Abstract
The expected development and massification of Local Energy Markets (LEM), in particular the ones associated with Renewable Energy Communities, poses new challenges, and requires new operations strategies to their promoters, aggregators, and end-consumers. One of the mechanisms that can be used to speed up the spreading of this kind of market is the use of Demand Response (DR) programs since they can be designed to increase the community's savings and profits. In this framework, the end customers are induced to change their normal consumption patterns by temporarily reducing and/or shifting their electricity consumption away from periods with low local generation in response to a signal from a service provider, i.e., aggregator. To this purpose, this paper presents an Agent Based Model (ABM) using the Q-Learning mechanism to implement and to simulate a LEM and its interaction with the Wholesale Market (WSM), using also and incentive-based DR program. The overall objective of this design is to decrease average energy costs by moving the demand to periods of large availability of wind or solar resources or to store energy for future use. The developed model was tested considering real data regarding energy consumption and PV generation. The proposed paper describes and discusses the obtained market strategy and the profits that can be obtained with this approach.

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.

Abstract
This paper describes a study developed to analyse the interest in investing in Li-ion batteries to perform price arbitrage in the power system of Portugal. In this context, it was developed a methodology to identify the most suitable hours for charging and discharging the energy, and the new market prices were estimated for these hours. It was concluded that at current investment costs in this storage technology, and current market prices, this investment would not be viable in the lifetime of the batteries despite the recent rise of electricity market prices and also the larger price spread. This spread is now larger given the depression of prices at sunny hours that is getting typical in the Iberian electricity market.

Abstract
The global push for environmental sustainability is driving substantial changes in power systems, prompting extensive grid upgrades. Policies and initiatives worldwide aim to reduce CO2 emissions, with a focus on increasing reliance on Renewable Energy Sources (RESs) and electrifying transportation. However, the geographical variability and uncertainties of RESs directly impact power generation and distribution, necessitating adjustments in transmission system planning and operation. This paper presents a Transmission Expansion Planning (TEP) model using the 2021 Texas snowstorm as a benchmark scenario, incorporating wind and solar energy penetration while addressing associated uncertainties. Climate Change (CC) and Extreme Weather Events (EWE) are integrated into the set of scenarios aiming at evaluating the proposed method's effectiveness. Comparisons in extreme operative conditions highlight the importance of network reliability and security, emphasizing the significance of merged grids. All simulations are conducted using the ACTIVSg2000 synthetic test system, which emulates the ERCOT grid, with comparisons made between TEP scenarios considering and disregarding CC and EWEs, supporting the concept of umbrella protection.

Abstract
In the last two decades, power systems have experienced several changes, mainly related to organizational and operational restructuring. The appearance of new actors contributes to developing new business models and modifies its traditional operation activities. As a direct result, there is a need for new control solutions and strategies to integrate these different players. Agent-Based Models (ABM) have been increasingly used to model complex systems since they are especially suited to model systems influenced by social interactions between flexible, autonomous, and proactive agents. This paper provides a review of the literature regarding ABM in power systems followed by an analysis in more detail regarding specific applications that are becoming relevant in this new paradigm.

Abstract
Energy allocation rules are one of the core aspects of collective self-consumption (CSC) regulations. It allows final consumers to share their surplus generation with other CSC members, while keeping their full rights as consumers, i.e., maintaining a supply contract with the retailers of their choice. Some European Union member states regulations use allocation coefficients so that local allocations are integrated with wholesale settlement and directly affect the retailers' billing. Several AC methods have been proposed so far, each one adapted to distribution system operators' settlement procedures with specific rules that can impact the benefits that each CSC member obtain. This paper analyses, assesses and compares two relevant AC methods, namely pre-delivery fixed AC and post-delivery dynamic AC, by developing a settlement formulation for a community with members with flexible assets and different opportunity costs. AC policy recommendations based on findings are provided.