Filipe Tadeu Oliveira

Abstract
To achieve carbon neutrality by 2050, commercial buildings have installed photovoltaic systems to reduce carbon emissions and operational costs. Nevertheless, PV generation does not always match the building’s energy demand profile, therefore storage systems are needed to store excess energy and supply it when necessary. This paper presents a Mixed Integer Linear Programming optimisation algorithm designed to schedule the operation of the electric storage system, aiming to minimise the building’s energy-related costs. An annual hourly simulation of the optimised system was performed to assess the cost reduction. To prevent excessive operation of the electric storage system, an approach to penalise low energy charging was studied, with results showing a significant increase in the system’s lifespan.

Abstract
This paper presents an optimization model for electric vehicle (EV) fleet charging under MIBEL (Iberian Electricity Market). The model integrates EV charging with day-ahead forecasting for grid energy prices, photovoltaic (PV) generation, and local power demand, combined with a battery energy storage system (BESS) to minimize total charging costs, reduce peak demand, and maximize renewable use. Simulations across Baseline, Certainty, and Uncertainty scenarios show that the proposed approach would reduce total charging costs by up to 49%, lower carbon emissions by 73.7%, and improve SOC compliance, while smoothing demand curves to mitigate excessive contracted power charges. The results demonstrate the economic and environmental benefits of predictive and adaptive EV charging strategies, highlighting opportunities for further enhancements through real-time adjustments and vehicle-to-grid (V2G) integration.

Abstract
Widespread fleet electrification is concentrating electricity demand at commercial depots that face volatile prices, tight feeder limits and scarce chargers. This paper proposes a forecast-aware mixed-integer linear program (MILP) that co-optimises vehicle charging, battery-energy-storage dispatch and photovoltaic self-consumption. The model minimises energy cost plus state-of-charge (SOC) penalties, while enforcing charger exclusivity, battery-health bounds and continuous priority weights. It is evaluated on a 48-interval weekday data set comprising 20 electric vehicles, two 11?kW chargers, half-hourly solar forecasts, factory-load predictions and Iberian day-ahead prices. Relative to an uncontrolled first-come/first-served baseline, the optimiser cuts total charging expenditure by 49?%, inceases SOC compliance from 35?% to 65?%, increases PV self-consumption from 33.4?% to 35.5?% and lowers grid-attributed CO2 emissions by 66?%. A modest rise in instantaneous demand is held within transformer limits through strategic battery discharge. These results confirm that predictive scheduling transforms depot charging from a passive load into a cost-optimal, carbon-aware asset and motivate future extensions that embed stochastic forecasts, vehicle-to-grid services. route-energy coupling and Keywords. EV fleet charging; mixed-integer linear programming; battery energy self-consumption; predictive scheduling

Abstract
This paper presents a methodology for assessing the long-term economic feasibility of renewable energy-based systems for green hydrogen and ammonia production. A key innovation of this approach is the incorporation of a predictive algorithm that optimizes day-ahead system operation on an hourly basis, aiming to maximize profit. By integrating this feature, the methodology accounts for forecasting errors, leading to a more realistic economic evaluation. The selected case study integrates wind and PV as renewable energy sources, supplying an electrolyser and a Haber-Bosch ammonia production plant. Additionally, all supporting equipment, including an air separation unit for nitrogen production, compressors, and hydrogen / nitrogen / ammonia storage devices, is also considered. Furthermore, an electrochemical battery is included, allowing for an increased electrolyser load factor and smoother operating regimes. The results demonstrate the effectiveness of the proposed methodology, providing valuable insights and performance indicators for this type of energy systems, enabling informed decision-making by investors and stakeholders.

Abstract
Reducing the gap between renewable energy needs and supply is crucial to achieve sustainable growth. Hydroelectric power production predictions in several Madeira Island catchment regions are shown in this article using Long Short-Term Memory, LSTM, networks. In order to foresee hydro reservoirs inflows, our models take into account the island's dynamic precipitation and flow rates and simplify the process of water moving from the cloud to the turbine. The model developed for the Socorridos Faja Rodrigues system demonstrates the proficiency of LSTMs in capturing the unexpected flow behavior through its low RMSE. When it comes to energy planning, the model built for the CTIII Paul Velho system gives useful information despite its lower accuracy when it comes to anticipating problems.