Integration Of EVs Research Articles

Electric vehicles as facilitators of grid stability and flexibility: A multidisciplinary overview

AbstractElectric vehicles (EVs), as facilitators of grid stability and flexibility, provide a critical solution to the energy infrastructure's evolving demands, underscored by the growing integration of renewable energy sources (RES) and the rapid increase in EV adoption worldwide. This trend is particularly evident in Europe which is experiencing dramatic increases in both the adoption of RES and EVs. Vehicle‐to‐grid (V2G) technology allows EVs to operate as a two‐way power flow to both draw and feed electricity into the grid. This multidisciplinary overview examines the role of V2G systems in enhancing grid performance, identifying corporate vehicle fleets as key flexibility providers, and integration with Smart Grid technologies as a key element for successful V2G implementation. In a scoping analysis of recent literature (2005–2024), we identify challenges such as privacy, security, and regulatory compliance as well as a critical gap in establishing economically sustainable models for aggregators, distribution system operators (DSOs), generation companies (GENCOs), and end‐users. Drawing from these insights, we then discuss the necessity for future research to develop models that ensure equitable benefits across stakeholders and the importance of models that can adapt to country‐specific mechanisms. The findings from our overview argue that the integration of EVs, V2G, and RES are essential components for developing future energy systems that are resilient, adaptable, decarbonized, and sustainable.This article is categorized under: Cities and Transportation > Electric Mobility Energy and Power Systems > Energy Infrastructure Energy and Power Systems > Energy Management

Voltage Stability in a Photovoltaic-based DC Microgrid with GaN-Based Bidirectional Converter using Fuzzy Controller for EV Charging Applications

Electric vehicles are growing in importance owing to their desirable characteristics leading to utilization. It is a significant challenge to maintain voltage for DC microgrids when integrating with EVs. The work aims to enhance voltage stability in a DC microgrid and the electric vehicle charging using GaN devices in the converter. This is done by developing a DC microgrid with better voltage regulation, loss reduction, and increased efficiency and these are analyzed using PLECS software. Under different operating conditions, the proposed converter can respond to load fluctuations and maintain its voltage profile stable. The approach meets the increasing demand for vehicle charging by upgrading DC microgrid technology. The use of GaN-based converters improves voltage stability while allowing for efficient integration of EVs into the grid thereby giving more options for transportation.

Abstract 6968: Extracellular vesicles as carriers of signaling factors in cancer

Abstract Extracellular vesicles (EVs) are small, membrane-bound structures released by cells. They contain various biomolecules and have emerged as mediators of cellular signaling. EVs of various origins can be isolated from body fluids and hold potential as novel biomarkers. However, their clinical implementation as biomarkers is hampered by complex isolation procedures, limited expertise in routine labs, and a lack of mechanistic understanding of signal transfer, particularly in vivo. To better understand how EVs carry oncogenic signaling factors, we performed mechanistic investigations into the role of EVs in WNT signaling as a representative cancer-associated signaling pathway, as well as ex vivo validation experiments analyzing patient-derived EVs. Hydrophobic WNT ligands can use EVs to travel the extracellular space. However, little is known about the quantities of WNT secretion onto different EV fractions and potentially other vehicles. To explore WNT secretory routes and potential functional differences, we generated fluorescently tagged marker constructs of the non-canonical ligand WNT5a, which has context-dependent tumor-suppressing or -promoting functions in cancer. In additional in vitro experiments, we tested how the WNT signaling activation state and cancer-specific WNT mutations, such as CRISPR/Cas9-induced APC truncations, quantitatively and qualitatively affect EV shedding in colorectal cancer cell lines. Further functional testing of EV-bound vs. non-EV WNT ligands is ongoing. For proof-of-concept analyses and to test EVs as biomarkers, we set up the liquid biopsy study EValuate. The project aims for cross-entity analysis of EV profiles and correlation of results to cancer presence, stage, and survival. For quality control and before pipeline implementation, we tested various EV isolation methods and compared different sample pre-processing steps. By combining multidisciplinary expertise in biophysics, molecular biology, and basic and clinical cancer research, we aim to gain a better mechanistic understanding of EV-mediated signal transfer and facilitate the integration of EVs as diagnostic and prognostic biomarkers into clinical studies. Citation Format: Antonia Schubert, Nadine Winkler, Ajaree Mongkolsittisilp, Matthias Schulz, Oksana Voloshanenko, Meike Schaffrinski, Dominique Kranz, Karin Nienhaus, Dirk Jäger, Lorenz Trümper, Judith Büntzel, Claudia Binder, Gerd Ulrich Nienhaus, Michael Boutros. Extracellular vesicles as carriers of signaling factors in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6968.

Optimization of Microgrid Energy Cost with Electrical Vehicles and Demand Response

Challenges with current power systems include increasing energy costs and Greenhouse gasses. Participation in distributed generators (DGs) and demand response (DR) strategies inside the distribution network may solve these issues. This paper's primary objective is to suggest an energy management system (EMS) strategy for campus microgrid reduce all the issues mentioned above. To practice this strategy a microgrid is considered. Conventionally the microgrid uses a utility grid to fulfill load demands. But in the proposed model the integration of a micro turbine, photovoltaic (PV) system, battery energy storage system (BESS), and Electric vehicles is proposed. Mix integer linear programming MILP technique is used in MATLAB to solve the non-linear optimization problem. The energy management system EMS and demand response policies are used to optimally schedule the available resources and reduce energy costs. The integration of EVs is also observed as source and load. The simulation results are discussed and different case studies are analyzed. Simulation results show that 46% cost saving is observed while using EVs in the system as source. The real-time energy interruption problem is also observed in this paper.

A Survey on Load Frequency Control of Multi-Area Power Systems: Recent Challenges and Strategies

Load frequency control (LFC) is well known for balancing the load demand and frequency for a multi-area power system. Studies have proven that LFC can improve the global performance of multi-area power systems. In recent years, the increasing proportion of renewable energy, integration of EVs, and cyber-attacks have become the main challenges in LFC power systems. Different strategies have been applied in the literature for LFC power systems and the possible impacts of renewable energy, EVs, and cyber-attacks. This survey paper is devoted to the research on directions in LFC multi-area power systems. The mathematical model of recent challenges in LFC multi-area power systems is summarized and the similarities and differences of these challenges are analyzed. The uncertainty of renewable energy is a frequently noted issue in LFC power systems; however, the uncertainty that exists in controller design is often ignored. In this survey, we analyze methods for treating the uncertainty of renewable energy and controller. This survey paper introduces the most recent research on LFC and acquaints anyone interested in its development, such that the most effective strategies can be developed by the researchers.

Design of a bi-directional DC/DC converter for EV chargers oriented to V2G applications

In order to address the problems caused by climate change, electric vehicles have gained international attention in recent years. Furthermore, special attention is being paid to the Vehicle to Grid (V2G) services that this technology can provide. In this context, the development of bi-directional chargers is gaining importance. Taking all this into consideration, in this work a simple and easy-to-implement solution of the DC/DC stage of a bidirectional electronic charger that allows the integration of EVs in V2G applications has been designed and validated through simulation.

A Review on Electric Vehicles Charging Strategies Concerning Actors Interests

Electric vehicles are becoming increasingly popular in societies and an important part of smart grids. Utility companies should be able to provide them with vital energy as they need electric energy instead of fuel, and this is where new challenges emerge in the network. In order to avoid causing utilities to incur additional energy and economic losses, researchers have proposed smart charging as a way to provide adequate energy to vehicles. When developing a charging schedule for a fleet of EVs, special considerations are made on variables such as energy, cost, and EVs milage. In this review paper, the importance of EVs integration into smart grids is studied, and then different methods to develop EVs charging scheduling are investigated. These methods can vary from optimization algorithms to learning-based, and game theory-based approaches. Then, as the considered system consists of three main actors, including EV users, the utility operator, and aggregators, a systematic review is conducted on these actors, and objectives related to each one are analyzed. Finally, research gaps related to the problem are studied. Researchers can use this review to conduct further research on the integration of EVs into smart grids.

Optimal Electric Vehicle Charging Control Strategy Powered by Grid Linked Hybrid PV Wind Battery Renewable Energy System

An advanced model is proposed for grid connectivity of an interconnected network consisting of a charging station for electric automobiles. To automate the discharge procedure of charging/ the battery energy storing system, a wind network, the photovoltaic system, and the battery energy storing system is developed to efficiently increase the consumption degree of solar and wind energy sources and create renewable inner-city capacity. On the basis of DC bus architecture, the power design was planned such that buffered storage systems and renewable energy resources can be incorporated. The proposed optimal control algorithm uses the Swarm Optimization Algorithm consists of Multi-Objective Particle, developed for electric vehicles charging or discharge behaviors to minimize the overall actual energy loss and increase the integration of EVs with power networks due to the efficiency and economy of network activity, taking into account the economic issue and the satisfaction of consumers, the voltage limits and the parking availability pattern. To test the proposed EV charging strategy, simulation studies based on efficiency, and assessed major energy fluxes within the device. Energy management approaches have also been developed to optimize the power requirements and charging times of various electric vehicles. Results suggest that proposed model will substantially reduce the power grid’s operational costs while meeting the charging criteria of the customer. Improved performance on global search capabilities is also checked, as is the desired outcome of enhanced particle swarm optimization algorithm. The findings show that the new approach is in a position to prepare EV charging times optimally, taking into account electronic knowledge and uncertainty.

Switching strategy of the low wind speed wind turbine based on real-time wind process prediction for the integration of wind power and EVs

Utilizing the secondary wind resources in cities and countryside is a significant way to promote wind power consumption and sustainable transportation. However, the probability of wind speed near the cut-in wind speed increases in such area and resulting in frequent on/off switches as well as large fatigue load of the wind turbine. To address these problems, this paper proposes a switching strategy of the low wind speed wind turbine based on real-time wind process prediction. First, the wavelet decomposition and neural network are employed to predict the time series of wind speed in a real-time manner. Second, based on the historical and predicted wind speed, the typical wind processes are extracted by using the x-means algorithm. Third, seven indexes are defined to quantify the characteristics of the wind process set before developing the corresponding switching strategy for each type of it. Data from NREL are used to validate the proposed models. The results show that, the proposed strategy increases the energy yield, reduces the number of wind turbine switches as well as the power fluctuation. Therefore, the proposed strategy is beneficial to both wind power projects development and electric vehicles charging.

Research on short-term wind power combined forecasting and its Gaussian cloud uncertainty to support the integration of renewables and EVs

Under the pressure of environmental pollution and energy shortage, wind power generation and EVs with clean and pollution-free characteristics have developed rapidly. However, the randomness of EVs charging and the volatility of wind power output will bring great challenges to the reliability and economy of grid operation. Especially the accuracy and range of wind power forecasting are critical to the operation of the power system with a high proportion of renewable energy and EVs. Aiming at improving the accuracy of short-term wind power forecasting and its uncertainty, this paper puts forward a combined forecasting model, including BP, Wavelet, and RVM by information fusion strategy, Gaussian Cloud model is used to reflect the uncertainty in the forecasting process. According to the measured data of two units, the results of short-term wind power forecasting are analyzed and compared with the single forecasting method. It’s found that the combined forecasting model can improve the forecasting accuracy with more reasonable confidence interval. The power grid can guide the EVs to dynamically adjust the EVs charging time according to the forecasting wind power and EVs charging power curves, so as to maximize the absorption of wind power, achieve the economic operation and reduce pollution emissions.