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This Special Issue “Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies” was in session from 1 May 2019 to 31 May 2020. For this Special issue, we invited articles on current state-of-the-art technologies and solutions in G2V and V2G, including but not limited to the operation and control of gridable vehicles, energy storage and management systems, charging infrastructure and chargers, EV demand and load forecasting, V2G interfaces and applications, V2G and energy reliability and security, environmental impacts, and economic benefits as well as demonstration projects and case studies in the aforementioned areas. Articles that deal with the latest hot topics in V2G are of particular interest, such as V2G and demand-side response control technique, smart charging infrastructure and grid planning, advanced power electronics for V2G systems, adaptation of V2G systems in the smart grid, adaptation of smart cities for a large number of EVs, integration, and the optimization of V2G systems, utilities and transportation assets for advanced V2G systems, wireless power transfer systems for advanced V2G systems, fault detection, maintenance and diagnostics in V2G processes, communications protocols for V2G systems, energy management system (EMS) in V2G systems, IoT for V2G systems, distributed energy and storage systems for V2G, transportation networks and V2G, energy management for V2G, smart charging/discharging stations for efficient V2G, environmental and socio-economic benefits and challenges of V2G systems, and building integrated V2G systems (BIV2G). Five manuscripts are published in this Special Issue, including “An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads” by Agyeman et al., “Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid, An MPC Scheme with Enhanced Active Voltage Vector Region for V2G Inverter” by Shipman et al., “Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids” by Xia et al., and “A Review on Communication Standards and Charging Topologies of V2G and V2H Operation Strategies” by Savitti et al.

History of engineering & technology --- vehicle-to-grid (V2G) --- vehicle-to-home (V2H) --- bi-directional charging topologies --- communication standards --- battery cycle --- smart grid --- optimization --- energy management --- electric vehicles --- distributed generation --- MPC --- AV2R --- V2G --- inverter --- vehicle-to-grid --- vehicle location prediction --- automated machine learning --- machine learning --- Bayesian --- deep neural network --- demand load forecast --- distributed load --- ensemble algorithm stochastic --- K-means --- vehicle-to-grid (V2G) --- vehicle-to-home (V2H) --- bi-directional charging topologies --- communication standards --- battery cycle --- smart grid --- optimization --- energy management --- electric vehicles --- distributed generation --- MPC --- AV2R --- V2G --- inverter --- vehicle-to-grid --- vehicle location prediction --- automated machine learning --- machine learning --- Bayesian --- deep neural network --- demand load forecast --- distributed load --- ensemble algorithm stochastic --- K-means

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This Special Issue “Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies” was in session from 1 May 2019 to 31 May 2020. For this Special issue, we invited articles on current state-of-the-art technologies and solutions in G2V and V2G, including but not limited to the operation and control of gridable vehicles, energy storage and management systems, charging infrastructure and chargers, EV demand and load forecasting, V2G interfaces and applications, V2G and energy reliability and security, environmental impacts, and economic benefits as well as demonstration projects and case studies in the aforementioned areas. Articles that deal with the latest hot topics in V2G are of particular interest, such as V2G and demand-side response control technique, smart charging infrastructure and grid planning, advanced power electronics for V2G systems, adaptation of V2G systems in the smart grid, adaptation of smart cities for a large number of EVs, integration, and the optimization of V2G systems, utilities and transportation assets for advanced V2G systems, wireless power transfer systems for advanced V2G systems, fault detection, maintenance and diagnostics in V2G processes, communications protocols for V2G systems, energy management system (EMS) in V2G systems, IoT for V2G systems, distributed energy and storage systems for V2G, transportation networks and V2G, energy management for V2G, smart charging/discharging stations for efficient V2G, environmental and socio-economic benefits and challenges of V2G systems, and building integrated V2G systems (BIV2G). Five manuscripts are published in this Special Issue, including “An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads” by Agyeman et al., “Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid, An MPC Scheme with Enhanced Active Voltage Vector Region for V2G Inverter” by Shipman et al., “Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids” by Xia et al., and “A Review on Communication Standards and Charging Topologies of V2G and V2H Operation Strategies” by Savitti et al.

vehicle-to-grid (V2G) --- vehicle-to-home (V2H) --- bi-directional charging topologies --- communication standards --- battery cycle --- smart grid --- optimization --- energy management --- electric vehicles --- distributed generation --- MPC --- AV2R --- V2G --- inverter --- vehicle-to-grid --- vehicle location prediction --- automated machine learning --- machine learning --- Bayesian --- deep neural network --- demand load forecast --- distributed load --- ensemble algorithm stochastic --- K-means

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Electric vehicles are only ‘green’ as long as the source of electricity is ‘green’ as well. At the same time, renewable power production suffers from diurnal and seasonal variations, creating the need for energy storage technology. Moreover, overloading and voltage problems are expected in the distributed network due to the high penetration of distributed generation and increased power demand from the charging of electric vehicles. The energy and mobility transition hence calls for novel technological innovations in the field of sustainable electric mobility powered from renewable energy. This Special Issue focuses on recent advances in technology for PV charging and storage for electric vehicles.

Technology: general issues --- electric vehicle --- demand forecasting --- peak shaving --- smart charging --- robust optimization --- Energy management and control --- particle swarm optimization (PSO) --- hybrid AC/DC microgrid --- electric vehicle charging and discharging control --- artificial physics optimization (APO) --- vehicle to grid --- V2G --- battery degradation --- Li-ion --- real-time --- moving horizon window --- battery charger --- electric bike --- photovoltaic system --- power converter --- wireless power transfer --- plug-in electric vehicle --- energy management system --- renewable energy --- vehicle-to-grid --- life cycle assessment --- CO2 emissions --- photovoltaic systems --- electric vehicles --- VIPV --- cost–benefit analysis

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Electric vehicles are only ‘green’ as long as the source of electricity is ‘green’ as well. At the same time, renewable power production suffers from diurnal and seasonal variations, creating the need for energy storage technology. Moreover, overloading and voltage problems are expected in the distributed network due to the high penetration of distributed generation and increased power demand from the charging of electric vehicles. The energy and mobility transition hence calls for novel technological innovations in the field of sustainable electric mobility powered from renewable energy. This Special Issue focuses on recent advances in technology for PV charging and storage for electric vehicles.

Technology: general issues --- electric vehicle --- demand forecasting --- peak shaving --- smart charging --- robust optimization --- Energy management and control --- particle swarm optimization (PSO) --- hybrid AC/DC microgrid --- electric vehicle charging and discharging control --- artificial physics optimization (APO) --- vehicle to grid --- V2G --- battery degradation --- Li-ion --- real-time --- moving horizon window --- battery charger --- electric bike --- photovoltaic system --- power converter --- wireless power transfer --- plug-in electric vehicle --- energy management system --- renewable energy --- vehicle-to-grid --- life cycle assessment --- CO2 emissions --- photovoltaic systems --- electric vehicles --- VIPV --- cost–benefit analysis --- electric vehicle --- demand forecasting --- peak shaving --- smart charging --- robust optimization --- Energy management and control --- particle swarm optimization (PSO) --- hybrid AC/DC microgrid --- electric vehicle charging and discharging control --- artificial physics optimization (APO) --- vehicle to grid --- V2G --- battery degradation --- Li-ion --- real-time --- moving horizon window --- battery charger --- electric bike --- photovoltaic system --- power converter --- wireless power transfer --- plug-in electric vehicle --- energy management system --- renewable energy --- vehicle-to-grid --- life cycle assessment --- CO2 emissions --- photovoltaic systems --- electric vehicles --- VIPV --- cost–benefit analysis

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With the Internet of Things and various information and communication technologies, a city can manage its assets in a smarter way, constituting the urban development vision of a smart city. This facilitates a more efficient use of physical infrastructure and encourages citizen participation. Smart energy and smart mobility are among the key aspects of the smart city, in which the electric vehicle (EV) is believed to take a key role. EVs are powered by various energy sources or the electricity grid. With proper scheduling, a large fleet of EVs can be charged from charging stations and parking infrastructures. Although the battery capacity of a single EV is small, an aggregation of EVs can perform as a significant power source or load, constituting a vehicle-to-grid (V2G) system. Besides acquiring energy from the grid, in V2G, EVs can also support the grid by providing various demand response and auxiliary services. Thanks to this, we can reduce our reliance on fossil fuels and utilize the renewable energy more effectively. This Special Issue “Smart Energy and Intelligent Transportation Systems” addresses existing knowledge gaps and advances smart energy and mobility. It consists of five peer-reviewed papers that cover a range of subjects and applications related to smart energy and transportation.

Technology: general issues --- History of engineering & technology --- electric vehicles --- PROSA --- PROMETHEE for Sustainability Assessment --- MCDA --- Multi-Criteria Decision Analysis --- stochastic analysis --- Monte Carlo --- uncertainty --- cargo bicycles --- loading hub --- facility location problem --- computer simulations --- Python programing --- electric vehicle charging --- vehicle-to-grid --- genetic algorithms --- particle swarm optimization --- demand-side management --- discrete choice theory --- revenue management --- road-railway accidents --- classification trees --- road safety --- transport means --- accidents victims --- condition monitoring --- vibroacoustic diagnostics --- gearbox --- power transmission systems --- neural networks --- deep learning --- electric vehicles --- PROSA --- PROMETHEE for Sustainability Assessment --- MCDA --- Multi-Criteria Decision Analysis --- stochastic analysis --- Monte Carlo --- uncertainty --- cargo bicycles --- loading hub --- facility location problem --- computer simulations --- Python programing --- electric vehicle charging --- vehicle-to-grid --- genetic algorithms --- particle swarm optimization --- demand-side management --- discrete choice theory --- revenue management --- road-railway accidents --- classification trees --- road safety --- transport means --- accidents victims --- condition monitoring --- vibroacoustic diagnostics --- gearbox --- power transmission systems --- neural networks --- deep learning