The rapid popularization of controllable loads such as electric vehicles (EVs), battery energy storage systems (BESS), and heating, ventilating and air conditioning (HVAC) has greatly improved the flexibility of active distribution networks, but it also brings challenges to safe and efficient real-time scheduling. However, most existing control strategies rely on either static optimization or single-agent reinforcement learning, which have shortcomings in scalability, adaptability to dynamic operating conditions, and ensuring network constraint satisfaction. To remedy these deficiencies, we propose a multi-agent reinforcement learning (MARL) -based scheduling framework combined with quadratic programming (QP) safety projection, which unifies flexible resource modeling, learns adaptive cost-aware and comfort-aware control policies, and guarantees real-time physical feasibility. The case study on the IEEE 123-node test system shows the superior performance of the proposed method compared to the existing methods. Specifically, in a typical scenario with 40% PV penetration, the proposed framework achieves 19.3% peak demand reduction and 13.4% operational cost savings. Moreover, it maintains a high EV user satisfaction of 96.5% and strictly guarantees network security with a zero voltage violation rate, which indicates its potential for reliable deployment in modern active distribution networks.
The shift from fossil-based to clean-energy infrastructure has escalated the significance of wind power. Expanding the power system to accommodate wind generation remains challenging due to the natural fluctuations and uncertainty of wind speeds. Effective short-term prediction of wind speeds is key to maintain system reliability, efficient power scheduling, and dependable wind-energy integration. This work introduces a hybrid EEMD-ConvLSTM framework that hybridize Ensemble Empirical Mode Decomposition for signal decomposition and a Convolutional LSTM for spatiotemporal feature learning. Using EEMD, the raw time-dependent and structurally stochastic and highly variable wind-speed measurements are separated into a collection of intrinsic oscillatory components each obtaining meaningful time-varying sequence of the underlying dynamics. These functions are subsequently fed as input to the ConvLSTM network, which models both spatial and temporal dependencies to improve forecasting. The model's accuracy is examined using site-specific wind-speed data sourced from a power plant facility in Telangana, India. The forecasting accuracy is reflected in the MAE (0.0983 m/s), RMSE (0.1025 m/s), and MAPE (1.5472%) obtained during testing. The method performs better and runs faster than EEMD-LSTM and other conventional models, making it suitable for instantaneous utilization in smart-grid environments and renewable energy platforms
Solar drying serves as a sustainable technique for food preservation through solar technology intervention. The main purpose of this investigation is to design and construct a high-performance forced convection direct-type solar dryer for drying fish products of 20 kg capacity in a hygienic condition. For validation of experimental results, a thermal analysis of the solar dryer has been performed through CFD simulation using ANSYS Fluent. The performance indicators, such as maximum drying rate, drying efficiency, and specific energy consumption, are evaluated as 0.85 kg/hr, 63.5%, and 1.005 kWh/kg respectively, at the highest mass flow rate of 0.019 kg/s. The coefficient of determination (R2) for drying chamber temperature variation is found to be above 99%, indicating a good correlation between parameters. The highest temperature of drying chamber at 1 PM from the experiment and simulation is evaluated as 60.3 degrees C and 59.8 degrees C respectively. The equivalent solar heat flux is determined as 649.5 W/m2 and 646.244 W/m2 respectively. It was found that there is a close correlation between the simulation and experiment result of temperature, with an error rate of less than 1%, and for solar heat flux, it is between 1% to 5%. This type of dryer can be extremely beneficial in providing sustainable energy to dry various food products because of its economic effectiveness. The dryer is currently utilized by fisherwomen in the coastal regions of South Odisha.
This study aims to find out how well a PV solar facility possessing 400 MW grid-connected designed in Riyadh city, Saudi Arabia, can meet the demands of an electric load. A dispatch method that forecasts future solar production and electricity consumption was created using the HOMER program. This study investigates the optimal operation of three configurations for a solar plant with three different module sizes. The solar plant was designed using PVsyst and the results were exported to the HOMER program. The proposed system is compared to the base system, which includes load following as well as cycle charging regarding HOMER, is executed utilizing techno-economic optimization and environmental perspectives to obtain the Total Net Present Cost, the minimum Levelized Cost of Energy, and the minimum CO2 emission. The optimization procedures were executed with zero unmet loads and excess energy. The optimization findings illustrated that the suggested system with a large PV module has better energy, economic, and environmental results with the grid purchases (2,347,286,329 kWh/year), LCOE (0.0477 $/kWh), and CO2 emission (1,480,778 ton/year). The optimization procedures were executed with different load levels, and in all cases, the results confirmed that the plant designed with large panels is more economical.
A high-precision electrochemical model of lead-acid batteries is the core foundation for enhancing the safety of battery management systems. However, the numerous parameters and strong coupling characteristics of the model make identification difficult. In order to explore the design basis for an efficient and accurate parameter identification framework, this paper conducted a parameter sensitivity analysis. The parameter value ranges for 23 variables were determined based on established lead-acid battery literature. The sensitivity analyses were then performed across different C-rates and depth of discharge regions, primarily evaluating parameter identifiability. Additionally, the impact of parameter uncertainty on critical model outputs, specifically electrolyte concentration and side reaction overpotential, was quantified under constant-current charging conditions. The results indicate that capacity and electrochemically relevant parameters with high sensitivity significantly influence the terminal voltage. Accordingly, this paper proposes a stepwise identification strategy and clarifies the optimal identification conditions for different parameters: capacity-related parameters are suitable for low-current conditions, while electrochemical parameters require identification under high-rate currents.
To address the decreased power generation efficiency caused by wake effect in onshore wind farms, a yaw coordination control method based on the GCH (Gaussian-Curl-Hybrid) wake model is proposed. The study uses actual SCADA operation data to finely correct the incoming wind speed and power curves, thereby enhancing the model's applicability in complex terrains and highturbulence environments. In addition, the SR (Serial-Refine) sequential optimization algorithm is introduced in the FLORIS (Flow Redirection and Induction in Steady-state) framework, and an efficient solution is achieved through a two-stage strategy of "global rough search - local fine adjustment". Taking 20 2.5MW wind turbines on a certain onshore wind farm as the research object, the optimization of yaw control for the WT01-WT03 series of wind turbines under different wind speeds and directions is analyzed. At a typical wind direction of 315 degrees, the optimal yaw angle of the upstream wind turbines increases with wind speed, and the gain efficiency shows a non-linear attenuation; under wind directions of 135 degrees and 315 degrees, the maximum power increases by 18.28% and 9.39% respectively, verifying the effectiveness and robustness of this method. The results provide theoretical support and engineering practice paths for intelligent operation control of onshore wind farms.
The challenge of managing peak electricity demand is becoming more pronounced owing to the rising consumption of electricity, which leads to a mismatch between supply and demand. This research explores the implementation of energy management systems (EMS) and the integration of microgrids (MGs) with utility grids as key strategies for achieving electric peak shaving. This study investigates the functioning of demand-side management (DSM) in conjunction with hybrid energy storage systems (HESS) that utilize supercapacitors and lithium-ion batteries under varying load conditions. In this study, a control strategy for droop voltage regulation of the DC bus voltage in MGs is proposed, which is affected by renewable energy sources that are geographically dispersed and intermittently available. The proposed approach was validated through comprehensive simulations under diverse conditions and compared with the baseline methods. The simulations demonstrate that the proposed method improves the voltage stability, prolongs the battery life, and enhances the dynamic response time. The evaluation of India's power sector includes an in-depth analysis of the country's electricity supply and illustrates the application of DSM in real-world peak-load management scenarios.
Combined the backpropagation (BP) neural network with the genetic algorithm (GA), an efficient dual-module regression prediction model is presented for power generation prediction in distributed solar intelligent microgrids. Two years of operation data were collected from a local microgrid system with eight environmental factors as input data. The input data is selected as input of the module, and the cumulative daily power generated by monocrystalline silicon module is used for prediction. The structural framework of the module adopts the dynamic mechanism of switching models. In normal mode, the shallow module (a single-hidden-layer BP network with the GA optimized) is adopted for rapid inference within 0.5 s, and the root mean square error (RMSE) is 62.02W. In more intricate or variable situations, the subsystem activates a deep module (a dual-hidden-layer network trained on 150 generations of GA), and the results are highly accurate and decrease the RMSE to 20.93W and increase the coefficient of determination (R2) to 0.943. The onset time of light intensity precedes that of ambient temperature by one to two hours, resulting in an average relative prediction error within 3%. The efficiency of power scheduling can reach more than 30% by adjusting power allocation and optimize energy curtailment. The proposed framework can obtain effective balance between computation and forecasting precision, providing a realistic and reliable solution for on-line energy management in microgrid applications.
With the increasing popularity of electric vehicles (EVs), there's a rising demand for advanced power conversion systems that boost efficiency, reliability, and safety in charging batteries. This research presents the development of a specialized isolated bipolar DC-DC converter designed specifically to enhance the integration of energy storage in electric vehicle battery charging systems. The bipolar power outputs, essential for ensuring balanced charging and increasing the lifespan of the batteries, will be stable as well as efficient due to switching functions controlled by the converter using the dsPIC30F2010 microcontroller. To enhance monitoring features in the main control unit, the system includes an ESP32 microcontroller, based on IoT. With this arrangement, you can keep an eye on key battery metrics like voltage and temperature in real time, all thanks to wireless technology. This IoT-connected system not only boosts clarity in how charging works but also helps manage issues by sending out warnings for problems like overheating or too much voltage. This monitoring system enhances battery safety and extends its lifespan during operation. Isolated design gives this converter the advantages of increased electrical safety and interference reduction, making it perfect for fragile automotive environments. In simulation, the converter stepped up a 50 V input to 320 V DC, delivering a battery current of 85 A at 80.6% SOC, verified under resistive loading with a 10 kHz switching frequency. A FOPID controller provided precise voltage regulation and stable operation across varying duty cycles. In hardware implementation, the prototype achieved output regulation between 12 V-34 Vat switching frequencies up to 25 kHz, with efficiency exceeding 90% and stable thermal performance under load variations. Real-time monitoring of battery voltage, current, and SOC% was realized through the Blynk IoT application, providing predictive fault alerts against overvoltage, overheating, and abnormal charging patterns. Hardware-based implementation in real conditions clearly demonstrates its practical application and efficiency. This converter combines strong power electronics with smart control and monitoring, improving EV charging systems by tackling both technical and practical issues related to high-performance and safe battery management systems.
The co-production of white charcoal and synthesis gas from rice husk was investigated in a dual-system furnace, which decouples pyrolysis and gasification processes. This study evaluated the relationship of air feed rate 0.08-0.16 kg/s on process efficiency and product quality. Increasing the air feed rate reduced production time from 141 to 69 minutes. An optimal air feed rate of 0.12 kg/s was identified for producing high-quality white charcoal, characterized by a maximum calorific value of 18.5 MJ/kg and an electrical resistance of 22.0 Omega. The charcoal yield ranged from 37-47%. A critical trade-off was observed in energy recovery, higher air feed rates favored syngas recovery and enhanced gasification efficiency at the expense of white charcoal energy recovery. This paper shows that the dual-system furnace, by separating the reaction zones, effectively optimizes the simultaneous production of both value-added products through precise control of the air feed rate.
A key problem in the use of hydrogen-blended natural gas is its potential for leakage. Therefore, a simplified model of gas cabin of urban underground comprehensive pipe gallery is established to study the effect of pipeline pressure on gas diffusion characteristics. It is found that the gas in the pipe gallery space flows upward to the top of the cabin, and then it moves along the wall to both sides in a symmetrical pattern when there is no wind. Hydrogen-blended natural gas reaches its highest diffusion rate and concentration at the leakage site, and the hydrogen gas first reaches the lower explosive limit, resulting in a higher risk of explosion. Pipeline pressure is positively correlated with leakage rate and velocity. The pressure positively correlates with both the concentration of gases and the extent of their distribution range. At 60 seconds of leakage of gas, the concentration of gas at a pressure of 1.6MPa is nearly twice that at 0.2 MPa, and the period needed to achieve the lower explosive limit is earlier than that at medium and low pressures. The farther the leakage point is from the ventilation opening, the less conducive it is to gas diffusion.
- This study develops a mathematical model to determine the optimum thickness of cellulose corrugated wetted pads in direct evaporative cooling systems by integrating heat transfer, airflow pressure drop, and total system cost. The model was validated against experimental data, achieving a minimum RMSE of 1.22 degrees C and a MAPE of 4.01%, confirming high predictive accuracy. Results show a U-shaped relationship between pad thickness and total cost, with an optimum point minimizing the combined expenses of pad material, fan, and pump energy. Sensitivity analysis identified inlet and outlet air temperatures as the most influential factors on optimum thickness. To avoid computationally intensive brute-force optimization, a third-degree polynomial correlation was formulated using saturation effectiveness as the input. The correlation predicted optimum thickness with MAPE below 4% and RMSE below 0.005 m for effectiveness values between 0.45-0.90. However, errors increased significantly beyond 0.90, with MAPS reaching 19.44%. Applicability analysis confirmed the correlation's reliability across conditioned volumes of 15-1,500 m3, air change rates of 10-40 h-1, and cost factors of 0.0090-0.0224, with errors under 12%. While the correlation provides a practical tool for preliminary design, comprehensive cost evaluation remains essential for final optimization.
Off-grid solar systems encounter difficulties during low solar irradiance periods, particularly at night when photovoltaic generation stops. This study evaluates and compares the performance of five machine learning models Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine (RBF), and Decision Tree for prioritizing load cut-offs in AI to maintain critical loads during energy shortages. Data averaged hourly from an off-grid solar setup in Chiang Mai, Thailand, for 2024, included photovoltaic output, battery metrics, and categorized load usage during rainy, winter, and summer seasons. Models were trained on rainy season data and tested on winter, summer, and October datasets. We evaluated performance through MAE, RMSE, R-2, classification reports, F1-scores, and k-fold cross-validation to ensure stability. Results indicate that Random Forest and Gradient Boosting consistently reached the highest accuracy (R-2 > 0.95 in most seasons) with low MAE and RMSE, whereas Decision Tree and Logistic Regression showed more variability. AI-driven scenarios greatly improved nighttime battery performance over non-AI approaches, especially during the rainy season. This method enhances energy reliability and battery longevity in off-grid settings, but outcomes vary by location. Future research should explore a wider range of climates and load profiles.
The research presents the design and implementation of a smart data management system tailored for charcoal briquette manufacturing. The system integrated a load cell, an HX711 amplifier, and a NodeMCU ESP8266 microcontroller to collect and transmit real-time data to the Google Sheets. Additionally, it employed LINE Notify for instant alerts on production status. The prototype system was tested on two briquette sizes (10 cm and 12 cm), yielding average weight deviations of 2.50% and 2.29%, respectively, and count deviations of less than 1.00%. Realtime data display, cloud-based storage, and automatic alerting significantly improved operational efficiency, accuracy, and monitoring capability. The proposed system was modular, scalable, and provided practical implications for low-cost digital transformation in biomass energy and similar industries.
This study presents the development and performance evaluation of an automated fluidized bed dryer for paddy, equipped with a microcontroller-based system (Arduino) for precise control of temperature and humidity. The dryer, with dimensions of 47 cmx 72 cm x 190 cm, was fitted with sensors at the inlet, middle, and outlet of the grain column. Drying experiments were conducted at temperatures of 40, 50, and 60 degrees C for 60, 90, and 120 minutes, using 1,500 grams of paddy with an initial moisture content ranging from 14% to 15%w.b. The optimal air velocity for fluidization was found to be 9.67 m/s, ensuring stable grain suspension and improved heat and mass transfer. The condition of 60 degrees C for 120 minutes yielded the highest moisture reduction of 70.62% (final moisture content 4.22% w.b.) with moderate energy use (SEC = 27.37 MJ/kg). This condition demonstrated a well-balanced trade-off between drying efficiency and energy consumption, making it suitable for real-world applications in medium-scale farms or small-scale agro-industrial facilities where both product quality and energy performance are critical, while also contributing to global efforts in sustainable energy use.
As the demand for renewable energy and effective waste management grows, this study investigates the potential of producing wood pellets from rice straw biomass blended with refuse-derived fuel (RDF). Six mixing ratios by weight of rice straw to RDF were tested: 100:0, 90:10, 80:20, 70:30, 60:40, and 50:50 (wt%). The RDF used comprised 48% plastic, 25% paper, 23% fabric, and 4% organic matter. Pellets were produced using a rotary flat die briquetting machine, and key properties-including higher heating value (HHV), durability, bulk density, equilibrium moisture content, and water absorption behavior-were evaluated. Results showed that increasing RDF content improved the HHV, reaching a maximum of 17.40+ 1.06 MJ/kg at 50 wt% RDF. However, higher RDF proportions reduced durability (lowest at 93.34 + 2.63%) and bulk density (545.09 + 31.85 kg/m3). Equilibrium moisture content also decreased with more RDF, reaching a minimum of 12.87+ 1.62% (dry basis). Notably, RDF addition enhanced water resistance by reducing pellet disintegration during immersion. These findings highlight a promising strategy for converting agricultural residues and municipal waste into high-quality solid biofuels with enhanced energy content, water resistance, and reduced moisture sensitivity. The integration of rice straw and RDF offers a sustainable solution for waste valorization, supporting both environmental protection and the development of decentralized renewable energy systems.
study investigates the improvement of physical characteristics and energy costs in bagasse pellets using agro-waste additives. The additives were coffee grounds (CG) and corn cobs (CC), with varying proportions. The bagasse pellets were evaluated according to the standards set by the Pellet Fuels Institute. The results demonstrate that increasing the additive content significantly improved both bulk density and durability. The study revealed that the addition of coffee grounds and corn cobs at 5% improved the physical properties compared to no additives (0%). The bulk density values increased by 5.06% for coffee grounds and 3.13% for corn cobs. Furthermore, the durability values increased by 0.76% for coffee grounds and 0.37% for corn cobs. However, coffee ground additives enhance the quality of pellet fuel more effectively than corn cob additives. Due to the fine and uniform particles of coffee grounds, this results in reduced void spaces between the particles. Additionally, the coffee grounds act as a natural binder during the densification process, which enhances the durability of the pellets. It was shown that the energy costs associated with the production process of bagasse pellets using additives decreased. The reductions were 8.32% for coffee grounds and 7.55% for corn cobs. Thus, the agro-waste additives were coffee grounds and corn cobs, which improve the physical properties of the pellet. Additionally, the additives lower energy costs and enhance the performance of bagasse-based biomass pellets, providing an energy solution for a sustainable energy future.
study investigates the performance testing of a shell-and-tube heat exchanger designed for thermal energy storage using a phase change material (PCM), specifically sodium hydroxide. The heat exchanger used biomass-derived heat from a gasification burner, with hot air flowing within the shell and cold air passing through the tubes in a cross-flow arrangement. PCM was positioned beneath the heat exchanger cover to store excess heat during the heating process and extend the heat exchange duration during cooling. The system was tested at three cold airflow speeds (1.717, 2.965, and 5.490 m/s), while maintaining a constant hot airflow of 0.663 m/s. Results demonstrated that the PCM-enhanced heat exchanger effectively retained heat, keeping the cold air temperature above 60 degrees C for up to 70 minutes at 1.717 m/s, compared to 40 minutes without PCM. These results suggest that incorporating PCM considerably improves heat retention and prolongs heat exchange duration.
This study presents the development of evaporative cooling pads made from water hyacinth fibers coated with Rocima 243, a broad-spectrum antifungal agent, aiming to create a sustainable and cost-effective alternative to commercial cooling media for tropical climates. Unlike conventional materials such as engineered cellulose, our approach valorizes an abundant invasive aquatic plant--Eichhornia crassipes--by transforming its fibrous stems into functional cooling media. The fibers were chemically treated to resist biodegradation and microbial colonization, addressing a key limitation of natural pads. Water hyacinth stems were collected, dried, and woven into 15 x 20 cm fiber pads, then coated with Rocima 243 to inhibit fungal degradation. Laboratory tests evaluated fungal inhibition using five isolates (including Aspergillus spp. and Rhizopus spp.), showing colony suppression to 0.5-2 cm. The pads were installed in a small evaporative cooling rig with an airflow of 3 m/s and water recirculation, operated for 7 days under simulated tropical conditions (similar to 33 degrees C, 60-80% RH). The treated pads consistently achieved a temperature drop of similar to 3.5 degrees C and a saturation efficiency of similar to 70% while maintaining their structural integrity. Compared to untreated hyacinth pads, which rapidly degraded under the same conditions, the treated pads-maintained shape, color, and airflow integrity. These results confirm that Rocima-coated hyacinth fiber pads can match or exceed the thermal performance of synthetic commercial media while offering ecological co-benefits such as waste valorization and invasive species management.
-This study investigates biodiesel production under applied electric fields using two common, cost-effective electrode materials-iron and aluminum-operated under identical conditions. Used palm oil was transesterified with methanol at a molar ratio of 5:1. The temperature was 40 degrees C for 2 minutes, with sodium hydroxide (0.5-0.7% w/w) as the catalyst and electric field intensities of 80-240 V. A 32 factorial design within Response Surface Methodology (RSM) was used to quantify individual and interaction effects and to identify optimal conditions. The highest yield with iron electrodes was 81.12% at 0.5% NaOH and 125 V; for aluminum, the maximum yield was 80.12% at 0.6% NaOH and 138 V. Iron required lower field strength and catalyst loading, consistent with better chemical stability in alkaline media. These results clarify how electrode material affects yield and separation performance in electric-field-assisted biodiesel production and inform material selection for cost-effective, energyefficient process design.
