The problems are that: (i) frequent power outage by inadequate infrastructure and outdated technologies led to poor supply, (ii) limited access to reliable electricity and high cost of installation of off-grid energy and fossil fuel caused financial burden on households in the region, (iii) lack of a reliable energy, low renewable sources and reliability misconceptions affected the usage of renewable energy in the region, (iv) overall reliance on fossil fuel such as: diesel and petrol despite its high cost had contributed to global warming, and lack of strong implementation strategies and research affected renewable energy applications in the region. The study is aimed at comparing models for non-conventional energy supply to a four storey Building using Gauss-Seidel Algorithm in Abo-Mbaise, Imo State, southeast region, Nigeria. The objectives of the study are to: (i) evaluate the current energy infrastructure and consumption pattern in a-six- person-household per flat in the region, (ii) identify the renewable energy resources available in the region, (iii) assess the technical, economic and viability of implementing different renewable energy systems in a-four-storey-Building and the environmental impact of utilization in the region, and (v) apply optimization algorithm on different renewable energy sources on design configurations and fabrication. The reliability of the research experiment was determined using Pearson product moment correlation (r). The experimental result of (r), was found to be approximately (r = 1.0) which confirmed 100% (percent) level of performance between the model and prototype (fabricated Flywheel). The result also shows that there exist a strong correlation between the designed (model) and the fabricated (prototype). The work concluded that: (i) the cost of energy per kwh from Enugu Electricity Distribution Company (EEDC) is ₦53.78, Solar/Battery option is ₦65.62, Flywheel option is ₦34.27, Biomass option is ₦49.97 and Wind option is ₦74.01.Therefore, cost of energy for a-4-hour supply per flat of 6-person-household per month from EEDC is ₦18,070, while (ii) the optimization (GA) results show that for every-24-hour: (i) Wind option has a minimum cost of N14.66million in 30years, N488,666:67K in one year, N40,722:22K in one month per Building of 8flats, and per flat of 6persons has to pay N5090:00K ($3.4 US Dollars) per month, (ii) Biomass option has a minimum cost of N20.62million in 30years, N687,333:33K in one year, N57,277:78K in one month per Building of 8flats, and per flat of 6persons has to pay N7,160:00K ($4.8 US Dollars) per month, (iii) Flywheel option has a minimum cost of N6.9million in 30years, N230,000:00K in one year, N19,166:67K in one month per Building of 8flats, and per flat of 6persons has to pay N2,396:00K ($1.6 US Dollars) per month and (iv) Solar/Battery has a minimum cost of N28.16million in 30years, N938,666:67K in one year, N78,222:22K in one month per Building of 8flats, and per flat of 6persons has to pay N9,778:00K ($6.5 US Dollars) per month. It further concluded that there is zero emission of Co2 and other green house gasses using the Flywheel energy generating system and also it is the most efficient option. The work recommended that: for federal government of Nigeria to realize her vision/policy on climate change and SDG 2030: the Federal Housing Authority should promulgate a law for the provision of power supply in any building meant for rent age to reduce over-reliance on national grid, and the said regulatory council should implement the collection of appropriate tariffs using off-grid power supply in collaboration with other organizations in the renewable energy field.

The increasing deployment of Internet of Things (IoT) sensing infrastructures and distributed renewable energy resources is enabling the emergence of Renewable Energy Communities (RECs), which require intelligent, adaptive, and decentralized energy management strategies. This study proposes a sensor-driven reinforcement learning (RL) framework for the coordinated management of residential RECs, aiming to jointly optimize thermal comfort, economic savings, and environmental sustainability. Each household is equipped with a network of IoT sensors monitoring indoor temperature, energy production and consumption, battery state of charge, and user presence, which collectively define a discretized state space for a tabular Q-learning agent controlling heating systems and programmable appliances. A stochastic simulation environment is developed to realistically reproduce weather variability, building thermal dynamics, user activity profiles, and photovoltaic generation. To address the instability typical of multi-agent learning, a two-stage training strategy is adopted: agents are first pre-trained at single-house level using synthetic sensor data and are subsequently deployed within the full community, where coordination is achieved through shared reward components without explicit inter-agent communication. Performance is evaluated on a heterogeneous Renewable Energy Community (REC) composed of eleven households, including both prosumers and consumers. The simulation results show that the proposed approach significantly outperforms rule-based control strategies, achieving lower energy consumption, improved thermal comfort stability, and higher global reward. Moreover, pre-trained agents maintain stable and cooperative behavior when operating concurrently at community level, with limited sensitivity to exploration. These findings demonstrate that sensor-driven, lightweight reinforcement learning represents a viable and scalable solution for decentralized energy management in IoT-enabled Renewable Energy Communities.

Forest fires in the Indian Himalayan Region (IHR) are intensified by the accumulation of highly flammable Chir pine (Pinus roxburghii Sarg.) needles. This biomass, often treated as waste, represents both a hazard and an untapped renewable energy resource. Converting pine needles into biofuel offers a dual solution—reducing fire risk while contributing to rural energy security. However, systemic gaps in technology adoption, governance, and resource management have limited progress. This study develops an integrated, community-centric framework for sustainable deployment of pine-based biofuel systems in the IHR. Four objectives guided the research: (i) characterization of pine needle fuel properties to establish a baseline for harvest planning; (ii) techno-economic and sustainability analysis of decentralized conversion pathways, with briquetting emerging as most viable under current constraints; (iii) evaluation of a participatory governance model integrating local knowledge with institutional mechanisms to ensure equitable benefit-sharing and community ownership; and (iv) synthesis into a scalable framework aligned with national forest policy and climate goals. Results show community-managed biofuel systems can reduce forest fire fuel loads while fostering livelihoods. Briquetting was identified as the most feasible pathway, while pyrolysis and gasification showed promise for cluster-level deployment with targeted investment. The study provides a replicable blueprint for transforming hazardous pine needle waste into energy security, ecological resilience, and inclusive development in fragile mountain ecosystems.

Techno-economic perspectives on hydrogen production systems via using renewable energy resources: A comprehensive review

Hierarchical control framework for offshore hybrid AC/DC microgrid integrating renewable energy resources and hybrid energy storage system

The present study aims to provide a comprehensive and integrated analysis of the potential of offshore renewable energy resources in the maritime sector located at the Danube mouth area in the Black Sea, one of the most complex and dynamic hydrological and climatic systems in Eastern Europe. In the current context of climate change, the Danube mouths are of strategic importance due to the specific morphology of the area and the high potential for harnessing multiple renewable sources such as wind, wave, and solar energy. Therefore, this research supports sustainable development and adaptation to climate change. At the same time, predicted climate change may increase the frequency of extreme events, such as storms, sudden changes in water levels, and increased wave heights, which can affect navigational safety, ecosystem integrity, and coastal infrastructure. Thus, this research seeks not only to identify the energy potential of renewable resources but also to assess their risks and vulnerabilities. Using a wide range of data types, three time periods were studied for the main Danube mouth: Sulina and St. George. Both Sulina and St. George present future wind and wave intensification trends, especially in high-emission scenarios, without significant changes in the dominant direction. St. George remains the area with the more intense regime, while Sulina has more moderate episodes, but with a slightly more evident increase in the frequency of 6–12 m/s winds. At the same time, solar radiation shows a slight increase in recent years, especially in the summer season. Harnessing these resources has the potential to, for example, power coastal communities and offshore installations, providing clean and reliable energy while reducing greenhouse gas emissions.

The accelerating deployment of distributed renewable energy resources and the pressing need for decarbonization of the electricity supply have intensified interest in smart grid-integrated microgrids. However, most existing Energy Management System (EMS) approaches have been developed and validated for European or North American grid conditions, leaving a significant gap for developing economies such as India, where Time-of-Use (TOU) tariff structures, grid reliability constraints, and solar irradiance profiles differ substantially. This paper proposes a novel rule-based EMS tailored specifically for Indian grid conditions, integrating a 10 kWp solar photovoltaic (PV) array, a 20 kWh Battery Energy Storage System (BESS), and a TOU-based Demand Response (DR) strategy for a residential microgrid. The proposed EMS employs a deterministic priority-based scheduling algorithm that optimally coordinates energy resources across a 24-hour scheduling horizon to minimize electricity cost, reduce grid dependency, and lower carbon dioxide (CO₂) emissions. The system is modelled and simulated in MATLAB/Simulink R2023a using Typical Meteorological Year (TMY) solar irradiance data representative of Bhopal, Central India, and a standardised residential load profile derived from published literature on Indian load patterns. Simulation results across four seasonal scenarios demonstrate that the proposed EMS achieves a 28.0% reduction in peak load demand, a 26.6% reduction in daily electricity cost, a 36.0% reduction in grid dependency, a 35.4% reduction in CO₂ emissions, and a 62.5% improvement in voltage deviation at the Point of Common Coupling (PCC) compared to a base case without EMS. Sensitivity analysis across five weather scenarios validates the robustness of the approach. Comparative analysis with existing Fuzzy Logic and Model Predictive Control (MPC) methods confirms the superiority of the proposed system. Economic analysis yields a positive Net Present Value of approximately INR 6.8 lakh with a payback period of 11.2 years over a 25-year project lifetime. These results establish the proposed EMS as a practical, cost-effective, and scalable solution for residential microgrids under Indian grid conditions.

Energy storage systems have come to prominence with the sudden rise in renewable energy resources and the power network’s growing complexity. The idea of a smart grid that aims at optimising the integration and management of multiple energy resources has been a focuss area in recent years. This research gives a comprehensive analysis of energy storage in smart grids with an emphasis on several technologies and discusses storage technologies used in the smart grid by highlighting their types, advantages, drawbacks, and economics as well. Therefore, it is intended that this review paper will provide a critical evaluation of Energy Storage System (ESS) advancements and identify any research gaps related to reliability studies in modern Renewable Energy (RE) integrated power networks. This research also discusses hybrid energy storage solutions (HESS) which merging various energy storage technologies to enhance operational efficiency and cost-effectiveness and becoming increasingly important as they can tackle both short-term power variations and long-term energy storage requirements. Energy storage can solve intermittent renewable energy problems by giving the system more flexibility and balancing. Moreover, energy storage systems used in smart grids are analysed and listed with several energy storage methods, including electrical, electrochemical, thermal, and mechanical systems are investigated.

This research presents a technical and economic assessment of a hybrid energy system for electricity generation, accessibility, sustainability and consumption in rural and semi urban locations in Nigeria. The aim is to determine the sizes, technical and economic considerations of the hybrid microgrid renewable energy infrastructure that could be suitable for 40 rural and semi-urban locations selected to cut across the Federal Capital Territory (FCT) and all the 36 states in the country. The cost of the components of the hybrid system and the energy generated per renewable energy (RE) source from the microgrid are determined for all the locations. The projected yearly electricity generated for each location for the hybrid system were determined using the hybrid optimization of multiple electric renewables (HOMER) energy modelling software for a period of 10 years from 2024 to 2033. TheWorld Bank population growth rate of 2.5% was used to estimate the population each year and the associated load demand. Each location was assumed to have a minimum electric load of 0.76 kWh per day per person. To simulate longterm continuous implementation of the hybrid system, average solar irradiation, wind speed and available biomass resources for the selected locations were used. The mean annual wind speed ranged from 3.45 to 7.15 m/s. The solar radiation ranged from 4.43 to 6.24 kWh/m2/day. The per capita net present cost (NPC) ranged from 776.37 to 4130.21 USD per kWh, while the cost of energy (COE) ranged from 0.00196 to 0.0231 USD /kWh, respectively for the period. The results show that Nigeria as a country has ample renewable energy resource availability to meet minimum electric power demand for the locations consdered.With a strong political determination, optimal utilization of these renewable resources (solar, wind and biomass) can be actualized. Researchers, Industrialists, Policy Makers and the Nigerian government should therefore take advantage of these abundant renewable energy resources in the country to develop a sustainable energy generation and consumption plan through its maximum utilization.

Energy storage systems have come to prominence with the sudden rise in renewable energy resources and the power network’s growing complexity. The idea of a smart grid that aims at optimising the integration and management of multiple energy resources has been a focuss area in recent years. This research gives a comprehensive analysis of energy storage in smart grids with an emphasis on several technologies and discusses storage technologies used in the smart grid by highlighting their types, advantages, drawbacks, and economics as well. Therefore, it is intended that this review paper will provide a critical evaluation of Energy Storage System (ESS) advancements and identify any research gaps related to reliability studies in modern Renewable Energy (RE) integrated power networks. This research also discusses hybrid energy storage solutions (HESS) which merging various energy storage technologies to enhance operational efficiency and cost-effectiveness and becoming increasingly important as they can tackle both short-term power variations and long-term energy storage requirements. Energy storage can solve intermittent renewable energy problems by giving the system more flexibility and balancing. Moreover, energy storage systems used in smart grids are analysed and listed with several energy storage methods, including electrical, electrochemical, thermal, and mechanical systems are investigated.

The planet cannot survive without a sustainable approach to development, and global energy systems are rapidly evolving to meet the growing demand for clean, renewable alternatives. In this context, wind energy plays a crucial role, particularly in regions rich in abundant wind resources. Since wind energy is free and has no emissions, it can provide the electricity needed to manufacture green hydrogen (GH2), which makes the combination of wind and hydrogen production a promising solution in the energy transition. GH2 is a renewable fuel that can significantly contribute to climate goals, energy security, and decarbonization of economies and societies. Produced through electrolysis powered by renewable energy, green hydrogen has the potential to be both a practical response to environmental challenges and a driver of long-term sustainability. The province of Newfoundland and Labrador (NL) has a unique position to produce sustainable green hydrogen because of its favorable geographic location and exceptional offshore wind potential. Large-scale renewable energy facilities have a unique opportunity in the North Atlantic offshore due to the ocean’s consistent wind patterns and the province’s proximity to the USA and European markets. Although the NL’s energy sector is mostly based on renewable energy resources (hydro), there is still a lack of thorough research that integrates the possibilities of wind energy and green hydrogen to boost the energy transition of the province of NL. This study is a narrative literature review, which highlights the vital role of wind energy in facilitating the sustainable energy transition in NL and explains the status of provincial GH2 projects. It emphasizes NL’s untapped potential to develop a robust, sustainable, and export-driven clean energy sector. Additionally, it uses a transdisciplinary review to co-produce knowledge that addresses practical sustainability challenges and provides some policy recommendations for the successful implementation of green hydrogen projects in the NL province.

Modern power networks contain an increasing amount of renewable energy resources that are connected to the grid via inverters (Inverter-Based Resources, IBR). As highlighted in the recent IEEE Standard 2800-2022, these resources behave differently compared to conventional power plants, which impact protection systems. For networks with a high proportion of IBR, existing protection systems may no longer be dependable and reliable. This research project investigated the behaviour of commercially available relays for distance protection applied to a power grid with a high proportion of IBR. A detailed numerical model was established for the power grid of the Gaspesian Peninsula in Québec, Canada, where there are numerous wind farms. Five power lines with different characteristics were selected, and 700 fault events were generated in COMTRADE format. These events were then converted into analog signals, applied to commercially available relays, and their tripping actions were analyzed. Several misoperations could be identified and classified. Proposals for improving protection performance were developed and validated with the experimental setup. This project highlights the importance of validating and eventually adapting the protection systems in power grids with a high proportion of IBR, as existing protection systems may be prone to misoperate. Various solutions are proposed to ensure the dependability and reliability of protection systems in modern power grids.

Innovative fuzzy reinforcement learning based energy management for smart homes through optimization of renewable energy resources with starfish optimization algorithm.

This study enhances wind speed forecasting by implementing the second-order Generalized Least Deviation Method (GLDM), focusing on wind turbines in Turkey. The research aims to improve predictive accuracy and operational efficiency in renewable energy systems through advanced mathematical modeling in meteorology. The GLDM, utilizing a quasilinear recurrence equation, addresses the inherent non-linearity and variability of wind speed data. By applying the method to extensive SCADA data, this study minimizes residuals in nonlinear big data environments, integrating both linear and nonlinear components to refine predictions. A critical aspect of this research is the comparison between the second-order GLDM and traditional forecasting models, including statistical methods and machine learning approaches. The results demonstrate the superior performance of GLDM, as indicated by lower prediction errors and greater accuracy across key metrics. The study also underscores the importance of GLDM coefficients, ????????????????, in improving predictive capabilities. The findings advocate for the adoption of GLDM in wind speed forecasting, highlighting its potential to significantly enhance wind energy management through increased accuracy. This study also sets a precedent for broader applications of advanced mathematical models in environmental science, illustrating the effectiveness of GLDM in optimizing renewable energy resources.

ABSTRACT The rapid penetration of renewable energy resources has introduced significant uncertainty into modern power systems, necessitating accurate pre‐assessment to ensure secure operation. Probabilistic power flow (PPF) analysis is a powerful technique for quantifying such uncertainty, but its reliance on repeated AC power‐flow solutions makes it computationally prohibitive. This study proposes a fast PPF framework that couples the linear DC power flow (DC method) model with an Extreme Learning Machine (ELM) surrogate. By augmenting ELM inputs with DC‐PF results and employing Latin Hypercube Sampling, the method achieves both high speed and high accuracy. Numerical experiments on benchmark IEEE systems confirm that the proposed approach preserves the precision of conventional Monte‐Carlo‐based PPF while reducing total computation time by approximately 140 times

Off-grid integrated energy systems offer a practical solution for remote regions lacking access to the main power grid; however, their planning and design are challenged by the inherent uncertainty of renewable energy resources. To address this issue, this paper proposes a stochastic optimization framework for off-grid integrated energy systems that explicitly accounts for wind speed and solar irradiance variability. Continuous probability distributions combined with Monte Carlo sampling are employed to generate stochastic scenarios, which are embedded into a bi-objective optimization model minimizing total system cost and pollutant emissions under power balance and device operational constraints. Unlike existing studies that primarily focus on cost–reliability trade-offs, this work introduces the Renewable Energy Penetration Rate (REPR) as a quantitative, planning-oriented indicator and systematically investigates its interactions with economic performance, pollutant emissions, and renewable uncertainty. The REPR is not only used to characterize renewable utilization levels, but also to support investment decision-making and the comparative assessment of Pareto-optimal configurations. A real-world off-grid service area is adopted as a case study. The results show that increasing the REPR leads to a significant reduction in carbon emissions while exhibiting a nonlinear impact on total system cost. Specifically, the proposed framework identifies a Pareto-optimal solution set in which the total system cost varies within 40–92 million ¥, carbon emissions are reduced by 86% compared with diesel-dominated configurations, and the REPR increases from 70% to 96.4% as renewable capacity expands. In addition, the analysis reveals that higher renewable volatility requires a larger stochastic sample size to ensure solution stability. These findings demonstrate that the proposed framework provides a more comprehensive and decision-relevant assessment of off-grid integrated energy systems under renewable uncertainty, thereby offering practical insights for low-carbon and economically viable system planning.

Introduction The study addresses the critical challenge of uneven digital technology adoption and its varied impact on sustainability in the agricultural sector globally. It investigates how digital technologies can be leveraged to enhance key sustainability metrics, including renewable energy integration and resource efficiency, within the bioeconomy framework. Focusing on the integration of digital tools and their efficacy in enhancing key sustainability metrics, this study aim to provide insights into how digital innovations can bolster sustainable agricultural practices across diverse environmental, regulatory, and socio-economic landscapes. Methods A mixed-methods research design was employed, combining quantitative analysis of sustainability indicators with qualitative assessment of regional socio-economic and infrastructural conditions. Empirical data were collected from multiple international regions and aggregated at the regional level. Correlation analysis and multivariable ordinary least squares regression models were applied to examine the relationship between digital technology adoption and sustainability outcomes, controlling for policy support, infrastructure availability, and economic context. Sensitivity analysis was conducted by systematically varying input parameters to assess the robustness of model results. Results and discussion Study results show that areas exhibiting elevated rates of technological adoption, including Europe and North America, exhibit significant advancements in sustainability metrics, such as enhanced integration of renewable energy sources and improved resource efficiency. In contrast, Asia and Africa exhibit modest improvements, which highlights the critical need for region-specific strategies to overcome infrastructural and adoption barriers. This study develops a model that quantifies the relationship between digital technology adoption and sustainability improvements across different agricultural contexts. Findings highlight the need for promoting technological parity to achieve holistic agricultural sustainability and leverage digital technologies for climate resilience, food security, and renewable energy use in agriculture.

Abstract Integration of renewable resources to meet growing energy demand is becoming a global priority under decarbonization mandates. This study contributes to ongoing efforts on this key subject by assessing the feasibility of using urban renewable energy resources, namely, offshore wind and rooftop photovoltaic systems, to meet electricity demand of New York City during the intense recent heat wave period of June 2025. A unified modelling framework, based on the Urbanized Weather Research and Forecasting model, is used to simulate climate, renewable resources and energy demand variables. Findings show significant energy load miss-match of approximately 20000 GWh over the month, between the demand and the combined renewable generation outcome. Three storage integration scenarios are analyzed to mitigate the deficits, reducing said deficits by a minimum of approximately 22% over the duration of the month. This study provides a transferable modelling framework tool for evaluating renewable integration in dense urban environments that can be used by grid operators to support grid resilience planning and management during extreme heat events.

The increasing penetration of renewable energy resources has amplified variability and uncertainty in power systems, reducing the effectiveness of conventional single-period Optimal Power Flow (OPF) strategies. Multi-period AC-OPF offers a more comprehensive framework by incorporating inter-temporal constraints and resource flexibility, but its high computational complexity and strong temporal coupling make large-scale applications challenging, often causing scalability issues and convergence difficulties in conventional solvers. We address these issues with a spatio-temporal deep learning model that combines a Graph Attention Network (GAT) for topology-aware feature learning with a Temporal Convolutional Network (TCN) for multi-period temporal modeling. The proposed model is trained on large-scale 500-bus and 1354-bus systems under both 8-period and 24-period settings, and it achieves robust scalability with consistently high prediction accuracy. Using the model’s predictions, we construct an initial solution and provide it to a conventional OPF solver, which improves convergence performance and demonstrates the model’s effectiveness as an auxiliary tool for complex MP-ACOPF problems.

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