This study presents the development of the Transit Analytics Lab Electric Bus (TALe-Bus) Dashboard, an integrated decision-support tool designed to support transit agencies in planning the transition from conventional diesel buses to battery-electric buses. The dashboard combines predictive modeling, data analytics, and interactive visualization to estimate electric bus energy consumption rates, fleet size requirements, replacement factors, and maximum operational range for the case of overnight depot charging. The dashboard was built based on real-world data collected from the operations of 60 battery-electric buses on 48 routes in Toronto. The modeling workflow includes data preprocessing, feature selection, and the development and comparison of multiple statistical and machine learning energy prediction models. Tree-based modeling techniques outperformed the other techniques, demonstrating strong capability in capturing the relationships between operational, environmental, vehicle, and route characteristics and the energy consumption rate, achieving a root mean square error (RMSE) of 0.13 kWh/km. These techniques were therefore adopted as the core predictive engine of the system. The fleet-sizing module integrates traditional transit planning formulations (for diesel fleets) with electric-bus energy and range constraints to estimate both electric and diesel fleet requirements and compute the replacement factor, a key indicator reflecting the relative fleet needs for electrification. The dashboard also estimates the maximum operational bus range based on the battery capacity and real-world operating conditions, supporting reliable service planning. The system is implemented as an interactive web-based platform that provides geospatial visualization of route electrification feasibility and a scenario-based interface for customized operational analysis. By translating complex predictive analytics into a practical planning tool, the TALe-Bus Dashboard supports informed, data-driven decision-making for fleet electrification and infrastructure planning.
This study presents a Particle Swarm Optimization (PSO) framework for the techno-economic design of photovoltaicu2013battery energy storage systemu2013hydrogen (PVu2013BESSu2013H2) residential systems targeting green hydrogen production. The methodology integrates National Renewable Energy Laboratory Annual Technology Baseline (NREL ATB) Advanced 2035 cost projections with time-varying electricity tariffs to optimize PV capacity, battery energy storage system (BESS) sizing, electrolyzer power, and hydrogen storage volume. The optimal configuration achieved a levelized cost of hydrogen (LCOH) of 4.09 USD/kg through strategic energy management, combining self-consumption maximization, price arbitrage via BESS charge/discharge cycles, and electrolyzer load balancing. The PVu2013BESSu2013H2 system demonstrated superior performance with high self-consumption rates and efficient PV-to-H2 conversion pathways, validated through comprehensive Sankey energy flow analysis and sensitivity studies on key techno-economic parameters. Results highlight the critical role of BESS in enabling competitive green hydrogen production at the residential scale under future cost scenarios.
With the increasing penetration of distributed energy resources, electric vehicles, and prosumers, the demand for secure, scalable, and low-latency transaction platforms in smart grid-based transactive energy systems has grown substantially. Although blockchain technology offers a promising solution for decentralized energy trading, the choice of consensus mechanism critically determines system performance and practical feasibility. This study evaluates the relative performance of Clique, Istanbul Byzantine Fault Tolerance (IBFT), and proof of work (PoW) consensus algorithms to identify the most suitable approach for transactive energy applications in a smart grid environments. Using a five-node blockchain network, key performance indicators (KPIs)-namely, latency, throughput, and transaction failure rate-are quantified for essential market functions, including bidding, market clearing, payment settlement, and balance queries. The results demonstrate that permissioned consensus mechanisms significantly outperform PoW in term of responsiveness, scalability, and reliability. Among the evaluated approaches, IBFT exhibits the highest throughput and the lowest latency, making it the most suitable choice for real-time and near-real-time energy market operations. Clique delivers satisfactory performance in small-scale deployments but exhibits scalability limitations as transaction volumes increase. In contrast, PoW suffers from excessive latency and high failure rates, rendering it unsuitable for smart grid service operations. Overall, the findings indicate that permissioned blockchain platforms employing IBFT can effectively support efficient, secure, and scalable transactive energy markets for future smart grid infrastructures.
Accurate solar irradiance forecasting is essential for renewable energy in tropical regions like Java-Bali, where weather variability poses major challenges. This study compared statistical (SARIMA) and neural network-based models (LSTM, GRU, NARNET, WNN), along with hybrid approaches, to identify the most effective prediction method. SARIMA was selected for its ability to capture consistent seasonal and linear trends, while NNVs model nonlinear relationships, especially in unstable weather. The proposed models were benchmarked against persistence and ARIMA baselines. The hybrid SARIMA-NARNET model achieved superior accuracy, with an MAE of 1.9287 W/m2, RMSE of 2.5197 W/m2, and a remarkably low MAPE of 0.3084%. Additionally, the DCL strategy demonstrated adaptive energy management, yielding daily energy savings of 16%-17% compared to static methods, with even greater efficiency at extreme confidence levels. These findings highlight the potential of hybrid modeling and adaptive control for optimizing solar energy use in tropical climates.
Increasing demand for sustainable energy is driving significant interest in biomass-powered ORC systems, especially for small-scale, off-grid power generation. However, due to the nonlinear coupling between the operating parameters, it remains difficult to predict many different performance indicators, or rank performance to identify the best system configurations. This study seeks to address these issues by developing a deep neural networks (DNN) model with thermodynamic simulation, multi-objective optimization, and decision analysis to predict biomass organic Rankine cycle (ORC) performance, with the aim of optimizing performance. Data was generated in matrix laboratory (MATLAB) using CoolProp based on two biomass fuels, coconut shell and cornstalk, and operating ranges that included pressures of 2.6-3.5 MPa, and lower cycle temperature of 300.15-310.15 K for R245fa as working fluid. The nine decision variables included mixture strength, biomass flowrate, and component efficiencies. The DNN predicted four exergy-based outputs: net power, cycle efficiency, exergy efficiency of heat transfer fluid-organic Rankine cycle (HTF-ORC) circuits, and the overall exergy efficiency of the biomass plant. Pareto-based optimization, was implemented using particle swarm optimization (PSO), which produced non-dominated solutions from which the optimal decision variables were identified based on technique for order preference by similarity to ideal solution (TOPSIS) method. The results indicated coconut shell performance was greater than that for cornstalk, with optimal net power ranging from 1350-1460 kW, cycle efficiency values around 18.4%, HTF-ORC exergy efficiency values between 26-27%, and plant exergy efficiency between 6.5-6.9%. The most efficient points correlated with the highest operating temperatures and pressures. The mixture strength and biomass mass flowrate showed proportional increases to power targets, reaffirming the validity of the thermodynamic analysis and optimization. The DNN-PSO-TOPSIS approach appropriately captures the multi-parametric interactions that govern ORC performance, providing a powerful and scalable framework for the design of efficient biomass-to-power systems for renewable energy uses.
The research analyzes how financial development and government effectiveness influence patterns of renewable energy adoption within a country's energy matrix. The study examines a sample of 16 Latin American countries based on 20 years of data (2002-2021). Using the two main variables, the Driscoll-Kraay estimator is applied; it is supplemented by complementary indices pertinent to governance and climate to facilitate a more comprehensive interpretation of the results and to integrate variable interactions to capture marginal effects. The direct results indicated that financial development has a significantly negative effect, as it does not, on its own, generate an investment pattern favoring renewable energy sources: meanwhile, governance effectiveness is shown to be statistically insignificant with respect to changes in the energy matrix. However, the results regarding the interaction between financial development and the complementary variable, climate readiness, indicate that the effect reverses once the interaction exceeds a threshold of +1.1 standard deviations (SD), turning positive at +1.2 SD. The dynamic between finance and governance interaction remains statistically insignificant. These results remain robust across tests employing various lag lengths and temporal segmentation. The study concludes that a transformation of the energy matrix, characterized by a greater share of renewable energy, is achievable when financial development aligns with investment planning facilitated by stable social, economic, and regulatory frameworks that enhance the capacity to channel financial resources into the execution of renewable energy projects. This result will help government entities, financial agents, and multilateral entities formulate and implement policies that are better aligned.
This study presents a Particle Swarm Optimization (PSO) framework for the techno-economic design of photovoltaic-battery energy storage system-hydrogen (PV-BESS-H2) residential systems targeting green hydrogen production. The methodology integrates National Renewable Energy Laboratory Annual Technology Baseline (NREL ATB) Advanced 2035 cost projections with time-varying electricity tariffs to optimize PV capacity, battery energy storage system (BESS) sizing, electrolyzer power, and hydrogen storage volume. The optimal configuration achieved a levelized cost of hydrogen (LCOH) of 4.09 USD/kg through strategic energy management, combining self-consumption maximization, price arbitrage via BESS charge/discharge cycles, and electrolyzer load balancing. The PV-BESS-H2 system demonstrated superior performance with high self-consumption rates and efficient PV-to-H2 conversion pathways, validated through comprehensive Sankey energy flow analysis and sensitivity studies on key techno-economic parameters. Results highlight the critical role of BESS in enabling competitive green hydrogen production at the residential scale under future cost scenarios.
Accelerating the oxygen evolution reaction (OER) is critical for efficient alkaline water electrolysis in green hydrogen production. Rational regulation of the electronic structure of transition metal (oxy)hydroxides offers a vital route to enhance their OER kinetics under alkaline conditions. Herein, we reported a defect-rich high-entropy layered double hydroxide, D-NiCoFeCu-LDH, constructed via a sequential electrodeposition-electrochemical etching strategy. Selective chromium leaching reconstructs a homogeneous Ni-Co-Fe-Cu high-entropy matrix while introducing abundant vacancies and lattice distortion. Structural characterization confirms uniform elemental distribution and defect-enriched nanosheet arrays, whereas X-ray photoelectron spectroscopy (XPS) analysis reveals pronounced electronic redistribution, manifested by increased high-valence Ni3+/Co3+ species and positive binding energy shifts. We proposed that highly dispersed Cu+/Cu2+ species act as electronic modulators, withdrawing electron density from neighboring Ni, Co, and Fe centers, while lattice distortion further promotes electronic reconfiguration. Electrochemical measurements demonstrated enhanced intrinsic activity and accelerated charge-transfer kinetics compared to quaternary counterparts. Importantly, in situ attenuated total reflection surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) directly verified facilitated *OOH intermediate formation at lower overpotential, establishing a molecular-level link between electronic modulation and improved OER kinetics. In this work, we provide mechanistic insight into how synergistic high-entropy regulation and defect engineering cooperatively enhance intrinsic catalytic activity, offering a framework for designing durable, high-performance OER electrocatalysts.
In this paper, we present an economic optimization and sizing study of a hybrid solar-wind system integrated with energy storage and grid connection for green hydrogen production. The proposed model used nonlinear constrained optimization to determine the optimal capacities of photovoltaic panels, wind turbines, storage devices, and electrolyzers to maximize the net present value (NPV) of the investment. The system operation was simulated over a multi-year horizon accounting for intermittent renewable generation profiles, electricity market prices, and operational constraints. The optimization yielded an optimal configuration with 85.95 kW of solar PV, 59.87 kW of wind power, 64.18 kW/100 kWh of battery storage, and 100 kW of electrolyzer capacity, achieving a cumulative hydrogen production of 318,545 kg over 20 years. The system achieved a NPV of 524,720 USD with a Levelized Cost of Hydrogen of 3.35 USD/kg. Sensitivity analyses revealed that NPV varied from approximately 60,000 USD to 180,000 USD as the discount rate increased from 2% to 16% and showed a strong positive correlation with hydrogen selling prices. The results demonstrated the techno-economic feasibility of hybrid renewable systems for sustainable hydrogen production, highlighting the trade-offs between capital expenditure and operational revenues.
Numerous research studies have been conducted to enhance fuel economy and reduce emissions by converting diesel engines to run on aqueous ammonia-diesel (AAD) blends. However, only a few studies have investigated the effect of the piston bowl geometry on combustion efficiency and emissions from AAD blends. This study investigated the effect of piston bowl geometry on the combustion efficiency of a diesel engine fueled with AAD blends. Five different types of piston bowls, namely the Double lip (Case 1), 10D100 (Case 2), Mexican hat (Case 3), Inveco F1C Rollbuch (Case 4), and Peugeot DW 10 (Case 5), were used. Diesel-RK software was used for modeling and simulating the fuel combustion inside the diesel engine. Based on the numerical findings, with the same combustion chamber, the results show that the NOx emission, smoke level, and peak pressure reduced with increased engine speed from 2000 to 3000 rpm, whereas peak temperature, CO2, and PM emissions increased. Moreover, among all designs, Case 5 achieved the highest combustion performance, with peak pressure and temperature, along with the lowest PM and smoke. However, it produced the highest NOx emissions. In contrast, Case 1 yielded the lowest NOx, peak pressure, and temperature, but the highest PM, indicating poor combustion. Case 3 achieves the second-highest peak pressure (53.13 bar at 2000 rpm and 52.61 bar at 3000 rpm) and second-highest temperature (1886.8 K at 2000 rpm and 1914 K at 3000 rpm), and only moderately elevated NOx. Case 3 offers the best overall emission balance while maintaining high combustion efficiency, making it the most suitable piston bowl geometry for sustainable operation with AAD blends.
Energy storage systems (ESSs) serve as flexible resources that significantly contribute to the integration of uncertain renewable energy sources. They can mitigate power fluctuations through fast charging and discharging in ramping services while providing frequency regulation reserves to maintain system stability. However, traditional methods often neglect the uncertainty of storage capacity and the coupling between ramping and frequency regulation constraints, leading to potential capacity violations and inefficient joint optimization. In this paper, we propose a coordinated ramping-frequency regulation optimization strategy considering ESS capacity uncertainty. A probabilistic confidence-based model was developed to characterize the stochastic nature of capacity, and unified ramping-frequency regulation constraints were formulated for ESSs and thermal units. Nonlinear terms were linearized to derive a tractable mixed-integer quadratic programming (MIQP) model. Case studies verified that the proposed approach effectively accounts for capacity uncertainty, enables shared utilization of ramping and regulation capabilities under unified constraints, and enhances the overall efficiency of system flexibility deployment.
The performance of vanadium redox flow batteries (VRFB) is affected by multiple factors, such as flow field design and electrode size. Providing a smooth electrolyte supply to a suitable electrode size is determinant for battery performance. In this work, the dual impact of channel height and electrode thickness on overall battery efficiency (in nine different combinations) was investigated numerically through a 3D model involving electrolyte motion. The individual role and the dual impact of both factors on electrolyte penetration into the electrode, overpotential, and pressure losses were evaluated to highlight how these affect the output charge-discharge voltages, optimal flow rate, energy efficiency, and overall system efficiency. Numerous operating conditions, like state of charge (SOC), volumetric flow rate, and current density, were implemented to identify the most efficient cell combination among the studied cases. In general, the voltage response is improved as the channel height is reduced and/or electrode thickness is increased due to the reduction in overpotential; however, large pump losses are also produced. A balance between enhanced voltage and pumping power increment is required to achieve maximum battery efficiency (at optimal flow rate), depending on the cell configuration and applied conditions. Results indicate that channel height has a positive effect on energy efficiency at low flow rate, more so than electrode thickness, whereas the opposite is noticed at high flow rate. Additionally, for the best battery efficiency, electrode thickness is more determinant, followed by channel height, based on operating conditions. Overall, case 7 (smallest channel height and largest electrode thickness) performed best in terms of energy/battery efficiency at a relatively low optimal flow rate, whereas case 3 (largest channel height and smallest electrode thickness) performed worst; however, this effect is lowered at high current density. This model can guide the design of optimal flow batteries and loading conditions.
This paper addresses the challenge of tracking an arbitrary power profile in a proton exchange membrane fuel cell (PEMFC) in the presence of measurement noise and disturbances. To this end, we used an extended Kalman filter (EKF) to estimate the internal states of the PEMFC in conjunction with an adaptive sliding mode controller (SMC) that has been shown to reduce chatter. The model used by the controller captures the internal dynamics and nonlinearly, and is accurate within 0.1% of the high-fidelity model. We developed the conditions necessary for the stability of the proposed controller based on the Lyapunov stability theorem. We also developed a systematic multi-objective optimization methodology of the controller hyperparameters to simultaneously minimizing tracking error, controller-chatter, and controller input using the non-dominated sorting genetic algorithm II (NSGA-II). The controller performance was demonstrated using multiple simulated experiments. Based on experimental results on desired signal data, we concluded that the proposed controller scheme can track desired power profiles within a 1% error.
The rapid integration of distributed energy resources and power-electronic-interfaced loads requires sophisticated planning techniques for modern radial distribution systems (RDS). In this paper, we propose a comprehensive multi-objective optimization framework for the coordinated placement and size of electric vehicle charging stations (EVCS), distributed generations (DGs), and soft open points (SOPs) in RDS. The developed objective function concurrently minimizes loss, improves the voltage profile, enhances the power factor, reduces harmonic distortion, and maximizes the utilization of substation capacity, subject to operational and technical constraints. Teaching Learning Based Optimization (TLBO) and Harris Hawks Optimization (HHO) were used and compared to evaluate solution resilience and convergence efficiency. The outcomes showed that system coordination consistently improved network efficiency, voltage stability, feeder balance, and power quality. Coordinated multi-device integration delivered better technical performance and ensured steady operation within regulatory voltage and harmonic limits. The robustness and dependability of the suggested optimization framework were validated through statistical analysis, which showed that HHO outperformed TLBO in terms of convergence behavior and solution quality. In addition to improving technical performance, the suggested framework supports the development of sustainable power systems by encouraging the integration of renewable energy sources, grid modernization, and the adoption of electrified transportation. The study supports SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure), 11 (Sustainable Cities and Communities), and 13 (Climate Action). Overall, the suggested coordinated optimization approach offers a scalable and long-lasting solution for active distribution networks prepared for the future.
Short-term wind power forecasting is essential for wind farm management and reliable grid operations. However, the accuracy of turbine-specific forecasting is often compromised by limitations in sequence modeling and misleading information from the surrounding wind turbines. To address these challenges, we proposed a novel DVTransformer (DTW-VARIMA-Transformer) framework for turbine specific forecast integrating Transformer neural networks and a predictive strategy. This approach integrates spatio-temporal dynamics using wind speed from the surrounding turbines to forecast the wind power of the target turbine in a wind farm. Wind turbines were selected using dynamic time warping (DTW) based metrics, which calculate the dynamic distance between wind speed time series, ensuring the reliability of spatial information. Additionally, we incorporated predicted wind speeds of surrounding turbines using vector autoregressive integrated moving average (VARIMA), alongside historical data, to better capture the influence of future wind conditions on the target turbine power output. The DVTransformer performance was assessed against parallel models under various metrics, including mean absolute error (MAE), mean squared error (MSE), and correlation coefficient (R), demonstrating significant improvements in a multi-step ahead forecasting task. The proposed DVTransformer was first validated on two representative turbines to assess turbine-specific forecasting performance. For 3-step-ahead forecasting, DVTransformer achieved an average MSE of 0.085 for turbine T01, corresponding to reductions of 34.92%, 24.77%, 2.20%, and 27.34% compared to the Fast Fourier Transformer (FFTransformer), Informer, Spatio-temporal Long Short-Term Memory (ST-LSTM), and Spatio-temporal Multi-Layer Perceptron (ST-MLP), respectively. Similarly, for turbine T05, the proposed model attained an average MSE of 0.090, achieving MSE reductions of 38.45%, 23.08%, 2.93%, and 25.96% against the same benchmark models, demonstrating robustness across turbines. To further evaluate computational efficiency and scalability, a training-budget sensitivity analysis was conducted by comparing a DVTransformer trained for a single epoch against a fully trained Transformer. The results showed that the DVTransformer achieved comparable prediction accuracy across all turbines while reducing significant computational time. Evident from incorporating reliable spatial information, employing predictive wind conditions and using Transformer to capture long and short-term dependencies within time sequences increased the overall performance of the proposed method.
