Abstract
This paper proposes a stochastic optimization model for industrial hubs to enable their participation in energy markets. The model aims to leverage the resources of multi-energy systems to minimize energy costs in the day-ahead market. It accounts for uncertainties in photovoltaic generation, electrical and heat demand, and outdoor temperatures. A comparison is made with a deterministic approach, along with an analysis of the impact of thermal storage and reserve market participation on costs and bidding strategies. The results show that the stochastic approach is more conservative than the deterministic, while the integration of thermal storage and reserve services help decrease costs.

Abstract
This paper introduces a comprehensive methodology for day-ahead planning of renewable energy systems geared toward green hydrogen and ammonia production. This approach is a forecasting algorithm that uses synthetic data, which feeds a short-term load forecasting (STLF) algorithm to predict the 24-hour hydrogen demand. This capability enables the optimization of hourly system operations, with the goal of maximizing profitability while maintaining system efficiency. The case study presented includes a renewable energy source - photovoltaic power plant (PV) - and a grid connection, which supply power to an electrolyser. Essential supporting infrastructure such as the auxiliary system of the electrolyser is incorporated into the model. Additionally, an electrochemical battery - a battery energy storage system (BESS) - is incorporated, which helps to keep a high electrolyser load factor and creates smoother operating profiles. This BESS also allows the system to contribute to the energy reserves market, enhancing its economic and operational viability.

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
Local flexibility markets are a promising solution to aid system operators in managing the network as it faces the growth of distributed resources and the resulting impacts on voltage control, among other factors. This paper presents and simulates a proposal for an intra-day local flexibility market based on grid segmentation. The design provides a market-based solution for distribution system operators (DSOs) to address near-real-time grid issues. The grid segmentation computes the virtual buses that represent each zone and the sensitivity indices that approximate the impact of activating active power flexibility in the buses within the zone. This approach allows DSOs to manage and publish their flexibility needs per zone and enables aggregators to offer flexibility by optimizing their resource portfolios per zone. The simulation outcomes allow for the assessment of market performance according to the number of zones computed and show that addressing overloading and voltage control through zonal approaches can be cost-effective and counterbalance minor errors compared to node-based approaches.

Abstract
Research around the co-location of different renewable energy technologies in offshore sites is increasing due to the potential complementarity of different sources that could decrease the power output variability, and increase reliability. However, further decrease of the power fluctuations and higher economic profitability could be achieved with energy storage. In this work, a model is developed for optimal sizing and energy management of energy storage and delivery solutions to accommodate the hybridisation of an offshore wind park. A set of options is considered for energy storage: the integration of a battery energy storage system (BESS), hydrogen production for direct sale or hydrogen/fuel cell system. For energy delivery, an expansion of the transmission cable, hydrogen pipeline or transportation by ship is evaluated. The case study used to test the model is the offshore farm WindFloat Atlantic located near the coast of Viana do Castelo, Portugal, which is proposed to be hybridised with wave energy converters (WEC). Sensitivity analyses are performed on possible components' cost variations, hydrogen shipping frequency or sale price. The results show that hydrogen production from the studied offshore hybrid park is profitable, and the transmission through submarine pipeline is competitive with electrical connections by cable. The highest profitability is achieved when pipeline and cable expansion are combined. Hydrogen transportation by ship also appears profitable, in the eventuality that additional submarine transmission facilities cannot be installed.