Search Results (275)

Hydrogen offers an effective pathway for the large-scale storage of renewable energy. For a tourist city located in a plateau region rich in renewable energy, hydrogen shows great potential for reducing carbon emissions and utilizing uncertain renewable energy. Herein, the wind–solar–hydrogen stand-alone and grid-connected systems in the plateau tourist city of Lijiang City in Yunnan Province are modeled and techno-economically evaluated by using the HOMER Pro software (version 3.14.2) with the multi-criteria decision analysis models. The system is composed of 5588 kW solar photovoltaic panels, an 800 kW wind turbine, a 1600 kW electrolyzer, a 421 kWh battery, and a 50 kW fuel cell. In addition to meeting the power requirements for system operation, the system has the capacity to provide daily electricity for 200 households in a neighborhood and supply 240 kg of hydrogen per day to local hydrogen-fueled buses. The stand-alone system can produce 10.15 × 10⁶ kWh of electricity and 93.44 t of hydrogen per year, with an NPC of USD 8.15 million, an LCOE of USD 0.43/kWh, and an LCOH of USD 5.26/kg. The grid-connected system can generate 10.10 × 10⁶ kWh of electricity and 103.01 ton of hydrogen annually. Its NPC is USD 7.34 million, its LCOE is USD 0.11/kWh, and its LCOH is USD 3.42/kg. This study provides a new solution for optimizing the configuration of hybrid renewable energy systems, which will develop the hydrogen economy and create low-carbon-emission energy systems. Full article

This study presents preliminary findings from an experimental campaign conducted on a pilot-scale green hydrogen production plant powered by a photovoltaic (PV) system. The integrated setup, implemented at the University “Mediterranea” of Reggio Calabria, includes renewable energy generation, hydrogen production via electrolysis, on-site storage, and reconversion through fuel cells. The investigation assessed system performance under different configurations (on-grid and selective stand-alone modes), focusing on key operational phases such as inerting, purging, pressurization, hydrogen generation, and depressurization. Results indicate a strong linear correlation between the electrolyser’s power setpoint and the pressure rise rate, with a maximum gradient of 0.236 bar/min observed at 75% power input. The system demonstrated robust and stable operation, efficient control of shutdown sequences, and effective integration with PV input. These outcomes support the technical feasibility of small-scale hydrogen systems driven by renewables and offer valuable reference data for calibration models and future optimization strategies. Full article

Standalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duration solutions. Green hydrogen, generated via proton exchange membrane (PEM) electrolyzers, offers a scalable alternative. This study proposes a stochastic energy management framework that leverages a Markov decision process (MDP) to coordinate PV generation, battery storage, and hydrogen production under variable irradiance and uncertain load demand. The strategy dynamically allocates power flows, ensuring system stability and efficient energy utilization. Real-time weather data from Goiás, Brazil, is used to simulate system behavior under realistic conditions. Compared to the conventional perturb and observe (P&O) technique, the proposed method significantly improves system performance, achieving a $99.9\%$ average efficiency (vs. 98.64%) and a drastically lower average tracking error of 0.3125 (vs. 9.8836). This enhanced tracking accuracy ensures faster convergence to the maximum power point, even during abrupt load changes, thereby increasing the effective use of solar energy. As a direct consequence, green hydrogen production is maximized while energy curtailment is minimized. The results confirm the robustness of the MDP-based control, demonstrating improved responsiveness, reduced downtime, and enhanced hydrogen yield, thus supporting sustainable energy conversion in off-grid environments. Full article

This study presents the design and techno-economic comparison of two standalone photovoltaic (PV) systems, each supplying a 1 kW critical load with 100% reliability under Cairo’s climatic conditions. These systems are modeled for both the constant and the night load scenarios, accounting for the worst-case weather conditions involving 3.5 consecutive cloudy days. The primary comparison focuses on traditional lead-acid battery storage versus green hydrogen storage via electrolysis, compression, and fuel cell reconversion. Both the configurations are simulated using a Python-based tool that calculates hourly energy balance, component sizing, and economic performance over a 21-year project lifetime. The results show that the PV/H₂ system significantly outperforms the PV/lead-acid battery system in both the cost and the reliability. For the constant load, the Levelized Cost of Electricity (LCOE) drops from 0.52 USD/kWh to 0.23 USD/kWh (a 56% reduction), and the payback period is shortened from 16 to 7 years. For the night load, the LCOE improves from 0.67 to 0.36 USD/kWh (a 46% reduction). A supplementary cost analysis using lithium-ion batteries was also conducted. While Li-ion improves the economics compared to lead-acid (LCOE of 0.41 USD/kWh for the constant load and 0.49 USD/kWh for the night load), this represents a 21% and a 27% reduction, respectively. However, the green hydrogen system remains the most cost-effective and scalable storage solution for achieving 100% reliability in critical off-grid applications. These findings highlight the potential of green hydrogen as a sustainable and economically viable energy storage pathway, capable of reducing energy costs while ensuring long-term resilience. Full article

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however, uncertainty remains about optimal grid architectures to minimize cost, including how and when to incorporate long-duration energy storage. This study implements a novel, multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue, which has already successfully deployed solar photovoltaics, wind turbines, and battery storage systems. Using real community load, resource, and generation data, we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation, considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs, but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase, but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue, we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability, sustainability, and security. Full article

The need for electrical energy is dramatically increasing, pushing researchers and industrial communities towards the development and improvement of microgrids (MGs). It also encourages the use of renewable energies to benefit from available sources. Thereby, the implementation of a photovoltaic (PV) system with a hybrid energy storage system (HESS) can create a standalone MG. This paper presents an MG that uses photovoltaic energy as a principal source. An HESS is required, combining batteries and supercapacitors. This MG responds “insure” both alternating current (AC) and direct current (DC) loads. The batteries and supercapacitors have separate parallel connections to the DC bus through bidirectional converters. The DC loads are directly connected to the DC bus where the AC loads use a DC-AC inverter. A control strategy is implemented to manage the fluctuation of solar irradiation and the load variation. This strategy was implemented with a new logic control based on Boolean analysis. The logic analysis was implemented for analyzing binary data by using Boolean functions (‘0’ or ‘1’). The methodology presented in this paper reduces the stress and the faults of analyzing a flowchart and does not require a large concentration. It is used in this paper in order to simplify the control of the EMS. It permits the flowchart to be translated to a real application. This analysis is based on logic functions: “Or” corresponds to the addition and “And” corresponds to the multiplication. The simulation tests were executed at T_au  =  6 s of the low-pass filter and conducted in 60 s. The DC bus voltage was 400 V. It demonstrates that the proposed management strategy can respond to the AC and DC loads. Full article

Solar energy is a promising and sustainable green energy source, showing significant advancements in photovoltaic (PV) system deployment. To maximize PV efficiency, robust maximum power point tracking (MPPT) methods are essential, as the maximum power point (MPP) shifts with changing irradiance and temperature. This paper proposes a novel MPPT control strategy for a 100 kW grid-connected PV system, based on the incremental conductance (IC) method and enhanced by a cascaded Fractional Order Proportional–Integral (FOPI) and conventional Proportional–Integral (PI) controller. The controller parameters are optimally tuned using the recently introduced RUNge Kutta optimizer (RUN). MATLAB/Simulink simulations have been conducted on the 100 kW benchmark PV model integrated into a medium-voltage grid, with the objective of minimizing the integral square error (ISE) to improve efficiency. The performance of the proposed IC-MPPT-(FOPI-PI) controller has been benchmarked against standalone PI and FOPI controllers, and the RUN optimizer is here compared with recent metaheuristic algorithms, including the Gorilla Troops Optimizer (GTO) and the African Vultures Optimizer (AVO). The evaluation covers five different environmental scenarios, including step, ramp, and realistic irradiance and temperature profiles. The RUN-optimized controller achieved exceptional performance with 99.984% tracking efficiency, sub-millisecond rise time (0.0012 s), rapid settling (0.015 s), and minimal error (ISE: 16.781), demonstrating outstanding accuracy, speed, and robustness. Full article

Photovoltaic systems are increasingly recognized as one of the most advanced, efficient, and rapidly developing methods of electricity generation, utilizing the limitless potential of solar radiation while offering environmentally sustainable solutions to contemporary energy challenges. However, despite their clear benefits, issues such as high initial investment costs and relatively low energy efficiency must be carefully addressed during the design phase. Key considerations include the quantity and type of panels, battery capacity and number, environmental conditions, site-specific factors, and the mathematical models and interconnection strategies of system components. This study proposes a two-stage optimization approach for standalone photovoltaic systems, employing three distinct optimization algorithms—NSGA-II, DEMO, and Particle Swarm Optimization—to minimize both the Loss of Load Probability (LLP) and the life cycle cost (LCC). In the second stage, optimal solutions from the Pareto front are evaluated using three multi-criteria decision-making techniques: the hybrid AHP-TOPSIS method, VIKOR, and PROMETHEE. The proposed framework is applied to systems with storage batteries designed for deployment in three Cypriot cities, aiming to meet energy demands of 10, 15, and 20 kWh. The findings reveal a strong correlation between economic and energy performance and the degree of load coverage, with the combination of the DEMO algorithm and the AHP-TOPSIS method emerging as the most effective solution. Full article

This study presents a preliminary analysis of an innovative system that combines indoor air conditioning with water recovery and storage. The device integrates Peltier cells with a horizontal Earth-to-Air Heat Exchanger (EAHX), exploiting the ground stable temperature to enhance cooling and promote condensation. Warm, humid air is pre-cooled via the geothermal pipe, then split by a fan into two streams: one passes over the cold side of the Peltier cells for cooling and dehumidification, while the other flows over the hot side and heats up. The two airstreams are then mixed in a water storage tank, which also serves as a thermal mixing chamber to regulate the final air temperature. The analysis investigates the influence of soil thermal conditions on condensation within the horizontal pipe and the resulting cooling effect in indoor spaces. A hybrid simulation approach was adopted, coupling a 3D model implemented in COMSOL Multiphysics^® with a 1D analytical model. Boundary conditions and meteorological data were based on the Typical Meteorological Year (TMY) for Palermo. Two scenarios were considered. In Case A, during the hours when air conditioning is not operating (between 11 p.m. and 9 a.m.), air is circulated in the exchanger to pre-cool the ground and the air leaving the exchanger is rejected into the environment. In Case B, the no air is not circulated in the heat exchanger during non-conditioning periods. Results from the June–August period show that the EAHXs reduced the average outdoor air temperature from 27.81 °C to 25.45 °C, with relative humidity rising from 58.2% to 66.66%, while maintaining nearly constant specific humidity. The system exchanged average powers of 102 W (Case A) and 96 W (Case B), corresponding to energy removals of 225 kWh and 212 kWh, respectively. Case A, which included nighttime soil pre-cooling, showed a 6% increase in efficiency. Condensation water production values range from around 0.005 g/s with one Peltier cell to almost 0.5 g/s with seven Peltier cells. As the number of Peltier cells increases, the cooling effect becomes more pronounced, reducing the output temperature considerably. This solution is scalable and well-suited for implementation in developing countries, where it can be efficiently powered by stand-alone photovoltaic systems. Full article

This paper presents a comprehensive optimization framework for modern distribution systems, integrating distribution system reconfiguration (DSR), soft open point (SOP) operation, photovoltaic (PV) allocation, and energy storage system (ESS) management to minimize daily active power losses. The proposed approach employs a novel hybrid metaheuristic algorithm, the Cheetah-Grey Wolf Optimizer (CGWO), which synergizes the global exploration capabilities of the Cheetah Optimizer (CO) with the local exploitation strengths of Grey Wolf Optimization (GWO). The optimization model addresses time-varying loads, renewable generation profiles, and dynamic network topology while rigorously enforcing operational constraints, including radiality, voltage limits, ESS state-of-charge dynamics, and SOP capacity. Simulations on a 33-bus distribution system demonstrate the effectiveness of the framework across eight case studies, with the full DER integration case (DSR + PV + ESS + SOP) achieving a 67.2% reduction in energy losses compared to the base configuration. By combining the global exploration of CO with the local exploitation of GWO, the hybrid CGWO algorithm outperforms traditional techniques (such as PSO and GWO) and avoids premature convergence while preserving computational efficiency—two major drawbacks of standalone metaheuristics. Comparative analysis highlights CGWO’s superiority over standalone algorithms, yielding the lowest energy losses (997.41 kWh), balanced ESS utilization, and stable voltage profiles. The results underscore the transformative potential of coordinated DER optimization in enhancing grid efficiency and reliability. Full article

This study presents the development and evaluation of a stand-alone solar dryer designed to improve the efficiency of bee bread dehydration. Unlike the electric prototype powered by conventional energy sources, the proposed system operates autonomously, utilizing solar energy as the primary drying agent. The drying chamber is equipped with solar collectors located in its lower section, which ensure convective heating of the product. Active convection is generated by a set of fans powered by photovoltaic panels, maintaining the drying agent’s temperature near 42 °C. The research methodology integrates both numerical simulation and experimental investigation. Simulations focus on the variations in temperature (288–315 K) and relative humidity (1–1.5%) within the honeycomb structure under convective airflow. Experimental trials examine the relationship between moisture content and variables such as bee bread mass, airflow rate, number of frames (5–11 units), and drying time (2–11 h). A statistically grounded analysis based on an experimental design method was conducted, revealing a reduction in moisture content from 16.2–18.26% to 14.1–15.1% under optimized conditions. Linear regression models were derived to describe these dependencies. A comparative assessment using enthalpy–humidity (I–d) diagrams demonstrated the enhanced drying performance of the solar dryer, which is attributed to its cyclic operation mode. The results confirm the potential of the developed system for sustainable and energy-efficient drying of bee bread in decentralized conditions. Full article

Solar photovoltaic (PV) and wind power generation are key contributors to the integration of renewable energy into modern power systems. The intermittent and variable nature of these renewables has a substantial impact on the power system’s reliability. In time-series simulation studies, inaccuracies in solar irradiation and wind speed parameters can lead to unreliable evaluations of system reliability, ultimately resulting in flawed decision making regarding the investment and operation of energy systems. This paper investigates the reliability deviation due to modeling uncertainties in a 100% renewable-based system. This study employs two methods to assess and contrast the reliability of a standalone microgrid (SMG) system in order to achieve this goal: (i) random uncertainty within a selected confidence interval and (ii) splitting the cumulative distribution function (CDF) into five regions of equal probability. In this study, an SMG system is modeled, and loss of load probability (LOLP) is evaluated in both approaches. Six different sensitivity analysis studies, including annual load demand growth, are performed. The results from the simulations demonstrate that the suggested methods can estimate the reliability of a microgrid powered by renewable energy sources, as well as its probability of reaching certain levels of reliability. Full article

Eliminating fossil fuel as early as possible and electrifying everything by green sustainable electric power are some of the primary solutions for tackling the climate emergency. Solar electricity generated by photovoltaics is now the cheapest source of electric power generation. The cost of electrochemical storage batteries is plummeting and the combination of photovoltaics and batteries at a utility scale can provide a lower cost than electrical power generated by fossil fuel in many parts of the world. Thus, today, we nearly have a solution in sustainable green electrical power generation and storage. Globally, we have adopted alternating-current electric power infrastructure over direct-current power due to the invention of the transformer. However, due to the advancements in power electronics and loads based on semiconductors, the situation is totally different today. Except for induction motors running at rated speed, all loads using variable-frequency drive are direct-current loads. Photovoltaics, batteries, and virtually all loads are based on direct-current power. Considering power generation, transmission, distribution, and utilization as a single entity, we are wasting a large amount of power by using our alternating-current power electricity infrastructure. By using end-to-end direct-current power networks, we can save energy and capital investment in electricity infrastructure as well as the cost of the loads, as compared to the existing power infrastructure. Based on an end-to-end direct-current power network, a new concept for community solar is proposed in this paper. Without connecting to the existing grid, community solar can provide 24 × 7 electric power to residents. The proposed infrastructure concept can also have a transformative role in areas which are providing very high growth of green electric power. This concept can have an immediate profound impact on any new constructions in most parts of the world. Full article

The integration of photovoltaic (PV) systems in buildings is crucial for reducing reliance on conventional energy sources while promoting sustainability. This study evaluates and compares three energy generation systems: rooftop PV, building-integrated photovoltaics (BIPV), and a hybrid combination of both. The analysis covers energy production, economic feasibility through the levelized cost of electricity (LCOE), and environmental impact by assessing unreleased carbon dioxide (UCD). A residential building in Kerman, Iran, serves as the case study. The results indicate that rooftop PV exhibits the lowest LCOE at USD 0.023/kWh, while BIPV has a higher LCOE of USD 0.077/kWh due to installation complexities. The hybrid system, combining both technologies, achieves a balance with an LCOE of USD 0.05/kWh while maximizing energy generation at 16.2 MWh annually. Additionally, the hybrid system reduces CO₂ emissions by 9.7 tons per year, surpassing the standalone rooftop PV (5.0 tons) and BIPV (4.7 tons). The findings highlight the synergistic benefits of integrating both PV systems, ensuring higher self-sufficiency and enhanced environmental impact. This research underscores the necessity of comprehensive urban energy planning to optimize renewable energy utilization and accelerate the transition toward zero-energy buildings. Full article

This study presents analysis and optimization of a standalone hybrid renewable energy system (HRES) for Adama Science and Technology University’s ICT center in Ethiopia. The proposed hybrid system combines photovoltaic panels, wind turbines, a battery bank, and a diesel generator to ensure reliable and sustainable power. The objectives are to minimize the system’s total annualized cost and loss of power supply probability, while energy reliability is maintained. To optimize the component sizing and energy management strategy of the HRES, we formulated a mathematical model that incorporates the variability of renewable energy and load demand. This optimization problem is solved using a hybrid genetic algorithm (HGA). Simulation results indicate that the HGA yielded the best solution, characterized by the levelized cost of energy of USD 0.2546/kWh, the loss of power supply probability of 0.58%, and a convergence time of 197.2889 s. Full article