Search Results (1,732)

In power distribution networks, the growing integration of renewable energy sources (RESs) presents a challenge for the electricity system and its operators, who need to make the energy sector more flexible and resilient. In this context, this paper proposes a novel flexibilization service for the distribution system leveraging the role of renewable energy communities (RECs), an emerging entity with the potential to facilitate the sustainable energy transition through Virtual Islanding operation. The concept of Virtual Islanding is investigated in the paper and a methodology for its validation is developed. Its effectiveness is then assessed using an IEEE-standard 33-node network with significant penetration of RESs, considering the presence of multiple RECs to prove its benefits on electrical distribution networks. The results showcase the advantages of the VI paradigm both from technical and sustainability viewpoint. Full article

This paper examines Krivelj, a copper mining village in Serbia, as a critical yet overlooked node within global extractive networks. Despite supplying copper essential for renewable energy and sustainable architecture, Krivelj experiences severe ecological disruption, forced relocations, and socio-spatial destabilization, becoming a “sacrifice zone”—an area deliberately subjected to harm for broader economic interests. Employing a hybrid methodology that combines ethnographic fieldwork with Geographic Information Systems (GISs), this study spatializes narratives of extractive violence collected from residents through walking interviews, field sketches, and annotated aerial imagery. By integrating satellite data, legal documents, environmental sensors, and lived testimonies, it uncovers the concept of “slow violence,” where incremental harm occurs through bureaucratic neglect, ambient pollution, and legal ambiguity. Critiquing the abstraction of Planetary Urbanization theory, this research employs countertopography and forensic spatial analysis to propose a counter-cartographic framework that integrates geospatial analysis with local narratives. It demonstrates how global mining finance manifests locally through tangible experiences, such as respiratory illnesses and disrupted community relationships, emphasizing the potential of counter-cartography as a tool for visualizing and contesting systemic injustice. Full article

As the global energy transition accelerates, the integration of solar power and artificial intelligence (AI) technologies offers new pathways for sustainable local development. This study examines four Serbian municipalities—Šabac, Sombor, Pirot, and Čačak—to assess how AI-enabled solar power systems can enhance energy resilience, reduce emissions, and support community-level sustainability goals. Using a mixed-method approach combining spatial analysis, predictive modeling, and stakeholder interviews, this research study evaluates the performance and institutional readiness of local governments in terms of implementing intelligent solar infrastructure. Key AI applications included solar potential mapping, demand-side management, and predictive maintenance of photovoltaic (PV) systems. Quantitative results show an improvement >60% in forecasting accuracy, a 64% reduction in system downtime, and a 9.7% increase in energy cost savings. These technical gains were accompanied by positive trends in SDG-aligned indicators, such as improved electricity access and local job creation in the green economy. Despite challenges related to data infrastructure, regulatory gaps, and limited AI literacy, this study finds that institutional coordination and leadership commitment are decisive for successful implementation. The proposed AI–Solar Integration for Local Sustainability (AISILS) framework offers a replicable model for emerging economies. Policy recommendations include investing in foundational digital infrastructure, promoting low-code AI platforms, and aligning AI–solar projects with SDG targets to attract EU and national funding. This study contributes new empirical evidence on the digital–renewable energy nexus in Southeast Europe and underscores the strategic role of AI in accelerating inclusive, data-driven energy transitions at the municipal level. Full article

As a key enabling technology for energy transition, the smart grid is propelling the global power system to evolve toward greater efficiency, reliability, and sustainability. Based on the three-dimensional analysis framework of “technology–policy–application”, this study systematically sorts out the technical architecture, regional development mode, and typical application scenarios of the smart grid, revealing the multi-dimensional challenges that it faces. By using the methods of literature review, cross-national case comparison, and technology–policy collaborative analysis, the differentiated paths of China, the United States, and Europe in the development of smart grids are compared, aiming to promote the integration and development of smart grid technologies. From a technical perspective, this paper proposes a collaborative framework comprising the perception layer, network layer, and decision-making layer. Additionally, it analyzes the integration pathways of critical technologies, including sensors, communication protocols, and artificial intelligence. At the policy level, by comparing the differentiated characteristics in policy orientation and market mechanisms among China, the United States, and Europe, the complementarity between government-led and market-driven approaches is pointed out. At the application level, this study validates the practical value of smart grids in optimizing energy management, enhancing power supply reliability, and promoting renewable energy consumption through case analyses in urban smart energy systems, rural electrification, and industrial sectors. Further research indicates that insufficient technical standardization, data security risks, and the lack of policy coordination are the core bottlenecks restricting the large-scale development of smart grids. This paper proposes that a new type of intelligent and resilient power system needs to be constructed through technological innovation, policy coordination, and international cooperation, providing theoretical references and practical paths for energy transition. Full article

Modern microgrids face critical challenges in maintaining stability and efficiency due to renewable energy intermittency and dynamic load demands. This paper proposes a novel real-time energy management framework that synergizes a bio-inspired T-Cell optimization algorithm with decentralized voltage-based droop control to address these challenges. A JADE-based multi-agent system (MAS) orchestrates coordination between the T-Cell optimizer and edge-level controllers, enabling scalable and fault-tolerant decision-making. The T-Cell algorithm, inspired by adaptive immune system dynamics, optimizes global power distribution through the MAS platform, while droop control ensures local voltage stability via autonomous adjustments by distributed energy resources (DERs). The framework is rigorously validated through Hardware-in-the-Loop (HIL) testing using OPAL-RT, which interfaces MATLAB/Simulink models with Raspberry Pi for real-time communication (MQTT/Modbus protocols). Experimental results demonstrate a 91% reduction in grid dependency, 70% mitigation of voltage fluctuations, and a 93% self-consumption rate, significantly enhancing power quality and resilience. By integrating centralized optimization with decentralized control through MAS coordination, the hybrid approach achieves scalable, self-organizing microgrid operation under variable generation and load conditions. This work advances the practical deployment of adaptive energy management systems, offering a robust solution for sustainable and resilient microgrids. Full article

Residents’ attitudes towards offshore wind farms have been researched extensively over the past few decades. In this research, the precept that offshore wind farms influence residents’ well-being is implicit. Only a few studies have directly examined residents’ knowledge, perceived benefits, and acceptance. This study attempts to go beyond attitude-based research and explicitly examines factors influencing acceptance decision-making. The data for this qualitative study was collected through face-to-face interviews at a proposed offshore wind farm site in Perth, Western Australia. Results from the study suggest that offshore wind farms are not perceived or responded to uniformly by residents. This study provides a more comprehensive understanding of the dynamics and complexities behind identifying and explaining how residents of designated communities perceive offshore wind farms in a nuanced manner. Therefore, this study proffers significant theoretical discussions and practical implications regarding developing sustainable renewable energy alternatives in cities across Australia. Full article

The large-scale integration of stochastic renewable energy sources necessitates enhanced dynamic balancing capabilities in power systems, positioning hydropower as a critical balancing asset. Conventional excitation systems utilizing hot-standby dual-redundancy configurations remain susceptible to unit shutdown events caused by regulator failures. To mitigate this vulnerability, this study proposes a peer-to-peer distributed excitation architecture integrating asynchronous traffic shaping (ATS) and Data Distribution Service (DDS) technologies. This architecture utilizes control channels of equal priority and achieves high redundancy through cross-communication between discrete acquisition and computation modules. This research advances three key contributions: (1) design of a peer-to-peer distributed architectural framework; (2) development of a real-time data interaction methodology combining ATS and DDS, incorporating cross-layer parameter mapping, multi-priority queue scheduling, and congestion control mechanisms; (3) experimental validation of system reliability and redundancy through dynamic simulation. The results confirm the architecture’s operational efficacy, delivering both theoretical foundations and practical frameworks for highly reliable excitation systems. Full article

Intensive energy consumption in the building sector remains one of the primary contributors to climate change and global warming. Within Renewable Energy Communities (RECs), improving energy management is essential for promoting sustainability and reducing environmental impact. Accurate forecasting of energy consumption at the community level is a key tool in this effort. Traditionally, engineering-based methods grounded in thermodynamic principles have been employed, offering high accuracy under controlled conditions. However, their reliance on exhaustive building-level data and high computational costs limits their scalability in dynamic REC settings. In contrast, Artificial Intelligence (AI)-driven methods provide flexible and scalable alternatives by learning patterns from historical consumption and environmental data. This study investigates three Machine Learning (ML) models, Decision Tree (DT), Random Forest (RF), and CatBoost, and one Deep Learning (DL) model, Convolutional Neural Network (CNN), to forecast community electricity consumption using real smart meter data and local meteorological variables. The study focuses on a REC in Loureiro, Portugal, consisting of 172 residential users from whom 16 months of 15 min interval electricity consumption data were collected. Temporal features (hour of the day, day of the week, month) were combined with lag-based usage patterns, including features representing energy consumption at the corresponding time in the previous hour and on the previous day, to enhance model accuracy by leveraging short-term dependencies and daily repetition in usage behavior. Models were evaluated using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination $R^2$. Among all models, CatBoost achieved the best performance, with an MSE of 0.1262, MAPE of 4.77%, and an $R^2$ of 0.9018. These results highlight the potential of ensemble learning approaches for improving energy demand forecasting in RECs, supporting smarter energy management and contributing to energy and environmental performance. Full article

Renewable energy communities (RECs) are increasingly recognized as pivotal instruments in the global energy transition, offering decentralized, participatory, and sustainable solutions for energy management, specifically regarding energy production and consumption. The present review provides a comprehensive examination of the REC concept, tracing its regulatory evolution, particularly within the European Union through the renewable energy directives (RED II and RED III) and by analyzing its practical implementation across various countries. This paper explores the diverse technologies integrated into REC projects, such as photovoltaic systems, wind turbines, biogas, hydroelectric, and storage solutions, while also considering the socioeconomic frameworks, management models, and local engagement strategies that underpin their success. Key case studies from Europe, Asia, Africa, and Australia illustrate the various approaches, challenges, and outcomes of REC initiatives in different geographic and policy contexts. The analysis also highlights barriers to implementing RECs, including regulatory uncertainty and market integration issues, and identifies the best practices and policies that support REC scalability. By synthesizing current trends and lessons learned, this review aims to inform policymakers, researchers, and practitioners about the transformative role of RECs in achieving decarbonization goals and accomplishing resilient energy systems. Full article

Renewable Energy Communities (RECs) are voluntary coalitions of citizens, small and medium-sized enterprises and local authorities, which cooperate to share locally produced renewable energy, providing environmental, economic, and social benefits rather than profits. Despite a favorable European and Italian regulatory framework, their development is still limited in the Member States. To this end, this paper proposes a methodology to identify optimal spatial configurations of RECs, based on proximity criteria and maximization of energy self-sufficiency. This result is achieved through the mapping of the demand, expressive of the energy consumption of residential buildings; the suitable areas for installing photovoltaic panels on the roofs of existing buildings; the supply; the supply–demand balance, from which it is possible to identify Positive Energy Districts (PEDs) and Negative Energy Districts (NEDs). Through an iterative process, the optimal configuration is then sought, aggregating only PEDs and NEDs that meet the chosen criteria. This method is applied to the case study of the Avellino Province in the Campania Region (Italy). The maps obtained allow local authorities to inform citizens about the areas where it is convenient to aggregate with their neighbors in a REC to have benefits in terms of energy self-sufficiency, savings on bills or incentives at the local level, including those deriving from urban plans. The latter can encourage private initiative in order to speed up the RECs’ deployment. The presented model is being implemented in the framework of an ongoing research and development project, titled Renewable Energy Communities Monitoring, Optimization, and Planning (RECMOP). Full article

This paper presents a study on open energy data in Spain and its contribution to sustainability, analyzing its content and its reuse potential. Since energy plays an important role in the sustainability and economic development of a country or region, energy strategies must be managed through public policies that promote the development of this sector. In this sense, open data is relevant for decision-making in the energy sector, especially in areas such as energy consumption and renewable energy policies. Our research aims to analyze the work of Spain’s autonomous communities in the field of energy information by conducting a population analysis of all datasets tagged in the energy category. After compiling the information and eliminating irrelevant datasets (those that are mislabeled, obsolete, or have a scope less than the level of the autonomous community), it can be seen that the supply is very scarce and that this category is one of the least populated among all existing categories. The typological analysis indicates that information on consumption is the one offering the most datasets, followed, at a short distance, by heterogeneous and difficult-to-classify information and by the set related to energy certificates or audits (the most recurrent, as it is offered only once by the autonomous communities). One of the main findings of the research is the heterogeneity of the initiatives and the significant differences in scores on an indicator created for this purpose. The ranking has taken into account both the existence of information and the quality of reuse, with Catalonia, the Basque Country, and Cantabria being the leaders (with Castilla y León, the performance reaches 60%, so the three remaining communities do not reach 40%). The research concludes with recommendations based on the gaps detected: more data should be published that can drive economic development and environmental sustainability, reduce heterogeneity, and facilitate the use of these data for greater applicability, which will increase the chances that open energy data can contribute more to sustainability. Full article

This article demonstrates how positioning residents as active co-designers fundamentally transforms both the process and outcomes of carbon-positive building development. Through structured collaborative workshops, shared decision-making protocols, and continuous partnership throughout the building lifecycle, the REN+HOMES Positive Carbon Building methodology challenges the conventional expert-driven approach to sustainable construction. Developed and validated through the H2020 REN+HOMES project, this resident-centered approach achieved remarkable technical performance—65.9% reduction in final energy demand—while simultaneously enhancing community ownership and long-term sustainability practices. By integrating participatory design with Zero Emissions Building (ZEB) criteria, renewable energy systems, and national carbon offset programs, the methodology proves that resident collaboration is not merely beneficial but essential for creating buildings that truly serve both environmental and human needs. This research establishes a new paradigm where technical excellence emerges from authentic partnership between residents and sustainability experts, offering a replicable framework for community-driven environmental regeneration. Full article

As part of the carbon peak and neutrality drive, an influx of renewable energy into the grid is imminent. However, the unpredictability of renewables like wind and solar can lead to significant curtailment if the power system relies solely on traditional generators. This paper presents a demand response mechanism to enhance renewable energy uptake by defining an optimal load curve for each node, considering the generator’s dynamic impact, system operations, and renewable energy projections. Once the ideal load curve is published, consumers, influenced by incentives, voluntarily align their consumption, steering the actual load to resemble the proposed curve. This strategy not only guides flexible generation resources to better utilize renewables but also minimizes the communication and control expenses associated with large-scale customer demand response. Additionally, a new evaluation metric for user response is proposed to ensure equitable incentive distribution. The model has been shown to lower both consumer power costs and system generation expenses, achieving a 22% reduction in renewable energy wastage. Full article

Recently, solar energy technologies are a cornerstone of the global effort to transition towards cleaner and more sustainable energy systems. However, in many rural areas of developing countries, unreliable electricity severely impacts healthcare delivery, resulting in reduced medical efficiency and increased risks to patient safety. This review explores the transformative potential of solar energy as a sustainable solution for powering healthcare facilities, reducing dependence on fossil fuels, and improving health outcomes. Consequently, energy harvesting is a vital renewable energy source that captures abundant solar and thermal energy, which can sustain medical centers by ensuring the continuous operation of life-saving equipment, lighting, vaccine refrigeration, sanitation, and waste management. Beyond healthcare, it reduces greenhouse gas emissions, lowers operational costs, and enhances community resilience. To address this issue, the paper reviews critical solar energy technologies, energy storage systems, challenges of energy access, and successful solar energy implementations in rural healthcare systems, providing strategic recommendations to overcome adoption challenges. To fulfill the aims of this study, a focused literature review was conducted, covering publications from 2005 to 2025 in the Scopus, ScienceDirect, MDPI, and Google Scholar databases. With targeted investments, policy support, and community engagement, solar energy can significantly improve healthcare access in underserved regions and contribute to sustainable development. Full article

Renewable energy projects are critical for sustainable development, yet their success often hinges on local community acceptance. This study refines the Community Acceptance Framework to classify and better understand the social and behavioral factors that shape community responses to renewable energy projects. To support the reclassification, we draw selectively on three psychological concepts to refine definition and streamline categories. Based on a systematic review of 212 studies, we identified 29 influencing factors and categorized them into a seven-dimensional framework: social, economic, environmental, political, process, project details, and temporal. The findings reveal that financial capital, which reflects economic gains, emerges as the most frequently cited factor influencing local acceptance. However, when viewed dimensionally, the social dimension encompassing factors such as social capital, cognitive response, and cultural capital accounts for the largest share of influencing factors. Additionally, the often-overlooked political and temporal dimensions highlight the importance of governance quality and timely community engagement. While the framework offers a more robust and context-sensitive tool for analyzing acceptance dynamics, empirical validation is needed to assess its practical applicability. Nevertheless, the refined CAF can guide policymakers, researchers, and practitioners in designing renewable energy initiatives that are both technically sound, economically viable, and socially inclusive. Full article