
Smart grids integrate advanced metering infrastructure and distributed energy resources to enhance grid flexibility and reliability. Demand response management and real-time load forecasting optimize energy consumption patterns. Microgrid control and vehicle-to-grid integration facilitate localized energy balancing and storage utilization. Grid automation and advanced distribution management systems improve fault detection, isolation, and restoration processes. Renewable energy integration challenges are addressed through smart inverters and voltage control techniques.
This is our latest selection of worldwide publications and patents in english on Smart Grids and Energy Management, between many scientific online journals, classified and focused on smart grids and energy management, advanced metering infrastructure, energy management, grid automation, microgrid control, energy load balancing, real-time load forecasting, grid state estimation, energy distribution, demand response management, energy storage integration, power quality monitoring, grid resilience, grid stability analysis, load shedding strategies, smart grid communication, cybersecurity in smart grids, grid topology, grid congestion, grid frequency control, distributed generation management, grid fault tolerance, grid voltage regulation and smart grid.
VMD-LSTM-Based Model Predictive Control for Hybrid Energy Storage Systems with Auto-Tuning Weights and Constraints
Published on 2025-10-22 by Yi Yang, Bin Ma, Peng-Hui Li @MDPI
Abstract: Enhancing ultra-capacitor (UC) utilization and mitigating battery stress are pivotal for improving the energy management efficiency and service life of hybrid energy storage systems (HESSs). Conventional energy management strategies (EMSs), however, rely on fixed parameters and therefore struggle to allocate power flexibly or reduce battery degradation. This paper proposes a VMD-LSTM-based EMS that incorporates auto-tuning weight and constraint to address these limitations. First, a VMD-LSTM pre[...]
Our summary: This paper presents a VMD-LSTM-based energy management strategy for hybrid energy storage systems. It features auto-tuning weights and constraints to enhance ultra-capacitor utilization and reduce battery stress. Simulation results indicate significant improvements in UC utilization and battery life compared to traditional methods.
VMD, LSTM, Model Predictive Control, Hybrid Energy Storage Systems
Publication
Electricity Demand Forecasting and Risk Assessment for Campus Energy Management
Published on 2025-10-20 by Yon-Hon Tsai, Ming-Tang Tsai @MDPI
Abstract: This paper employs the Grey–Markov Model (GMM) to predict users’ electricity demand and introduces the Enhanced Monte Carlo (EMC) method to assess the reliability of the prediction results. The GMM integrates the advantages of the Grey Model (GM) and the Markov Chain to enhance prediction accuracy, while the EMC combines the Monte Carlo simulation with a dual-variable approach to conduct a comprehensive risk assessment. This framework helps decision-makers better unde[...]
Our summary: This paper presents a framework for predicting electricity demand using the Grey-Markov Model (GMM) and assessing risks with the Enhanced Monte Carlo (EMC) method. The GMM outperforms traditional models in accuracy, as indicated by lower Mean Absolute Percentage Error (MAPE) values. The study provides valuable insights for decision-makers in managing electricity demand and planning contract capacities effectively.
Electricity Demand, Grey-Markov Model, Risk Assessment, Monte Carlo Simulation
Publication
An Energy Management Strategy for FCHEVs Using Deep Reinforcement Learning with Thermal Runaway Fault Diagnosis Considering the Thermal Effects and Durability
Published on 2025-10-18 by Yongqiang Wang, Fazhan Tao, Longlong Zhu, Nan Wang, Zhumu Fu @MDPI
Abstract: Temperature control plays a critical role in mitigating the lifespan degradation mechanisms and ensuring thermal safety of lithium-ion batteries (LIBs) and proton exchange membrane fuel cells (PEMFCs). However, current energy management strategies (EMS) for fuel cell hybrid electric vehicles (FCHEVs) generally lack comprehensive thermal effect modeling and thermal runaway fault diagnosis, leading to irreversible aging and thermal runaway risks for LIBs and PEMFCs stacks under complex operating c[...]
Our summary: This paper presents an energy management strategy for fuel cell hybrid electric vehicles using deep reinforcement learning. It integrates thermal runaway fault diagnosis and thermal effects modeling to enhance safety and durability of lithium-ion batteries and fuel cells. Simulation results show significant reductions in energy consumption and state-of-health degradation compared to existing strategies.
Energy Management, Deep Reinforcement Learning, Thermal Runaway, Fault Diagnosis
Publication
A Colombian Case Study
Published on 2025-10-18 by Eduardo Gómez-Luna, Mario A. Palacios, Juan C. Vasquez @MDPI
Abstract: This paper proposes a novel microgrid (MG) architecture designed for telecommunication base stations in non-interconnected regions, with the main objective of mitigating mobile service interruptions caused by power outages. This research consists of three key modules: the first module on resources and components, the second module on characterization, and the third module on design and methodology. The first module presents a comprehensive identification and description of the resources and comp[...]
Our summary: This paper presents a novel microgrid architecture for telecommunication base stations in non-interconnected regions. It includes modules on resources, characterization, and design methodology to mitigate service interruptions from power outages. The research highlights economic and social benefits, aiming to improve mobile service reliability in underserved areas.
microgrid, telecommunication, power outages, energy management
Publication
Bridging Technological, Economic, Environmental, Social, and Regulatory Dimensions
Published on 2025-10-17 by Kenneth Chukwuma Nwala, Moses Jeremiah Barasa Kabeyi, Oludolapo Akanni Olanrewaju @MDPI
Abstract: Renewable energy integration is no longer a solely technical endeavor; it necessitates a multidimensional transformation that spans technological, economic, environmental, social, and regulatory dimensions. This review presents a visual and strategic framework for addressing the complex challenges of integrating solar, wind, hydro, geothermal, and biomass energy systems. The objective is to redefine traditional approaches by linking specific integration barriers to tailored strategies and measur[...]
Our summary: This review presents a multidimensional framework for integrating renewable energy systems. It employs visual tools to clarify complex dynamics and enhance stakeholder collaboration. The study emphasizes the importance of adaptive planning and community engagement for sustainable energy transitions.
Renewable Energy, Integration Framework, Stakeholder Collaboration, Visual Tools
Publication
Improved Multi-Faceted Sine Cosine Algorithm for Optimization and Electricity Load Forecasting
Published on 2025-10-17 by Stephen O. Oladipo, Udochukwu B. Akuru, Abraham O. Amole @MDPI
Abstract: The sine cosine algorithm (SCA) is a population-based stochastic optimization method that updates the position of each search agent using the oscillating properties of the sine and cosine functions to balance exploration and exploitation. While flexible and widely applied, the SCA often suffers from premature convergence and getting trapped in local optima due to weak exploration–exploitation balance. To overcome these issues, this study proposes a multi-faceted SCA (MFSCA) incorpo[...]
Our summary: The study presents a Multi-Faceted Sine Cosine Algorithm (MFSCA) that enhances the original SCA by improving exploration and exploitation balance. MFSCA incorporates dynamic opposition, chaotic logistic maps, and adaptive parameters to optimize performance. It successfully forecasts electricity load using a fuzzy c-means MFSCA-based adaptive neuro-fuzzy inference system, demonstrating superior accuracy in energy consumption predictions.
Multi-Faceted Sine Cosine Algorithm, Optimization, Electricity Load Forecasting, Adaptive Neuro-Fuzzy Inference System
Publication
Radiation image processing device, radiation image processing method, and radiation image processing program
Patent published on the 2025-09-17 in EP under Ref EP4616810 by FUJIFILM CORP [JP] (Takahashi Tomoyuki [jp])
Abstract: A processor is configured to: acquire first to (n - 1)th (n ≥ 3) radiation images acquired by imaging a subject, which includes a first component consisting of a plurality of compositions and second to nth components each consisting of a single composition, with n - 1 types of radiation having different energy distributions; derive a characteristic of the first component in at least a region of the subject in the first to (n - 1)th radiation images; acquire a body thickness of the subject;[...]
Our summary: A processor acquires multiple radiation images of a subject to analyze its components. It derives characteristics and thicknesses of these components using the acquired images and body thickness. Finally, it enhances the images of each component based on the derived thicknesses.
radiation image processing, image enhancement, component analysis, body thickness measurement
Patent
Radiation image processing device, radiation image processing method, and radiation image processing program
Patent published on the 2025-09-17 in EP under Ref EP4616807 by FUJIFILM CORP [JP] (Takahashi Tomoyuki [jp])
Abstract: A processor is configured to: acquire first to (n - 1)th (n ≥ 3) radiation images acquired by imaging a subject, which includes first to nth components each consisting of a single composition, with n - 1 types of radiation having different energy distributions; acquire a body thickness of the subject; derive thicknesses of the first to nth components by using the body thickness and the first to (n - 1)th radiation images; and derive first to nth component images in which the first to nth c[...]
Our summary: A processor acquires multiple radiation images of a subject and its body thickness. It derives the thicknesses of various components using these images. Enhanced images of each component are produced based on their respective thicknesses.
radiation image processing, image enhancement, body thickness analysis, multi-energy imaging
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