Search Results (355)

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

The growing demand for low-emission maritime transport and efficient onboard energy management has intensified research into advanced control strategies for hybrid shipboard microgrids. These systems integrate both AC and DC power domains, incorporating renewable energy sources and battery storage to enhance fuel efficiency, reduce greenhouse gas emissions, and support operational flexibility. However, integrating renewable energy into shipboard microgrids introduces challenges, such as power fluctuations, varying line impedances, and disturbances caused by AC/DC load transitions, harmonics, and mismatches in demand and supply. These issues impact system stability and the seamless coordination of multiple distributed generators. To address these challenges, we proposed a hierarchical control strategy that supports sustainable operation by improving the voltage and frequency regulation under dynamic conditions, as demonstrated through both MATLAB/Simulink simulations and real-time hardware validation. Simulation results show that the proposed controller reduces the frequency deviation by up to 25.5% and power variation improved by 20.1% compared with conventional PI-based secondary control during load transition scenarios. Hardware implementation on the NVIDIA Jetson Nano confirms real-time feasibility, maintaining power and frequency tracking errors below 5% under dynamic loading. A comparative analysis of the classical PI and sliding mode control-based designs is conducted under various grid conditions, such as cold ironing mode of the shipboard microgrid, and load variations, considering both the AC and DC loads. The system stability and control law formulation are verified through simulations in MATLAB/SIMULINK and practical implementation. The experimental results demonstrate that the proposed secondary control architecture enhances the system robustness and ensures sustainable operation, making it a viable solution for modern shipboard microgrids transitioning towards green energy. Full article

This study provides a comprehensive overview of vertical-axis wind turbines (VAWTs) for emerging energy applications by combining a bibliometric analysis and a thematic mini-review. Scopus-indexed publications from 1979 to 2025 were analyzed using PRISMA guidelines and bibliometric tools (Bibliometrix, CiteSpace, and VOSviewer) to map global research trends, and a parallel mini-review distilled recent advances into five thematic areas: aerodynamic strategies, advanced materials, urban integration, hybrid systems, and floating offshore platforms. The results reveal that VAWT research output has surged since 2006, led by China with strong contributions from Europe and North America, and is concentrated in leading renewable energy journals. Dominant topics include computational fluid dynamics (CFD) simulations, performance optimization, wind–solar hybrid integration, and adaptation to turbulent urban environments. Technologically, active and passive aerodynamic innovations have boosted performance albeit with added complexity, remaining mostly at moderate technology readiness (TRL 3–5), while advanced composite materials are improving durability and fatigue life. Emerging applications in microgrids, building-integrated systems, and offshore floating platforms leverage VAWTs’ omnidirectional, low-noise operation, although challenges persist in scaling up, control integration, and long-term field validation. Overall, VAWTs are gaining relevance as a complement to conventional turbines in the sustainable energy transition, and this study’s integrated approach identifies critical gaps and high-priority research directions to accelerate VAWT development and help transition these turbines from niche prototypes to mainstream renewable solutions. Full article

As the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling. An energy trading framework is developed, involving integrated energy microgrids (IEMS), shared energy storage operators (ESOS), and user aggregators (UAS). A mixed game model combining master–slave and cooperative game theory is constructed in which the ESO acts as the leader by setting electricity prices to maximize its own profit, while guiding the IEMs and UAs—as followers—to optimize their respective operations. Cooperative decisions within the IEM coalition are coordinated using Nash bargaining theory. To enhance the generality of the user aggregator model, both electric vehicle (EV) users and demand response (DR) users are considered. Additionally, the model incorporates renewable energy output uncertainty through distributionally robust chance constraints (DRCCs). The resulting two-level optimization problem is solved using Karush–Kuhn–Tucker (KKT) conditions and the Alternating Direction Method of Multipliers (ADMM). Simulation results verify the effectiveness and robustness of the proposed model in enhancing operational efficiency under conditions of uncertainty. Full article

The increasing integration of renewable energy sources (RES) in power systems presents challenges related to variability, stability, and efficiency, particularly in smart microgrids. This systematic review, following the PRISMA 2020 methodology, analyzed 66 studies focused on advanced energy storage systems, intelligent control strategies, and optimization techniques. Hybrid storage solutions combining battery systems, hydrogen technologies, and pumped hydro storage were identified as effective approaches to mitigate RES intermittency and balance short- and long-term energy demands. The transition from centralized to distributed control architectures, supported by predictive analytics, digital twins, and AI-based forecasting, has improved operational planning and system monitoring. However, challenges remain regarding interoperability, data privacy, cybersecurity, and the limited availability of high-quality data for AI model training. Economic analyses show that while initial investments are high, long-term operational savings and improved resilience justify the adoption of advanced microgrid solutions when supported by appropriate policies and financial mechanisms. Future research should address the standardization of communication protocols, development of explainable AI models, and creation of sustainable business models to enhance resilience, efficiency, and scalability. These efforts are necessary to accelerate the deployment of decentralized, low-carbon energy systems capable of meeting future energy demands under increasingly complex operational conditions. Full article

In order to improve energy utilization efficiency and the flexibility of resource transfer in oceanic-island-group microgrids, a water–electricity–hydrogen flexible scheduling strategy based on a multi-rate hydrogen-powered ship is proposed. First, the characteristics of the seawater desalination unit (SDU), proton exchange membrane electrolyzer (PEMEL), and battery system (BS) in consuming surplus renewable energy on resource islands are analyzed. The variable-efficiency operation characteristics of the SDU and PEMEL are established, and the effect of battery life loss is also taken into account. Second, a spatio-temporal model for the multi-rate hydrogen-powered ship is proposed to incorporate speed adjustment into the system optimization framework for flexible resource transfer among islands. Finally, with the goal of minimizing the total cost of the system, a flexible water–electricity–hydrogen hybrid resource transfer model is constructed, and a certain island group in the South China Sea is used as an example for simulation and analysis. The results show that the proposed scheduling strategy can effectively reduce energy loss, promote renewable energy absorption, and improve the flexibility of resource transfer. Full article

This research focuses on optimizing a grid-connected hybrid microgrid system (HMGS) for The Neotia University (TNU), West Bengal, India, utilizing renewable energy sources to improve sustainability and energy efficiency. The system integrates solar panels, wind turbines, and an existing diesel generator (DG) to meet campus energy demands, including electric vehicle (EV) charging facilities for residents and staff. The pelican optimization algorithm (POA) is employed to determine the optimal capacity of PV and wind turbine units for reducing energy costs, enhancing reliability, and minimizing carbon emissions. The results reveal a substantial decrease in the cost of energy (COE) from INR 11.74/kWh to INR 5.20/kWh. Full article

The need for electrical energy is dramatically increasing, pushing researchers and industrial communities towards the development and improvement of microgrids (MGs). It also encourages the use of renewable energies to benefit from available sources. Thereby, the implementation of a photovoltaic (PV) system with a hybrid energy storage system (HESS) can create a standalone MG. This paper presents an MG that uses photovoltaic energy as a principal source. An HESS is required, combining batteries and supercapacitors. This MG responds “insure” both alternating current (AC) and direct current (DC) loads. The batteries and supercapacitors have separate parallel connections to the DC bus through bidirectional converters. The DC loads are directly connected to the DC bus where the AC loads use a DC-AC inverter. A control strategy is implemented to manage the fluctuation of solar irradiation and the load variation. This strategy was implemented with a new logic control based on Boolean analysis. The logic analysis was implemented for analyzing binary data by using Boolean functions (‘0’ or ‘1’). The methodology presented in this paper reduces the stress and the faults of analyzing a flowchart and does not require a large concentration. It is used in this paper in order to simplify the control of the EMS. It permits the flowchart to be translated to a real application. This analysis is based on logic functions: “Or” corresponds to the addition and “And” corresponds to the multiplication. The simulation tests were executed at T_au  =  6 s of the low-pass filter and conducted in 60 s. The DC bus voltage was 400 V. It demonstrates that the proposed management strategy can respond to the AC and DC loads. Full article

Grid stability in microgrids represents a critical challenge, particularly with the increasing integration of variable renewable energy sources and the loss of systematic inertia. This study analyzes the use of vehicle-to-grid (V2G) technology and supercapacitors as complementary solutions to improve grid stability. A hybrid approach is proposed in which electric vehicles act as temporary storage units, supplying energy to regulate grid frequency. Supercapacitors, due to their rapid charging and discharging capabilities, are used to mitigate power fluctuations and provide immediate support during peak demand. The proposed management model integrates two strategies for frequency control, leveraging the linear relationship between power and frequency. Power smoothing is combined with Kalman filter-based frequency control, allowing for accurate estimation of the dynamic system state, even in the presence of noise or load fluctuations. This methodology improves grid stability and frequency regulation accuracy. A frequency variability analysis is also included, highlighting grid disturbance events related to renewable-energy penetration and demand changes. Furthermore, the effectiveness of the Kalman filter in improving grid stability control, ensuring an efficient dynamic response, is highlighted. The results obtained demonstrate that the combination of V2G and supercapacitors contributes significantly to reducing grid disturbances, optimizing energy efficiency, and enhancing system reliability. Full article

Power systems need to meet the ever-increasing demand for higher quality and reliability of electricity in distribution systems while remaining sustainable, secure, and economical. The globe is moving toward using renewable energy sources to provide electricity. An evaluation of the influence of artificial intelligence (AI) on the accomplishment of SDG7 (affordable and clean energy) is necessary in light of AI’s development and expanding impact across numerous sectors. Microgrids are gaining popularity due to their ability to facilitate distributed energy resources (DERs) and form critical client-centered integrated energy coordination. However, it is a difficult task to integrate, coordinate, and control multiple DERs while also managing the energy transition in this environment. To achieve low operational costs and high reliability, inverter control is critical in distributed generation (DG) microgrids, and the application of artificial neural networks (ANNs) is vital. In this paper, a power management strategy (PMS) based on Inverter Control and Artificial Neural Network (ICANN) technique is proposed for the control of DC–AC microgrids with PV-Wind hybrid systems. The proposed combined control strategy aims to improve power quality enhancement. ensuring access to affordable, reliable, sustainable, and modern energy for all. Additionally, a review of the rising role and application of AI in the use of renewable energy to achieve the SDGs is performed. MATLAB/SIMULINK is used for simulations in this study. The results from the measures of the inverter control, m, VL-L, and Vph_rms, reveal that the power generated from the hybrid microgrid is reliable and its performance is capable of providing power quality enhancement in microgrids through controlling the inverter side of the system. The technique produced satisfactory results and the PV/wind hybrid microgrid system revealed stability and outstanding performance. Full article

The integration of renewable energy into residential microgrids presents significant challenges due to solar generation intermittency and variability in household electricity demand. Traditional forecasting methods, reliant on historical data, fail to adapt effectively in dynamic scenarios, leading to inefficient energy management. This paper introduces a novel adaptive energy management framework that integrates streaming machine learning (SML) with a hierarchical fractal microgrid architecture to deliver precise real-time electricity demand forecasts for a residential community. Leveraging incremental learning capabilities, the proposed model continuously updates, achieving robust predictive performance with mean absolute errors (MAE) across individual households and the community of less than 10% of typical hourly consumption values. Three battery-sizing scenarios are analytically evaluated: centralised battery, uniformly distributed batteries, and a hybrid model of uniformly distributed batteries plus an optimised central battery. Predictive adaptive management significantly reduced cumulative grid usage compared to traditional methods, with a 20% reduction in energy deficit events, and optimised battery cycling frequency extending battery lifecycle. Furthermore, the adaptive framework conceptually aligns with digital twin methodologies, facilitating real-time operational adjustments. The findings provide critical insights into sustainable, decentralised microgrid management, emphasising improved operational efficiency, enhanced battery longevity, reduced grid dependence, and robust renewable energy utilisation. Full article

The adoption of wind energy is pivotal for advancing sustainable power systems, particularly in off-grid microgrids where infrastructure limitations hinder conventional energy solutions. The inherent variability of wind generation, however, challenges grid reliability and demand–supply balance, necessitating accurate forecasting models. This study proposes a hybrid framework for short-term wind speed prediction, integrating deep learning (Long Short-Term Memory, LSTM) and ensemble methods (random forest, Extra Trees) to exploit their complementary strengths in modeling temporal dependencies. A multivariate approach is adopted using meteorological data (including wind speed, temperature, humidity, and pressure) to capture complex weather interactions through a structured time-series design. The framework also includes a feature selection stage to identify the most relevant predictors and a hyperparameter optimization process to improve model generalization. Three wind speed variables, maximum, average, and minimum, are forecasted independently to reflect intra-day variability and enhance practical usability. Validated with real-world data from Cuenca, Ecuador, the LSTM model achieves superior accuracy across all targets, demonstrating robust performance for real-world deployment. Comparative results highlight its advantage over tree-based ensemble techniques, offering actionable strategies to optimize wind energy integration, enhance grid stability, and streamline renewable resource management. These insights support the development of resilient energy systems in regions reliant on sustainable microgrid solutions. Full article

This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm progresses through the optimization hyperspace. This modification addresses issues of poor convergence and suboptimal search in the original algorithm. Both the modified and standard algorithms were employed to design an HRES system comprising photovoltaic panels, wind turbines, fuel cells, batteries, and hydrogen storage, all connected via a DC-bus microgrid. The components were integrated with the microgrid using DC-DC power converters and supplied a designated load through a DC-AC inverter. Multiple operational scenarios and multi-objective criteria, including techno-economic metrics such as levelized cost of energy (LCOE) and loss of power supply probability (LPSP), were evaluated. Comparative analysis demonstrated that mSAO outperforms the standard SAO and the honey badger algorithm (HBA) used for the purpose of comparison only. Our simulation results highlighted that the PV–wind turbine–battery system achieved the best economic performance. In this case, the mSAO reduced the LPSP by approximately 38.89% and 87.50% over SAO and the HBA, respectively. Similarly, the mSAO also recorded LCOE performance superiority of 4.05% and 28.44% over SAO and the HBA, respectively. These results underscore the superiority of the mSAO in solving optimization problems. Full article

Various sectors focus on transitioning to clean and renewable energy sources, particularly airport microgrids (AMGs), which offer the potential for highly reliable and resilient operations. As airports increasingly integrate renewable energy sources, ensuring stable and efficient power becomes a critical challenge. In this context, maintaining proper frequency is essential for the reliable operation of AMGs, which helps maintain grid stability and reliable operation. This paper proposes a new hybrid disturbance observer-based controller with a fractional-order controller (DOBC/FOC) for operating AMGs with high levels of renewable energy integration and advanced frequency regulation (FR) capabilities. The proposed controller utilizes DOBC coupled with a non-integer FOC for load frequency control (LFC), optimized for peak performance under varying operational conditions. In addition, a decentralized control strategy is introduced to manage the participation of electric vehicles and lithium-ion battery systems within the airport’s energy ecosystem, enabling effective demand response and energy storage utilization. Furthermore, the parameters of these controllers are optimized simultaneously to ensure optimal performance in both transient and steady-state conditions. The proposed DOBC/FOC controller demonstrates strong performance and reliability according to simulation outcomes, showcasing its superior performance in maintaining frequency stability, reducing fluctuations, and ensuring continuous power supply in diverse operating scenarios, such as 55.5% and 76.5% in step load perturbations when compared to the utilization of electric vehicles (EVs) and electric aircraft (EAC), respectively. These results underline the potential of this approach in enhancing the resilience and sustainability of AMG and contributing to more intelligent and eco-friendly airport infrastructure. Full article

This paper proposes a supercapacitor-battery hybrid energy storage scheme based on a series-parallel hybrid compensation structure and model predictive control to address the increasingly severe power quality issues in oilfield microgrids. By adopting the series-parallel hybrid structure, the voltage compensation depth can be properly improved. The model predictive control with a current inner loop is employed for current tracking, which enhances the response speed and control performance. Applying the proposed hybrid energy storage system in an oilfield DC microgrid, the fault-ride-through ability of renewable energy generators and the reliable power supply ability for oil pumping unit loads can be improved, the dynamic response characteristics of the system can be enhanced, and the service life of energy storage devices can be extended. This paper elaborates on the series-parallel compensation topology, operational principles, and control methodology of the supercapacitor-battery hybrid energy storage. A MATLAB/Simulink model of the oilfield DC microgrid employing the proposed scheme was established for verification. The results demonstrate that the proposed scheme can effectively isolate voltage sags/swells caused by upstream grid faults, maintaining DC bus voltage fluctuations within ±5%. It achieves peak shaving of oil pumping unit load demand, recovery of reverse power generation, stabilization of photovoltaic output, and reduction of power backflow. This study presents an advanced technical solution for enhancing power supply quality in high-penetration renewable energy microgrids with numerous sensitive and critical loads. Full article