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
The integration of inverter-based distributed generation challenges the implementation of an reliable protection This work proposes an adaptive protection method for coordinating protection systems using directional overcurrent relays, where the settings depend on the distribution network operating conditions. The coordination problem is addressed through a specialized genetic algorithm, aiming to minimize the total operating times of relays with time-delayed operation. The pickup current is also optimized. Coordination diagrams from diverse fault scenarios illustrate the method's adaptability to different operational conditions, emphasizing the importance of employing multiple setting groups for optimal protection system performance. The proposed technique provides high-quality solutions, enhancing reliability compared to traditional protection schemes.

Abstract
The integration of Distributed Energy Resources (DER) imposes challenges to the operation of distribution networks. This paper conducts a systematic assessment of the impact of DER on distribution network overcurrent protection, considering the behavior of Inverter Based Resources (IBR) during faults in the coordination of medium voltage (MV) feeders' overcurrent protection. Through a detailed analysis of various scenarios, we propose adaptive protection solutions that enhance the reliability and resilience of distribution networks in the face of growing renewable energy integration. Results highlight the advantages of using adaptive protection over traditional methods and topology changes, and delve into current protection strategies, identifying limitations and proposing mitigation strategies. © The Institution of Engineering & Technology 2024.

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.

Abstract
As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as shoeboxes using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning.

Abstract
At a time when European countries try to cope with escalating energy prices while decarbonizing their economies, waste heat recovery and reuse arises as part of the solution for sustainable energy transitions. The lack of appropriate assessment tools has been pointed out as one of the main barriers to the wider deployment of waste heat recovery projects and as a reason why its potential remains largely untapped. The EMB3Rs platform emerges as an online, open-source, comprehensive and novel tool that provides an integrated assessment of different types of waste heat recovery solutions, (e.g. internal or external) and comprises several analysis dimensions (e.g. physical, geographical, technical, market, and business models). It has been developed together with stakeholders, and tested in a number of representative contexts, covering both industrial and heat network applications. This has demonstrated the enormous potential of the tool in dealing with complex simulations, while delivering accurate results within a significantly lower time-frame than traditional analysis. The EMB3Rs tool removes important barriers such as analysis costs, time and complexity for the user, and aims at supporting a wider investment in waste heat recovery and reuse by providing an integrated estimation of the costs and benefits of such projects. This paper describes the tool and illustrates how it can be applied to help unlock the potential of waste heat recovery across European countries.