Charging Strategy Research Articles

Research on the charging strategy of battery swapping stations under the optimization of peak-valley electricity price of electrical technology

Research on the charging strategy of battery swapping stations under the optimization of peak-valley electricity price of electrical technology

A Two-Stage Operation Strategy for Energy Storage under Extreme-Heat-with-Low-Wind-Speed Scenarios of a Power System

Changes in weather conditions directly impact the output of wind power, photovoltaic systems, and other forms of uncontrollable power generation. During extreme weather events, the output from wind and photovoltaic sources is typically reduced. In light of this, this paper proposes a two-stage operational strategy for energy storage, under scenarios of extreme-heat-with-low-wind-speed, in power systems. Firstly, historical data on wind and solar power, along with weather characteristics, are collected to analyze the power output during multi-day periods of extreme heat and low wind speed. Then, Monte Carlo simulations are employed to generate multi-day load curves with inherent uncertainties, based on regional load characteristics of the power system. Finally, a two-stage operation strategy for energy storage charging and discharging is established. In the first stage, normal operations are conducted to identify periods of power shortage across various types of loads. In the second stage, based on the identified moments of power shortage from the first stage, charging and discharging constraints are applied to the energy storage systems. The feasibility and effectiveness of this two-stage operational strategy are then validated through simulations, using historical data to generate scenarios of multi-day extreme-heat-and-low-wind-speed conditions.

Optimal power dispatching for a grid-connected electric vehicle charging station microgrid with renewable energy, battery storage and peer-to-peer energy sharing

The paper proposes an optimization approach and a modeling framework for a PV-Grid-integrated electric vehicle charging station (EVCS) with battery storage and peer-to-peer vehicle charging strategies. The main objective of the paper is to optimize the system for reliability and profitability while minimizing operational costs. The model considers factors such as energy demand, grid stability, battery health, and charging costs to efficiently utilize resources. A notable aspect of the proposed model is the introduction of a peer-to-peer energy sharing mechanism. Prosumers are allowed to trade energy with each other, enabling efficient utilization of resources and fostering a collaborative energy ecosystem. The dynamic power demand and pricing environment facilitate fair and optimized energy exchanges. The paper outlines the development of a mathematical algorithm formulation for the proposed model and validates its effectiveness and performance using a real-world case study conducted at Nelson Mandela University in South Africa. This comprehensive approach considers daily varying weather and load conditions, to ensure a realistic analysis of system performance and economic feasibility. The simulation results demonstrate significant energy demand cost savings, with a 38.14 % reduction considering time-of-use tariffs, and a 30.5 % reduction in maximum demand through optimal control and conducts a detailed techno-economic analysis over a 20-year period. The results indicate substantial reductions in grid costs and a break-even point within 8 years, highlighting the economic advantages of the optimization model. Furthermore, the results demonstrate significant cost savings, including energy demand cost savings and time-of-use savings.

Dynamic Charging Scheduling and Path Planning Scheme for Multiple MC-enabled On-demand Wireless Rechargeable Sensor Networks

With the advancement of wireless energy transfer, Wireless Rechargeable Sensor Networks (WRSNs) have become increasingly popular for efficiently charging sensor nodes. In WRSNs, determining the charging schedule for Mobile Chargers (MCs) is critical for reducing maintenance costs and improving charging efficiency. This is termed the Charging Scheduling Problem (CSP), which is proven to be NP-hard in nature. The existing schemes lack a comprehensive approach to determine the optimal number of MCs and often set a fixed charging threshold for the sensor nodes, degrading charging efficiency in dynamic networks. Additionally, relying on a single MC is unrealistic and impractical for large-scale networks, necessitating a more advanced charging strategy. Thus, this study proposes a dynamic and multi-node charging scheduling scheme named Partitioning-based Charging Schedule for Multiple Mobile Chargers (PCSMMC). The PCSMMC utilizes the traffic load of sensor nodes and energy load of MCs to estimate the optimal number of MCs and computes the progressive threshold for sensor nodes to improve the charging efficiency. Moreover, potential sojourn locations are determined and multiple network factors are integrated into a multi-attribute decision-making process to achieve an efficient charging scheduling and path planning scheme for multiple MCs. The objective of PCSMMC is to enhance the survivability rate of sensor nodes and decrease the traveling path followed by MCs to charge the sensor nodes within the network. Empirical simulation results confirm the superiority of PCSMMC in terms of charging response time, survivability rate, energy utilization efficiency, and network lifetime by a significant margin when compared to alternative approaches.

Theoretical evaluation of newly predicted VC4 monolayer for Li-ion batteries

Carbon-rich materials could increase the capacity and improve stability by decreasing the Li+-metal repulsive interactions. Herein, a new two-dimensional (2D) vanadium tetra carbide (VC4) was predicted using the first-principles calculations. The electrical characteristics, Li-anodic applications, and structural stability of VC4 were evaluated. With a comparatively less band gap, the bare VC4 displayed a semiconductor, while the Li+-loaded VC4 possessed a metallic nature. Using a multilayer Li top/bottom adsorption technique, a high Li storage capacity of 1353 mA h g−1 was observed in the VC4 monolayer. Notably, Bader's charge scheme confirmed a favorable and reversible electrochemical reaction between Li5VC4 and VC4. Significantly, VC4 serves as a host for high-performance lithium-ion batteries (LIBs), featuring a low barrier energy of 0.18 eV and an average open circuit voltage (OCV) of 0.52 V. Impressively, the variations of the structural parameters are within 10 % for a maximum Li+ intercalation, indicating high structural stability. These enthralling findings indicate that the monolayer VC4 sheet can be realized as an efficient Li host material for rechargeable LIBs.

Real-time V2G simulator for grid system frequency and voltage regulation with fault ride-through capabilities

Electric vehicles (EVs) have become one of the promising power grid ancillary services. They can help reduce the intermittent of renewable energy sources by supporting active power and frequency control services. Additionally, bi-directional chargers included into EV batteries can supply/consume reactive power for voltage regulation. To improve voltage quality, a unique control approach is devised to offer fault ride-through. In this study, real-time charging optimization schemes based on particle swarm optimization are developed to coordinate the EVs charging/discharging power with the grid power dispatches depending on the charging priority and preferential electricity prices. Five different car user profiles including different initial state of charge and plug in state have been depicted. Different optimum issues are carried out with comprehensive simulations employing hardware in the loop via real-time simulation for a modified IEEE 14-bus test system. Where different frequency and voltage controls, management strategies, variant fault scenarios, benefits, and challenges for power system regulation with EVs are illustrated. According to the simulation results, the proposed optimized charging strategies reduce the total expenses of EV parking station by 40 % compared to other cases, hence maximizing EV owner satisfaction. The net load curve is smoothed and reduced by 11 %, particularly during peak period.

Context-aware voltage sag waveform decomposition method using hybrid models for complex power quality detection

To analyze the voltage sag characteristics of distribution networks, automotive companies need an accurate and efficient non-stationary signal analysis tool to extract voltage sag feature information from power quality monitors and fault recorders, which is crucial for automotive companies to analyze grid characteristics and improve localized charging strategies. We innovatively propose a hybrid decomposition method to decompose voltage sag waveforms from composite disturbance data. Subsequently, we design a context-aware mechanism to adapt the proposed decomposition method to the complex power quality data in different regions. In addition, tests were conducted on an integrated power quality simulation system to verify that this method can accurately decompose the voltage sag waveform from the composite disturbance signals. Finally, the experimental simulation results also show the better performance of the method in decomposing voltage sag waveforms with different causes in different composite disturbance signals compared with other mainstream methods.

A charge and discharge control strategy of gravity energy storage system for peak load Cutting

Gravity energy storage is a type of energy storage method that utilizes gravitational potential energy to store energy. In recent years, it has been widely concerned by scholars and enterprises at home and abroad for its unique advantages. From the perspective of long-term profit, the economic analysis of the gravity energy storage system is essential. In previous studies, only some specific economic models are available for describing the gravity energy storage system. This article proposes a revenue model for the gravity energy storage system first. Then, suggest a method for operating and scheduling a decentralized slope-based gravity energy storage system based on peak valley electricity prices. This method aligns with the current business model of using user-side energy storage to participate in power system auxiliary services. Last, verify the feasibility of the process through analysis. This study will make a beneficial attempt for the market promotion and application of gravity energy storage.

Excitonic Configuration Interaction: Going Beyond the Frenkel Exciton Model.

We present the excitonic configuration interaction (ECI) method─a fragment-based analogue of the CI method for electronic structure calculations of multichromophoric systems. It can also be viewed as a generalization of the exciton approach, with the following properties: (i) It constructs the effective Hamiltonian exclusively from monomer calculations. (ii) It employs the strong orthogonality assumption and is exact within McWeeny's group function theory, thus requiring only one-electron density matrices of the monomer states. (iii) It is agnostic of the monomer electronic structure method, allowing us to use/combine different methods. (iv) It includes an embedding point charge scheme (called excitonic Hartree-Fock, EHF) to improve the accuracy of the monomer states, but such that the effective full-system Hamiltonian is not explicitly dependent on the embedding. (v) It is systematically improvable, by expanding the set of monomer states and by including configurations where two or more monomers are excited (defining the ECIS, ECISD, etc., methods). The performance of ECI is assessed by computing the absorption spectrum of two exemplary multichromophoric systems, using CIS as the monomer electronic structure method. The accuracy of ECI significantly depends on the chosen embedding charges and the ECI expansion. The most accurate assessed combinations─ECIS or ECISD with EHF embedding─yield spectra that agree qualitatively and quantitatively with full-system direct calculations, with deviations of the excitation energies below 0.1 eV. We also show that ECISD based on CIS monomer calculations can predict states where two monomers are excited simultaneously (e.g., triplet-triplet double-local excitations) that are inaccessible in a full-system CIS calculation.

Hierarchical control for collaborative electric vehicle charging to alleviate network congestion and enhance EV hosting in constrained distribution networks

This paper introduces a novel hierarchical architecture aimed at enhancing coordination between distribution system operators and electric vehicle aggregators in order to minimize Electric Vehicle (EV) charging costs for users while optimizing EV hosting capacity to alleviate network congestion. Real-world distribution networks are employed to evaluate EV charging strategies and their impact on medium and low-voltage networks. Two distinct EV charging optimization strategies are proposed to ensure fair power allocation among EV Aggregators (EVAs), alleviating congestion while managing EV charging power efficiently. Results demonstrate that the proposed collaborative EV charging effectively flattens the load curve, reducing peak power and avoiding grid congestion. The main findings underscore the importance of incentivizing EV flexibility to support Distribution System Operator (DSO) objectives beyond static tariffs. Furthermore, a battery degradation model is introduced into the optimization problem, reducing high currents and capacity decay. Despite capturing a higher mean electricity price, the total cost of EV charging is reduced.

Investigation of Fe-Ni Battery/Module for Grid Service Duty Cycles.

Iron-nickel (Fe-Ni) batteries are renowned for their durability and resilience against overcharging and operating temperatures. However, they encounter challenges in achieving widespread adoption for energy storage applications due to their low efficiency and the need for regular maintenance and electrolyte replacement, which adds to maintenance costs. This study evaluates and demonstrates the capabilities of Fe-Ni batteries for participating in grid energy storage applications. Stable performance was observed frequency regulation (FR) testing at 100% and 50% state of charge (SOC)s, while at 50% SOC, there was a 14% increase in efficiency compared to 100% SOC. Although 25% SOC achieved higher efficiency, limited cyclability was observed due to reaching the discharge cutoff voltage. Optimal SOC selection, battery monitoring, maintenance, and appropriate charging strategies of Fe-Ni batteries seem to be crucial for their FR applications. Fe-Ni batteries exhibit stable peak shaving (PS) results, indicating their suitability and reliability under various load conditions for PS testing. Extended cycling tests confirm their potential for long-term grid-scale energy storage, enhancing their appeal for PS and FR applications.

Research on charging strategy based on improved particle swarm optimization PID algorithm

Aiming at the electric vehicle charging pile control system has the characteristics of multi-parameter, strong coupling and non-linearity, and the existing traditional PID control and fuzzy PID control methods have the problems of slow charging speed, poor control performance and anti-interference ability, as well as seriously affecting the service life of the battery, this paper designs a kind of improved particle swarm algorithm to optimize the PID controller of the charging control system for electric vehicle charging piles, and utilizes the improved particle swarm algorithm to Adaptive and precise adjustment of proportional, integral and differential parameters, so that the system quickly reaches stability, so as to improve the accuracy of the system control output current or voltage. Simulation results show that the optimized system response speed of the improved particle swarm algorithm is improved by 3.077 s, the overshooting amount is reduced by 1.01%, and there is no oscillation, which has strong adaptability and anti-interference ability, and can significantly improve the control accuracy and charging efficiency of the charging pile control system.

Study on charge and discharge control strategy of improved PSO for EV

In view of the negative influence of electric vehicle (EV) random charging on power grid load stability and charging cost of users, on the premise of guaranteeing users' travel demand, in this paper, an improved PSO model is proposed with the objective function of optimizing the daily load variance of the power grid and minimizing the charge cost of the vehicle owner. In this model, chaotic mapping is used to initialize the position of particles so that the particles are uniformly distributed in space and the diversity of particle solutions is increased, based on the global search ability and local search ability of the improved algorithm, combined with the analysis of a numerical example, the results show that the improved multi-objective PSO algorithm converges quickly and can jump out of the local optimum, better multi-objective optimization to reduce the peak-valley load difference and charging costs.

A digital twin for advancing battery fast charging based on a Bayesian optimization-based method

The optimization of fast charging protocols is regarded as a key technology for promoting the use of electric vehicles because it can balance battery charging time and health. Optimizing such charging protocols through electrochemical models is a mainstream approach and can demonstrate high accuracy in simulating battery characteristics. However, the high complexity of the corresponding models makes the calculation and optimization processes difficult to perform in real time. To address this problem, this study presents an online closed-loop fast charging strategy optimization scheme that combines a battery digital twin model (BDTM) and Bayesian optimization (BO). Because the BO can quickly find a near-optimal solution in a few iterations, the optimization time is shortened, thereby reducing the computational burden incurred by highly complex models. To further improve the efficiency of the online optimization, a parallel multichannel optimization strategy is proposed, which further accelerates the process of finding the optimal protocol by simultaneously executing multiple optimization algorithms. Additionally, we analysed the effects of ageing parameters and ambient temperature on the optimization results. The results show that BO can obtain relatively stable optimization and has the highest efficiency when using four parallel channels. Specifically, the number of convergence evaluations for single-channel optimization is 2.5 times that of the four-channel optimization. Compared with the reference charging protocol, charging using the protocol optimized based on the Doyle-Fuller-Newman (DFN) model can effectively suppress the loss of lithium inventory (LLI) by up to 4.76 % within 90 cycles.

Investigation of Lithium-Ion Battery Negative Pulsed Charging Strategy Using Non-Dominated Sorting Genetic Algorithm II

To address the critical issue of polarization during lithium-ion battery charging and its adverse impact on battery capacity and lifespan, this research employs a comprehensive strategy that considers the charging duration, efficiency, and temperature increase. Central to this approach is the proposal of a novel negative pulsed charging technique optimized using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This study initiates the creation of an intricate electrothermal coupling model, which simulates variations in internal battery parameters throughout the charging cycle. Subsequently, NSGA-II is implemented in MATLAB to fine-tune pulsed charging and discharging profiles, generating a Pareto front showcasing an array of optimal solutions tailored to a spectrum of goals. Leveraging the capabilities of the COMSOL Multiphysics software 6.2 platform, a high-fidelity simulation environment for lithium-ion battery charging is established that incorporates three charging strategies: constant-current (CC) charging, a multi-stage constant-current (MS-CC) charging protocol, and a pulsed-current (PC) charging strategy. This setup works as a powerful instrument for assessing the individual effects of these strategies on battery characteristics. The simulation results strongly support the superiority of the proposed pulsed-current charging strategy, which excels in increasing the battery temperature and amplifying battery charge capacity. This dual achievement not only bolsters charging efficiency significantly but also underscores the strategy’s potential to augment both the practical utility and long-term viability of lithium-ion batteries, thereby contributing to the advancement of sustainable energy storage solutions.

Global Trends in Electric Vehicle Battery Efficiency and Impact on Sustainable Grid

Over the past decade, transportation electrification has emerged as a pivotal focus of the article. Electric vehicles (EVs) have progressively gained traction in the market, displacing conventional internal combustion engine vehicles. This surge in EV popularity has led to a corresponding increase in the number of charging stations, thereby significantly influencing the power grid (PG). Various charging strategies and grid integration approaches are being devised to mitigate the potential negative impacts of EV charging while optimizing the advantages of integrating EVs with the grid. This paper provides a comprehensive overview of the current state of the EV market, standards, charging infrastructure, and the PG’s response to the impact of EV charging. The article provides a comprehensive assessment of how forthcoming advancements in EV technology, including connected vehicles, autonomous driving, and shared mobility, will intricately influence the integration of EVs with the PG. Ultimately, the article concludes by meticulously analyzing and summarizing both the challenges and recommendations pertinent to the prospective expansion of EV charging infrastructure and grid integration. The proliferation of venture capital investments in nascent start-up ventures specializing in EV and battery technologies has experienced a pronounced surge, reaching an impressive sum of nearly USD 2.1 billion in 2022. This notable increase represents a substantial uptick of 30% compared to the figures recorded in 2021. Furthermore, these investments have been directed towards two key areas: advancements in battery technology and the acquisition of critical minerals. This discernible shift in investment trends underscores the growing recognition of the strategic importance and potential profitability associated with innovations in EV and battery technologies. In 2022, global expenditures on EVs surpassed USD 425 billion, marking a substantial 50% increase compared to the previous year, 2021. Remarkably, a mere 10% of these expenditures can be attributed to governmental support, with the bulk stemming from consumer investments.

Multi-objective optimization and scheduling research on electric bicycle charging and discharging in distribution networks

Abstract With the continuous development of urbanization, the rapid growth of electric bicycles has become a prominent trend. The number of battery-swapping stations for convenient battery replacement has also increased. The large-scale and unregulated connection of electric bicycles and battery-swapping stations to the power grid can significantly impact the distribution network, leading to localized overloads and affecting grid stability. Therefore, this paper proposes a multi-objective optimization scheduling strategy for electric bicycle charging and dis-charging based on both the transmission and distribution systems. Firstly, in the transmission system, the objective is to reduce the operating costs of generators and user charging costs. Sub-sequently, in the distribution system, the goal is to minimize network losses, considering system constraints and the spatial migration characteristics of electric bicycles. The study establishes a multi-objective optimization problem. Simulation analyses were conducted on the power system models based on a standard 10-machine transmission network and an IEEE 33-node distribution network to verify the effectiveness of the proposed multi-objective optimization scheduling strategy.

Optimal Bivariate Control Strategy of Multistage Constant Current Charging for IPT-Based Wireless Electric Vehicle Charging

Optimal Bivariate Control Strategy of Multistage Constant Current Charging for IPT-Based Wireless Electric Vehicle Charging

Development of Novel MSCCCTCV Charging Strategy for Health-Aware Battery Fast Charging Using QOGA Optimization

Development of Novel MSCCCTCV Charging Strategy for Health-Aware Battery Fast Charging Using QOGA Optimization

Cost optimization of electricity in energy storage system by dynamic programming

This paper presents a dynamic programming solution for the cost optimization of an electric storage system. The objective is to minimize the total cost of meeting electricity demand over a specified time interval, considering energy constraints and costs. The proposed algorithm efficiently determines the optimal energy discharge and charge strategies for the storage system, resulting in reduced overall costs. The effectiveness and efficiency of the algorithm are demonstrated through various test cases, highlighting its potential for real-world applications in energy storage systems and electric grid management. It also provides an overview of different types of electrical storage systems, review recent research on optimization techniques for energy storage, and examines recent studies on the optimization of electrical storage systems for specific applications, such as peak load shaving and grid stability. Through this comprehensive analysis, we hope to shed light on the current state of the field and identify areas for further research and improvement.