Renewable Energy Systems Research Articles

Abstract The sustainability of the electricity market relies on enhanced forecasting of renewable energy sources combined with energy storage arbitrage. This paper introduces a bi-level approach with a novel contribution to wind farms and energy storage to generate maximum revenue in the day-ahead (DA) electricity market. In the first stage, the maximum revenue of the wind farm is estimated by forecasting wind power and market prices. The second stage models the optimal energy arbitrage strategy for storage devices. To solve the first problem, a well-organized Long-Short-Term-Memory (LSTM) combined with recurrent neural network (RNN)—Adam optimizer model is used, and for the second level by using Monte-Carlo optimization, the superior frontier of the revenue is estimated with locational based marginal prices (LBMPs). The solution to the second-level problem delivers a solitary value of the foremost boundary of the revenue and the equivalent charging/discharging schedule. An effective and feasible solution for improving the financial performance of renewable energy and storage systems in the DA market is offered by the suggested framework, which presents a novel approach to incorporating analytical prediction and optimization methodologies.

A hybrid renewable energy system utilizes an SI engine fueled by a syngas–biogas–hydrogen mixture with a wide range of compositional variations. Due to the significantly different stoichiometric air/fuel (A/F) ratios of these fuels, conventional fuel supply systems cannot meet the engine's operational requirements. Simulation results indicate that a 4 mm injector is suitable for biogas/hydrogen but not for syngas, whereas a 9 mm injector is appropriate for syngas but unsuitable for the other fuels. To handle the large variations in composition, a twinning injector system, consisting of two injectors, has been proposed. When the syngas content is low, only one injector operates; as the syngas proportion increases, the second injector is activated. This paper presents the simulation results of the ECU-controlled twinning injector system, along with experimental validation on a Honda GX390 engine.

This research presents a transformative approach to precision current measurement in modern energy systems through the development of high-accuracy nanocrystalline core current transformers (TVT type, accuracy classes 0.2/0.2S). Addressing critical limitations of conventional ferrite and silicon-steel transformers—such as excessive hysteresis losses (>2%) and poor low-current accuracy—the proposed design leverages nanocrystalline alloys (Fe-Si-B-Nb-Cu) to achieve unprecedented metrological performance: ≤0.2% error even at 5% rated current, 38% lower hysteresis losses, and >50,000 relative permeability for enhanced linearity across dynamic loads (20–120% In). Prototypes, validated at Technology Readiness Level (TRL) 6, comply with IEC 61869-10 and GOST R 57462-2021 standards, demonstrating durability in 2,000-hour accelerated aging tests (−40°C to +85°C). The transformers integrate digital interfaces (RS-485/Modbus) for real-time data acquisition, enabling seamless fusion with AI-driven grid management systems. In simulated smart grid environments, this integration reduced renewable-induced instability by 22% during cloudy-day PV intermittency through LSTM-based power forecasting and adaptive load balancing. Supported by a 322-million-tenge grant and industrial co-financing, the technology bridges material innovation with Industry 4.0 demands, offering 15–20% higher system-level efficiency compared to legacy designs. Its compact, thermally stable architecture ensures scalability for applications ranging from distributed renewable energy systems to HVDC metering. By resolving long-standing gaps in low-current metrology and digital interoperability, this work positions nanocrystalline-core CTs as foundational components for next-generation smart grids, while contributing to Kazakhstan’s strategic leadership in energy technology.

The project aims at applying Model Predictive Control (MPC) to increase the power quality of grid-tied inverters connected to photovoltaic (PV) and wind systems. The presence of increased renewable energy on the power grid has led to an increased possibility of occurrence of harmonic distortion, voltage variations and a decline in quick response that may compromise the grid and violate power quality norms such as IEEE 519. Conventional controllers like proportional--integral (PI) and hysteresis control cannot always produce the correct result in a response to system dynamics and various properties of systems. These problems are eliminated in the framework which predicts what will take place in the system and switch the inverter appropriately to minimize a given cost function. With this solution, harmonics rejection and efficient current monitoring as well as rapid adaptation to grid issues and changed demand are realized. Simulation study In MATLAB / Simulink, the comparison of the proposed MPC controller against conventional control methods was achieved through simulation with different irradiance variations and loads being switched on / off. The results state that MPC-based inverter control introduces THD of less than 3%, ensures that the voltage remains in a range of +/- 2 percent of the nominal value and enables quicker transient responses, renovating renewable energy systems more dependable and compliant. Besides, MPC enables safety and control properties that makes it flexible to suit the future needs in smart grids. This method brings a practical solution that highly benefits a power grid to operate normally, provide high power quality and operate effectively with increased renewable energy, promoting the usage of intelligent inverters within the power supply system.

Due to the rapid development of intermittent renewables and emergence of new types of load, flexibility becomes a crucial element for reliable and cost-effective power system operation. This paper proposes a dual-index flexibility evaluation metric (incorporating flexibility margin and insufficiency probability) that considers the dynamic supply-demand balance under net load uncertainty. Additionally, a robust two-stage power planning model is presented to enhance system flexibility, utilizing the proposed metric. The model is solved iteratively using the column generation algorithm and strong duality theory. Case studies on a Northeast China power grid demonstrate that, by optimally configuring generation and storage capacity guided by flexibility and other indicators, the proposed method reduces curtailment/load shedding costs and system flexibility insufficiency probability by 45% and 4.3% respectively. Furthermore, incorporating energy storage planning achieves additional significant reductions of 27% and 1.1% in these metrics, verifying its effectiveness. Comparative analysis confirms the superiority of the proposed robust-probabilistic hybrid model over traditional uncertainty quantification methods in balancing computational efficiency, risk control, and curtailment reduction.

This study evaluates the performance of a Solar-Wind-Biomass Hybrid Renewable Energy System (SWB-HRES) optimized for Hoa Bac conditions. The system comprises a 15 kW solar panel, 9 kW wind turbine, 8.3 kW syngas generator, 20 kW electrolyzer, 24 kW converter, and a 1 kg hydrogen storage tank. It supplies 7,300 kWh/year of electricity and produces 1,183 kg/year of hydrogen. When integrated with a hydrogen production grid, the solar-biomass (SB-H2) configuration demonstrates superior economic and environmental performance, offering double the profit and half the payback period compared to the wind-biomass (WB-H2) option. The economic viability of hydrogen production matches that of grid electricity sales when hydrogen is priced at $4.5/kg (non-continuous engine operation) or $5/kg (intermittent operation). Incorporating biomass significantly reduces greenhouse gas emissions: while a solar-wind system without hydrogen production cuts 33 tons CO₂-eq/year, the solar-wind-biomass system with hydrogen production achieves a reduction of 217 tons CO₂-eq/year.

Green architecture, also known as "sustainable architecture" or "green building," is a multi-aspect method of approaching the idea generation, scientific analysis, and design of the built environment where environmental sensitivity, resource conservation, and energy efficiency are indivisibly woven together.This review discusses the evolution, principles, and benefits of sustainable architecture, enumerating its use in solving environmental, social, and economic challenges of contemporary construction practices. The evolution from conventional energy utilization to the environmentalist movement of the twentieth century is discussed, along with it marking a transition towards green building technology, particularly post-1970s energy crisis. The key characteristics of green architecture, water conservation, energy efficiency, sustainable materials, indoor environmental quality, and waste management are covered together with their application through technologies like renewable energy systems, rainwater harvesting, and recycled materials. The research also identifies the visual value of green designs, reconciling Biophilic principles and indigenous materials to bring buildings into harmony with their environments (Tabb & Deviren, 2017). Green buildings are examined for social, economic, and environmental advantages, with findings indicating high energy and water conservation, healthier occupants, and more ecological balance. This research reiterates the role of sustainable architecture in playing a part in achieving the Sustainable Development Goals, pushing for ongoing innovation and cooperation.

Abstract Background Energy hardship can be broadly understood as a situation in which individuals or households are unable to afford basic energy services necessary for sustaining their wellbeing. Despite conceptual challenges and overlaps with similar narratives (e.g., fuel poverty), the literature on energy hardship continues to expand. It represents a critical intersection with sustainable energy systems that reveals both challenges and opportunities in the transition towards clean energy solutions. However, few energy hardship programmes have been examined from a policy perspective. Our study aims to address this knowledge gap by providing a systematic analysis of a sample of 67 energy hardship programmes implemented across Australia, Canada, the United Kingdom, the United States, and more than 20 European countries. Guided by specific research questions and supported by directed content analysis, we focus on five areas: dominant policy rationales, main policy goals, supportive policy instruments, stakeholders, and key performance indicators (KPIs). Results Despite an important degree of heterogeneity among the reviewed programmes, findings revealed commonalities across significant design and implementation areas. Policy rationales often rely on two significant pillars: narratives related to energy poverty (and related concepts), and market barriers and failures related to energy efficiency or decentralised renewable energy systems. Policy goals encompass three distinct areas: finance, knowledge, and technology/infrastructure. Policy instruments supporting energy hardship programmes are predominantly economic in nature. However, the review of programmes reveals a significant gap in robust estimates of cost-effectiveness or economic efficiency. Results also show that the design and implementation of programmes often involve a diversity of stakeholders. The review reveals that there is an abundance of KPIs that can (potentially) support the monitoring and assessment of programmes. Conclusions Overall, our study reveals significant policy lessons regarding the links, dynamics, and complexities associated with the design and implementation of energy hardship programmes. It underscores the importance of evidence-based evaluations to enhance the ability of policymakers and managers to effectively alleviate the suffering of those facing energy hardship. Results can be of particular interest to countries where policy discussions about energy hardship are emerging, and where there is a need for knowledge to inform decision-making on future programmes that support just and inclusive clean energy transitions.

Recently, there has been a surge in the popularity of renewable energy systems due to their lucrative and sustainable attributes. Among these, photovoltaic (PV) systems stand out as prominent examples. Nevertheless, it is imperative to ascertain the management of waste produced by these systems in order to mitigate environmental pollution and harness their economic potential. This study aims to assess the present status and forecast the accumulation of waste generated by PV power plants in Chile. Utilizing openly available public data, a database is constructed to track the accumulation of waste. Two scenarios, namely, early-loss and regular-loss scenarios are employed to estimate the projected accumulation of PV waste. The findings indicate that by the years 2035 and 2043, the accumulation of waste is estimated to reach 100,000 tons under the early-loss scenario and regular-loss scenario. The total anticipated waste from solar PV modules is projected to be 284,906 tons, with c-Si PV modules contributing 175,595 tons to this total in Chile. Remarkably, it is determined that more than 235,000 tons of materials from this waste is recoverable, amounting to nearly USD 781 million in economic value. Silver is projected to bring the most economic value, with nearly USD 379 million, while lead, tin, cadmium, and zinc are each valued at less than USD 1 million. This study highlights the importance of promoting the sustainable development of PV systems, particularly in alignment with Sustainable Development Goals 7 (Affordable and Clean Energy) and 13 (Climate Action). Future research is expected to place greater emphasis on eco-design approaches in PV module production.

Because of its high energy content, adaptability, and clean burning characteristics, hydrogen has become a promising energy source for a cleaner and more sustainable future. This study investigates the possibilities for hydrogen production from Egypt’s renewable power plant’s hybrid energy system. Four distinct sites were built up using HOMER Pro software to investigate off-grid combinations of photovoltaic solar, wind turbines, and batteries in order to minimize energy costs, carbon emissions, levelized hydrogen costs, and net current costs. The best system configuration was implemented in Alexandria and had the lowest net present cost of $24,735. The ideal system values are $0.109/kWh and $200.14/kg for the cost of energy and cost of hydrogen, respectively. A yearly reduction of 12,017 kg in carbon dioxide emissions and an annual output of 121,367 kg of green hydrogen are achieved by the optimized system. If sensitivity analysis is applied to increasing wind velocity, the net present value and energy cost will decrease to $0.0598/kWh and $15,354, respectively. When the best solar–wind and diesel generator (DG) scenarios are compared, it is found that the solar–wind scenario is best for the suggested system since the net present cost of the DG is $122,870, and the energy cost is $0.509/kWh. The payback period of the proposed system is 7.5 years. By connecting the grid and delivering the extra electricity generated by renewable source, these costs can be further decreased. Green hydrogen has the potential to revolutionize the energy sector for hard-to-decarbonize businesses which helps secure a sustainable energy future.

This study models different hybrid systems based on renewable energies that can be supported by diesel generators to meet the energy needs of isolated homes in the Canary Islands. The research will cover the energy requirements of a residential house, including the production of fresh water using a reverse osmosis desalination plant. The system is designed to operate independently of the electrical grid. The HOMER software package was used to model and optimize the hybrid systems. The model was fed with data on the electrical demands of residential homes (including the consumption by the small reverse osmosis desalination plant) as well as the technical specifications of the various devices and renewable energy sources, such as solar radiation and wind speed potentials. The software considers various configurations to optimize hybrid systems, selecting the most suitable one based on the available renewable energy sources at the selected location. The data used in the research were collected on the eastern islands of the Canary Islands (Gran Canaria, Lanzarote and Fuerteventura). Based on the system input parameters, the simulation and optimization performed in HOMER, taking into account the lowest “Levelized Cost of Energy”, it can be concluded that the preferred hybrid renewable energy system for this region is a small wind turbine with a nominal power of 1.9 kW, eight batteries, and a small diesel generator with a nominal power of 1.0 kW. The knowledge from this research could be applied to other geographical areas of the world that have similar conditions, namely a shortage of water and plentiful renewable energy sources.

With growing urban populations and rapid technological advancement, major cities worldwide are facing pressing challenges from surging energy demands. Interestingly, substantial unused space within residential buildings offers potential for installing renewable energy systems coupled with energy storage. This study innovatively proposes a grid-connected photovoltaic (PV) system integrated with pumped hydro storage (PHS) and battery storage for residential applications. A novel optimization algorithm is employed to achieve techno-economic optimization of the hybrid system. The results indicate a remarkably short payback period of about 5 years, significantly outperforming previous studies. Additionally, a threshold is introduced to activate pumping and water storage during off-peak nighttime electricity hours, strategically directing surplus power to either the pump or battery according to system operation principles. This nighttime water storage strategy not only promises considerable cost savings for residents, but also helps to mitigate grid stress under time-of-use pricing schemes. Overall, this study demonstrates that, through optimized system sizing, costs can be substantially reduced. Importantly, with the nighttime storage strategy, the payback period can be shortened even further, underscoring the novelty and practical relevance of this research.

ABSTRACT This study presents an advanced load and energy management system for a grid-connected hybrid energy system (HES) supplying hospital loads. The proposed configuration integrates solar, wind, battery storage, and grid power to ensure uninterrupted supply to critical, emergency, and non-critical hospital loads. To optimize energy dispatch under India’s Time-of-Day (ToD) pricing scheme, a novel Hybrid Firefly Genetic Algorithm (HFAGA) is developed. The HFAGA optimizes the power transmission ratio, balancing battery charging, load supply, and grid interaction to maximize cost efficiency. Performance is benchmarked against the Ant Lion Optimization (ALO) algorithm and conventional load management strategies using MATLAB simulations with real-world weather and load profiles. Results demonstrate that HFAGA achieves a 30.4% daily cost reduction compared to conventional methods and outperforms ALO, which achieves a 27.6% reduction. Additionally, a 150 W experimental prototype using Delta HMI and a PIC microcontroller validates the system’s operational effectiveness in real-time conditions. The proposed HFAGA-based system offers a robust, economically viable solution for energy management in critical healthcare infrastructure.

ABSTRACT This paper introduces an innovative high-gain DC–DC converter designed for 100 W applications to support tri-hybrid renewable energy systems comprising solar, wind, and underwater turbines. The conventional DC–DC converters struggle to achieve high-voltage gain without compromising efficiency and component lifespan, which can merely support single or dual input sources topologies, limiting their application in the hybrid renewable energy systems. The proposed converter integrates switched inductor and capacitor voltage-boosting techniques, achieving a voltage gain of 14 at a duty cycle of 0.5, with an efficiency of 92.90%. It effectively operates across a duty cycle range of 0.35 to 0.66, an input voltage range of 12 V to 96 V, and resistive loads between 1.3 kΩ and 3.5 kΩ. Furthermore, the converter maintains a stable output voltage through the integration of an external battery storage system. The simulation and experimental studies validate the converter’s performance across varying power levels, while experimental results at low power closely match the simulation data, confirming the converter’s effectiveness under various conditions. The converter exhibits a cost-to-voltage ratio of 0.324, demonstrating superior economic feasibility compared to existing topologies. In general, the converter provides a scalable-solution for hybrid renewable energy with multiple-input, high-gain, cost-efficient, and smaller installation areas.

The global energy landscape is undergoing a critical transformation driven by the urgent need to mitigate climate change, reduce greenhouse gas (GHG) emissions, and ensure long-term energy security [...]

This study presents deep learning models that are frequently used in the literature for the detection and classification of damage types in wind turbines and a new deep learning model (SatNET) that offers computational efficiency and rapid inference. Wind turbines, which are critical components of renewable energy systems, are sensitive to various damages (paint damage, erosion, serration, vortex, and vortex damage) that may endanger their operational efficiency and lifespan. The dataset consists of 1,794 high-resolution images taken under different weather conditions and angles, including damage and types. The images were increased by four times to 7,176 images using data augmentation techniques. Damage and types were detected using the developed SatNET deep learning model, 11 deep learning models, and the Faster Region-based Convulational Neural Network (R-CNN) object detection algorithm. Each of the models was evaluated with average sensitivity. Accordingly, SatNET achieved avarage precision (AP) values of 55.7% for paint damage, 76.7% for erosion, 95.2% for serration, 66.1% for vortex, and 27.3% for vortex damage. It demonstrated superior performance when compared to deep learning models frequently used in the literature, such as ResNet50 and VGG19. In addition, it has been shown that the model requires less computational cost than other models, with a memory requirement of 192 MB. The results show that SatNET’s computational efficiency and accuracy are competitive with other models. The model is suitable for systems with limited memory and computational capacity, which require real-time operation, and for systems with resource constraints. The results obtained can contribute to sustainability in renewable energy production by providing low-cost monitoring of damage and types in wind turbines.

As global energy demand continues to rise and the need to transition from fossil fuels becomes increasingly urgent, integrating solar farms efficiently into power grids presents a significant challenge. This study introduces a novel graph-theoretic framework for designing optimal interconnection networks among distributed solar farms. By utilizing Prim’s algorithm to construct a minimum spanning tree, the proposed method effectively reduces transmission losses and infrastructure costs. The performance of this deterministic approach is benchmarked against Particle Swarm Optimization (PSO), a widely applied metaheuristic technique. To assess network robustness under potential line failures, a new graph-based reliability metric is developed. Case studies involving a cluster of solar farms demonstrate that Prim’s algorithm outperforms PSO in minimizing both power losses and capital investment, while also offering higher topological reliability. Although PSO achieves better load balancing, the graph-based approach proves more effective for loss-sensitive and cost-driven design scenarios. The proposed framework naturally accommodates constraints such as terrain limitations and is scalable to hybrid renewable energy systems. By integrating classical graph theory with practical power system considerations, this work offers a computationally efficient and economically viable solution for the optimal physical integration of large-scale solar energy infrastructure. The proposed methodology also lays a foundation for future integration of AI and machine learning techniques to enable dynamic network optimization under uncertainty.

ABSTRACT The concept of Positive Energy Districts (PEDs) has gained momentum in global energy optimization strategies; however, its application in diverse climatic regions, particularly hot and humid zones, remains underexplored. This study addresses this gap by investigating renewable energy optimization strategies on Lavan Island, leveraging its geographic advantages such as high solar irradiance and coastal proximity. A multi-phase methodology is adopted, beginning with an evaluation of the applicability of current PED systems in a hot and humid climate. Climate-adapted models are developed, and energy production solutions are assessed using key energy performance indicators. The study also emphasizes the importance of clearly defining PED boundaries and selecting appropriate Key Performance Indicators (KPIs) to evaluate energy exchange efficiency. Comprehensive planning scenarios, energy audits, and the integration of renewable energy systems are proposed, targeting the development of a PED for a community of shift-working employees. Optimization methods – including energy demand reduction strategies and a genetic algorithm – are applied to enhance the energy performance of residential units and the district as a whole. The findings demonstrate the feasibility of establishing a sustainable PED in hot and humid climates and provide a transferable framework for similar regions worldwide.

The atom arrangement in carbon electrocatalysts is crucial for enhancing the intrinsic activity toward oxygen reduction reactions (ORRs), a key process in multiple renewable energy systems. However, the challenge of designing electrocatalysts with improved performance by manipulating atomic arrangement has been limited by synthetic constraints and a lack of understanding of the catalytic phase formation. Herein, we gain atomic-level insight into the origin of a highly active site by creating a model catalyst with a heteroatom-decorated carbon matrix of a specific configuration. The introduction of fluorine (F) during the synthesis of the nitrogen (N)-decorated carbon matrix induces structural rearrangement, converting most pyrrolic-N (Pr-N) into highly stable graphitic-N (G-N), thereby achieving a N configuration predominantly composed of pyridinic nitrogen (Py-N) and G-N. The multidopant synergistic effect of F, Py-N, and G-N causes a destabilized π-conjugated electron network of the carbon matrix, resulting in a more localized electronic structure. As a result, multiple dopant configurations with high ORR activity have been explored, among which the asymmetric Py-N and G-N configurations feature the lowest theoretical ORR overpotential, ultimately enabling the optimized F@NC catalyst to exhibit excellent oxygen reduction activity. This work establishes a foundation for the rational design of metal-free carbon-based electrocatalysts toward ORR.

Hydrogen storage is the technique of keeping hydrogen for use in energy applications, which is critical in the shift to sustainable energy. Innovative materials, such as metal hydrides, assure storage system efficiency and safety. In this paper, the physical traits of (Mg/Ca/Sr)2FeH6 are examined by employing the different potentials. The optimization curves for each hydride reveal complete structural stability, and the obtained optimized lattice constants are 6.34, 6.98 and 7.39 Å. The stress–strain compliance matrix is reduced to C[Formula: see text], C[Formula: see text] and C[Formula: see text] to obtain the mechanical properties of (Mg/Ca/Sr)2FeH6. The electronic properties reveal direct bandgaps for Z2FeH6 (Mg/Ca/Sr)2FeH6 with both potentials. The optical properties revealed that these hydrides are extremely useful in optical devices such as ultraviolet-based lenses and anti-reflective coatings. The hydrogen storage capacities for the studied hydrides depict that Mg2FeH6has a high volumetric density of 78.46 gH2/L and a superior hydrogen capacity of 5.15[Formula: see text]wt.% with a desorption temperature of 221.4[Formula: see text]K, making it an effective hydrogen storage material as compared to Ca2FeH6 and Sr2FeH6. These materials demonstrate the ability to adjust hydrogen storage capabilities via compositional modifications. Their various properties make them suitable options for certain hydrogen storage requirements in renewable energy systems.