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.

Large-scale renewable power supply system design for remote hydrogen production is a challenging task due to the 100% power electronics sending-end subsystem. The proper grid-forming strategy for a sending-end system to achieve large-scale remote hydrogen production still remains a research gap. This study first designs two grid-forming strategies for the concerned renewable power supply system, with one being based on virtual synchronous generator (VSG) and another one being based on V/f control. Then, the impedance analysis is carried out for ensuring the small-signal stable operation of the sending-end system including wind power plant and PV plant. Numerical simulation results implemented on PSCAD verify that the VSG-based grid-forming strategy configured on the sending-end modular multilevel converter (MMC) station of the MMC-based high-voltage direct-current (HVDC) link has a larger transient stability margin. Hence, the MMC-HVDC-based grid-forming strategy is a better choice for the power supply of large-scale remote hydrogen production. The enhanced stability margin ensures more robust operation under disturbances, which is critical for maintaining continuous power supply to large-scale electrolyzers.

Abstract An increasing number of countries are committing to carbon neutrality and plan a massive rollout of solar PV to meet this goal. Owing to its seasonal production patterns, solar PV electricity will become less valuable during summer and more valuable during winter, when electricity prices increase accordingly. Consequently, optimizing PV plants to balance seasonal production variability will become increasingly important. This can be achieved via deploying PV in locations with higher irradiation in winter and/or by changing the angle of PV panels. Current solar PV support policies overlook production seasonality, risking a lock-in of projects optimized for annual production, which is dominated by irradiation during the summer. Here, we analyze household-scale rooftop PV in Switzerland, applying a cash flow model to evaluate policies designed to boost winter solar production. Our findings show that seasonally differentiated feed-in tariffs increase the economic incentives for winter electricity generation in areas with the highest winter solar potential. By contrast, investment subsidies, which are resource agnostic, prove less effective in directing PV to high-resource locations. This analysis underscores the importance of regionally tailored policies to promote winter-adjusted PV plants, an approach transferable to regions with seasonal solar variation, and large geographic altitude differences.

In this work, it is shown how a reliable nitrate based composite phase change material (PCM) of low melting point and extended operating temperature range can be formulated, to be used in applications for medium-low temperature thermal energy storage. A eutectic ternary nitrate system of (NaNO3-KNO3-LiNO3) has been used as the phase change matrix, to which nanoparticulate graphene has been used as a functionalized additive material. The thermophysical characterization and morphological tests show that the doping of graphene can effectively manipulate the thermal properties and micro morphology of the ternary nitrate composite. It is shown that the addition of graphene results in a significant increase in the initial decomposition temperature compared to the neat salt system. Of the composite systems made, the one with 1.0 wt% of graphene loading gave the highest initial decomposition temperature, maximum melting temperature (Tm), peak enthalpy (ΔH) of phase transition. Microscopic studies have shown that a graphene loading of 0.5 wt% gives the most uniform grain distribution within the composite. The results presented here give a conceptual basis for the use of composite ternary nitrate/graphene systems in concentrating solar power (CSP) energy systems. Demonstrated are the results obtained which show the potential of these materials for efficient thermal energy storage in renewable power technologies. This study describes the preparation of a low melting point and thermally stable nitrate composite phase change material (PCM) usable for medium-low temperature thermal energy stores. The eutectic ternary nitrate system based on NaNO3-KNO3-LiNO3 having the finely adjusted composition of 12:53:35 by weight building upon the well-known Solar Salt and Hitec salt, was employed as the phase change matrix while the functional additive was graphene nanosheets. Thermophysical characterisation showed that the inclusion of graphene enhanced the thermal properties of the matrix substantially: the composite containing 1.0 wt% of graphene showed an increase in phase change enthalpy of 30.2% as compared to the pure ternary nitrate (from 4.00J/g to 5.21J/g), There was an increase of the initial decomposition temperature (from 597°C to 617°C) and a decrease of the melting temperature (from 97.6°C to 76.6°C) of 21.5%. The microstructural analysis showed that the addition of 0.5 wt% graphene favoured the most uniform grain distribution in the matrix. These results provide technical support for the use of ternary nitrate/graphene composites in CSP systems.

Accurate forecasting of solar power in utility-scale photovoltaic (USPV) systems is critical for grid stability but remains challenging due to meteorological variability and the large spatial scale of these systems. However, the choice of sliding window size in time-series forecasting remains underexplored. This study introduces a deep learning-based forecasting framework that systematically evaluates the impact of sliding window size on forecasting accuracy using multivariate time-series data. The data collected from a 25 MWac USPV system in Malaysia between August 2022 and April 2023, comprises 5-minute interval measurements of solar irradiance, module temperature and solar power output. Multiple deep learning (DL) models, namely LSTM, CNN and GRU across window sizes ranging from 12 to 288 steps and forecasting horizons of 1 to 12 hours were investigated. Results show that a 144-step window consistently improves accuracy over conventional one-step input methods, with LSTM outperforming other models by achieving up to 23.1% RMSE reduction, 30.7% MAE reduction and a 8.6% increase in R² at 60 minutes forecasting horizon. This work emphasizes the importance of window size selection in optimizing forecasting accuracy for USPV systems and supporting renewable energy grid integration. By improving forecasting capabilities, this research is expected to provide critical insights to enhance renewable energy integration into the grid system.

Abstract As the global demand for renewable energy sources increases, sea wave energy converters have emerged as a promising solution for harnessing the power of ocean waves. This review paper provides an in-depth analysis of ocean and sea wave energy harvesting with a focus on some key aspects of wave energy converters, including different types of converters and power take-off systems, a critical review of control methods, challenges and limitations of extracting sea wave power, and the potential for integration with other renewable energy resources like wind turbines. In this paper, a wide range of wave energy converters is considered, with a focus on their operational principles, benefits, and drawbacks. Additionally, various power take-off systems are discussed, highlighting their structure and efficiency in converting the captured wave energy into electricity. Furthermore, this review examines the control strategies employed to maximize the extracted power and protect the system from potential damage caused by harsh ocean conditions. The challenges of implementing wave energy converters, including environmental impact, economic feasibility, and technical constraints, are also addressed, along with potential solutions to address related issues. Finally, prospects, including harvesting wave energy from an array of converters, developing strategies to integrate wave energy converters with other renewable energy resources such as wind energy and solar power, and innovative construction of wave harvesters, are examined. The future research directions and areas of progress are outlined.

The shift towards renewable energy demands decision-making tools that unite economic performance with environmental stewardship and social equity. The conventional evaluation methods fail to consider these interconnected factors, which results in substandard investment results. The paper establishes a sustainability accounting system that uses the Elimination and Choice Expressing Reality (ELECTRE) method to optimize investment distribution between solar power, wind power, and bioenergy systems. The evaluation framework uses six performance indicators, which include cost efficiency and return on investment, together with CO2 emissions intensity, job creation, energy output, and financial sustainability indicators, like Net Present Value (NPV) and payback period. The barrier optimization algorithm solved the model in 10 iterations, which took 0.10 s to achieve an optimal objective value of 1.6929. The wind energy source demonstrated superior performance in every evaluation criterion because it achieved the highest concordance scores, lowest discordance levels, best payback period, and strongest NPV. The maximum allocation went to wind at 53.3%, while bioenergy received 31.0%, and solar received 16.7%. The optimized portfolio reached a total sustainability index (SI) of 1.70, which validates the method’s strength. The research shows that using ELECTRE with sustainability accounting creates an exact and open system for renewable energy investment planning. The framework reveals wind as the core alternative yet demonstrates how bioenergy and solar work together to support sustainable development across environmental and economic and social dimensions.

Advancements in solar power tower technology: innovations in optical systems and heliostat field design

This article presents the results of the suitability assessment and identification of favorable sites for photovoltaic solar power plants connected to the electricity grid in northern Benin. The integration of renewable energies into Benin's energy park constitutes a major challenge for which those in power are seeking an optimal solution. The north of the country has strong solar potential, we based our study on this region in order to evaluate the technical and economic feasibility of photovoltaic power plants. The method adopted consists first of analyzing the opportunity to choose potential sites through the combination of a geographic information system (GIS) and a hierarchical analytical process (AHP) approach. In this process, nine (9) factors were weighted, namely solar irradiation, grid connection infrastructure, topography, land cover and use, surface and soil characteristics, environmental risks, flooding, restricted areas and distances from the road and power grid to determine potential sites. Then thanks to the Technique of Order of Preference by Similarity with the Ideal Solution (TOPSIS), the distances were optimized for the identification of the most favorable sites. The results of the application of the selection criteria applied to the northern zone around the substations of the Beninese Energy Electrical located in Bembereke, Djougou, Kandi, Natitingou and Parakou made it possible to identify 25 potential sites then 15 favorable sites. This hybrid method has the advantage of determining both favorable sites and the capacities of the resulting power plants.

As the primary interface for integrating renewable energy sources such as wind and solar power into the grid, inverters are prone to inducing sub-/super-synchronous or medium-to-high-frequency oscillations during grid-connected operation under weak grid conditions. Optimizing the control structure of a single wind turbine inverter struggles to address multi-mode resonance issues comprehensively. Therefore, a cooperative control strategy for parallel-coupled inverters is proposed. First, a frequency-domain impedance reconstruction method for parallel wind turbines is proposed based on the phase-neutralizing characteristics and damping variation patterns of parallel-coupled impedances. Second, the damping characteristics of inverters are enhanced through the design of an additional damping controller, while the phase-frequency characteristics of wind turbines are improved using active damping based on notch filters. Finally, simulation models based on 2.5 MW permanent magnet synchronous generator (PMSG) units validate the effectiveness of the control strategy. Research results demonstrate that this cooperative control strategy effectively suppresses sub-/super-synchronous and medium-to-high-frequency oscillations: In the 0~300 Hz key oscillation band, the amplitude suppression rate of oscillating current reaches ≥60%, the total harmonic distortion (THD) of the 5th harmonic at the grid connection point decreases from 4.465% to 3.518%.

In the pursuit of sustainable industrial operations, efficient energy management has become a critical challenge, particularly under scenarios where the electrical grid is restricted to serving industrial loads. This study addresses the urgent need for intelligent forecasting and scheduling frameworks by proposing a hybrid Gene Expression Programming Adaptive Neuro-Fuzzy Inference System (GEP-ANFIS) for predictive energy management in hybrid renewable energy systems. The model was evaluated using standard forecasting metrics. For solar PV prediction, GEP-ANFIS achieved low short- and long-term error rates, with MAPE values below 6% and 8%, respectively. For industrial load forecasting, the model exhibited high precision, maintaining MAPE values under 2.5% (short-term) and under 3.5% (long-term). These results demonstrate consistent improvements over conventional ANFIS and GEP models. Economic evaluation confirmed significant cost benefits. In a Grid-only configuration, GEP-ANFIS reduced daily energy costs by 7.4% compared to ANFIS. Greater efficiency was observed in PV and Battery-only and Grid-connected PV-Battery setups, where GEP-ANFIS achieved daily cost reductions of 6.5% and 6.3%, respectively. Over a 20-year planning horizon, the system recorded a 6.5% reduction over ANFIS and a 37.7% improvement over HOMER. A sensitivity analysis was also conducted to assess the robustness of the GEP-ANFIS model under varying solar PV power, and battery storage capacity. Results indicated the robustness, efficiency, and scalability of the GEP-ANFIS controller, especially in resource-constrained, PV-dominated microgrids, making it a strategic solution for sustainable industrial energy management while preserving battery longevity by avoiding deep discharge scenarios.

Abstract Integration of electricity based on intermittent renewable sources such as solar power to a grid can have adverse effect on electric power grid. In this work, we investigated the impact of integration of solar photovoltaic (SPV) on voltage stability. Six transmission buses (Kano, Kaduna, Gwagwalada, New Haven, Birnin Kebbi and Lokoja) with shortest distance to each of the 13 proposed locations have been identified and each of the Solar PV farms was integrated to the transmission bus closest to the proposed solar farm sites. The effects of SPV integration on Transmission lines loading have been performed and the Nigeria 56-bus transmission network was used for the investigation. Voltage stability analysis was carried out using the load margin obtained from the PV curve at each of the six identified buses and effect of SPV integration on the system voltage profile was identified. Sensitivity analysis was also performed in order to obtain the impact of increasing penetration on the voltage stability. The investigation was conducted using DigSilent Power Factory and MATLAB. The result shows that the safe region of integration for the six identified transmission buses is between 10 % (365.8 MW) at Gwagwalada bus and 19 % (695 MW) of base load power at Kaduna. 1 % of SPV was integrated simultaneously at Egbin, Ikeja West, Akangba, Sakete, Kano, Aja, Alagbon, and Osogbo with voltages lesser than 0.95 pu at the base case and the result reveals that all the buses in the system are within acceptable voltage level of 0.95–1.05 pu. Highest improvement in load margin is achieved when 1 % SPV is integrated at Kaduna bus among the six transmission buses considered. Different locations affect system load margins and voltage stability differently. 1 % integration of SPV at different buses significantly improve the load margin from 1,107.7 MW (Birnin Kebbi) to 1,448.9 MW (Kaduna).

Predicting solar power production accurately is becoming more and more crucial for efficient power management and the grid's integration of renewable energy sources. Using data from an Australian photovoltaic (PV) power station, this study employs a variety of machine learning (ML) ensemble techniques, such as gradient boosting (GB), random forest (RF), and extreme gradient boosting (XGBoost), to forecast solar power production. ML models are developed utilizing pertinent information from electricity and meteorological data in order to forecast solar power. The predictive performance of trained ML models is verified in terms of metrics like mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R<sup>2</sup>). With higher R<sup>2</sup> values and lower error results (MAE and RMSE), XGBoost performs better than GB and RF. Optimizing the hyperparameters of the XGBoost model significantly improves its performance. The tweaked XGBoost model shows a significant improvement in R2 (more than 5% to 10%) and error results (reduced MAE and RMSE by 0.01 to 0.06), when compared to other ensemble approaches. Compared to other ensemble approaches, the tuned XGBoost methodology is more robust and generates more accurate forecasts in solar power.

<p><span lang="EN-US">This paper presents a hybrid one-step voltage-adjustable transformerless inverter designed to efficiently integrate both solar photovoltaic (PV) and wind energy sources into a single-phase grid. The primary objective is to enhance power conversion efficiency while minimizing system complexity and cost. The proposed architecture combines a buck-boost DC-DC converter with a full-bridge inverter in a compact and modular design, enabling voltage regulation across a wide input range typical of hybrid renewable systems. By grounding the PV negative terminal, the system effectively eliminates leakage currents and ensures compliance with IEEE harmonic standards. The inverter operates with reduced switching losses and supports multiple operational modes tailored for variable solar and wind conditions. Simulation of a 300 W prototype demonstrates reliable performance, achieving a total harmonic distortion (THD) below 1%, validating its compatibility with grid requirements. Key contributions include the development of a unified topology for hybrid energy sources, in-depth analysis of energy storage components, and implementation of efficient modulation strategies. This work addresses significant challenges in renewable energy integration and provides a scalable solution for next-generation grid-connected hybrid power systems</span><span lang="EN-US">.</span></p>

Solar electricity without solar panels: Changes in consumption behavior due to community solar programs

Integration of solar energy into the grid is the most important aspect for achieving sustainable energy systems. This paper presents an artificial neural network-based maximum power point tracking (ANN-MPPT) system with battery storage to enhance grid efficiency. The proposed ANN-MPPT is dynamically adapted to the varying irradiance and temperature, hence ensuring optimal power extraction from the photovoltaic system. Excess energy is stored in batteries during high solar radiation and discharged when solar generation is low or grid demand is high, maintaining a stable power supply. This system enhances the grid performance in terms of supporting real-time energy exchange, load balancing, and grid stability. Efficient management of the energy fluctuations ensures reliability even at times of grid failures. Further, integration of ANN-based MPPT with battery storage reduces dependence on non-renewable sources and harmonizes solar energy utilization. It can be achieved through enabling smarter energy management and thus contributing to the resilience and efficiency of a grid for better integration of renewable energies. The proposed system can tolerate fluctuating grid demands apart from supporting the features of smart grid, hence viable for increasing stability and sustainability in the grid.

Spatial optimization of solar PV and wind power capacity in Finland and correlation analysis

Performance evaluation of progressively eccentric double-coated absorber tubes on boosting the techno-economic metrics of solar thermal power systems

Thermal storage integrated solar hybrid power plant capacity planning and coordinated peak regulation analysis

Firefly-optimized ensemble learning framework for accurate solar PV power forecasting