Abstract
Energy Communities (ECS) and Self- consumption structures are receiving significant attention in Europe due to their potential contribution to a sustainable energy transition and the decarbonization process of the energy system. They are considered a powerful instrument to involve end-consumers in active participation in the energy system by becoming self-producers of renewable electricity and increasing their awareness of their potential contribution by adapting their energy behavior to the global or local power system needs. An EC can also contribute to alleviating energy poverty, which occurs when low incomes and poorly efficient buildings and appliances place a high proportion of energy costs on households. The main driver would be the reduction in energy costs obtained if some members agree to share their surplus electricity at a lower price with vulnerable members. Similarly, a renewable EC (REC) can facilitate access to energy assets by sharing the investments among the community members and exploiting existing complementarities. For example, vulnerable members could share their roofs with others to install solar panels in exchange for low-cost electricity. RECs can also help vulnerable members by reducing the barriers to accessing subsidies for building efficiency investments thanks to collective community initiatives, easing information dissemination and helping with bureaucratic processes. © 2003-2012 IEEE.

Abstract
Hybrid storage systems that combine high energy density and high power density technologies can enhance the flexibility and stability of microgrids and local energy communities under high renewable energy shares. This work introduces a novel approach integrating rule-based (RB) methods with evolutionary strategies (ES)-based reinforcement learning. Unlike conventional RB methods, this approach involves encoding rules in a domain-specific language and leveraging ES to evolve the symbolic model via data-driven interactions between the control agent and the environment. The results of a case study with Li-ion and redox flow batteries show that the method effectively extracted rules that minimize the energy exchanged between the community and the grid. © 2024 IEEE.

Abstract
Energy Communities (ECS) and Self- consumption structures are receiving significant attention in Europe due to their potential contribution to a sustainable energy transition and the decarbonization process of the energy system. They are considered a powerful instrument to involve end-consumers in active participation in the energy system by becoming self-producers of renewable electricity and increasing their awareness of their potential contribution by adapting their energy behavior to the global or local power system needs. An EC can also contribute to alleviating energy poverty, which occurs when low incomes and poorly efficient buildings and appliances place a high proportion of energy costs on households. The main driver would be the reduction in energy costs obtained if some members agree to share their surplus electricity at a lower price with vulnerable members. Similarly, a renewable EC (REC) can facilitate access to energy assets by sharing the investments among the community members and exploiting existing complementarities. For example, vulnerable members could share their roofs with others to install solar panels in exchange for low-cost electricity. RECs can also help vulnerable members by reducing the barriers to accessing subsidies for building efficiency investments thanks to collective community initiatives, easing information dissemination and helping with bureaucratic processes.

Abstract
Hosting capacity (HC) describes the electricity network's ability to accommodate distributed generation (DG) without deteriorating electrical performance indicators. Distribution system operators typically express their networks' HC as a single threshold, called static hosting capacity (SHC). SHC is determined via conservative regulatory criteria, increasing connection costs and time. This paper explores the potential for additional energy injection into the network via dynamic hosting capacity (DHC). A network node's DHC is derived from the hourly operation of the network, accounting for the time variability of existing distributed generation (DG) output and demand. The methodology considers the network assets' N-1 contingencies and their probabilities, defining the security-aware DHC (SDHC). The SDHC definition is technologically neutral. Through a case study of a radial medium voltage distribution network, the paper highlights the significant limitations of SHC due to conservative calculation criteria mandated by regulators. Annual injectable energy is increased by 62% to 76% when comparing DHC to SHC. Variations between average DHC and SDHC are below 0.01% due to low N-1 probabilities. This finding points out the potential of dynamic hosting capacity definitions, allowing more efficient use of the existing network and facilitating the integration of new DG capacity with reduced connection costs and time.

Abstract
This paper proposes a planning optimization model to help distribution system operators (DSOs) decide on the most cost-effective investments to handle the wholesale market participation of distributed energy resources (DERs). Two investment options are contemplated: market redesign; and network augmentation. The market redesign is employed through a DSO framework used to coordinate the network-secure participation of DERs in wholesale markets. Network augmentation is achieved by investing in new HV/MV OLTC and MV/LV transformers. To evaluate the performance of our planning model, we used the IEEE 69-bus network with three DER aggregators operating under different DER scenarios. Our tests show that the planning problem suggests investment decisions that can help DSOs guarantee network security. Market redesign has shown to be the most cost-effective option. However, this option is not always viable, namely in scenarios where not enough DERs are available to provide network support services. In such scenarios, hybrid investment solutions are required.

Abstract
This paper presents a methodology for assessing the long-term economic feasibility of renewable energy-based systems for green hydrogen and ammonia production. A key innovation of this approach is the incorporation of a predictive algorithm that optimizes day-ahead system operation on an hourly basis, aiming to maximize profit. By integrating this feature, the methodology accounts for forecasting errors, leading to a more realistic economic evaluation. The selected case study integrates wind and PV as renewable energy sources, supplying an electrolyser and a Haber-Bosch ammonia production plant. Additionally, all supporting equipment, including an air separation unit for nitrogen production, compressors, and hydrogen / nitrogen / ammonia storage devices, is also considered. Furthermore, an electrochemical battery is included, allowing for an increased electrolyser load factor and smoother operating regimes. The results demonstrate the effectiveness of the proposed methodology, providing valuable insights and performance indicators for this type of energy systems, enabling informed decision-making by investors and stakeholders.