Abstract
In the context of the R3EA project, funded by the Portuguese Foundation for Science and Technology (FCT), we analyse a set of selected future power system scenarios to assess the impact, on the Iberian electricity market (MIBEL), of installing wind and solar generation capacity in Portugal's Centro Region. We use the long-term MIBEL operation and planning model CEVESA. The scenarios are designed based on the current economic situation and the last National Energy and Climate Plan drafts for Portugal and Spain, by distributing the expected new wind and solar generation capacity differently among Portugal regions, also considering the flexible demand for producing electrolytic hydrogen. Market prices, capture prices and production per technology are analysed to assess this impact. Results show that regional investments have no significant impact on the MIBEL variables analysed.

Abstract
Hydrogen production through renewable energy-powered electrolysis is pivotal for fostering a sustainable future hydrogen market. In the electricity sector, hydrogen production bears an additional demand that affects electricity price, and mathematical models are needed for the joint simulation, analysis, and planning of electricity and hydrogen sectors. This study develops a Cournot and a perfect competition model to analyze the links of the electricity and hydrogen sectors. In contrast to other solving methods approaches, the Cournot model is solved by convex reformulation techniques, substantially easing the resolution. The case studies, focusing on the Iberian Peninsula, demonstrate the region's potential for competitive hydrogen production, and the advantages of perfect competition to maximize the use of renewable energies, in contrast to Cournot's oligopoly, where the exercise of market power raises electricity prices. Sensitivity analyses highlight the importance of strategic decision-making in mitigating market inefficiencies, with valuable insights for stakeholders and policymakers.

Abstract
This research assesses the behaviour of alternative objectives related to maximising the energy self-consumed in renewable energy communities. Three different objective functions are proposed: minimising the grid-supplied energy to the community members, reducing the energy surplus of the community injected into the grid, and maximising the self-consumed energy according to its definition in the Portuguese regulation. Two additional objectives were also considered for comparison purposes, the maximisation of the equivalent CO2 emissions saved and the minimisation of the total community energy cost. The methodology involves formulating and implementing the optimisation problems and discussing the results with a case example, including decreased grid dependency, utilisation of battery storage, and differences in energy trading strategies within the REC. Overall, this research contributes to understanding some alternative objectives that could be considered for the management of the flexible resources of a REC. © The Institution of Engineering & Technology 2024.

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Abstract

Abstract
Microgrids are poised to become the building blocks of the future control architecture of electric power systems. As the number of controllable points in the system grows exponentially, traditional control and optimization algorithms become inappropriate for the required operation time frameworks. Reinforcement learning has emerged as a potential alternative to carry out the real-time dispatching of distributed energy resources. This paper applies one of the continuous action-space algorithms, proximal policy optimization, to the optimal dispatch of a battery in a grid-connected microgrid. Our simulations show that, though suboptimal, RL presents some advantages over traditional optimization setups. Firstly, it can avoid the use of forecast data and presents a lower computational burden, therefore allowing for implementation in distributed control devices.