Most rural communities in Nigeria still face inadequate power supply, while others await connection to the national grid due to their remote locations. To meet the energy requirements in these areas, the adoption of renewable energy sources has become crucial for society and the nation at large. Renewable energy resources are largely attractive because of their availability, environmentally friendly nature, and cost-effectiveness through continuous supply. However, due to their intermittent availability, hybrid renewable energy systems are employed to mitigate the drawback caused by their intermittency. In this study, the reliability of a complex hybrid renewable energy system involving five subsystems components is evaluated. The minimal cut-sets of the complex system were first determined, followed by the construction of the fault tree diagram. The failure variables associated with the parameters of each component were assumed to follow Weibull failure laws. The system's reliability was assessed for various arbitrary parameter values, such as failure rate (λ), shape parameter (β), and operating time (t) of the components. The results show that for λ = 0.01, the system reliability ranges from 0.97474 to 0.84816 for β values from 0.1 to 0.2, and t values from 10 to 20. For λ = 0.02, reliability ranges from 0.96671 to 0.57295 over the same parameter ranges. For λ = 0.03, the reliability varies from 0.95568 to 0.34419; for λ = 0.04, from 0.94058 to 0.19465; and for λ = 0.05, from 0.92004 to 0.10677, with β values between 0.1 and 0.2 and t between 10 and 20. The dynamics of these reliability indices are presented both graphically and numerically, based on arbitrary values of the system components' parameters

The increasing demand for reliable and cost-effective energy in oil production facilities has increased the need for optimized Hybrid Renewable Energy Systems (HRES). This study evaluates the technical and economic performance of a hybrid power system for an offshore oil facility using the Hybrid Optimization of Multiple Energy Resources (HOMER) software. The proposed system integrates solar photovoltaic (PV), diesel generators, battery storage, and wind energy. A two-dimensional sensitivity analysis was conducted by varying diesel fuel prices between $0.50–$1.00 per litre and inflation rates between 15–25% to assess their impact on system optimization and economic indicators. Simulation results show that the optimal configuration consists of 25.8 kW solar PV, a 50-kW diesel generator, 15 battery units, and a 22.6 kW converter, supplying the facility over a 20-year project lifetime. The Total Net Present Cost (NPC) increases significantly with rising fuel prices and inflation rates. At lower fuel prices and inflation rates, the PV/Diesel/Battery system is the most economical option, while higher inflation rates favour a PV/Diesel/Wind/Battery configuration. Spider plot analysis indicates that inflation rate has a stronger influence on NPC than fuel price, highlighting the importance of macroeconomic stability in long-term energy planning. These findings provide practical insights for energy planners and policymakers seeking sustainable and resilient power solutions for oil and gas facilities.

Ports located within dense urban environments face a major challenge in achieving deep decarbonization without compromising the reliability and safety of critical maritime operations. This study develops and validates a resilience-oriented control and sizing typology for Hybrid Renewable Energy Systems (HRESs), supporting the transition of a medium-sized Mediterranean port toward a Nearly Zero Energy Port (nZEP). The framework integrates five years of measured electrical demand at 15 min resolution to capture stochastic load variability, seasonal effects, and safety-critical peak events. Thirty-five HRES configurations are simulated using HOMER Pro, assessing photovoltaic and wind generation combined with alternative Energy Storage System (ESS) technologies under two grid-interface control strategies: Net Metering (NM) and non-NM curtailment-based operation. Conventional Lead–Acid batteries are compared with inherently safer Vanadium Redox Flow Batteries (VRFBs), while autonomy constraints of 24 h and 48 h are imposed to represent operational resilience. System performance is evaluated through a multi-criteria framework encompassing economic viability (Levelized Cost of Energy), environmental impact (Lifecycle Assessment-based carbon footprint), and operational reliability. Results indicate that NM-enabled HRES architectures significantly outperform non-NM configurations by exploiting the external grid as an active balancing layer. The optimal NM configuration achieves a Levelized Cost of Energy of 0.063 €/kWh under a 24 h autonomy constraint, while reducing operational carbon intensity to approximately 70 gCO2,eq/kWh, corresponding to a reduction exceeding 90% relative to baseline grid-dependent operation. In contrast, non-NM systems require substantial storage and generation oversizing to maintain resilience, resulting in higher curtailment losses and Levelized Cost of Energy values of 0.12–0.15 €/kWh. Across both control regimes, VRFB-based systems consistently exhibit superior robustness and safety performance compared to Lead–Acid alternatives. The proposed typology provides a transferable framework for resilient and low-carbon port microgrid design under real-world operational constraints.

This book collects papers published in the Special Issue of Energies on “New Insights into Hybrid Renewable Energy Systems in Buildings” [...]

Optimization of hybrid renewable energy systems: Reliability, cost, and environmental trade-offs using PSO and GJO algorithms

The optimal sizing of hybrid renewable energy systems for constant hydrogen supply – particularly suited for decarbonizing hard-to-abate industrial processes – requires managing temporal fluctuations of renewable energy sources through intermediate storage and is often tackled using mathematical optimization. However, efficiently finding a design that reliably meets constant hydrogen demand over long time horizons remains challenging. This work addresses the challenge using robust optimization techniques deriving a tractable robust counterpart for a constant supply system based on box uncertainty sets considering meteorological parameters as uncertainties. The investigated system comprises wind turbines, photovoltaic panels, an electrolyzer and electrical energy and hydrogen storage. To account for uneven geographical availability, three archetypal locations with distinct renewable resource profiles are investigated. As part of the comprehensive analysis, the impact of varying techno-economic assumptions and different operation scenarios, including grid connection and variable hydrogen supply, is examined. Results show baseline levelized cost of hydrogen values of 3.7–4.3 € kg −1 , rising to 7 € kg −1 under pessimistic techno-economic assumptions. Allowing variable demand trims costs by up to 1.4 € kg −1 , while grid interconnection yields up to 0.7 € kg −1 savings. Uncertainty consideration through robust optimization removes up to 23 % of the hourly shortfalls seen in nominal designs evaluated on historic profiles, but adds roughly 1 € kg −1 , an insurance premium for reliable supply. Sites with a consistently strong, complementary renewable mix (particularly wind-rich regions augmented by solar) turn out to be the most economical. This unified framework reveals the cost trade-offs among flexibility, grid interaction and robustness, equipping developers and policymakers with actionable insights for green hydrogen production. • Optimization framework for sizing hybrid renewable energy systems. • Location-specific optimal design for three archetypal locations. • Investigation of varying techno-economic assumptions and operation scenarios. • Robust designs for reduction of demand shortfall on historic weather profiles. • Cost trade-offs with respect to flexibility, grid connection and supply robustness.

Retraction notice to "Economic and technical analysis of an HRES (Hybrid Renewable Energy System) comprising wind, PV, and fuel cells using an improved subtraction-average-based optimizer" [Heliyon 10 (2024) e32712

Rising electricity demand, fossil fuel depletion, and environmental concerns highlight the need for sustainable rural electrification. The Elenjere community in Kwara State, Nigeria, depends on costly diesel generation and limited grid access, creating an urgent demand for reliable and affordable alternatives. This study designs and optimizes a hybrid renewable energy system (HRES) for the community using hybrid optimization model for electric renewables (HOMER) Pro simulation. The proposed system combines photovoltaic (PV), wind turbines (WT), battery storage (BAT), inverter (INV), and a diesel generator (DG) as backup. Field data on load demand, solar radiation, and wind speed were used for realistic modeling. System performance was evaluated using levelized cost of energy (LCOE), net present cost (NPC), and system capital cost (SCC). Results show the PV/WT/BAT/INV/GEN configuration achieved the lowest LCOE of USD 0.455/kWh, an NPC of USD 2.98 million, and 86.2% renewable penetration, significantly reducing diesel use. Sensitivity analysis revealed that reducing battery costs and increasing PV capacity could lower the LCOE to USD 0.227–0.325/kWh. The study demonstrates how modest wind resources (4.19 m/s at 10 m) complement PV in low-wind regions while addressing inflation realism (25.5% discount rate, foreign exchange (FX) volatility). Future work will include dynamic control simulation and lifecycle analysis to enhance scalability and sustainability.

In recent years, the demand for clean and reliable electricity in educational buildings has grown quickly and help to run educational building having low COE value. Present study focuses on designing and analyzing a Grid-Connected Hybrid Renewable Energy System (HRES) for an educational institute in Jaipur (India). The HRES system includes solar panels, wind turbines, battery energy storage, and conventional electrical generator, all connected to the main electricity grid. The goal is to provide a continuous, cost-effective, and eco-friendly power supply with good power quality. The current research was done in two stages. First, HOMER software was used to analyze different combinations of energy sources. The best solution was selected based on overall cost, fuel savings, and environmental benefits which was LCOE (levelized cost of electricity). The results of the present study showed that the optimal hybrid system had a Net Present Cost (NPC) of ₹172 million, Initial Capital of ₹89.1 million, Operation & Maintenance cost of ₹6.39 million per year, and a Levelized Cost of Energy (LCOE) of ₹5.72/kWh. These values are significantly better than the base case system (only grid purchased electricity), which had higher costs in all categories. In the second stage, Python-based modeling and control techniques were used to check how the system performs under different weather and load conditions. Special attention was given to maintaining stable voltage and frequency, and to reducing harmonic distortion (THD) for the protection of sensitive classroom and lab equipment. The battery’s State of Charge (SOC) was managed using an adaptive control method to improve energy reliability. Current study shows that a hybrid system combining renewable sources with a grid connection can reduce both electricity bills and greenhouse gas emissions. It also improves the power quality and reliability for educational institutions, making it a strong solution for future energy needs in India and similar regions.

Cost-optimal hydrogen refueling scheduling for bus fleet in a grid-connected hybrid renewable energy system

Abstract This study offers a detailed methodological framework for optimizing renewable energy integration in Qatar, focusing on spatial and techno-economic aspects to boost sustainability. The analysis uses electricity consumption data from 1,000 residential units across various zones in Qatar, including Al-Wakra, Doha Al-Jadeda, and Mushareb. Using geospatial analysis in ArcGIS Pro, this study examined electricity consumption patterns and the role of green spaces in urban cooling. A nine-step process combines high-resolution consumption data, socioeconomic indicators, and spatial regression techniques to evaluate demand changes and guide targeted energy strategies. The results reveal a high concentration of electricity use in Doha, an unequal distribution of green spaces, and an anticipated increase in energy demand due to urban growth. Electricity consumption levels ranged from very low (<10,000 kWh) to over 145,000 kWh in Zone 71 (Umm Slal/Khreteat), showing significant spatial variation in demand. These findings identify key areas for intervention and infrastructure development. They also emphasize the importance of hybrid renewable energy systems and district cooling technologies in reducing dependence on fossil fuels. The study advocates policy measures that include demand-side management, emission-reduction strategies, and dynamic modeling of population growth and climate impacts. By aligning spatial energy planning with Qatar National Vision 2030, this research promotes data-driven decisions for a more resilient and sustainable energy system.

This study introduces a novel bio-inspired metaheuristic algorithm, named JBO-OSN (Jackal–Badger–Octopus with Optimized Synaptic Network), for addressing the multi-objective optimization of a hybrid floatovoltaic–battery–diesel energy system. The target application is the Tuz Gölü (Salt Lake) region in Türkiye, where arid climatic conditions and unique resource availability present challenges for sustainable energy planning. The aim is to reduce cost, minimize carbon emissions, and ensure battery longevity in off-grid and semi-grid contexts. The system is modeled using realistic meteorological and demand profiles, incorporating water surface effects on photovoltaic performance such as reflectivity and thermal regulation. JBO-OSN is designed by integrating biological cooperation, synaptic decision-making, and chaotic dynamics to enhance exploration and convergence. The algorithm is implemented in MATLAB and benchmarked against widely used optimization techniques including PSO, GWO, and WOA. Simulation results demonstrate that JBO-OSN achieves superior convergence speed, improved solution stability, and more effective trade-offs among objectives compared to conventional swarm-based approaches. The algorithm efficiently balances system cost, emission reduction, and battery cycling stability under arid environmental conditions. JBO-OSN shows promise as a robust decision-support tool for the design and optimization of hybrid renewable energy systems in resource-constrained, arid regions. Its bio-inspired and synaptic-based framework provides advantages over traditional algorithms, supporting future applications in sustainable energy planning.

Intelligent Coordination Approach for Hybrid Renewable Energy Systems Towards Sustainable Power Supply

تواجه ليبيا تحدياً في امداد الطاقة الكهربائية الامن والمستقر، وبلغت نسبة العجز المسجلة عام 2024 نحو 32.5%، مما اضطر الشركة العامة للكهرباء إلى اللجوء لسياسة تقنين الإمداد بفترات قطع قد تصل إلى ثماني ساعات يومياً. تتجه الرؤية الاستراتيجية للدولة لتخطي هذه الإشكالية عبر التحول نحو مصادر الطاقة المتجددة، وتبني اللامركزية في نظم التوليد. ويتجسد هذا التوجه جليا في الاستراتيجية الوطنية للطاقة للفترة 2025–2050، التي تستهدف رفع حصة المصادر المتجددة في مزيج الطاقة الإجمالي إلى أكثر من 50% بحلول عام 2050. في هذا السياق، تقدم هذه الورقة البحثية اقتراحاً لتصميم نظام هجين متكامل للطاقة المتجددة، يجمع بين تقنيتي الخلايا الكهروضوئية، وتوربينات الرياح، مدعومة بنظام تخزين بواسطة الضخ الكهرومائي وأظهرت نتائج الدراسة أن التركيبة المثالية للنظام تتألف من حقل خلايا شمسية بسعة 1000 ميجاوات، ومزرعة رياح بقدرة 200 ميجاوات، مدمجا مع نظام تخزين بسعة 854 ميجاوات ساعة. يُنتج هذا النظام طاقة سنوية إجمالية تقدر بـ 1515.761 جيجاوات ساعة، تغطي بالكامل العجز الكهربائي الحالي المقدر بـحوالي 15575 ميجاوات/ساعة. كما وتم حقن الشبكة العامة للكهرباء بطاقة قدرها مع الالتزام ببرتكول الشبكة الكهربائية والذي ينص على عدم الطلب من الشبكة في حالة العجز وعدم التصدير اليها في حالة تشبع الشبكة والعكس صحيح. حقق التصميم الأمثل امداد مستدام %100 للحمل, كما وسجل التصميم أدنى قيمة لتكلفة انتاج الطاقة المستوية 49.9 دولار/ميجاوات ساعة, وقدرت تكلفة المشروع حوالي 19.923 مليار دولار، مع فترة استرداد تبلغ 11.10 سنة. كما اسهم النظام المقترح في خفض الانبعاثات الكربونية بمقدار 11,624 كيلوطن، مما يوفر ما يقدر بـحوالي 816.246 مليون دولار من التكاليف الاجتماعية المرتبطة بالانبعاثات، بالإضافة إلى توفير ما يُقدّر بـحوالي 2,189 مليون دولار ناتجة عن خفض استهلاك الوقود التقليدي.

This paper analyzes the functional feasibility and strategic value of hybrid hydrogen storage and photovoltaic (PV) energy systems at isolated areas, specifically at Egypt’s Shalateen station. The paper is significant as it formulates a solution to the energy independence coupled with economic feasibility issue in regions where the basic energy infrastructure is non-existent or limited. Through the integration of a portfolio of advanced optimization algorithms—Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Multi-Objective Genetic Algorithm (MOGA), Pattern Search, Sequential Quadratic Programming (SQP), and Simulated Annealing—the paper evaluates the performance of two scenarios. The first evaluates the PV system in the absence of hydrogen production to demonstrate how system parameters are optimized by Pattern Search and PSO to achieve a minimum Cost of Energy (COE) of 0.544 USD/kWh. The second extends the system to include hydrogen production, which becomes important to ensure energy continuity during solar irradiation-free months like those during winter months. In this scenario, the same methods of optimization enhance the COE to 0.317 USD/kWh, signifying the economic value of integrating hydrogen storage. The findings underscore the central role played by hybrid renewable energy systems in ensuring high resilience and sustainability of supplies in far-flung districts, where continued enhancement by means of optimization is needed to realize maximum environmental and technological gains. The paper offers a futuristic model towards sustainable, dependable energy solutions key to the energy independence of the future in such challenging environments.

This study presents a comprehensive techno-economic assessment to optimize a hybrid renewable energy system for green hydrogen production in Jordan. Using the Hybrid Optimization Model for Electric Renewables (HOMERs) and System Advisor Model (SAM) software, this study evaluates multiple cost projections for 2030 technology costs. Key parameters such as capital cost, efficiency, and lifetime are varied extensively. Highlighted results show a wide range in the Levelized Cost of Hydrogen (LCOH), reaching 1.59 to 3.49 USD/kg, and the Levelized Cost of Energy (LCOE) from 0.0072 to 0.0301 USD/kWh. Furthermore, Net Present Value (NPV) spans from USD 424 to 927 million, depending on the scenario and sensitivity case. Technically, the system’s optimized capacities vary significantly. PV ranges from 203 to 457 MW, wind capacities range from 0 to 220 MW, and electrolyzers range from 192 to 346 MW, demonstrating the flexibility required to meet different cost and performance assumptions. The study’s broad relevance extends to developing countries with grid constraints, where off-grid green hydrogen production is feasible. Its framework can be adapted globally, offering valuable insights.

The increasing global demand for clean and sustainable energy has intensified research focus on offshore renewable energy systems, particularly those integrating wind, solar, and wave resources. Offshore hybrid renewable energy systems represent a transformative opportunity to harness diverse marine energy sources, aiming to improve energy yield, capacity factor, and reliability compared to single-technology solutions. Evaluation of past literature reveals a critical gap in comprehensive evaluations of fully integrated hybrid offshore platforms that simultaneously deploy floating solar photovoltaic (FPV), offshore wind turbines, and wave energy converters (WECs), including their techno-economic performance and environmental impacts. This study addresses this gap by systematically reviewing the current state-of-the-art offshore floating solar, wind, and wave energy technologies and analyzing key commercial pilot hybrid projects such as Hollandse Kust Noord, W2POWER, and the Hybrid Floating POSEIDON system. A mixed-methods approach combining qualitative literature review, multi-criteria technical, economic, and environmental evaluation, and case study analysis was employed to assess the design innovations, integration strategies, and deployment challenges. Results demonstrate that hybrid offshore systems leveraging synergies between solar, wind, and wave resources can achieve up to five times higher energy output than single-source systems, with floating wind currently leading in maturity and energy production scale, complemented effectively by floating solar and wave converters to enhance seasonal and operational stability. Novel modular floating platforms and advanced mooring systems enable scalable, durable solutions capable of withstanding harsh marine environments. Environmental considerations, including biofouling, corrosion, and ecosystem impacts, can be addressed via mitigation strategies and adaptive site selection.

Hydrogen is gaining prominence as a key enabler in the global shift toward low-carbon energy systems, yet its role in heating—particularly in residential, commercial, and industrial contexts—remains underdeveloped. This paper explores the potential of hydrogen-based technologies to decarbonize heating, focusing on technological innovations, economic feasibility, and regulatory frameworks. Drawing on a comprehensive review of literature, policy documents, and case studies such as the EU-supported H2Heat project, the paper examines developments in hydrogen production, storage, and distribution, with a special emphasis on green hydrogen and its integration into Combined Heat and Power (CHP) systems and heat pump technologies. The findings demonstrate the quick advancements in infrastructure prepared for hydrogen, electrolyzer efficiency, and renewable energy-based hybrid energy systems. High costs, infrastructure retrofitting, safety issues, and regulatory fragmentation are still problems, though. Hydrogen heating has a lot of potential, especially for hard-to-electrify industries and seasonal storage requirements, the study concludes, but its success hinges on concerted policy action, investment incentives, and international cooperation. By providing strategic recommendations for scaling hydrogen heating solutions and establishing them as a feasible part of sustainable energy transitions, the paper adds to the current conversation.

Optimal design of a photovoltaic–diesel–battery hybrid renewable energy system for sustainable off-grid electrification of a village in Ethiopia

Abstract Power quality is a critical concern in modern microgrids, particularly those integrating renewable energy sources such as photovoltaic arrays, wind turbines, and batteries. However, variability in renewable generation often introduces problems such as voltage instability, harmonics, and current imbalance, which degrade system efficiency and reliability. To address these challenges, this study proposes an Artificial Neural Network (ANN)-based Reinforcement Learning Brainstorm Optimization (RLBSO) controller enhanced with a reward function for a Unified Power Quality Conditioner (UPQC). The RLBSO algorithm is employed for efficient offline training of the ANN, while the reinforcement learning reward function ensures adaptive control under grid-connected microgrid operations. MATLAB/Simulink simulations demonstrated that the ANN-based RLBSO controller significantly outperformed traditional proportional-integral (PI) controllers in key performance metrics. The proposed method reduced Total Harmonic Distortion from 4.2% to 2.59% and decreased current imbalance from 5% to 2% while maintaining voltage stability within ± 2% of the nominal value. Compared with PI and Fuzzy Logic controller (FLC), the ANN-based RLBSO achieved superior performance under varying load conditions owing to its dynamic control mechanism. These results highlight the potential of the proposed controller as a promising approach to improve power quality and efficiently integrate renewable energy into microgrids, thereby setting a new benchmark for intelligent control in hybrid renewable energy systems.