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.

Abstract
Ensuring robust semantic interoperability is essential for efficient data exchange in the energy sector. This paper introduces SEMAPTIC, a lightweight framework that simplifies semantic interoperability by providing a standardized approach for attaching metadata to exchanged data. SEMAPTIC utilizes ontologies to define the meaning of data elements and employs a new structured metadata map to guide data interpretation. This approach simplifies data exchange, minimizes maintenance effort, and fosters unambiguous data understanding across heterogeneous systems. Compared to traditional methods that often require complex data transformations, SEMAPTIC offers greater flexibility and reduced overhead. The paper explores the benefits of SEMAPTIC, including simplified integration, minimal maintenance, enhanced interoperability, reduced misinterpretation, facilitated data reuse, and future-proofing. A practical example showcases how SEMAPTIC enriches a JSON data structure with semantic context without the need of modifying the original structure and without inflating data size. Finally, the importance of well-defined ontologies is emphasized, highlighting how SEMAPTIC empowers the energy sector to achieve seamless and reliable data exchange, paving the way for a more efficient and intelligent energy ecosystem. © The Institution of Engineering & Technology 2024.