Search Results (8,044)

This paper proposes an optimal operational framework for enhancing the economic, technical, and environmental performance of a renewable energy-based microgrid. The proposed system integrates photovoltaic (PV) generation, wind turbines (WTs), battery energy storage systems (BESSs), diesel generators (DGs), and utility grid interaction. Three multi-objective optimization algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Genetic Algorithm (MOGA), and Multi-Objective Celestial Orbit Optimization (MOCOO), are employed to minimize the total operating cost and grid dependency. The obtained results demonstrate that MOPSO achieves the best techno-economic performance with a minimum operating microgrid cost of 2.2 M$/year and a low grid dependency ratio of 0.0333. The operational analysis confirms that the proposed renewable-priority scheduling strategy significantly reduces operational emissions and reliance on the utility grid through coordinated BESS charging/discharging and efficiency-aware DG dispatch. The microgrid (MG) achieves zero-emission operation during operating periods dominated by renewable generation. Furthermore, the DG operates within an efficiency range of 36.8–39.3%, improving fuel utilization and reducing unnecessary emissions. The battery degradation analysis indicates high lifetime cycle capability under shallow depth-of-discharge operation, demonstrating improved long-term operational sustainability. Overall, the proposed framework provides a reliable and economically balanced solution for sustainable microgrid energy management. Full article

The accelerating integration of renewable energy sources into modern power grids has created unprecedented operational challenges, with significant system cost volatility under extreme uncertainty events. To address this challenge, this paper presents a risk-aware stochastic approximate dynamic programming (SADP) algorithm based on machine learning and parallel computing architectures. The algorithm learns optimal coordination strategies for source-grid-load-storage resources while explicitly quantifying and mitigating tail risk events that conventional approaches overlook. First, a risk-averse stochastic optimization model is constructed, which captures the complex interdependencies between renewable generation uncertainty, demand variability, and flexible resource coordination through second-order cone programming formulations. This model integrates the GlueVaR (Glued Value-at-Risk) metric, enabling simultaneous optimization across multiple risk horizons with adjustable conservatism parameters. Second, to solve the established model efficiently, an SADP algorithm based on risk-averse approximate value functions (RAVFs) is proposed, in which the training process of the RAVFs employs machine learning principles to directly encode risk preferences into operational decisions. By integrating GlueVaR into offline training across 5000 probabilistically weighted scenarios, the algorithm discovers emergent coordination patterns between distributed resources, which are rarely identified by human operators. Third, a large-scale parallel computing architecture is implemented for the SADP algorithm. This architecture decomposes the multi-period optimization problem into single-period coordinated sub-problems. During offline training, parallel computing of a series of single-period sub-problems can be performed across all probabilistic scenarios, significantly reducing training time. Extensive validation on both the modified IEEE 33-bus and 69-bus systems with integrated wind turbines, photovoltaic plants, energy storage systems, and demand response capabilities demonstrates remarkable performance improvements. Convergence analysis reveals that the AVFs stabilize within 30 training iterations, achieving sub-160 s solution times in online application even for complex networks with heterogeneous resources. By enabling real-time risk-aware decision-making under severe uncertainty, the proposed method provides grid operators with actionable strategies that balance economic efficiency and operational resilience. Full article

To alleviate grid peak-shaving pressure from high-penetration renewable energy integration, coupling thermal energy storage (TES) with coal-fired power plants is an effective approach for enhancing operational flexibility. This paper systematically investigates the peak-shaving performance of a coal-fired unit integrated with molten salt storage and heat pump storage systems, focusing on load response characteristics, peak-shaving capability, and the influence of discharge strategies on thermodynamic performance under various rated turbine heat acceptance (THA) conditions. The results indicate that, under identical peak-shaving capacity, the molten salt system exhibits greater storage capacity, which increases with rising THA levels, whereas the heat pump storage capacity remains largely THA-independent. Regarding discharge strategies, replacing high-pressure extraction steam achieves the fastest ramp rate and largest incremental power output, introducing steam into the intermediate-pressure cylinder yields the slowest response but highest round-trip efficiency, and replacing low-pressure extraction steam delivers the smallest peak-shaving capacity and lowest round-trip efficiency. Although TES integration slightly reduces thermal efficiency due to heat exchange losses, this trade-off is justified by significant flexibility improvement, demonstrating clear engineering value for high-renewable grids. Full article

This study presents a comparative evaluation of geopolymer foams incorporating either commercially available shape-stabilized phase change materials (PCMs) or laboratory-developed diatomite–paraffin PCM granules with controlled particle size fractions ranging from <1.6 mm to >2.5 mm. All PCM variants were incorporated at a constant dosage of 7.5 wt.% to isolate the influence of PCM type on the properties of the resulting composites. The commercial materials comprised PX-4, PX15, and PX20 (Rubitherm Technologies GmbH), whereas the laboratory-developed PCM consisted of paraffin immobilized within a porous diatomite matrix to produce granular shape-stabilized composites. The experimental program included the determination of bulk density, total porosity, pore size distribution, thermal conductivity (λ), thermal resistance (R), specific heat capacity (Cp), and compressive strength. The pore structure was characterized by mercury intrusion porosimetry (MIP), while the morphology and dispersion of PCM particles within the geopolymer matrix were investigated using scanning electron microscopy (SEM). All mixtures were produced using the same alkali-activated matrix and identical curing conditions, with the PCM content maintained at 7.5 wt.%. The results demonstrated that the type of PCM significantly affected the microstructure and thermophysical performance of the geopolymer foams. The laboratory-developed diatomite–paraffin PCM provided the most favorable thermal insulation performance, exhibiting the lowest thermal conductivity (0.095 W/m·K) together with the highest thermal resistance (0.278 m²·K/W). In contrast, the commercial PX15 and PX20 materials exhibited the highest specific heat capacities (1.740 and 1.778 kJ/kg·K, respectively), indicating superior thermal energy storage capability. In addition, the estimated production cost of the laboratory-developed PCM (2.5–4.0 EUR/kg) was substantially lower than that of the commercial PX materials (approximately 20 EUR/kg), highlighting its potential as a cost-effective alternative for sustainable, energy-efficient building materials. These findings demonstrate that both commercial and laboratory-developed PCM systems can effectively enhance the functionality of geopolymer foams, although they provide different balances between thermal insulation, heat storage capacity, and production cost. Full article

With the rapid development of commercial aerospace, emerging applications such as satellite constellations, space-based communications, and orbital computing platforms have significantly increased the demand for efficient and reliable spacecraft power systems. Abundant exploitable energy exists in the space environment, including Air Mass Zero (AM0) solar radiation, spacecraft surface temperature gradients, ambient electromagnetic radiation, and radioisotope thermal energy, making multi-source energy harvesting a promising approach for improving satellite energy autonomy and system redundancy. This paper reviews the following four key space energy harvesting technologies: photovoltaic power generation, radio frequency (RF) energy harvesting, thermoelectric energy harvesting, and radioisotope thermoelectric generators (RTGs). The impacts of harsh space environmental factors on device performance and reliability are analyzed, and the applicability of different technologies in low Earth orbit (LEO), geostationary orbit (GEO), and deep-space missions is discussed. Furthermore, a multi-source self-powered satellite energy architecture integrating energy harvesting, energy storage, and power management is proposed. Finally, the major challenges and future development trends of satellite energy harvesting systems are summarized. Full article

This scientific article presents a comprehensive case study detailing the design of a fully off-grid household in Poland, utilizing an energy solution that combines high-pressure hydrogen energy storage and photovoltaic (PV) technology. In response to the growing demand for sustainable and self-sufficient energy sources, the current study investigates the efficiency and yearly energy balance of this innovative system. The off-grid household is powered by a hybrid system that seamlessly integrates PV panels to harness solar energy and a high-pressure hydrogen energy storage system for long-term energy management. The presented case study examines the design and performance of a system integrating solar energy production with hydrogen storage. Through an analysis of real-world data and operational parameters, this research contributes valuable insights into the viability of such an off-grid solution in Polish environmental conditions. These findings provided an interesting approach to off-grid residential systems, offering a glimpse into the possible future of residential energetic autonomy in the pursuit of a greener and more resilient energy landscape. Full article

This review shows that lignocellulosic biomass is not merely an abundant feedstock for nanocellulose production but a strategic platform for building the next generation of sustainable, high-performance materials, integrating feedstock diversity, processing logic, characterization, market direction, and translational applications into a single narrative. Comparing woody and non-woody biomass through the lens of processability, recalcitrance, and value creation while showing why agricultural residues are increasingly central to low-cost, circular nanocellulose production beyond the usual acid-hydrolysis-centered discussion by emphasizing enzymatic hydrolysis as a lower-energy, lower-toxicity alternative while still acknowledging the persistent industrial advantages and environmental costs of chemical and mechanical routes. A further strength of this review is its effort to bridge structure and function: it links extraction strategy to morphology, crystallinity, thermal stability, and surface chemistry, then connects these properties to real applications in packaging, drug delivery, electronics, filtration, energy storage, and biomedical systems. Its distinctive contribution lies in showing that the future of nanocellulose depends not only on how it is extracted but also on how intelligently the biomass source, processing route, material performance, and market need are aligned. Full article

This paper presents a comprehensive review of state-of-the-art developments in electric vehicle (EV) charging technologies, charging stations, and charging protocols, with particular emphasis on their integration with renewable energy sources (RESs). EV chargers are generally classified into on-board and off-board configurations. This study examines recent designs and advanced control strategies for both AC/DC and DC/DC power conversion stages, highlighting key technical aspects, recent innovations, and existing challenges. Furthermore, it provides an in-depth discussion of emerging multiport EV charger architectures that integrate photovoltaic (PV) systems, energy storage units, EVs, and the power grid within a unified framework. A comparative analysis is also presented to evaluate various converter topologies and energy management strategies used in the AC/DC and DC/DC stages of EV charging systems. Critical performance indicators such as power rating, output voltage level, efficiency, economic feasibility, and system complexity are also discussed. A comprehensive comparison is conducted among 13 review papers between 2015 and 2026, identifying key trends, methodological differences, and common findings. Full article

Conventional soft open points (SOPs) suffer from limited transfer capacity during distribution network faults. To address this issue, this paper proposes an integrated energy storage system and soft open point (ES-SOP) along with a power path dynamic reconfiguration method. The device consists of an M × N AC switch matrix, N AC/DC converters, and a common DC bus with energy storage. This structure provides three distinct power paths: a mechanical direct path, a third-party grid path, and an energy storage path. A seamless reconfiguration technology is developed to eliminate inrush currents during mechanical switching. It combines multi-unit virtual synchronous generator (VSG) pre-synchronization with a DC bus voltage droop coordination mechanism. The overall control follows a two-time-scale strategy. On a long time scale, a heuristic rule selects the most suitable healthy grid as the mechanical source. On a short time scale, the droop parameters of the converters are optimized to autonomously share the remaining power between the third-party grid path and the energy storage path. This allocation minimizes losses and requires no fast communication. Hardware-in-the-loop experiments verify the performance: the proposed method completely suppresses inrush current, keeps DC bus voltage fluctuation below 20 V during mode transitions, and achieves a transfer efficiency of approximately 98.5%. Full article

Smart microgrids combining photovoltaic arrays, wind turbines, and battery storage generate telemetry that existing open-source monitoring tools cannot process with per-mechanism energy loss visibility in real time. This paper presents the design, implementation, and evaluation of an IoT monitoring framework. The framework incorporates a physics-based microgrid simulator, a hierarchical MQTT communication architecture, and a React-based web-based user interface that supports WebSocket-based real-time data visualization. The framework consists of ten containerized microservices that can be started with a single command: docker compose up -d. All stack performance testing was conducted using a simulated 1 h test case based on a 100 kWp PV system, 10 kW wind turbine, and 50 kWh battery-powered campus microgrid. Median P50 publisher-to-subscriber latency was 27.2 ms and 99th percentile (P99) latency was 48.3 ms, with 100% message delivery across 5840 test messages, with per-topic analysis revealing a 25 ms serialization-order effect in sequential MQTT publishing. Comparative analysis against nine existing platforms including OpenEMS, VOLTTRON, Eclipse Ditto, and pymgrid confirms that, among the platforms surveyed, none unifies physics-based loss telemetry, IoT communication, time-series storage, and real-time visualization in a single reproducible deployment. Full article

With the rapid development of grid-scale energy storage, aqueous polysulfide/iodide redox flow batteries (SIFBs) have attracted extensive attention owing to their low cost, high safety, and suitable output voltage. However, the sluggish redox kinetics of iodine and polysulfide couples and the severe shuttle effect seriously restrict their performance. Here, an ultrathin nanoflower-like CuCo₂S₄ electrocatalyst supported on graphite felt (GF) is rationally designed and synthesized via a hydrothermal method combined with high-temperature sulfurization. Benefiting from the unique open nanoflower structure, abundant multivalent metal sites, and strong Cu–Co bimetallic synergy, the as-prepared CuCo₂S₄ exhibits excellent adsorption capacity for polysulfide and polyiodide intermediates, small redox peak potential separation, and low charge transfer resistance. When applied in SIFBs, the CuCo₂S₄ electrode delivers a remarkably low voltage gap of 0.29 V at 20 mA cm⁻², stable energy efficiency of 62–66% over 50 cycles, and superior long-term cycling stability with high energy efficiency above 70% after 400 cycles. This work provides an effective strategy for constructing high-efficiency bifunctional electrocatalysts toward high-performance and long-life SIFBs for large-scale energy storage applications. Full article

Microgrid scheduling is a challenging optimization problem because renewable energy generation, energy storage behavior, load demand, and grid interaction must be coordinated under nonlinear and constrained operating conditions. To improve the search performance of the original Butterfly Optimization Algorithm (BOA), this paper proposes an Improved Butterfly Optimization Algorithm (IBOA) for global optimization and microgrid scheduling. Three strategies are embedded into the BOA framework. First, the Dance Synchronization Guidance Strategy uses both the current global best solution and the dominant-group center to reduce excessive dependence on a single leader and improve population cooperation. Second, the Dance Emotion Disturbance Strategy introduces an adaptive perturbation term into the local search process, which helps the algorithm escape stagnant regions. Third, the Exponential Fragrance Decay Strategy dynamically adjusts the sensory modality parameter, allowing the search process to gradually shift from global exploration to local refinement. The performance of IBOA is evaluated through the IEEE CEC2017 and CEC2022 benchmark suites under different dimensions. The Friedman ranking results show that IBOA achieves the best mean ranks on CEC2017 with values of 1.13, 1.23, and 1.87 for 30-, 50-, and 100-dimensional cases, respectively. On CEC2022, IBOA also ranks first, with mean ranks of 1.50 and 1.00 for 10- and 20-dimensional cases. In the microgrid scheduling case, IBOA obtains the lowest average operating cost of 1443.56 with a standard deviation of 69.61 over 30 independent runs. Compared with CCO, CBSO, and GWCA, the average cost is reduced by approximately 13.58%, 14.98%, and 15.39%, respectively. Moreover, compared with the original BOA, the average cost is reduced from 28,338.69 to 1443.56. These results indicate that IBOA provides a more stable and cost-effective optimization approach for both benchmark optimization and microgrid scheduling problems. Full article

The Lantana genus, especially L. camara, has emerged as a potential yet underutilized lignocellulosic feedstock for various catalytic thermochemical conversion products and advanced carbon materials. This study reviews recent developments in the valorization of Lantana biomass to generate biofuels, bio-oil, syngas, and engineered biochar materials through pyrolysis, gasification, hydrothermal processing, and integrated biorefinery processes, in a critical manner. Particular focus will be on nanocomposite-modified, metal-doped biochar with catalytic elements such as ZSM-5, Fe₃O₄, TiO₂, and Ni-, Co-, and Zn-based oxides to enhance deoxygenation, catalytic cracking, tar reforming, pollutant remediation, and energy storage. Recent developments in catalyst synthesis techniques, such as impregnation, hydrothermal deposition, and in situ functionalization, are reviewed, along with characterization methods including BET, XRD, SEM/TEM, Raman spectroscopy, and XPS. The review further examines the impact of pore structure, surface chemistry, the presence of redox-active centers, and catalyst stability on product selectivity, syngas quality, and upgrading bio-oil performance. The effects of biochar on microbial immobilization, anaerobic digestion, and integrated biochemical conversion are discussed in detail, excluding thermochemical effects. The challenges of catalyst deactivation, biomass heterogeneities, scalability, techno-economic viability, and decentralized biomass logistics are also discussed. In summary, the development and implementation of catalytic reaction engineering, the design of nanocomposite biochar, and circular bioeconomy strategies have great potential to facilitate the conversion of invasive Lantana biomass into renewable fuels, multifunctional carbon materials, and environmentally friendly bioeconomy products. Full article

This study addresses the power dispatching of a DC-coupled three-phase grid-connected photovoltaic (PV) and energy storage-integrated system by proposing a peak shaving and valley-filling control architecture based on time-of-use (TOU) pricing. This research involves achieving maximum power-point tracking (MPPT) for PVMAs using a boost converter combined with the perturb and observe (P&O) method. A lithium-iron phosphate battery pack is integrated into the DC link via a bidirectional buck-boost converter, where charging and discharging control is executed according to peak and off-peak periods to regulate and stabilize the DC link voltage. Furthermore, bidirectional power flow control for peak and off-peak electricity consumption is realized using hysteresis current control and sinusoidal pulse-width modulation (SPWM) technologies within a smart inverter. By integrating the aforementioned power control architecture, the grid system can store energy from the utility during off-peak hours and release the stored energy during peak hours to reduce the load demand on the utility side. Initially, a simulation environment was established using Matlab/Simulink (2024b version) software, followed by control verification of the proposed system on a physical platform. The simulation and experimental results confirm that the integrated control architecture can precisely control the system’s DC link voltage at 800 V and stabilize the grid-connected AC voltage at an effective value (RMS) of 380 V. Moreover, under conditions of peak/off-peak switching and load variations, the system effectively demonstrates its stability and efficacy in performing valley filling and peak shaving. The proposed strategy achieves a power factor above 0.99 and a total harmonic distortion (THD) below 5%, regulates the DC-link voltage at 800 V with a steady-state error within 1.75%, and prevents up to 66.4 kWh of over-contract energy consumption per day under a 35 kW contract capacity, thereby contributing to sustainable energy management and economic savings. Full article

The large-scale integration of converter-interfaced renewable energy sources has significantly reduced power system inertia, posing challenges to frequency stability. Although virtual inertia and primary frequency control can enhance the frequency support capability of renewable energy units, their actual performance often deviates from set values due to dynamic response differences among various energy sources (e.g., energy storage, photovoltaic, and wind power) and coupling between inertia and primary regulation power. Existing evaluation methods fail to accurately decouple these components or account for unit-specific dynamic characteristics, leading to considerable estimation errors. To address these issues, this paper proposes a novel estimation method for the frequency regulation capability of renewable energy stations. First, the dynamic frequency response characteristics of synchronous and renewable generators are compared. Then, a decoupling method is developed to separate virtual inertia power from primary frequency regulation power by leveraging their distinct response features. A first-order plus delay time (FOPDT) model is employed to characterize the external frequency response of different renewable energy units. The primary frequency regulation coefficient is estimated using a sliding window integration method, and the virtual inertia time constant is identified via a gradient descent algorithm based on the decoupled inertia power. A hardware-in-the-loop experimental platform is constructed using a real-time digital simulator (RTDS) and phasor measurement units (PMUs) to validate the proposed method. Simulation results show that the estimation errors for energy storage, photovoltaic, and wind power units are 0.63%, 6.38%, and 8.38% for the virtual inertia time constant and 0.45%, 0.72%, and 3.81% for the primary frequency regulation coefficient, respectively. Field test data further confirm the practical applicability and accuracy of the approach. The proposed method enables precise frequency control capability estimation, providing a reliable basis for parameter setting and capacity configuration of frequency regulation resources in low-inertia power systems. Full article