Technical analysis for optimum hydrogen production using nuclear–renewable hybrid energy system

Techno-Economic assessment of distributed hybrid renewable energy systems across Nigeria's regions using measured resource data

Cascade hydropower retrofitting for pumped storage in high-penetration renewable energy systems: Techno-economic perspectives

This dataset presents experimental data on the performance of a photovoltaic (PV) solar-powered water pumping system installed in a coffee plantation in Chiang Mai province, Thailand. The system performance was evaluated through controlled experiments using response surface methodology (RSM). Three independent variables were systematically varied: solar irradiance (300-900 W/m²), panel inclination (15-35°), and panel surface temperature (30-60°C). A total of 15 experimental runs were conducted, and the pumping efficiency (%) was recorded under each condition. Statistical analyses, including analysis of variance (ANOVA) and regression modeling, were applied to evaluate the effects of the individual variables and their interactions on system performance. The dataset includes raw and processed measurements, regression coefficients, and response surface parameters, enabling replication and further analysis. Perturbation plots, 3D surface plots, and contour plots provide detailed visualizations of the relationships between environmental factors and system efficiency. The optimal operating conditions were identified at a solar irradiance of 600 W/m², a panel inclination of 25°, and a panel surface temperature of 45°C, corresponding to a predicted maximum efficiency of 76.3-77.0%. This dataset can be reused for designing optimized solar water pumping systems, validating predictive models, and comparing system performance under different environmental conditions or geographic locations. It also serves as a reference for researchers in renewable energy system optimization and agricultural water management. The data provide high-resolution, experimentally validated information on the combined effects of solar irradiance, panel inclination, and panel surface temperature on PV water pumping efficiency. Unlike previous studies, it includes detailed quantitative analysis specific to coffee-growing regions in Northern Thailand, along with regression models and visualizations that can guide both experimental replication and predictive modeling under similar climatic and agricultural conditions.

Bi-objective optimization of hybrid renewable energy systems with ice storage vs. battery storage for sustainable dairy farming

Short-term forecasting of solar and wind power generation is critical for smart grid management but challenging due to non-stationarity and extreme generation events. This study addresses a multi-task learning problem: regression-based forecasting of power output and binary detection of extreme events defined by a quantile-based threshold (q = 0.90). A hybrid spatio-temporal model, DP-STH++, is proposed, implementing parallel causal fusion of LSTM, GRU, a causal Conv1D stack, and a lightweight causal transformer. The architecture employs regression and classification heads, while an uncertainty-weighted mechanism stabilizes multitask optimization in the regression tasks; extreme event detection performance is evaluated using AUC. Training and evaluation follow a leakage-safe protocol with chronological data processing, calendar feature integration, time-aware splitting, and training-only estimation of scaling parameters and extreme thresholds. Experimental results obtained with a one-hour forecasting horizon and a 24 h context window demonstrate that DP-STH++ achieves the best regression performance on the hold-out set (RMSE = 257.18, MAE = 174.86–287.90, MASE = 0.2438, R2 = 0.9440) and the highest extreme event detection accuracy (AUC = 0.9896), ranking 1st among all compared architectures. In time-series cross-validation, the model retains the leading position with a mean MASE = 0.3883 and AUC = 0.9709. The advantages are particularly pronounced for wind power forecasting, where DP-STH++ simultaneously minimizes regression errors and maximizes AUC = 0.9880–0.9908.

Processes for generating clean hydrogen from waste plastics through thermochemical methods such as pyrolysis and gasification are a promising solution for both waste management and clean energy initiatives. Then, this derived hydrogen powers the fuel cell, which produces electricity that can be directly fed to charge electric vehicles (EVs). Although this complex process has many challenges related to energy efficiency during the conversion processes—starting from the generation of hydrogen from thermochemical processes and hydrogen storage and followed by fueling the fuel cells and charging EV infrastructure—the simplistic conceptual modeling developed for this research demonstrates how an ecosystem of such processes can be made feasible commercially. Clean hydrogen generated using known techniques reported in the literature is promising for commercialization, but harnessing hydrogen from plastics offers additional benefits, such as reducing greenhouse gas (GHG) emissions. Overall, the feasibility of clean hydrogen using this methodology is not limited by potential cost inefficiencies, especially when savings from GHG emissions reduction are taken into account. EVs have become commercially viable thanks to high-energy-density Li-ion batteries. And therefore, research continues to optimize charging performance through the integration of renewable energy and battery storage systems. This study examines another potential of clean hydrogen: its use as a power source in grids, especially V-2-G (vehicle-to-grid) systems. Additionally, direct current (DC) power from a fuel cell powers an EV charger at DC input voltages for e-ambulances. In particular, this designed system operates on DC voltages throughout the power system, combining high-voltage direct current (HVDC) lines, renewable energy sources, DC-DC converters, DC EV chargers, and other supporting components. The literature review identified gaps in plastics production, waste management, and processes for converting them into useful energy. The presented model is a stepping stone towards a novel, innovative process for clean hydrogen production to power electric vehicle charging infrastructure for emergency response systems in healthcare, thereby improving public safety. The limitations of the study would be governed by the effective establishment of locations where waste management services are performed (for example, landfills) and adoption by local government authorities with deregulated power systems.

To enhance the accommodation capability and operational flexibility of renewable energy systems, address the insufficient architectural integration of existing ammonia-based energy systems, and overcome the limitations of current optimization algorithms in tackling complex nonlinear multi-objective problems, this paper proposes a synergistic integrated energy system with liquid ammonia as the central hub. The system integrates multi-energy flows encompassing electricity, hydrogen, ammonia, and heat, leveraging ammonia fuel cell power generation, ammonia cracking, and ammonia-blended gas turbines for both electricity and heat production. A bi-level optimization model is formulated, coupling upper-layer multi-objective capacity planning with lower-layer stochastic chance-constrained scheduling. To solve this model, a hybrid algorithm, designated as LSDBO-WOA, is developed by integrating an improved dung beetle optimizer (LSDBO) with the whale optimization algorithm (WOA). Case study results demonstrate that the proposed algorithm achieves markedly superior convergence performance compared to benchmark algorithms such as non-dominated sorting genetic algorithm II (NSGA-II), with an improvement of approximately 18.6% in comprehensive performance metrics. Furthermore, the proposed electricity-hydrogen-ammonia-heat system attains an overall energy efficiency exceeding 97.66% and reduces carbon emissions by 7.3% relative to the original system without ammonia integration.

Deep learning models often encounter two key challenges in developing intelligent and scalable forecasting frameworks for renewable energy systems: input feature space dimensionality and sensitivity to hyperparameter settings. These limitations increase computational cost and compromise generalization and robustness. This paper presents a hybrid deep learning-optimization framework that leverages cognitively inspired metaheuristics to address these challenges, employing the Binary iHow Optimization Algorithm (biHOW) for feature selection and its continuous counterpart, iHOW, for hyperparameter tuning. Both variants emulate human cognitive phases-data absorption, information analysis, reinstitution, and adaptive knowledge development enabling efficient traversal of complex search spaces. Using the Multi-Scale Attention Network (MSAN) as the forecasting backbone, which is well suited for modeling renewable energy time series due to its ability to capture multi-scale temporal dependencies ranging from short-term fluctuations to long-term seasonal patterns, the proposed framework achieved high accuracy for wind and solar generation prediction. The MSAN model attained Mean Squared Errors (MSE) of 0.0105 for wind and 0.0976 for solar forecasting. Applying biHOW for feature selection reduced the average misclassification rate to 0.3925 (wind) and 0.4161 (solar) while identifying compact, interpretable feature subsets. The iHOW optimizer further fine-tuned architectural and training parameters, decreasing MSE to [Formula: see text] for wind and [Formula: see text] for solar, outperforming state-of-the-art metaheuristics including HHO, GWO, PSO, and JAYA. These findings demonstrate the effectiveness of iHOW-based optimization in enhancing forecasting accuracy and computational scalability. The proposed hybrid framework supports adaptive forecasting for intelligent energy management within modern smart grids.

The “Almirante Padilla” Naval Academy (ENAP), on the occasion of its 90th anniversary, launches the OnBoard Knowledge Journal, an open-access platform for the dissemination of research in engineering, technology, and social sciences. This first issue highlights advances in renewable energy systems, software development planning, virtual reality for education, Wi-Fi network optimization through AI, as well as studies on illegal fishing, digital transformation, maritime security, and education supported by digital technologies. With this initiative, ENAP strengthens its role as Colombia’s Maritime University and reaffirms its commitment to knowledge generation, scientific development, and institutional leadership.

Large Language Models (LLMs) are emerging as a new class of intelligent systems capable of reasoning over heterogeneous knowledge and interacting with human operators, yet their role in renewable energy systems remains insufficiently synthesized. This review provides a dedicated, systematic examination of LLMs as knowledge-centric, human-oriented decision-support tools for renewable energy infrastructure. In contrast to existing surveys that primarily emphasize numerical optimization, forecasting, or conventional machine learning methods, this work focuses on how LLMs enable textual reasoning, regulatory interpretation, operational intelligence, and interactive support across energy system lifecycles. We present a structured overview of recent literature, categorizing LLM applications by their functional roles in analysis, control, operation, and policy support. Furthermore, we analyze the contributions of LLMs to key decision-support tasks, including information retrieval, incident analysis, operational coordination, and strategic planning in smart grids and microgrids. The review also critically examines current limitations and risks associated with deploying LLMs in energy systems, including hallucination, reliability, domain adaptation, explainability, and real-time operational constraints. Finally, we identify emerging research directions, including energy-efficient LLM deployment, sustainability-aware AI design, and the alignment of LLM-based solutions with the goals of resilient, low-carbon, and environmentally sustainable energy systems.

The problems are that: (i) frequent power outage by inadequate infrastructure and outdated technologies led to poor supply, (ii) limited access to reliable electricity and high cost of installation of off-grid energy and fossil fuel caused financial burden on households in the region, (iii) lack of a reliable energy, low renewable sources and reliability misconceptions affected the usage of renewable energy in the region, (iv) overall reliance on fossil fuel such as: diesel and petrol despite its high cost had contributed to global warming, and lack of strong implementation strategies and research affected renewable energy applications in the region. The study is aimed at comparing models for non-conventional energy supply to a four storey Building using Gauss-Seidel Algorithm in Abo-Mbaise, Imo State, southeast region, Nigeria. The objectives of the study are to: (i) evaluate the current energy infrastructure and consumption pattern in a-six- person-household per flat in the region, (ii) identify the renewable energy resources available in the region, (iii) assess the technical, economic and viability of implementing different renewable energy systems in a-four-storey-Building and the environmental impact of utilization in the region, and (v) apply optimization algorithm on different renewable energy sources on design configurations and fabrication. The reliability of the research experiment was determined using Pearson product moment correlation (r). The experimental result of (r), was found to be approximately (r = 1.0) which confirmed 100% (percent) level of performance between the model and prototype (fabricated Flywheel). The result also shows that there exist a strong correlation between the designed (model) and the fabricated (prototype). The work concluded that: (i) the cost of energy per kwh from Enugu Electricity Distribution Company (EEDC) is ₦53.78, Solar/Battery option is ₦65.62, Flywheel option is ₦34.27, Biomass option is ₦49.97 and Wind option is ₦74.01.Therefore, cost of energy for a-4-hour supply per flat of 6-person-household per month from EEDC is ₦18,070, while (ii) the optimization (GA) results show that for every-24-hour: (i) Wind option has a minimum cost of N14.66million in 30years, N488,666:67K in one year, N40,722:22K in one month per Building of 8flats, and per flat of 6persons has to pay N5090:00K ($3.4 US Dollars) per month, (ii) Biomass option has a minimum cost of N20.62million in 30years, N687,333:33K in one year, N57,277:78K in one month per Building of 8flats, and per flat of 6persons has to pay N7,160:00K ($4.8 US Dollars) per month, (iii) Flywheel option has a minimum cost of N6.9million in 30years, N230,000:00K in one year, N19,166:67K in one month per Building of 8flats, and per flat of 6persons has to pay N2,396:00K ($1.6 US Dollars) per month and (iv) Solar/Battery has a minimum cost of N28.16million in 30years, N938,666:67K in one year, N78,222:22K in one month per Building of 8flats, and per flat of 6persons has to pay N9,778:00K ($6.5 US Dollars) per month. It further concluded that there is zero emission of Co2 and other green house gasses using the Flywheel energy generating system and also it is the most efficient option. The work recommended that: for federal government of Nigeria to realize her vision/policy on climate change and SDG 2030: the Federal Housing Authority should promulgate a law for the provision of power supply in any building meant for rent age to reduce over-reliance on national grid, and the said regulatory council should implement the collection of appropriate tariffs using off-grid power supply in collaboration with other organizations in the renewable energy field.

Hybrid renewable energy systems (HRESs), mixing conventional and renewable power sources and occasionally storage units, have become the norm regarding electricity generation. Robust long-term planning of such systems requires stakeholders to test different layouts and system configurations, while their operational management relies on forecasting surpluses and deficits to achieve optimal decision making. However, both tasks, which in fact constitute a flow allocation problem across power networks, are subject to multiple peculiarities, arising from the nonlinear dynamics of the underlying processes, subject to numerous technical and operational constraints. Interestingly, a mutual problem emerges in water resource systems, also comprising network-type storage, abstraction and conveyance components. In this vein, triggered from well-established simulation approaches from the water domain, we introduce a generic (i.e., topology-free) and time-agnostic framework, the key methodological elements of which are: (a) the graph-based representation of the power fluxes; (b) the effective handling of energy uses and constraints through virtual nodes and edges; (c) the implementation of priorities via proper assignment of virtual costs across all graph components; and (d) the configuration of the overall problem as a network linear programming context, which allows the use of exceptionally fast solvers. Specific adjustments are required to address highly complex issues within HRESs, particularly the representation of conventional thermal and pumped-storage hydropower units, as well as the power losses across transmission lines. The modeling approach is stress-tested by means of configuring a hypothetical HRES in a non-interconnected Aegean island, i.e., Sifnos, Greece.

Global climate change has intensified the need for clean and stable energy sources. Geothermal energy, with its consistent availability, is crucial for the transition to renewable energy systems. This study aims to numerically evaluate the enhancement of heat extraction in a mid-deep coaxial geothermal heat exchanger (GHE) when using water-based Al2O3 and SiO2 nanofluids. A comprehensive 1D pipe flow- and 3D subsurface heat transfer-coupled model was developed and validated against field experimental data. The results demonstrate that the nanofluids significantly enhanced heat extraction. The water–SiO2 nanofluid achieved the highest outlet temperature, exceeding pure water by approximately 0.2 °C after 2000 h. A lower inlet temperature of 5 °C increased heat extraction by 88.57% compared to 25 °C, despite a lower outlet temperature. The thermal influence radius expanded from <2 m at 300 h to ~6 m at 1800 h. This study provides quantitative insights and a validated framework for optimizing GHE performance through nanofluid selection and operational control.

Tropical commercial buildings are characterized by cooling-dominated energy consumption due to persistently high temperatures, elevated humidity, and increasing urbanization. These conditions intensify reliance on air-conditioning systems, contributing to high energy use, greenhouse gas emissions, and degraded indoor environmental quality. This paper reviews the environmental–energy nexus in tropical commercial buildings, focusing on the interactions between climatic conditions, building design, occupant behavior, and energy systems. Key themes include thermal environment challenges, HVAC performance, ventilation and humidity control, adaptive thermal comfort models, and urban heat island mitigation strategies. The study also examines the role of renewable and low-carbon energy technologies, such as solar photovoltaic systems, solar-assisted cooling, hybrid renewable energy systems, and thermal energy storage, in reducing cooling energy demand. Regional case studies from tropical Southeast Asia highlight how policy frameworks, technological maturity, and market conditions influence energy performance outcomes. The findings demonstrate that improving energy efficiency in tropical commercial buildings requires a holistic, climate-responsive approach that integrates passive design, advanced control technologies, and renewable energy deployment within an environmental–energy nexus framework to support sustainable development and long-term decarbonization goals.

Power outages are a persistent and widespread issue in many developing countries, significantly affecting economic development, public health, education, and overall quality of life. These outages, often referred to as "load shedding," occur due to a combination of inadequate infrastructure, poor maintenance, limited energy production, and mismanagement of resources. The increasing frequency of power outages and the growing interest in renewable energy technologies have driven the adoption of hybrid backup power systems for residential applications. However, the economic performance of such systems under subsidized fuel and electricity prices has not been sufficiently examined. This paper presents an optimal sizing and economic analysis of a hybrid photovoltaic (PV), battery, and diesel generator system designed to supply residential loads in Libya during grid outages lasting from 1 to 7 hours. The system is modelled and optimized using HOMER Pro software. Since electricity, gas, and diesel are subsidized for the public in Libya, the optimization is conducted for three different load cases: 2,912.7, 4,855.89, and 7,538.434 kWh/yr. A comparison of the economic performance of the three residential backup power system configurations under varying power outage durations, along with sensitivity analyses, is conducted to evaluate system performance and the impact of variations in diesel and renewable energy component costs. Despite subsidized electricity and fuel prices, the results indicate that incorporating renewable energy with energy storage, in addition to a diesel generator, is more economical and reliable for residential backup power systems in Libya. Furthermore, integrating renewable energy systems reduces generator operating hours, thereby lowering maintenance costs and pollution. Renewable energy penetration generally increases with outage duration, with the medium-load case achieving the highest renewable fraction of 36.1% during extended outages.

Retraction Note: Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia.

Driven by the demand for green ammonia and sustainable energy technologies, electrochemical ammonia synthesis has attracted increasing attention due to its mild operating conditions, compatibility with renewable energy, and low-carbon potential. However, prior studies have focused primarily on catalyst development, while systematic analyses of reactor engineering remain limited, constraining industrial translation. This review provides a comparative examination of electrochemical ammonia synthesis reactors, including single-chamber and H-type electrolyzers, continuous-flow reactors, and membrane electrode assemblies, with an emphasis on mass transfer, current density, faradaic efficiency, and scalability. Key reactor engineering strategies-such as interface optimization, three-phase regulation, and membrane and flow-channel design-are summarized. Finally, future perspectives are discussed, highlighting durable catalysts, stable and low-cost membranes, modular continuous reactors, and integration with renewable energy systems to enable efficient, low-carbon, and scalable ammonia production.

With the increasing share of intermittent renewable electric energy sources, such as wind and solar power, the electric grid risks becoming imbalanced. In regions where hydropower provides a significant share of the renewable electric power production, hydropower has a great potential to mitigate some of this imbalance through flexible and fast-responsive operation. This involves frequent starts and stops, continuous regulation, and off-design operating conditions, for which the machines were not designed. New questions arise for hydropower, such as how much the lifetime of the machines is reduced, how the maintenance intervals should be determined, the costs, and the limits of safe operation. This review paper investigates the extent to which lifetime analysis has been used to answer these questions whenever hydropower is used to stabilize a renewable electric energy system. The focus is on fatigue damage, which is a lifetime reduction mechanism strongly connected to the new kind of operation of hydraulic turbines in a renewable energy system. The review summarizes both experimental and numerical methods and lists the alternative steps required for a complete lifetime analysis of hydro turbines. It is found that a few studies do indeed indicate quantitatively that the lifetime of hydraulic turbines is reduced by operating at off-design, but most studies do not come close to a complete lifetime prediction. This reveals an important gap in research and highlights the need for further studies that quantitatively answer the questions related to potential problems for hydropower as a regulating resource in a renewable electric energy system. • Summarizes procedures for lifetime estimations of hydro turbines. • Categorizes experimental and numerically based fatigue damage calculation methods. • Reveals a lack of complete residual lifetime calculations in the literature. • Points out directions for future hydro turbine lifetime analysis.

This paper investigates the use of Quantum Annealing (QA) devices for optimizing unit scheduling in renewable energy systems, addressing the challenge of energy balancing with intermittent generation. We compare two approaches for encoding inequality constraints in Quadratic Unconstrained Binary Optimization (QUBO) formulations: (a) the conventional slack-variable-based method and (b) a novel soft-encoding technique called unbalanced penalization. This first application of unbalanced penalization in the energy and power systems domain, aims to disseminate knowledge about near-term applications of quantum-computing and how to make best use of it within the community. Through computational experiments, we find that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unbalanced Penalization</i> consistently outperforms the conventional method of using slack variables by reducing the number of variables in the QUBO formulation. Despite this, its inherent nature penalizes feasible solutions unevenly, preventing the ability to handle much larger problem sizes. More broadly, we observe that both methods-like most pure quantum approaches-struggle with scalability on current QA hardware, as solution quality declines with increasing problem size. In light of these limitations, we assess the feasibility of purely quantum solutions and argue that hybrid approaches remain the most practical path forward.