duration. The demand response policy is also proposed to reshape the original charging demand into an economical and reliable way to improve operational efficiency and mitigate the power overload issues caused by massive electric vehicle adoption. Further, we conduct a real-world case study in Amsterdam, the Netherlands, to explore the social-operational impacts of vehicle planning optimization model integrating the demand response, robust charging facility planning in three areas: (1) The demand response integration promote electric vehicle planning operations on cost-saving for about 3%. (2) Data richness of serviceability towards charging piles influence all decisions through the shared electric vehicle charging station planning. (3) A trade-off exists between technical progress on charging rate and charging technology stability.

A Consortium Blockchain-Enabled Secure and Privacy-Preserving Optimized Charging and Discharging Trading Scheme for Electric Vehicles

This article utilizes consortium blockchain to design a decentralized, secure, and privacy-preserving scheme for bidirectional power trading between electric vehicles (EVs) and the power grid. To reduce adverse effects induced by disordered charging of massive EVs to the power grid, we minimize the total load deviation through optimizing the charging and discharging time period of EVs. Our optimization scheduling is a large-scale mixed-integer programming problem of which the number of variables and constraints are enormous. Hence, we propose to adopt the heuristic algorithm, an improved krill herd (KH) algorithm to solve it. Simulation results indicate that our model can effectively smooth the load fluctuations, and improved KH can improve the rate and accuracy of solving this model effectively. Implementation of Hyperledger Fabric evaluates the performance and scalability of our scheme. Qualitative security and privacy analysis demonstrate that our scheme helps to improve the security and privacy of power trading.

Techno Economic Analysis of Vehicle to Grid (V2G) Integration as Distributed Energy Resources in Indonesia Power System

High penetration of electric vehicles (EVs) leads to high stress on a power grid, especially when the supply cannot cover and actively respond to the unpredictable demand caused by charging EVs. In the Java-Madura-Bali (JAMALI) area, Indonesia, the capability of the grid to balance its supply and demand is very limited, and massive EV charging additionally worsens the condition because of unbalanced load profiles. Ancillary services of EVs have led to the idea of utilizing EV batteries for grid support, owing to their high-speed response to the fluctuating power system. In this study, a techno-economic analysis of the vehicle-to-grid (V2G) system in the JAMALI grid is conducted in terms of the changes in the feed-in tariff schemes, including regular, natural, and demand response tariffs. The results show that by utilizing EVs, the supply during peak hours can be reduced by up to 2.8% (for coal) and 8.8% (for gas). EVs owned by business entities as operating vehicles with a natural tariff show the highest feasibility for ancillary services, and can potentially reduce the cost of charging by up to 60.15%. From a power company perspective, V2G also potentially improves annual revenue by approximately 3.65%, owing to the replacement of the fuel.