Battery Protective Electric Vehicle Charging Management in Renewable Energy System

The adoption of grid-connected electric vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient vehicle-to-grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This article develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. Meanwhile, an online V2G regulator is built to facilitate the real-time scheduling of GEVs' charging. The extreme learning machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a U.K. microgrid with actual energy generation and consumption data. This article can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarbonization at low costs.</p

A Case Study of the Use of Smart EV Charging for Peak Shaving in Local Area Grids

Electricity storage systems, whether electric vehicles or stationary battery storage systems, stabilize the electricity supply grid with their flexibility and thus drive the energy transition forward. Grid peak power demand has a high impact on the energy bill for commercial electricity consumers. Using battery storage capacities (EVs or stationary battery systems) can help to reduce these peaks, applying peak shaving. This study aims to address the potential of peak shaving using a PV plant and smart unidirectional and bidirectional charging technology for two fleets of electric vehicles and two comparable configurations of stationary battery storage systems on the university campus of Saarland University in Saarbrücken as a case study. Based on an annual measurement of the grid demand power of all consumers on the campus, a simulation study was carried out to compare the peak shaving potential of seven scenarios. For the sake of simplicity, it was assumed that the vehicles are connected to the charging station during working hours and can be charged and discharged within a user-defined range of state of charge. Furthermore, only the electricity costs were included in the profitability analysis; investment and operating costs were not taken into account. Compared to a reference system without battery storage capacities and a PV plant, the overall result is that the peak-shaving potential and the associated reduction in total electricity costs increases with the exclusive use of a PV system (3.2%) via the inclusion of the EV fleet (up to 3.0% for unidirectional smart charging and 8.1% for bidirectional charging) up to a stationary battery storage system (13.3%)

Hidden Effects and Externalities of Electric Vehicles

The global drive towards sustainability has ushered in a new era of transportation, prominently featuring the rise of Battery Electric Vehicles (BEVs). The rapid rise of BEVs has been widely hailed as a crucial milestone in promoting sustainable transportation and combating climate change. The existing empirical evidence provides undeniable support for the essential role of BEVs to support net zero targets. However, like any disruptive technology, BEVs are not without their hidden effects. This paper seeks to explore and analyse these lesser-known repercussions (i.e., externalities) of BEV adoption. In doing so, it sheds light on the environmental, infrastructure, socio-economic and safety externalities of BEVs, aiming to foster a holistic understanding of their impact and facilitate informed decision-making. Furthermore, it highlights the critical role of public awareness and user education in maximising the benefits of BEVs and the importance of maintaining a balance of information in developing such campaigns. Empowering individuals, communities, and policymakers with accurate information, dispelling misconceptions, and fostering responsible BEV practices are essential for realising maximal environmental impacts of BEVs. The discussion emphasises the need for moving beyond climate-change targets that are merely based on tailpipe emissions, towards life-cycle-based approaches

Metodologia para detecção e localização de faltas em rede real de distribuição considerando a inserção de veículos elétricos

The present master’s thesis proposes an application of artificial neural networks in the problem of detecting and locating high impedance defects in a real electrical energy distribution system with the presence of electric vehicles. For this, electric vehicles with their charging stations were used and the daily charging curve of electric vehicles was varied in order to assess the impact on the problems of detecting and locating high impedance defects, on the premise of adaptive protection through application of RNA. Among the curves in the specialized literature, three were chosen, with special emphasis on the type of recharge in which the electric vehicle is considered, both in the charging condition and in the condition of power injection in the distribution network. In addition to the charging curves, in order to increase the applicability of the method, the load levels were also varied for the simulation of power flow via OpenDSS. Finally, we investigated different types of topologies for the installation of smart meters installed throughout the electrical energy distribution system studied and the results were analyzed using evaluation metrics and descriptive statistics tools. The case study considers a real electrical network from a Brazilian distribution concessionaire.A presente dissertação de mestrado propõe uma aplicação de redes neurais artificiais (RNA) no problema de detecção e localização de defeitos de alta impedância de um sistema de distribuição de energia elétrica real com a presença de veículos elétricos (VE). Para isso, utilizou-se veículos elétricos com seus postos de carregamento e variou-se a curva de recarga diária dos veículos elétricos com o objetivo de avaliar o impacto nos problemas de detecção e localização de defeitos de alta impedâncias, na premissa de proteção adaptativa através da aplicação de RNA. Dentre as curvas existentes na literatura especializada escolheu-se três, com destaque especial para o tipo de recarga em que se considera o veículo elétrico tanto na condição de carregamento, quanto na condição de injeção de potência na rede de distribuição. Além das curvas de recarga, visando aumentar a aplicabilidade do método, variou-se também os patamares de carga, para a simulação de fluxo de potência via OpenDSS. Por fim, investigou-se diferentes tipos de topologias de instalação de medidores inteligentes instalados ao longo do sistema de distribuição de energia elétrica estudado e os resultados foram analisados através de métricas de avaliação e ferramentas da estatística descritiva. O estudo de caso considera uma rede elétrica real de uma concessionária de distribuição brasileira

A comprehensive review on electric vehicles smart charging: solutions, strategies, technologies, and challenges

The role of electric vehicles (EVs) in energy systems will be crucial over the upcoming years due to their environmental-friendly nature and ability to mitigate/absorb excess power from renewable energy sources. Currently, a significant focus is given to EV smart charging (EVSC) solutions by researchers and industries around the globe to suitably meet the EVs' charging demand while overcoming their negative impacts on the power grid. Therefore, effective EVSC strategies and technologies are required to address such challenges. This review paper outlines the benefits and challenges of the EVSC procedure from different points of view. The role of EV aggregator in EVSC, charging methods and objectives, and required infrastructure for implementing EVSC are discussed. The study also deals with ancillary services provided by EVSC and EVs' load forecasting approaches. Moreover, the EVSC integrated energy systems, including homes, buildings, integrated energy systems, etc., are reviewed, followed by the smart green charging solutions to enhance the environmental benefit of EVs. The literature review shows the efficiency of EVSC in reducing charging costs by 30 %, grid operational costs by 10 %, and renewable curtailment by 40 %. The study gives key findings and recommendations which can be helpful for researchers and policymakers

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Peak-Load Management in Commercial Systems with Electric Vehicles

© 2007-2012 IEEE. Electric vehicles (EVs) are getting popular as one of the effective solutions for increased energy efficiency in commercial systems. This paper proposes an improved algorithm for commercial peak-load management using EVs, battery-energy-storage systems, and photovoltaic units. It uses the bidirectional vehicle-to-grid technique to utilize the energy from EVs in a parking lot. The proposed system has been tested in a real power distribution network in realistic load and weather conditions. The financial benefit of the system is also investigated, and it is found that the industrial peak load can be reduced by 50%, and the energy cost can be reduced by up to 27.3%. It also enhances the load factor by 9%. The performance of the proposed control algorithm is compared with that of an artificial-neural-network-based technique and tested in a laboratory prototype. From simulated and experimental results, it is found that the proposed approach provides substantial savings, while reducing the peak demand of the existing grids

Peak-Load Management in Commercial Systems With Electric Vehicles

Electric vehicles (EVs) are getting popular as one of the effective solutions for increased energy efficiency in commercial systems. This paper proposes an improved algorithm for commercial peak-load management using EVs, battery-energy-storage systems, and photovoltaic units. It uses the bidirectional vehicle-to-grid technique to utilize the energy from EVs in a parking lot. The proposed system has been tested in a real power distribution network in realistic load and weather conditions. The financial benefit of the system is also investigated, and it is found that the industrial peak load can be reduced by 50%, and the energy cost can be reduced by up to 27.3%. It also enhances the load factor by 9%. The performance of the proposed control algorithm is compared with that of an artificial-neural-network-based technique and tested in a laboratory prototype. From simulated and experimental results, it is found that the proposed approach provides substantial savings, while reducing the peak demand of the existing grids