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

The decarbonization of electricity supply has intensified interest in standalone microgrids capable of achieving high renewable penetration while maintaining strict reliability. This study addresses the research questions of how cost-optimal standalone hybrid microgrids emerge under near-zero unmet-load constraints, how renewable variability and storage dynamics influence system behavior, and how cost-optimal designs compare with emissions-minimizing alternatives. A hybrid photovoltaic–wind–battery microgrid with dispatchable generation supplying a hospital facility in Carbondale, Illinois, USA, is analyzed under islanded operation. Site-specific data are combined with a constrained techno-economic optimization framework implemented in the Hybrid Optimization Model for Electric Renewables (HOMER) to minimize net present cost (NPC) while enforcing hourly power balance and battery state-of-charge constraints. Sensitivity analysis on photovoltaic derating evaluates robustness under performance uncertainty. Results show that the cost-optimal hybrid configuration achieves a renewable fraction of 74.6%, with a renewable utilization index of approximately 0.78 and excess electricity of 22.4%. Limited and intermittent use of dispatchable generation reduces lifecycle cost to approximately $38.2 M. In contrast, a diesel-free configuration nearly doubles net present cost to $71 M under identical reliability constraints. The findings demonstrate that economically viable decarbonization of standalone microgrids is best achieved through diversified hybrid architectures rather than fully renewable extremes.

The Philippines, which is rich in natural resources, has significant biomass potential. Among the country’s renewable energy sources, biomass is currently the slowest-growing in terms of power generation. Various types of biomass resources with full or partial use in Laguna Province include bagasse, sweet sorghum, coconut, rice husk, corn cobs, and municipal solid waste. Additionally, the adoption and implementation of HRESs (hybrid renewable energy systems) are mainly achieved through large-scale projects. This paper intentionally showcases highly optimized hybrid configurations for off-grid microgrids to promote rural electrification in Laguna, with a focus on various technoeconomic parameters, specifically the minimization of net present costs and the levelized cost of electricity across all simulations. Each off-grid scenario was compared with scenarios featuring hybrid renewable energy systems incorporating a biomass generator. Laguna, one of the few provinces in the Philippines with all forms of renewable energy systems present, each with high renewable energy potential and renewable fraction values, was selected as the primary study site in this paper. After optimizing and analyzing technoeconomic parameters such as the net present cost and the levelized cost of electricity, a hybrid biomass-solar-wind energy system is proposed to power off-grid areas in Laguna, thereby supporting rural electrification and decarbonization goals. Scenario simulations and comparisons using hybrid optimization demonstrate that adding battery backup systems improves both economic and environmental performance. This paper highlights two key benefits of including a biomass generator: (1) a 17.0% reduction in long-term carbon emissions for the entire system and (2) approximately 9.4% savings in operation and maintenance costs after seven years. The optimization results support the goal of providing Laguna with power through off-grid, decentralized, community-based hybrid renewable energy systems.

As buildings account for nearly one-third of global energy consumption, improving their energy performance and renewable integration is essential for achieving sustainability targets. Traditional building energy management systems (BEMS), often rule-based and static, struggle to adapt to fluctuating demands, variable tariffs, and the intermittency of solar resources. This study introduces an integrated, artificial intelligence (AI)-driven BEMS framework that jointly optimizes rooftop photovoltaic (PV) sizing and adaptive demand-side management (DSM) using reinforcement learning (Q-Learning) and benchmarks its performance against two established deterministic tools: HOMER Pro for techno-economic PV sizing and particle swarm optimization (PSO) for DSM load scheduling. Using realistic hourly building loads, meteorological data, and time-of-use pricing, the Q-Learning model converged to a PV–inverter configuration closely aligned with HOMER Pro’s optimum, achieving a slightly lower net present cost (–1.85%) and a modest increase in renewable fraction (+3.1%). In DSM applications, Q-Learning consistently outperformed PSO by shifting a larger share of flexible loads and securing higher daily cost reductions. Under grid-only conditions, Q-Learning reduced energy costs by 7.58% in winter and 8.27% in summer, while PV-integrated scenarios achieved savings of 35.14% and 26.89%, respectively. These results demonstrate that reinforcement learning can effectively enhance the performance of conventional BEMS approaches by providing more adaptive scheduling aligned with tariff structures and solar availability. The proposed framework supports more efficient, flexible, and sustainable building operations, highlighting the practical potential of AI-driven energy management in modern grid-interactive environments.

In this study, a biomass-based hybrid renewable energy system (HRES) combining photovoltaic (PV) panels, a biogas generator, and battery storage was designed and optimized to meet the electricity demand of an off-grid livestock facility located in northern Diyarbakır, Turkey. The analysis was performed using HOMER Pro software, incorporating technical, economic, and environmental evaluations. The system was configured with 43.9 kW PV, a 50 kW biogas generator, battery storage, and a converter, supplying a daily load of 165 kWh entirely from renewable sources. Annual energy production reached 90,866 kWh, achieving a 100% renewable fraction. The system’s Net Present Cost (NPC) and Levelized Cost of Energy (LCOE) were determined as $289,055 and 0.2326 $/kWh, respectively, confirming its strong economic competitiveness. Sensitivity analyses revealed that solar radiation was the most influential factor affecting system economics, where increased irradiance improved cost efficiency by approximately 7–8%. Variations in biomass price produced moderate effects of around 3%, while energy demand and nominal discount rate had secondary yet measurable impacts. The environmental assessment indicated a 99.97% reduction in CO₂ emissions, with the hybrid configuration achieving near-zero greenhouse gas output and only negligible levels of SO₂, NOₓ, and CO. Furthermore, the grid extension analysis demonstrated that beyond a 14.6 km distance, the off-grid HRES becomes more cost-effective than extending the utility grid. Overall, the results confirm that integrating solar and biogas energy provides a technically reliable, economically viable, and environmentally sustainable solution for rural electrification in regions with high solar potential and strong agricultural livestock activity.

The increasing environmental and economic drawbacks of fossil fuels have accelerated the global transition to renewable energy sources. In this context, the optimal design of hybrid renewable energy systems (HRES) that combine solar, wind, and energy storage technologies is critical for achieving sustainable and cost-effective power generation. This study addresses the problem of optimally sizing a grid-connected HRES composed of photovoltaic (PV) panels, wind turbine (WTs), batteries (BTs), and supercapacitors (SCs). A mathematical model is developed to minimize the annual cost of the system (ACS) while ensuring high renewable energy utilization and system efficiency. To solve this optimization problem, five advanced meta-heuristic algorithms-Hunger Games Search (HGS), Spider Wasp Optimizer (SWO), Kepler Optimization Algorithm (KOA), Fire Hawk Optimizer (FHO), and Coronavirus Disease Optimization Algorithm (COVIDOA)-were applied and statistically validated. The model was tested on real meteorological and load data from a university campus in Turkey. Results show that HGS achieved the most favorable performance, with an ACS of $603,538.44, a cost of energy (COE) of $0.23801/kWh, and a renewable energy fraction (REF) of 80.04%. This configuration offers significant economic advantages compared to purchasing electricity directly from the grid at $0.35/kWh. The proposed system proves commercially viable for large consumers and demonstrates the practical effectiveness of meta-heuristic methods in energy system design. MATLAB was used for simulation, while R programming was employed for statistical validation of the algorithmic performance. The study establishes a reproducible and validated framework that can guide future research and implementation in the field of hybrid energy optimization.

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.

Purpose This study aims to develop a sustainable renewable energy strategy for Nakhon Ratchasima (KORAT), Thailand, in response to growing energy demands driven by rapid population growth and industrialisation. The research explores the optimal mix of renewable energy sources to maximise energy efficiency and sustainability in the region. Design/methodology/approach The hybrid optimisation of multiple energy resources (HOMER) Software was employed to simulate a microgrid system tailored for KORAT. The model integrated local demand profiles and climatic data to evaluate the performance and cost-effectiveness of various renewable energy technologies, including solar, hydropower, wind and energy storage systems. Findings Simulation results indicated that solar power systems are the most effective and cost-efficient renewable option for the region, closely followed by hydropower systems. Wind power demonstrated lower performance and economic viability due to local wind speeds falling below the cut-in speed of the selected turbines. Similarly, battery storage did not significantly enhance the renewable energy fraction due to limited surplus energy, indicating lower cost-effectiveness. Research limitations/implications This study is limited to a single province – Nakhon Ratchasima – which may not fully represent the diverse geographic and climatic conditions across Thailand. Despite these limitations, the findings offer a replicable framework for regional energy planning and highlight the importance of site-specific data in designing cost-effective hybrid renewable systems for Thailand and similar developing regions. Practical implications This study provides a practical framework for designing region-specific hybrid renewable energy systems using real-world data and HOMER software. The findings support policymakers, utility providers and investors in making informed decisions about energy planning in Thailand. Social implications The transition to hybrid renewable energy systems in Thailand, as demonstrated in this study, can significantly improve energy access, affordability and reliability for local communities. Reducing dependence on fossil fuels helps lower greenhouse gas emissions and air pollution, contributing to better public health outcomes. Originality/value This study presents the first HOMER-based microgrid simulation specifically focused on KORAT, providing a replicable framework for integrating renewable energy in similar regions across Thailand. It contributes valuable insights for policymakers and energy planners aiming to advance renewable energy adoption through evidence-based system design.

ABSTRACT The research considers an hourly residential load demand with a daily average of 988 kWh/day and investigates possible standalone systems, including solar panels (photovoltaic [PV]), wind turbines (WTs), diesel generator (DG), biogenerator (BG), and battery bank (Bat), to provide the load demand, for a case study located in Tabuk, Saudi Arabia, where the monthly solar radiation and wind speed are 5.74 kWh/m 2 /day and 5.33 m/s, respectively. In this study, enviroeconomic factors, including inflation and discount rates, capacity shortage and load demand, CO 2 and SO 2 penalties, diesel and biomass prices are considered, while they were not considered in the previous studies in Saudi Arabia. The results show that the net present cost and cost of energy of the optimized system are $1.03 M and 0.178 $/kWh, respectively. Additionally, the prices of diesel fuel and biomass have a significant impact on the CO 2 emissions of the system, even with a 10% increase in the renewable fraction. The results of sensitivity analyses show that increasing the CO 2 emission penalty from 20 to 80 $/ton leads to a decrease in CO 2 emissions by 50%. The effect of the initial cost of WT on the configuration of the optimal system is higher than that of PV, and increasing both prices significantly leads to an increase in CO 2 emissions.

This study investigates the design and performance of a hybrid solar-wind energy system optimised for the electricity needs of Nigerian universities. In response to widespread grid unreliability, escalating diesel fuel costs, and the pressing need for sustainable energy solutions in the education sector, a decentralised hybrid configuration was modelled and simulated using HOMER Pro and MATLAB/Simulink. The system integrates photovoltaic modules, wind turbines, and lithium-ion battery storage to ensure uninterrupted energy supply across a typical 24-hour academic cycle. Simulated weather and load profiles were developed based on regional climatic data and institutional energy consumption patterns. Results indicate that the hybrid system reliably met a daily energy demand of 3,840 kWh, achieving 100% renewable fraction and zero unmet load. Solar PV contributed significantly during daylight hours, while wind generation ensured supply during early morning and night-time periods. The battery storage system operated within a safe state-of-charge range (71%–100%), ensuring system stability and extended battery life. The diesel generator remained inactive throughout the simulation, leading to zero operational emissions. The calculated levelised cost of energy (LCOE) was $0.148/kWh, demonstrating cost competitiveness when compared to diesel-based systems commonly used in Nigerian campuses. The findings affirm that hybrid solar-wind systems are technically feasible, economically viable, and environmentally sustainable for higher education institutions in Nigeria. The proposed framework offers a scalable, replicable solution capable of enhancing energy security, reducing operational costs, and supporting national energy transition goals.

Hybrid Renewable Energy Systems (HRESs) are a practical solution for providing reliable, low-carbon electricity to off-grid and remote communities. This review examines the role of energy storage within HRESs by systematically comparing electrochemical, mechanical, thermal, and hydrogen-based technologies in terms of technical performance, lifecycle cost, operational constraints, and environmental impact. We synthesize findings from implemented off-grid projects across multiple countries to evaluate real-world performance metrics, including renewable fraction, expected energy not supplied (EENS), lifecycle cost, and operation & maintenance burdens. Special attention is given to the emerging role of hydrogen as a long-term and cross-sector energy carrier, addressing its technical, regulatory, and financial barriers to widespread deployment. In addition, the paper reviews real-world implementations of off-grid HRES in various countries, summarizing practical outcomes and lessons for system design and policy. The discussion also includes recent advances in metaheuristic optimization algorithms, which have improved planning efficiency, system reliability, and cost-effectiveness. By combining technological, operational, and policy perspectives, this review identifies current challenges and future directions for developing sustainable, resilient, and economically viable HRES that can accelerate equitable electrification in remote areas. Finally, the review outlines key limitations and future directions, calling for more systematic quantitative studies, long-term field validation of emerging technologies, and the development of intelligent, Artificial Intelligence (AI)-driven energy management systems within broader socio-techno-economic frameworks. Overall, this work offers concise insights to guide researchers and policymakers in advancing the practical deployment of sustainable and resilient HRES.

Abstract The presented research covers the work carried out in the frame of the TCology project, conducted under funding of the Swiss Federal Office of Energy SFOE, evaluating the potential of thermochemical networks (TCNs) as an alternative to classical, hydronic district heating networks (DHN). The primary aim was to understand how TCNs can enhance the utilization of renewable energy in district heating systems through lossless energy distribution and long-term storage. The challenge was approached by numerical simulation of TCNs and 4th generation hydronic networks for performance comparison using Modelica as a modelling platform. Two case studies were defined for the performance comparison; i) space heating and domestic hot water application of 190 residential buildings / single family homes with a cumulated heat demand of 1.53 GWh per year and a peak power demand of 0.92 MW and ii) the application from i) extended with an industrial herb drying application with an annual heat demand of 0.1 GWh and a peak demand of 0.1 MW, featuring an open sorption process. The results identify biggest benefits for using TCN for low-temperature space heating applications with up to 5 times higher exergy efficiency and 2-3 times higher volumetric energy storage density compared to a classical DHN. Further, the possibility of providing the annual heat demand from a thermochemical storage with solar regeneration has been confirmed and quantified in terms of required storage volumes.

The growing global energy demand, particularly in large public infrastructures, necessitates a transition toward more sustainable and cost-effective energy solutions. This study investigates a large load profile of Makkah railway station to identify the optimal system that minimizes cost and environmental impact while maintaining energy reliability. Two hybrid renewable configurations are proposed: (1) Grid-connected Photovoltaic (PV/grid) and (2) PV/battery/grid. Hybrid Optimization of Multiple Energy Resources (HOMER) software was used to carry out the study. Simulation results reveal that the PV/grid system is the most effective configuration, achieving a Net Present Cost (NPC) of 27.9 million USD and a Levelized Cost of Energy (LCOE) of 0.0755 USD/kWh. This setup ensures 0% unmet load and delivers a renewable fraction of 26.7%, leading to a 26.72% reduction in Carbon dioxide emissions (CO₂ emissions) compared to the grid-only baseline. Its Internal Rate of Return (IRR) stood at 9.3%, confirming its strong financial sustainability. This makes it both a cost-effective and environmentally favorable choice. It is therefore recommended for high-load public facilities in solar-rich regions like Saudi Arabia, where integration of renewable energy is essential to achieving long-term sustainability targets under initiatives such as Vision 2030.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-22003-4.

Nusmapi Island is one of Indonesia's isolated islands that rely on Diesel Power Plant (PLTD) with a capacity of 20 kW to meet the electricity needs of its 50 customers. However, this PLTD only operates 12 hours a day due to high operational costs, reaching IDR 215,240,980/year with fuel consumption of 20,889 liters of diesel, equivalent to 17.56 tons with a COE of IDR 7,132/kWh in 2023. The reliance on diesel generators exacerbates ecological harm by releasing COâ‚‚ emissions"”a critical contradiction to Indonesia"™s nationally determined contribution (NDC) under the Paris Agreement, which mandates carbon neutrality by 2060. This research seeks to determine the most effective hybrid energy system design and configuration for Nusmapi Island, evaluating both technical feasibility and economic viability. The technical feasibility was assessed based on the unmet electric load, while the economic feasibility was evaluated using operational costs and the Cost of Energy (COE). The analysis indicates that the optimal system configuration comprises a 8,1-kW solar photovoltaic array, a 20-kW diesel generator, a 12-kW inverter, and five battery units housed within a single compartment. This configuration in real implementation will be able to produce 61,193 kWh/year, thereby increasing the power hours to 24 hours and reducing the unmet electric load to 0%. It will have a COE of IDR 3,280/kWh and will result in a fuel consumption reduction of 3,661 liters/year and operational costs of IDR 30,692,119/year. In addition, this configuration has environmental advantages with a renewable fraction reaching 18.3%

Myanmar’s electricity supply relies mainly on hydropower and gas-fired generation, yet rural electrification remains limited, with national access at approximately 60%. The National Electrification Plan (NEP) aims for universal access via nationwide grid expansion, but progress in remote areas is constrained by financial limits and suspended external funding. This study evaluates the techno-economic feasibility of decentralized microgrids as an alternative to conventional grid extension under current budgetary conditions. We integrate a terrain-adjusted MV line-cost model with (i) PLEXOS capacity expansion and chronological dispatch for centralized supply and (ii) HOMER Pro optimization for PV–diesel–battery microgrids. Key indicators include LCOE, NPC, CAPEX, OPEX, reliability (ASAI/max shortage), renewable fraction, and unserved energy. Sensitivity analyses cover diesel, PV, and battery prices, as well as discount rate variations. The results show microgrids are more cost-effective in terrain-constrained regions such as Chin State, particularly when accounting for transmission and delayed generation costs, whereas grid extension remains preferable in flat, accessible regions like Nay Pyi Taw. Diesel price is the dominant cost driver across both regions, while battery cost and discount rate affect Chin State more, and PV cost is critical in Nay Pyi Taw’s solar-rich context. These findings provide evidence-based guidance for rural electrification strategies in Myanmar and other developing countries facing similar financial and infrastructural challenges.

This study is aimed to measure hybrid renewable energy system (HRES) potential windiest regions of Türkiye including Çanakkale, Balıkesir and İzmir. Photovoltaics (PV) panel two different wind turbine are used to increase renewable energy fraction. In addition to PV panels and wind turbine a thermal load controller (TLC) a boiler and a micro gas turbine (MGT) are used to arrange excess electricity and waste heat recovery. Over the several HRES configurations for three different regions, most optimum HRES configuration consist of PV panels, wind turbines, electrolyser, battery, hydrogen storage tank, MGT, TLC, and boiler. The lowest NPC and COE values which is $10.80 M and $0.0677/kW whereas the lowest LCOH values is calculated in İzmir region with the value of $1.20/kg.

In southern Algeria, diesel generators (DGs) and small-scale off-grid systems are the primary power sources for powering remote regions. These systems are unreliable, uneconomical, and environmentally unsustainable for achieving the goals of Algeria's renewable energy transition. For this context, this paper aims to explore the techno-economic feasibility of three hybrid energy systems using advanced storage systems to electrify households and agricultural lands in the Indalek area. These systems are a photovoltaic (PV)/diesel (DG)/battery energy storage system (BESS), a hydrogen-based PV/DG/fuel cell (FC)/BESS, and a hydrogen-based PV/DG/FC/pump hydro-storage (PHS) system. First, Hybrid Optimization of Multiple Electric Renewable (HOMER) software is used to conduct the techno-economic and environmental analysis to select the appropriate system. Second, the Particle Swarm Optimization (PSO) method is applied to optimize the environmental performance of the optimal configuration based on the derating factor (Fd) and the reliability level [reliability 1-loss of power supply probability (LPSP)]. The optimization is formulated as a single-objective problem, aiming to maximize renewable fraction (RF), which indirectly leads to reduced CO2 emissions. The simulation results indicate that the hydrogen-based PV/DG/FC/PHS system is the optimal system, consisting of 41.2 kW PV arrays, 100 kW DG, 250 kW FC, 50 kW electrolyzer, 19.2 kW converter, 10 kg of the hydrogen tank, and 3000 m3 reservoir. This system achieves the most techno-economic and environmental performance, with the net present cost of $284 143.20, cost of energy of 0.364 $/kW h, operating cost of $1216, reliability level of 97.28% (LPSP = 0.0272), unmet load of 0%, autonomy energy of 98.65%, RF of 91.8%, and CO2 emissions of 67 440 kg/yr. The environmental performance optimization outcomes of the hydrogen-based PV/DG/FC/PHS configuration demonstrate that at the reliability level of 100% (LPSP = 0) and the Fd of 80%, the RF increases to 99.999%, while the CO2 decreases to 62 289 kg/yr compared to the scenario with the Fd of 60%.

Rural communities in Bangladesh face persistent energy access challenges due to geographic isolation and inadequate infrastructure. This study investigates the design and optimization of off-grid hybrid renewable energy systems for five distinct rural locations, utilizing solar photovoltaic (PV), wind turbines (WT), and four types of battery energy storage systems (BESS): ZnBr Flow, Li-Ion NMC, Lead-Acid, and LiFePO4. Using HOMER Pro (version 3.14.2), simulations were performed based on real hourly load profiles and resource data (solar irradiance, wind speed, and temperature) from NASA. Each system configuration was assessed for economic feasibility, renewable energy penetration, and environmental impact. Results show that the PV-WT-ZnBr Flow battery configuration outperformed others at all sites, achieving the lowest Net Present Cost (NPC) of $171,720, Cost of Energy (COE) of $0.0688/kWh, and 100% Renewable Fraction (RF) with zero carbon emissions. ZnBr Flow batteries demonstrated high efficiency, long lifespan (30 years), and low maintenance requirements. Sensitivity analysis revealed the influence of resource variability, load profiles, and component costs. This study confirms that ZnBr-based hybrid microgrids offer a viable, cost-effective, and scalable solution for sustainable rural electrification in Bangladesh and other remote or underdeveloped regions worldwide.

This study presents a techno-economic optimization of hydrogen production using hybrid wind-solar systems across six Australian cities, highlighting Australia’s green hydrogen potential. A hybrid PV-wind-electrolyzer-hydrogen tank (PV-WT-EL-HT) system demonstrated superior performance, with Perth achieving the lowest Levelized Cost of Hydrogen (LCOH) at $0.582/kg, Net Present Cost (NPC) of $27.5k, and Levelized Cost of Electricity (LCOE) of $0.0166/kWh. Perth also showed the highest return on investment, present worth, and annual worth, making it the preferred project site. All locations maintained a 100% renewable fraction, proving the viability of fully decarbonized hydrogen production. Metaheuristic validation using nine algorithms showed the Mayfly Algorithm improved techno-economic metrics by 3–8% over HOMER Pro models. The Gray Wolf and Whale Optimization Algorithms enhanced system stability under wind-dominant conditions. Sensitivity analysis revealed that blockchain-based dynamic pricing and reinforcement learning-driven demand response yielded 8–10% cost savings under ± 15% demand variability. Nevertheless, regional disparities persist; southern cities such as Hobart and Melbourne exhibited 20–30% higher LCOH due to reduced renewable resource availability, while densely urbanized cities like Sydney presented optimization ceilings, with minimal LCOH improvements despite algorithmic refinements. Investment in advanced materials (e.g., perovskite-VAWTs) and offshore platforms targeting hydrogen export markets is essential. Perth emerged as the optimal hub, with hybrid PV/WT/B systems producing 200–250 MWh/month of electricity and 200–250 kg/month of hydrogen, supported by policy incentives. This work offers a blueprint for region-specific, AI-augmented hydrogen systems to drive Australia’s hydrogen economy toward $2.10/kg by 2030.

Saudi Vision 2030, a strategic plan to diversify Saudi Arabia's economy and reduce its dependency on oil, emphasises the development of renewable energy sources (RESs) to promote sustainability and reduce greenhouse gas emissions. This study assesses the technical, economic, and environmental feasibility of three hybrid energy system (HES) configurations for a hospital in Tabuk, northern Saudi Arabia: (1) Grid/PV/Hydrogen Electrolyzer/Fuel Cell (GCS1), (2) Grid/PV/Inverter (GCS2), and (3) Standalone PV/Inverter/Pumped Storage Hydropower (SAS). Using HOMER software, the systems were simulated and optimised. Results show that GCS1 achieved the lowest cost of energy (COE) at $0.0563/kWh with 73.3% renewable fraction and 2.63 million kg/year of CO2 emissions. GCS2 had a COE of $0.0576/kWh and the lowest initial capital cost ($3.82M), but the highest emissions (2.74 million kg/year CO2). It also effectively maintains a balance between energy generation and consumption. SAS, while environmentally superior with zero emissions, had the highest COE at $0.120/kWh and notable unmet and excess energy. This research supports informed decision-making by providing stakeholders with a comprehensive evaluation of HES configurations, integrating technical, economic, and environmental factors to promote sustainable, optimal, and cost-effective energy planning.