Search Results (595)

The study establishes a comprehensive mathematical modeling framework for solar-driven hydrogen production by integrating a triple-diode photovoltaic (PV) model, an alkaline electrolyzer, and a hydrogen turbine (H₂T), subsequently using hybrid power utilization to optimize hydrogen output. The Triple-Diode Model (TDM) accurately reproduces the electrical performance of a 144-cell photovoltaic module under standard test conditions (STC), enabling precise calculations of hourly maximum power point outputs based on real-world conditions of global horizontal irradiance and ambient temperature. The photovoltaic system produced 1.07 MWh during the summer months (May to September 2025), which was sent straight to the alkaline electrolyzer. The electrolyzer, using Specific Energy Consumption (SEC)-based formulations and Faraday’s law, produced 22.6 kg of green hydrogen and used around 203 L of water. The generated hydrogen was later utilized to power a hydrogen turbine (H₂T), producing 414.6 kWh, which was then integrated with photovoltaic power to create a hybrid renewable energy source. This hybrid design increased hydrogen production to 31.4 kg, indicating a substantial improvement in renewable hydrogen output. All photovoltaic, electrolyzer, and turbine models were integrated into a cohesive MATLAB R2024b framework, allowing for an exhaustive depiction of system dynamics. The findings validate that the amalgamation of H₂T with photovoltaic-driven electrolysis may significantly improve both renewable energy and hydrogen production. This research aligns with Saudi Vision 2030 and global clean-energy initiatives, including the Paris Agreement, to tackle climate change and its negative impacts. An integrated green hydrogen system, informed by this study’s findings, could significantly improve energy sustainability, strengthen production reliability, and augment hydrogen output, fully aligning with economical, technical, and environmental objectives. Full article

Despite the growing adoption of hybrid energy systems integrating solar photovoltaic (PV), pumped storage hydropower (PSH), and battery energy storage (BES), comprehensive studies on their dynamic stability and interaction mechanisms remain limited, particularly under weak grid conditions. Due to the high impedance of weak grids, ensuring stability across varied operating scenarios is crucial for advancing grid resilience and energy reliability. This paper addresses these research gaps by examining the interaction dynamics between PV, PSH, and BES on the DC side and the utility grid on the AC side. The study identifies operating-region-dependent instability mechanisms arising from negative incremental resistance behavior and weak grid interactions and proposes a virtual-impedance-based active damping control strategy to suppress poorly damped oscillatory modes. The proposed controller effectively reshapes the converter output impedance, shifts unstable eigenmodes into the left-half plane, and improves phase margins without requiring additional hardware components or introducing steady-state power losses. System stability is analytically assessed using root-locus, Bode, and Nyquist criteria within a developed small-signal state-space model, and further validated through large-signal real-time simulations on an OPAL-RT platform. The main contributions of this study are threefold: (i) a comprehensive stability analysis of a utility-scale grid-connected hybrid PV–PSH–BES system under weak grid conditions, (ii) identification of operating-region-dependent instability mechanisms associated with DC–link interactions, and (iii) development and real-time validation of a practical virtual-impedance-based active damping strategy for enhancing system stability and grid integration reliability. Full article

This study examines whether green hydrogen production using combined wind and solar energy on Marmara Island can meet the island’s electricity demand and fuel the fuel needs of a hydrogen-powered ferry. A hybrid system consisting of a 10 MW wind farm, a 3 MW solar PV system, and a PEM electrolyzer sized to meet the island’s hydrogen demand was modeled for the island, located in the southwestern Sea of Marmara. The hydrogen production potential, energy flows, and techno-economic performance were evaluated using HOMER-Pro 3.18.4 version. According to the simulation results, the hybrid system generates approximately 62.6 GWh of electricity annually, achieving an 82.8% renewable energy share. A significant portion of the produced energy is transferred to the electrolyzer, producing approximately 729 tons of green hydrogen annually. The economic analysis demonstrates that the system is financially viable, with a net present cost of USD 61.53 million and a levelized energy cost of USD 0.175/kWh. Additionally, the design has the potential to reduce approximately 2637 tons of CO₂ emissions over a 25-year period. The results demonstrate that integrating renewable energy sources with hydrogen production can provide a cost-effective and low-carbon solution for isolated communities such as islands, strengthening energy independence and supporting sustainable transportation options. It has been demonstrated that hydrogen produced by PEM electrolyzers powered by excess energy from the hybrid system could provide a reliable fuel source for hydrogen-fueled ferries operating between Marmara Island and the mainland. Overall, the findings indicate that pairing renewable energy generation with hydrogen production offers a realistic pathway for islands seeking cleaner transportation options and greater energy independence. Full article

The increasing penetration of variable renewable energy sources intensifies grid imbalance and challenges the reliability of small-scale power systems. This study addresses these challenges by developing and analyzing a fully integrated hybrid energy system that combines biogas upgrading to biomethane, photovoltaic (PV) generation, hydrogen production via alkaline electrolysis, hydrogen storage, and a gas-steam combined cycle (CCGT). The system is designed to supply uninterrupted electricity to a small municipality of approximately 4500 inhabitants under predominantly self-sufficient operating conditions. The methodology integrates high-resolution, full-year electricity demand and solar resource data with detailed process-based simulations performed using Aspen Plus, Aspen HYSYS, and PVGIS-SARAH3 meteorological inputs. Surplus PV electricity is converted into hydrogen and stored, while upgraded biomethane provides dispatchable backup during periods of low solar availability. The gas-steam combined cycle enables flexible and efficient electricity generation, with hydrogen blending supporting dynamic turbine operation and further reducing fossil fuel dependency. The results indicate that a 10 MW PV installation coupled with a 2.9 MW CCGT unit and a hydrogen storage capacity of 550 kg is sufficient to ensure year-round power balance. During winter months, system operation is sustained entirely by biomethane, while in high-solar periods hydrogen production and storage enhance operational flexibility. Compared to a conventional grid-based electricity supply, the proposed system enables near-complete elimination of operational CO₂ emissions, achieving an annual reduction of approximately 8800 tCO₂, corresponding to a reduction of about 93%. The key novelty of this work lies in the simultaneous and process-level integration of biogas, hydrogen, photovoltaic generation, energy storage, and a gas-steam combined cycle within a single operational framework, an approach that has not been comprehensively addressed in the recent literature. The findings demonstrate that such integrated hybrid systems can provide dispatchable, low-carbon electricity for small communities, offering a scalable pathway toward resilient and decentralized energy systems. Full article

Accurate short-term forecasting of photovoltaic (PV) output is critical to managing the variability of PV generation and ensuring reliable grid operation with high renewable integration. We propose an enhanced hybrid deep learning framework that combines Temporal Convolutional Networks (TCNs), Gated Recurrent Units (GRUs), and Random Forests (RFs) in an optimized weighted ensemble strategy. This approach leverages the complementary strengths of each component: TCNs capture long-range temporal dependencies via dilated causal convolutions; GRUs model sequential weather-driven dynamics; and RFs enhance robustness to outliers and nonlinear relationships. The model was evaluated on high-resolution operational data from the Yulara solar plant in Australia, forecasting horizons from 5 min to 1 h. Results show that the TCN-GRU-RF model consistently outperforms conventional benchmarks, achieving R² = 0.9807 (MAE = 0.0136; RMSE = 0.0300) at 5 min and R² = 0.9047 (RMSE = 0.0652) at 1 h horizons. Notably, the degradation in R² across forecasting horizons was limited to 7.7%, significantly lower than the typical 10–15% range observed in the literature, highlighting the model’s scalability and resilience. These validated results indicate that the proposed approach provides a robust, scalable forecasting solution that enhances grid reliability and supports the integration of distributed renewable energy sources. Full article

Many studies on solar heating systems have examined individual techniques to enhance the performance of solar water collectors, such as flow obstructions, increased turbulence, nanofluids, and investment in thermal storage. The benefits of integrating these sustainability strategies into a single, sustainable system have yet to be fully established. This work displays a hybrid water-heating system that contains a solar water collector (SWC) and an electric water heater (EWH), a photovoltaic panel (PV), and nano-additives to increase the outlet water temperature and improve thermal efficiency. Numerical and experimental analyses were used to estimate the influence of water flow rate (2.5, 3.5, and 4.5 L/min) and different Al₂O₃ concentrations (0.1%, 0.2%, and 0.3%) on system performance using U-shaped pipe in SWC model. The results highlight that lower flow rates consistently yield higher ΔT values because water spends a longer time in the collector, allowing it to absorb more heat. Also, when using water only, the collector efficiency increases pro-aggressively with flow rate. A significant performance enhancement is observed upon incorporating Al₂O₃ nanoparticles into the fluid, with a 0.1% Al₂O₃ volume concentration improving efficiency by ~7.4% over water. At 0.3%, the highest improvement is recorded, yielding a ~9.3% gain in efficiency compared to the base case. Full article

In photovoltaic (PV) power generation, the intermittency and uncertainty caused by meteorological factors pose challenges to grid operations. Accurate PV power prediction is crucial for optimizing power dispatching and balancing supply and demand. This paper proposes a PV power prediction model based on Density Peak Clustering Algorithm (DPCA)–Crested Porcupine Optimizer (CPO)–Random Forest (RF)–Gated Recurrent Unit (GRU)–Kolmogorov–Arnold Network (KAN). First, the DPCA is used to accurately classify weather conditions according to meteorological data such as solar radiation, temperature, and humidity. Then, the CPO algorithm is established to optimize the factor screening characteristic variables of the RF. Subsequently, a hybrid GRU model with a KAN layer is introduced for short-term PV power prediction. The Shapley Additive Explanation (SHAP) method values evaluating feature importance and the impact of causal features. Compared with other contrast models, the DPCA-CPO-RF-KAN-GRU model demonstrates better error reduction capabilities under three weather types, with an average fitting accuracy R² reaching 97%. SHAP analysis indicates that the combined average SHAP value of total solar radiation and direct solar radiation contributes more than 70%. Finally, the Kernel Density Estimation (KDE) is utilized to verify that the KAN-GRU model has high robustness in interval prediction, providing strong technical support for ensuring the stability of the power grid and precise decision-making in the electricity market. Full article

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work. Full article

The increasing electrification of mobility within smart cities has accelerated the need for intelligent energy management strategies that jointly address cost, emissions, and battery health. This study develops a health-aware hybrid reinforcement–predictive energy manager (H-RPEM) designed for photovoltaic–electric vehicle (PV-EV) microgrids. The proposed controller unifies model-based predictive optimisation with adaptive reinforcement learning to achieve both short-term operational efficiency and long-term asset preservation. A comprehensive dataset of solar generation, EV charging behaviour, and stochastic load profiles was employed to train and validate the hybrid control framework under realistic operating conditions. Quantitative results indicate that the proposed H-RPEM controller achieves an 18.7% reduction in total operating cost and a 22.5% decrease in carbon emissions, whilst maintaining the battery state-of-health above 0.95 throughout a 24 h operational cycle. When benchmarked against standard predictive control, the hybrid strategy converges 30–40 episodes faster and delivers a 25% improvement in reward stability, demonstrating enhanced robustness and learning efficiency. The results confirm that H-RPEM achieves robust and balanced performance across economic, environmental, and technical domains, establishing it as a scalable and health-conscious control solution for next-generation smart city microgrids. Full article

Energy deficits have been a major challenge in Sub-Saharan Africa (SSA), particularly in Nigeria. Consequently, the integration of renewable energy (RE) is a crucial strategy for achieving energy transition goals and addressing climate change issues. Therefore, this article investigates the technical, energy, economic, and environmental impact of PV/Wind/BS/Converter, a standalone hybrid energy mix for electrifying a single-family residential building prototype in multi-regional parts of Nigeria. This study aims to examine the renewable energy potential of three locations using HOMER Pro. The results indicate that Kano exhibits the lowest economic performance indices, with a net present cost (NPC) of USD 32,212.52 and a cost of energy (COE) of USD 0.6072/kWh, followed by Anambra (NPC: USD 45,671.68; COE: USD 0.8609/kWh) and Lagos (NPC: USD 47,184.62; COE: USD 0.8706/kWh). Technically, this study shows that the higher the renewable potential of a site, the lower the energy cost and vice versa. The sensitivity cases of key energy parameters—including solar PV cost, wind turbine cost, wind speed, solar radiation, and inflation rate—were considered to compare multiple scenarios and assess renewable energy potential variability under certain decision-making conditions. Economically, the Kano system shows the feasible capital cost of the energy produced, replacement cost, and operation and maintenance cost (O&M) for wind turbines, compared to the nil cost for Anambra and Lagos. Environmentally, the energy systems revealed 100% renewable fractions (RFs) with zero emissions at the three sites under study, which can enhance Nigeria’s energy transition plan and help in achieving the Sustainable Development Goals. Integrating RE supports the successful implementation of the recommended energy policy strategies for Nigeria. Full article

Short-term photovoltaic (PV) power forecasting is pivotal for grid stability and high renewable-energy integration, yet existing hybrid deep-learning models face three unresolved challenges: they fail to balance accuracy, computational efficiency, and interpretability; cannot mitigate iTransformer’s inherent weakness in local feature capture (critical for transient events like minute-level cloud shading); and rely on linear concatenation that mismatches the nonlinear correlations between global multivariate trends and local fluctuations in PV sequences. To address these gaps, this study proposes a novel lightweight hybrid framework—DSC-Chebyshev KAN-iTransformer—for 15-min short-term PV power forecasting. The core novelty lies in the synergistic integration of Depthwise Separable Convolution (DSC) for low-redundancy local temporal pattern extraction, Chebyshev Kolmogorov–Arnold Network (Chebyshev KAN) for adaptive nonlinear fusion and global nonlinear modeling, and iTransformer for efficient capture of cross-variable global dependencies. This design not only compensates for iTransformer’s local feature deficiency but also resolves the linear fusion mismatch issue of traditional hybrid models. Experimental results on real-world PV datasets demonstrate that the proposed model achieves an $R^2$ of 0.996, with root mean square error (RMSE) and mean absolute error (MAE) reduced by 19.6–62.1% compared to state-of-the-art baselines (including iTransformer, BiLSTM, and DSC-CBAM-BiLSTM), while maintaining lightweight characteristics (2.04M parameters, 3.90 GFLOPs) for urban edge deployment. Moreover, Chebyshev polynomial weight visualization enables quantitative interpretation of variable contributions (e.g., solar irradiance dominates via low-order polynomials), enhancing model transparency for engineering applications. This research provides a lightweight, accurate, and interpretable forecasting solution, offering policymakers a data-driven tool to optimize urban PV-infrastructure integration and improve grid resilience amid the global energy transition. Full article

Despite growing interest in positive-energy and net-zero-energy buildings (NZEBs), few studies have addressed the integration of biobased construction with building-integrated photovoltaics (BIPV) under hot–dry climate conditions, particularly in Morocco and North Africa. This study fills this gap by presenting a simulation-based evaluation of energy performance and renewable energy integration strategies for a residential building in the Fes-Meknes region. Two structural configurations were compared using dynamic energy simulations in DesignBuilder/EnergyPlus, that is, a conventional concrete brick model and an eco-constructed alternative based on biobased wooden materials. Thus, the wooden construction reduced annual energy consumption by 33.3% and operational CO₂ emissions by 50% due to enhanced thermal insulation and moisture-regulating properties. Then multiple configurations of the solar energy systems were analysed, and an optimal hybrid off-grid hybrid system combining rooftop photovoltaic, BIPV, and lithium-ion battery storage achieved a 100% renewable energy fraction with an annual output of 12,390 kWh. While the system incurs a higher net present cost of $45,708 USD, it ensures full grid independence, lowers the electricity cost to $0.70/kWh, and improves occupant comfort. The novelty of this work lies in its integrated approach, which combines biobased construction, lifecycle-informed energy modelling, and HOMER-optimised PV/BIPV systems tailored to a hot, dry climate. The study provides a replicable framework for designing NZEBs in Morocco and similar arid regions, supporting the low-carbon transition and informing policy, planning, and sustainable construction strategies. Full article

Various maximum power point tracking (MPPT) techniques have been proposed to optimize the efficiency of solar photovoltaic (PV) systems. These techniques differ in several aspects such as design simplicity, convergence speed, implementation types (analog or digital), decision optimal point accuracy, effectiveness range, hardware costs, and algorithmic modes. Choosing the most suitable MPPT controller is crucial in PV system design, as it directly impacts the overall cost of PV solar modules. This paper presents a comprehensive exploration of 64 MPPT techniques for PV solar systems, covering optimization, traditional, intelligent, and hybrid methodologies. A comparative analysis of these techniques, considering cost, tracking speed, and system stability, indicates that hybrid approaches exhibit higher efficiency albeit with increased complexity and cost. Amidst the existing PV system review literature, this paper serves as an updated comprehensive reference for researchers involved in MPPT PV solar system design. Full article

South Africa’s persistent electricity shortages and recurrent load shedding remain among the most pressing challenges to national economic growth and social stability. This paper presents a techno-economic framework to assess how optimized deployment of photovoltaic (PV) and battery energy storage systems (BESSs) can mitigate these disruptions under realistic grid and regulatory constraints. Despite recent operational improvements at Eskom—including a 10-month period without load shedding in 2024—energy insecurity persists due to aging coal assets, limited transmission capacity, and slow renewable integration. Using hourly demand and solar-resource data for 2023, combined with Eskom’s load-reduction records, a Particle Swarm Optimization (PSO) model identifies cost-optimal hybrid system configurations that minimize the Levelized Cost of Electricity (LCOE) while maximizing coverage of unserved energy. Three deployment scenarios are analyzed: (i) constrained regional grid capacity, (ii) flexible redistribution of capacity across six provinces, and (iii) unconstrained national deployment. Results indicate that constrained deployment covers about 86% of curtailed load at 1.88 USD kWh⁻¹, whereas flexible and unconstrained scenarios achieve over 99% coverage at ≈0.58 USD kWh⁻¹. The findings demonstrate that targeted PV–BESS expansion, coupled with selective grid reinforcement, can effectively eliminate load shedding and accelerate South Africa’s transition toward a resilient, low-carbon electricity system. Full article

Unmanned aerial vehicles (UAVs) are increasingly utilized across civilian and defense sectors due to their versatility, efficiency, and cost-effectiveness. However, their operational endurance remains constrained by limited onboard energy storage. Recent research has focused on electric propulsion systems integrated with hybrid energy sources, particularly the combination of solar cells and advanced battery technologies to overcome this limitation. This review presents a comprehensive analysis of the latest advancements in electric propulsion architecture, solar-based power integration, and hybrid energy management strategies for UAVs. Key components, including motors, electronic speed controllers (ESCs), propellers, and energy storage systems, are examined alongside emerging technologies such as wireless charging and flexible photovoltaic (PV) materials. Power management techniques, including maximum power point tracking (MPPT) and intelligent energy control algorithms, are also discussed in the context of long-endurance missions. Challenges related to energy density, weight constraints, environmental adaptability, and component integration are highlighted, with insights into potential solutions and future directions. The findings of this review aim to guide the development of efficient, sustainable, and high-endurance UAV platforms leveraging electric-solar hybrid propulsion systems. Full article