• Energy Research

This article investigates the contribution to the self-consumption achievable appropriately exploiting the battery of an Electric Vehicle (EV) in a residential nano-grid with photovoltaic generation. A battery energy management algorithm is presented and different scenarios of EV usage for different kinds of users have been investigated. Two households located in Northern and Southern Italy, respectively, are taken in examination. The results show that, even in the worst case, a remarkable increment on self-consumption is achieved with respect to the case without EV.

Electric vehicles (EVs) are pivotal in the global shift towards sustainable energy systems. As EV adoption rises, effective integration into the power grid becomes vital. Vehicle-to-grid (V2G) operations, enabling bidirectional power flow between EVs and the grid, enhance grid resilience and leverage EVs as distributed energy resources. This article presents a comprehensive framework addressing gaps in EV integration. It combines EV travel energy forecasts and electricity market engagement, coordinating forecast-based optimization with real-time management. Our approach introduces a novel prioritization mechanism, optimizing power allocation based on forecasted state-of-charge for upcoming trips. The optimization follows a multi-objective function that aims to minimize the operational costs and maximize both the upward and downward flexibility to provide ancillary services. The study assesses EV participation in Replacement Reserve, Secondary Reserve, and energy arbitrage. Simulations compare scenarios, unveiling optimal performances. The framework showcases EVs' potential to bolster grid stability, yielding economic value for EV owners and the power system. This article pioneers a holistic approach to EV integration, bridging current limitations, and emphasizing economic and technical benefits.

The maximum self-sufficiency by PV modules, wind turbines and storage is investigated, that can be obtained by aggregated tertiary sector users. Each renewable source (sun and wind) is assessed in terms of hours of availability with respect to the annual hours. The primary goal of aggregated users, now prosumers, is the achievement of the best match between profiles of generation and consumption. Power ratings of PV and wind generators and energy capacities of batteries are chosen to reach the highest self-sufficiency and the lowest power exchange with the grid. PV and wind powers are simulated by using measured meteorological data.

The current issues of climate change and air pollution are pushing the electricity sector to move towards the deployment of renewable energy sources. However, shifting from traditional to renewable energy resources like solar energy introduces new aspects and challenges. Weather dependency and the need for expensive storage infrastructure to cope with power fluctuations appear among the main aspects for effectively using solar energy. When storage cannot be used, in the presence of high diffusion of renewable energy it could be necessary to resort to energy curtailment in the peak hours of production. In this paper, we propose a methodology which helps the reduction of energy curtailment in the peak production hours of photovoltaic panels by controlling the charging power of electric vehicles connected to the grid without reaching the line loading limits and without exceeding the voltage magnitude limits in the nodes of the grid.

This article proposes a procedure for the control of electric vehicle (EV) batteries, aiming to have an optimal matching between local renewable production, domestic loads, and EV consumption. The procedure starts with the analysis of historical photovoltaic (PV), EV, and domestic load profiles. Load and PV profiles are forecasted using statistical-based algorithms, while the expected patterns of EV usages are forecasted using a combination of statistics and clustering techniques. Then, the forecasted profiles are used to estimate future energy balances trough an optimization process. Finally, the real-time management corrects the forecasting logic and checks the parameters of the EV storage to guarantee its correct and safe operation. Three different EV usage profiles (obtained by the clustering of 215 real users) are shown and their impact on the energy balance of EV–PV–home systems is quantified. The results are finally compared with those obtained with a traditional rule-based logic working without forecasts, by also reporting a detailed analysis of the main aspects having an impact on the results.