Abstract
Reducing office buildings' energy consumption can contribute significantly towards carbon reduction commitments since it represents similar to 40% of total energy consumption. Major components of this are lighting, electrical equipment, heating, and central cooling systems. Solid evidence demonstrates that individual occupants' behaviors impact these energy consumption components. In this work, we propose the methodology of using virtual choreographies to identify and prioritize behavior-change interventions for office users based on the potential impact of specific behaviors on energy consumption. We studied the energy-related office behaviors of individuals by combining three sources of data: direct observations, electricity meters, and computer logs. Data show that there are behaviors with significant consumption impact but with little potential for behavioral change, while other behaviors have substantial potential for lowering energy consumption via behavioral change.

Abstract
This paper investigates the contribution of hydrogen electrolysers (HEs) as highly controllable loads in the context of the Frequency Containment Reserve (FCR), in future operation scenarios on the Iberian Peninsula (IP). The research question is whether HEs can mitigate system insecurity regarding frequency or Rate of Change of Frequency (RoCoF) in critical periods of high renewable energy penetration (i.e. low system inertia), due to the fact that these periods will coincide with high volume of green hydrogen production. The proposed simulation platform for analysis consists of a simplified dynamic model developed in MATLAB/Simulink. The results obtained illustrate how HEs can outperform conventional generators on the provision of FCR. It is seen that the reference incident of 1GW loss in the IP in a 2040 low inertia scenario does not lead to insecure values of either frequency or Rate of Change of Frequency (RoCoF). On the other hand, an instantaneous loss of inverter-based resources (IBR) generation following a short-circuit may result in RoCoF violating security thresholds. The obtained results suggest that the HEs expected to be installed in the IP in 2040 may contribute to reduce RoCoF in this case, although this mitigation may be insufficient. The existing FCR mechanism does not fully exploit the fast-ramping capability of HEs; reducing measurement acquisiton delay would not improve results.

Abstract
The increasing reliance of modern power systems on heterogeneous renewable energy and decreasing contribution of inertial thermal resources necessitates the availability of planning tools to ensure the continued operational stability of these systems. The abundance of historical data allows the estimation of behaviour during contingencies in common network configurations, but overlooks feasible but rare combinations of generation. Surveys of operation levels at regular intervals can ignore critical areas of operation without high resolution, which requires a significant computational overhead. This paper seeks to address the need for reliable dynamic security assessment to inform grid operator decisions on contingency planning. The aim is to demonstrate the creation of an off-line database that surveys the possible network operation configurations drawing on statistical historical analysis and efficient generic sampling. A high degree of accuracy is achieved in identifying energy mixes that can be expected to result in unstable operation during an unanticipated network outage through the implementation of importance sampling.

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.