Search Results (808)

The increasing demand for electricity in multi-tenanted residential areas has placed unforeseen strain on sub-transformers, particularly in dense urban environments. This strain compromises overall grid performance and challenges utilities with shifting and rising peak demand periods. This study presents a novel approach to enhance the operation of a virtual power plant (VPP) comprising a microgrid (MG) integrated with renewable energy sources (RESs) and energy storage systems (ESSs). By employing an advanced monitoring and control system, the proposed topology enables efficient energy management and demand-side control within apartment complexes. The system supports controlled electricity distribution, reducing the likelihood of unpredictable demand spikes and alleviating stress on local infrastructure during peak periods. Additionally, the model capitalizes on the large number of tenancies to distribute electricity effectively, leveraging locally available RESs and ESSs behind the sub-transformer. The proposed research provides a systematic framework for managing electricity demand and optimizing resource utilization, contributing to grid reliability and a transition toward a more sustainable, decentralized energy system. Full article

This study evaluates the socio-economic impact of rural electrification through photovoltaic (PV) systems in Morocco. As part of the country’s broader energy transition strategy, decentralized renewable energy solutions like PV kits have been deployed to improve energy access in isolated rural areas. Using quasi-experimental econometric techniques, specifically propensity score matching (PSM) and estimation of the Average Treatment Effect on the Treated (ATT), the study measures changes in household income, expenditures, and economic activities resulting from PV electrification. The results indicate significant positive effects on household income, electricity spending, and productivity in agriculture and livestock. These findings highlight the critical role of decentralized renewable energy in advancing rural development and poverty reduction. Policy recommendations include expanding PV access with complementary support measures such as microfinance and technical training. 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

This article presents a comprehensive review of methods used for forecasting electricity consumption. The studies analyzed by the authors encompass both classical statistical models and modern approaches based on artificial intelligence, including machine-learning and deep-learning techniques. Electricity load forecasting is categorized into four time horizons: very short term, short term, medium term, and long term. The authors conducted a comparative analysis of various models, such as autoregressive models, neural networks, fuzzy logic systems, hybrid models, and evolutionary algorithms. Particular attention was paid to the effectiveness of these methods in the context of variable input data, such as weather conditions, seasonal fluctuations, and changes in energy consumption patterns. The article emphasizes the growing importance of accurate forecasts in the context of the energy transition, integration of renewable energy sources, and the management of the evolving electricity system, shaped by decentralization, renewable integration, and data-intensive forecasting demands. In conclusion, the authors highlight the lack of a universal forecasting approach and the need for further research on hybrid models that combine interpretability with high predictive accuracy. This review can serve as a valuable resource for decision-makers, grid operators, and researchers involved in energy system planning. Full article

Biomass is gaining increasing importance as a renewable energy source in the global energy mix, offering a viable alternative to fossil fuels and contributing to the decarbonization of the energy sector. Among various types of biomass, agricultural residues such as bean stalks represent a promising feedstock for the production of solid biofuels. This study analyzes the impact of particle size and selected briquetting parameters (pressure and temperature) on the physical quality of briquettes made from bean stalks. The experimental procedure included milling the raw material using #8, #12, and #16 mesh screens, followed by compaction under pressures of 27, 37, and 47 MPa. Additionally, the briquetting die was heated to 90 °C to improve the mechanical durability of the briquettes. The results showed that both particle size and die temperature significantly influenced the quality of the produced briquettes. Briquettes made from the 16 mm fraction, compacted at 60 °C and 27 MPa, exhibited a durability of 55.76%, which increased to 82.02% when the die temperature was raised to 90 °C. Further improvements were achieved by removing particles smaller than 1 mm. However, these measures did not enable achieving a net calorific value above 14.5 MJ·kg⁻¹. Therefore, additional work was undertaken, involving the addition of biomass with higher calorific value to the bean stalk feedstock. In the study, maize straw and miscanthus straw were used as supplementary substrates. The results allowed for determining their minimum proportions required to exceed the 14.5 MJ·kg⁻¹ threshold. In conclusion, bean stalks can serve as a viable feedstock for the production of solid biofuels, especially when combined with other biomass types possessing more favorable energy parameters. Their utilization aligns with the concept of managing local agricultural residues within decentralized energy systems and supports the development of sustainable bioenergy solutions. Full article

This paper investigates the problem of computation offloading and resource allocation in an integrated space–air–sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud–edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15–60% specifically. Full article

This paper presents a decentralized energy management system (EMS) based on Multilayer Perceptron Artificial Neural Networks (MLP-ANNs) and a Modified Cheetah Optimizer (MCO) to account for uncertainty in renewable generation and load demand. The proposed framework applies an MLP-ANN with Levenberg–Marquardt (LM) training for high-precision forecasts of photovoltaic/wind generation, ambient temperature, and load demand, greatly outperforming traditional statistical methods (e.g., time-series analysis) and resilient backpropagation (RP) in precision. The new MCO algorithm eliminates local trapping and premature convergence issues in classical optimization methods like Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). Simulations on a test microgrid verily demonstrate the advantages of the framework, achieving a 26.8% cost-of-operation reduction against rule-based EMSs and classical PSO/GA, and a 15% improvement in forecast accuracy using an LM-trained MLP-ANN. Moreover, demand response programs embodied in the system reduce peak loads by 7.5% further enhancing grid stability. The MLP-ANN forecasting–MCO optimization duet is an effective and cost-competitive decentralized microgrid management solution under uncertainty. Full article

Distributed energy resource systems offer improved energy utilization and reduced transmission losses by decentralizing power generation and load management. However, the power quality is often compromised by inefficient customer-side equipment, such as single-phase induction motors, which suffer from low efficiency and poor power factor. To address this issue, this paper proposes a permanent magnet retrofitting method for single-phase induction motors, which replaces the squirrel-cage rotor with a permanent magnet rotor while preserving the original stator and winding structure. The proposed method aims to enhance motor performance without significant structural changes. A single-phase induction motor was retrofitted using the proposed method, and its performance was evaluated through finite element simulations to verify the effectiveness of the design approach. This study also investigated the key factors influencing motor starting performance after the introduction of permanent magnets. This study presents a practical and effective method for the permanent magnet retrofitting of single-phase induction motors, which contributes to improving motor efficiency and enhancing power quality in distributed energy resource systems. Full article

This paper presents a practical framework for the design and real-time implementation of a Power Management System (PMS) for microgrids based on Supervisory Control Theory (SCT) for discrete-event systems. A detailed step-by-step methodology is provided, which covers the entire process from defining discrete events, modeling microgrid components, synthesizing supervisory controllers, and realizing them in MATLAB (R2024b) Stateflow. This methodology is applied to a case study, where a decentralized supervisor controller is designed for a microgrid containing a Battery Energy Storage System (BESS), a generator set (Genset), a wind and a solar generation system, critical loads, and noncritical loads. Unlike previous works based on SCT, the proposed PMS addresses the following functionalities: $\left(i\right)$ grid-connected and islanded operation; $\left(ii\right)$ peak shaving; $\left(iii\right)$ voltage support; $\left(iv\right)$ load shedding. Finally, a CHIL testing is employed to validate the synthesized SCT-based PMS. 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

Background: The transition to renewable energy is intensifying demand for lithium-ion batteries (LIBs), thereby increasing the need for sustainable lithium sourcing. Traditional mining practices pose environmental and health risks, which can be mitigated through efficient end-of-life recycling systems. Methods: This study proposes a modified lithium reverse logistics network that decentralizes black-mass production at distributed facilities before centralized extraction, contrasting with conventional models that transport raw LIBs directly to central processing sites. Using the United States as a case study, two mathematical optimization (mixed-integer linear programming) models were developed to compare the traditional and modified networks in terms of cost efficiency and carbon emissions. Results: The model indicates that the proposed network significantly reduces both operational costs and emissions. Conclusions: This study highlights its potential to support a greener economy and inform policy development. Full article

Handover (HO) management plays a key role in ensuring uninterrupted connectivity across evolving wireless networks. While previous generations such as 4G and 5G have introduced several HO strategies, these techniques are insufficient to meet the rigorous demands of sixth-generation (6G) networks in ultra-dense, heterogeneous smart city environments. Existing studies often fail to provide integrated HO solutions that consider key concerns such as energy efficiency, security vulnerabilities, and interoperability across diverse network domains, including terrestrial, aerial, and satellite systems. Moreover, the dynamic and high-mobility nature of smart city ecosystems further complicate real-time HO decision-making. This survey aims to highlight these critical gaps by systematically categorizing state-of-the-art HO approaches into AI-based, fuzzy logic-based, and hybrid frameworks, while evaluating their performance against emerging 6G requirements. Future research directions are also outlined, emphasizing the development of lightweight AI–fuzzy hybrid models for real-time decision-making, the implementation of decentralized security mechanisms using blockchain, and the need for global standardization to enable seamless handovers across multi-domain networks. The key outcome of this review is a structured and in-depth synthesis of current advancements, which serves as a foundational reference for researchers and engineers aiming to design intelligent, scalable, and secure HO mechanisms that can support the operational complexity of next-generation smart cities. Full article

As global climate change intensifies and the transition to clean energy accelerates, lithium-ion batteries—critical components of electric vehicles—are becoming increasingly vital in international trade networks. This study investigates the structural evolution and determinants of the electric vehicle lithium-ion battery trade network among European Union (EU) member states from 2012 to 2023, employing social network analysis and the multiple regression quadratic assignment procedure method. The findings demonstrate the transformation of the network from a centralized and loosely connected structure, with Germany as the dominant hub, to a more interconnected and decentralized system in which Poland and Hungary emerge as the leading players. Key network metrics, such as the density, clustering coefficients, and average path lengths, reveal increased regional trade connectivity and enhanced supply chain efficiency. The analysis identifies geographic and economic proximity, logistics performance, labor cost differentials, energy resource availability, and venture capital investment as significant drivers of trade flows, highlighting the interaction among spatial, economic, and infrastructural factors in shaping the network. Based on these findings, this study underscores the need for targeted policy measures to support Central and Eastern European countries, including investment in logistics infrastructure, technological innovation, and regional cooperation initiatives, to strengthen their integration into the supply chain and bolster their export capacity. Furthermore, fostering balanced inter-regional collaborations is essential in building a resilient trade network. Continued investment in transportation infrastructure and innovation is recommended to sustain the EU’s competitive advantage in the global electric vehicle lithium-ion battery supply chain. Full article

Standalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duration solutions. Green hydrogen, generated via proton exchange membrane (PEM) electrolyzers, offers a scalable alternative. This study proposes a stochastic energy management framework that leverages a Markov decision process (MDP) to coordinate PV generation, battery storage, and hydrogen production under variable irradiance and uncertain load demand. The strategy dynamically allocates power flows, ensuring system stability and efficient energy utilization. Real-time weather data from Goiás, Brazil, is used to simulate system behavior under realistic conditions. Compared to the conventional perturb and observe (P&O) technique, the proposed method significantly improves system performance, achieving a $99.9\%$ average efficiency (vs. 98.64%) and a drastically lower average tracking error of 0.3125 (vs. 9.8836). This enhanced tracking accuracy ensures faster convergence to the maximum power point, even during abrupt load changes, thereby increasing the effective use of solar energy. As a direct consequence, green hydrogen production is maximized while energy curtailment is minimized. The results confirm the robustness of the MDP-based control, demonstrating improved responsiveness, reduced downtime, and enhanced hydrogen yield, thus supporting sustainable energy conversion in off-grid environments. Full article

As global electricity demand increases and concerns over fossil fuel usage intensify, renewable energy sources have gained significant attention. Solar energy stands out due to its low installation costs and suitability for deployment. However, solar power generation remains difficult to predict because of its dependence on weather conditions and decentralized infrastructure. To address this challenge, this study proposes a flexible hybrid ensemble (FHE) framework that dynamically selects the most appropriate base model based on prediction error patterns. Unlike traditional ensemble methods that aggregate all base model outputs, the FHE employs a meta-model to leverage the strengths of individual models while mitigating their weaknesses. The FHE is evaluated using data from four solar power plants and is benchmarked against several state-of-the-art models and conventional hybrid ensemble techniques. Experimental results demonstrate that the FHE framework achieves superior predictive performance, improving the Mean Absolute Percentage Error by 30% compared to the SVR model. Moreover, the FHE model maintains high accuracy across diverse weather conditions and eliminates the need for preliminary validation of base and ensemble models, streamlining the deployment process. These findings highlight the FHE framework’s potential as a robust and scalable solution for forecasting in small-scale distributed solar power systems. Full article