Rising grid congestion raises concerns that conventional grid expansion may not keep pace with projected increases in demand and renewable generation. This research proposes a supplementary solution: leveraging the inherent mobility of Electric Vehicles to enable energy transfer within different locations. Through an analytical modelling approach, a stochastic energy transport model using Monte Carlo sampling is developed. This model generates synthetic weekly charging and driving profiles to quantify the energy transport potential of private EV fleets, accounting for battery capacity, mobility patterns, and energy consumption. Building on this concept, the study introduces Flexible Energy Communities (FlexECs), where members share the same living location but commute to different workplaces or vice versa. By enabling electric vehicles to charge at one location and discharge at another during routine daily travels, Flexible Energy Communities exploit private EV mobility as a mechanism for spatial energy transfer, extending Energy Community operation beyond purely grid energy exchange.Results reveal that approximately 35 kWh, equivalent to 60% of the total energy stored in the EV battery at the time of departure, can be discharged upon arrival. Additionally, when charging patterns are optimized, up to 30 kWh, representing 70% of the energy charged prior to the journey, can be effectively transported and discharged at the destination. By quantifying the impact of EV-imported energy on cost, peak demand, and overall energy consumption reduction, this study underscores the potential of EVs as dynamic energy transporters, providing a technical foundation for energy sharing through private EVs daily travelling patterns.

Decarbonizing the transport sector is crucial for achieving sustainability goals, and two key strategies are the adoption of Electric Vehicles (EVs) and the expansion of Car Sharing Systems (CSS). EVs eliminate tailpipe emissions, while CSS reduces overall vehicle ownership and usage, leading to lower carbon emissions. Combining these two solutions into Electric Car Sharing Systems (ECSS) enhances their environmental and economic benefits. The integration of Vehicle-to-Grid (V2G) technology further strengthens this synergy by enabling EVs to support the energy grid, optimize charging costs, and improve system efficiency.This study investigates the integration of Vehicle-to-Grid technology within an Electric Car Sharing System to enhance Car Sharing Operator (CSO) profitability. A mathematical model is developed to optimize the financial performance of a CSO managing a station-based ECSS with EVs across five stations. The model considers vehicle driving, relocation, charging, and discharging under time-varying electricity prices. Results show that V2G increases profitability by enabling energy sales during periods of low driving demand and high electricity prices. These findings provide insights for optimizing EV-sharing systems in dynamic electricity markets and highlight the need for advanced vehicle management strategies.

The study presents Electric Vehicle (EV) Point-of-View (POV) Vehicle to Grid (V2G) optimisation, where the system knows the trips and energy demand of the EV. This allows it to be less conservative in the departure State of Charge (SoC) of the EV battery. The peak and cost reduction of Vehicle POV optimisation are compared with state-of-the-art Location Point-of-View optimisation, this is, the car can do V2G but it has to depart each location with at least 80% SoC as the next trip energy demand is unknown. A Mix Integer Linear Programming (MILP) V2G optimisation approach is presented to reduce peak demand and total system cost of a fleet of EVs travelling between several residential locations and one common workplace. Simulations were conducted with 26 EVs unevenly distributed between five residential locations and commuters to a common workplace. The results indicate little differences in the reduction of peak demand between both POVs of V2G optimisation. However, the system cost can be further reduced with Vehicle Point-of-View optimization, saving an additional 200 euros annually per household by transporting cheaper energy from workplaces to residential locations.

European cities have adopted different solutions to address the challenges of charging infrastructure for electric vehicles, depending on their specific characteristics and needs. The widespread adoption of effective solutions could accelerate the transition towards more sustainable urban mobility. However, as cities differ in socio-economic, infrastructural, and environmental aspects, a one-size-fits-all approach may not be suitable. Currently, there is a lack of studies in the literature that identify similarities among cities to support the development of shared strategies for sustainable electric mobility. This paper contributes to filling this gap by proposing a methodology based on Key Performance Indicators (KPIs) to classify and compare cities according to their electric vehicle infrastructure. Using quantitative data from 80 European cities across civil, social, and transport-related factors, as well as electric vehicle charging characteristics, we identified five reference city clusters. A sensitivity analysis, conducted across 30 scenarios, validated the robustness of the KPI framework. This approach provides a tool for policymakers to monitor the evolution of charging infrastructure, supporting data-driven decision-making for sustainable urban mobility. By promoting efficient and adaptable electric vehicle policies, this study aligns with the objectives of the 2030 Agenda for Sustainable Development, particularly in fostering sustainable cities and clean energy adoption.

This study explores the potential of mobile charging systems to overcome the challenges of traditional Electric Vehicle (EV) charging infrastructures, such as the scarcity of charging points, lengthy charging times, and urban space constraints. It introduces an autonomous mobile system tailored to satisfy daily charging demands in various conditions, presenting a flexible alternative to fixed charging stations. Through an optimization process, the operational effectiveness of a robot-like mobile charging system is assessed under different grid capacities and battery configurations. The results indicate that these systems can significantly reduce peak grid demand and improve the charging experience by increasing availability and reducing waiting times. Profitability varies with seasonal changes and grid capacity. A switchable battery configuration, which utilizes fewer carriers to mobilize batteries, is shown to lower investment costs and boost financial returns when compared to traditional charging poles, making mobile charging systems a viable and efficient solution to meet the increasing demands of urban EV charging.

In the context of building electrification the operation of distributed energy resources integrating multiple energy carriers poses a significant challenge. Such an operation calls for an energy management system that decides the set-points of the primary control layer in the best way possible. This is done by fulfilling user requirements, minimizing costs, and balancing local generation with energy storage. This last component is what enables building flexibility. This paper presents a novel aging-aware strategy for operating grid-connected buildings that combine multiple energy carriers (heat and electricity), storage devices (electric vehicles, batteries, and thermal storage), and power sources (solar photovoltaics, solar collectors). The novel energy management algorithm presented considers the aging of the batteries to enhance the operational differences between storage technologies, thus making explicit the trade-off between the services provided by the hybrid energy storage system and its degradation. This unlocks grid cost reductions between 20–45 % depending on the season when compared to state-of-the-art solutions.

This paper presents a Mix Integer Linear Programming (MILP) optimization approach to reduce peak demand and maximize revenue in a grid-connected building with a PV-equipped charging station for Shared EVs. The study investigates the impact of EV availability on the effectiveness of the system by comparing the results for different connection times of a fleet of Shared EVs, a private EV used for commuting, and a stationary battery. Results from the case study conducted in The Netherlands demonstrate that not only the duration but also the timing of EV connection significantly influence system effectiveness, emphasizing the need for accurate availability estimation. The trade-off between peak reduction and Peak-to-Average Ratio (PAR) reduction is also highlighted, underscoring the importance of considering both factors for optimizing charging station usage. These findings provide valuable insights for optimizing energy management, reducing peak loads, and increasing the utilization of renewable energy sources in the context of Shared EVs and V2G technology.