Renewable Energy Focus最新文献

{"title":"Assessing second-life li-ion batteries for hybrid PV-diesel systems in remote amazonian communities","authors":"Fábio L.F. Faria,&nbsp;Daniel O. dos Santos,&nbsp;Aline K.V. de Oliveira,&nbsp;Ricardo Rüther","doi":"10.1016/j.ref.2025.100770","DOIUrl":"10.1016/j.ref.2025.100770","url":null,"abstract":"<div><div>Electrifying remote Amazonian communities entails substantial logistical and financial hurdles. Diesel generators remain the dominant power source in most isolated sites. Solar photovoltaic (PV) reduces fuel use, operating costs, and greenhouse-gas emissions relative to diesel-only operation. Because solar generation is intermittent, dependable supply requires efficient energy storage. Second-life lithium-ion batteries (SLBs), repurposed from electric-vehicle (EV) packs, can reduce system costs without compromising technical performance. We investigate SLB integration in PV–diesel hybrids using two Brazilian Army Special Border Platoons (SBPs) as case studies, which differ in renewable penetration. Based on HOMER Pro© simulations, a fully renewable setup with diesel as backup favors SLBs over new Li-ion batteries when the SLB price per kWh is below approximately 75 % of the price per kWh of new Li-ion batteries. For every 10 % reduction in SLB price, the project net present cost (NPC) falls by 3.2 %. In a configuration with approximately 40 % renewable penetration and active diesel generation, the NPC falls 1.4 % for each 10 % reduction in SLB price. It rises about 2.0 % for each 10 % increase in diesel price. In this context, increasing PV capacity is particularly effective between 125 % and 300 % of the baseline. Within this range, NPC falls by approximately 28 % per doubling of PV capacity. Beyond 300 % of the baseline, additional PV yields no further significant NPC reductions. Consequently, SLBs deliver a dual benefit: lower-cost PV intermittency management and reduced waste through delayed end-of-life.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100770"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145332528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Location, location, location: optimal placement of new electricity production in the nordic energy system amidst large-scale electrification","authors":"Joel Bertilsson,&nbsp;Lisa Göransson,&nbsp;Filip Johnsson","doi":"10.1016/j.ref.2025.100765","DOIUrl":"10.1016/j.ref.2025.100765","url":null,"abstract":"<div><div>Renewable electricity generation is expected to play a pivotal role in the global shift toward electrification. However, the inherent variability of renewable energy sources, in addition to factors such as local weather patterns and grid limitations, poses a significant challenge in terms of determining the optimal size and placement of distributed generation units. This study tackles this issue by applying a novel, high-resolution energy systems model that is tailored to the Nordic region. The model is designed to capture with high accuracy local nuances in relation to grid infrastructure, weather patterns, and demand profiles. The model minimizes the total system costs, accounting for both investment and operational expenditures, through the optimal integration of variable renewable energy sources and dispatchable generation units. The findings indicate that the siting of renewable generation is primarily influenced by a combination of a high number of full-load hours and proximity to the electricity demand, with the latter becoming increasingly important under high-demand conditions. Among renewable technologies, solar photovoltaic systems exhibit the strongest correlation with demand center proximity, whereas offshore wind is mainly constrained by a high potential annual production capacity. In addition, assumptions regarding the availability of electricity grid capacity are shown to have a significant impact on the results, with up to 26% of production being relocated when 100 % thermal grid capacity is available, as compared to when 30% of grid capacity is reserved for contingency events.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100765"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart control of standalone solar-powered oil press: Applying Reinforcement Learning for productivity and energy utilization improvement","authors":"Wiomou Joévin Bonzi ,&nbsp;Zhangkai Wu ,&nbsp;Sebastian Romuli ,&nbsp;Klaus Meissner ,&nbsp;Joachim Müller","doi":"10.1016/j.ref.2025.100772","DOIUrl":"10.1016/j.ref.2025.100772","url":null,"abstract":"<div><div>In resources constrained rural areas, solar-powered oil extraction can be enhanced through recent advances in artificial intelligence for energy optimization. This study introduces <em>SolPrInt</em>, a deep reinforcement-learning (DRL) based controller for a standalone, photovoltaic-battery powered mechanical oil press. A proximal policy optimization (PPO) agent was trained in MATLAB/Simulink using 15 years of PVGIS-SARAH2 radiation data and peanut-oil extraction benchmarks. A primary training phase followed by an adversarial phase on the 5% least-sunny days reinforced robustness under low-irradiance conditions. The developed agent adapts press rotational speed to real-time PV availability, battery state of charge, and system behavior to ensure energy-efficient use of solar resources. In-silico validation achieved stable rewards and simulated throughput of 96 ± 13.5<!--> <!-->kg/d under sunny days and 90 ± 20.5<!--> <!-->kg/d under cloudy days. Compared with conventional fixed-schedule operation (08:00–18:00) under sunny and cloudy conditions, <em>SolPrInt</em> extends operating time, and reduces power outages, while improves oil yield by 0.7 percentage points. Experimental validation on a PV-simulator bench confirmed real-time deployment feasibility on a low-cost ESP32 microcontroller interfaced with a Kern Kraft KK20 press. These findings demonstrate the potential of PV-sensitive DRL control to improve the performance of standalone renewable energy systems, supporting reliable decentralized energy use and contributing to sustainable energy access. Supplementary materials supporting this work, are available at <span><span>https://bonjoe.github.io/solprint.demo/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100772"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic viability of large-scale solar PV implementation in the Nordic power market: Case Finland","authors":"Dilshika Heenatigala Kankanamge ,&nbsp;Jaakko Jääskeläinen ,&nbsp;Sami Jouttijärvi ,&nbsp;Sanna Syri","doi":"10.1016/j.ref.2025.100750","DOIUrl":"10.1016/j.ref.2025.100750","url":null,"abstract":"<div><div>This study analyses how the rapid growth of utility-scale solar PV in the Nordic region will impact its economic viability by 2030, using Finland as a case study. The analysis is based on modelling the Nordic electricity market. Solar energy is crucial for the energy transition in the Nordic region; however, high penetration levels pose significant economic challenges. The lack of feed-in tariffs for solar PV, limited energy system flexibility, high shares of nuclear and wind power and ambitious solar expansion plans make Finland a topical case study. Using PLEXOS advanced electricity market simulation tool, this study models the Nordic and Baltic multinational electricity system in detail and its connections to the Central Western European power market. The study includes significant electricity demand changes from rapid increases in electric vehicles, district heating electrification, and hydrogen production. Multiple scenarios representing different solar PV levels across the Nordic regions are analysed by 2030, including one scenario with various shares of vertically mounted, east-west-oriented bifacial solar PV. Results indicate that large-scale solar PV integration in the Nordic region could collapse Finnish electricity prices, specifically in the summer months. This expansion would reduce solar capture rates to 40% by 2030, highlighting the solar cannibalisation in Finland. These findings indicate the potential economic risks of extensive solar PV deployment and the need for market adaptation. Integrating vertical bifacial panels to adjust the PV production profile and optimising the electric vehicle charging are found to be important to improve the economic viability of solar generation.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100750"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A spatial-techno-economic assessment modeling framework for optimal planning of rooftop photovoltaic systems in urban areas: the case of New Assiut City, Egypt","authors":"Mohammed Hussien Yadem Lamien ,&nbsp;Hooman Farzaneh","doi":"10.1016/j.ref.2025.100752","DOIUrl":"10.1016/j.ref.2025.100752","url":null,"abstract":"<div><div>Rooftop photovoltaic (PV) power generation can provide an efficient solution to urban energy needs, benefiting both individual homeowners and the broader community in urban areas. However, identifying suitable rooftop spaces and optimal planning of the rooftop PV systems requires a comprehensive approach. Unlike most previous studies that have concentrated on the technical aspects of rooftop PV systems, this study emphasizes both spatial and techno-economic factors. It explores how the optimal placement of rooftop PV systems can benefit customers and contribute to a more resilient and reliable energy system in newly constructed buildings. To this aim, this study presents a detailed methodology for determining the optimal placement of rooftop PV systems in newly constructed residential areas. By employing a mixed-integer linear programming (MILP) approach, the study aims to maximize the Net Present Value (NPV) of rooftop PV owners through strategically allocating solar PV panels in conjunction with designated central service areas, while adhering to spatial, technical, and economic constraints. The developed model is validated using site-specific data from a residential area, including 12 identical residential buildings in New West Assuit City, Egypt, a newly constructed but currently unoccupied district with significant solar energy potential. The results reveal that, with a latitude-based tilt angle of 27°, each building can support between 9 and 15 PV modules. In a scenario with 15% occupancy, up to 72% of the generated electricity can be sold back to the grid, resulting in the highest NPV. Conversely, a scenario with 100% occupancy leads to the lowest NPV due to the limited surplus energy available for export. Finally, a detailed sensitivity analysis is carried out to assess the impact of the tilt angle of PV panels and feed-in tariff (FiT) on NPV outcomes.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100752"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How do environmental values and attributes influence coastal community acceptance of tidal energy? Evidence from the Bristol Channel, UK","authors":"Andrew Edwards-Jones ,&nbsp;Caroline Hattam ,&nbsp;Tara Hooper ,&nbsp;Nicola J. Beaumont","doi":"10.1016/j.ref.2025.100748","DOIUrl":"10.1016/j.ref.2025.100748","url":null,"abstract":"<div><div>Early consideration of potential societal issues, including public acceptance, is important for the effective implementation of energy policies and technologies. Conversely, lack of public acceptance can act as a barrier to their uptake, and deployment of renewable technologies has frequently been marked by public opposition at the local level. Levels of support for renewable energy are currently high, although expressions of public support do not always translate into approval for local developments, and there is significant variability in acceptance depending on a wide range of attributes. This paper provides novel insights into potential contributing factors to public acceptance of tidal energy amongst residents of coastal communities along a major inlet in the UK with high potential for tidal energy development. Adopting a largely qualitative empirical approach, nineteen participants from three coastal towns took part in photo-elicitation interviews that utilised self-taken photographs to drive discussions around local environmental (marine and coastal) attributes of importance and how these might influence participants’ attitudes toward tidal energy. Selected data from a previous related energy survey provided a mixed-methods lens to reinforce specific issues raised by participants. Key findings on participants’ perceptions of tidal energy included overall general positivity toward this technology, as well as recognition of its significance for sustainable energy. A range of trade-offs between issues of personal importance and the wider significance of tidal energy were also apparent. The perceived impacts of developments on environmental attributes of greatest importance to participants were thematically analysed revealing particular concerns regarding local environmental impacts and impacts on wildlife. Presented as key influencing issues on participant’s perceptions of tidal energy developments, this new qualitative data improves our understanding of the issues that can lead to acceptance or rejection of proposals and are thus of relevance to a range of users, including decision-makers, consultants and developers.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100748"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-driven framework for microgrid design integrating machine learning model with economic-energy-environmental parameters","authors":"Abba Lawan Bukar ,&nbsp;Mahmoud Kassas ,&nbsp;Mohammad A. Abido ,&nbsp;Ahmed S. Menesy ,&nbsp;Babangida Modu ,&nbsp;Mukhtar Fatihu Hamza ,&nbsp;Djamal Hissein Didane","doi":"10.1016/j.ref.2025.100785","DOIUrl":"10.1016/j.ref.2025.100785","url":null,"abstract":"<div><div>This study proposes a data-driven framework for designing community microgrids that integrate photovoltaic systems, wind turbines, diesel generators, and battery storage. The framework optimizes microgrid configurations based on economic, energy, and environmental (3E) sustainability performance indicators (3E-SPI). To achieve these objectives, we developed a data-driven model that combines Homer-Pro with a custom Python tool integrating extreme gradient boosting (XGBoost) machine learning algorithm and thirteen 3E-SPI calculations for community microgrid systems. Subsequently, a multi-objective optimization model with a two-layer multi-criteria decision-making (MCDM) approach was employed to evaluate microgrid configurations based on thirteen 3E-SPI to support stakeholders in the decision-making process. In the first layer, Best Worst Method (BWM) determines the weights of the 3E-SPI, whereas in the second layer, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and VIˇsekriterijumsko Kompromisno Rangiranje (VIKOR) methods are used to rank microgrid alternatives. The predictive performance of XGBoost was compared with that of random forest (RF), support vector regression (SVR), and deep neural network (DNN). The analysis revealed that XGBoost outperformed other models, achieving superior predictive performance, with a coefficient of determination (R²) exceeding 0.95. The MCDM results indicate that hybrid photovoltaic/wind/battery/diesel microgrid is the optimal solution for the studied community, yielding a total net present cost of approximately $1.3 million, a levelized cost of energy of $0.29/kWh, and annual CO₂ emissions of 169.11 kg. Overall, the proposed framework provides a practical tool for policymakers and energy planners to design cost-effective, reliable, and sustainable microgrids.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100785"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Deep Geothermal Energy: Trends, Insights, and Future Perspectives","authors":"Danial Sheini Dashtgoli ,&nbsp;Michela Giustiniani ,&nbsp;Martina Busetti ,&nbsp;Claudia Cherubini ,&nbsp;Giulia Alessandrini ,&nbsp;Guillermo A. Narsilio","doi":"10.1016/j.ref.2025.100781","DOIUrl":"10.1016/j.ref.2025.100781","url":null,"abstract":"<div><div>Deep geothermal energy, known for its stable base load power and resilience to environmental fluctuations, is increasingly recognized as an important renewable energy source. Yet, its development is constrained by subsurface variability, high exploration costs, and operational inefficiencies. Artificial intelligence (AI) can analyze complex data, reveal patterns, and support predictive modeling to lower costs, shorten timelines, and improve efficiency. This review aims to evaluate how AI can address these barriers by systematically synthesizing its applications in deep geothermal research. A structured Web of Science search and multi-stage screening yielded 183 peer-reviewed journal papers, classified across eight research areas: reservoir characterization, exploration and resource identification, system optimization, seismic monitoring and risk assessment, drilling optimization, hybrid energy systems, environmental impact and sustainability, and techno-economic analysis. Our analysis shows that since 2020, AI applications in geothermal energy have expanded exponentially, surpassing overall AI growth rates. China and the United States dominate research output, followed by Germany, Turkey, Canada, and India. Advanced algorithms are increasingly preferred: convolutional neural networks for spatial modeling and image interpretation, recurrent neural networks for time-series forecasting, physics-informed AI, Bayesian frameworks, and autoencoders advance uncertainty quantification and data reconstruction. The novelty of this review lies in its comprehensive cross-domain synthesis of AI applications in deep geothermal energy, using a unified algorithm–input–output–performance lens. This structured mapping enables comparisons not possible in earlier overviews, reveals methodological strengths, identifies effective approaches for different geothermal tasks, and uncovers underexplored areas such as environmental assessment and techno-economic analysis.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100781"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SHapley Additive exPlanations-guided rule-based energy management: bridging machine learning interpretability and adaptive control strategies","authors":"Abdallah Abdellatif ,&nbsp;Hamza Mubarak ,&nbsp;Harikrishnan Ramiah ,&nbsp;Hazlie Mokhlis ,&nbsp;Saad Mekhilef ,&nbsp;Hassan Muwafaq Gheni ,&nbsp;Jeevan Kanesan","doi":"10.1016/j.ref.2025.100779","DOIUrl":"10.1016/j.ref.2025.100779","url":null,"abstract":"<div><div>The effective integration of photovoltaic (PV) systems with battery storage is essential for advancing sustainable energy adoption yet translating forecasts into adaptive and interpretable control remains a key challenge. This study introduces a SHapley Additive exPlanations–Guided Energy Management System (SHAP-EMS) that directly embeds model interpretability into real-time control for residential solar-battery systems. A hybrid Linear Regression–eXtreme Gradient Boost (LR-XGBoost) model provides one-hour-ahead PV forecasts, while a SHAP-weighted rule-based controller dynamically adjusts decision priorities based on feature importance, system state, and temporal interactions. Results demonstrate that SHAP-EMS achieved an 18.3% reduction in peak grid imports (63.2% to 44.9%), a 4.5% decrease in total imports compared with Mixed-Integer Linear Programming optimization, and consistently high self-consumption ratios under polycrystalline PV conditions. By efficiently adapting to temperature fluctuations and generation variability, the framework illustrates how SHAP values can be leveraged to transform black-box forecasts into transparent, computationally efficient, and adaptive control strategies, establishing a novel paradigm for explainable energy management.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100779"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robust framework for financial risk management of PV–EV systems under uncertainty in electricity markets","authors":"Vahid Parvaz","doi":"10.1016/j.ref.2025.100774","DOIUrl":"10.1016/j.ref.2025.100774","url":null,"abstract":"<div><div>The increasing penetration of Electric Vehicles (EVs) and Photovoltaic (PV) systems has introduced new energy management and financial challenges in the electricity market. The inherent fluctuations in PV generation and the unpredictable behavior of the EV charging load carry a significant risk of imbalance and heavy financial penalties. This paper proposes a comprehensive risk management framework for the optimal energy procurement decision of a PV-EV facility in the electricity market, aiming to minimize operational costs. The methodology was developed along two parallel paths: (1) heterogeneity analysis of EV charging load using K-Means clustering and (2) PV generation uncertainty modeling.</div><div>A hybrid statistical-machine learning model was adopted for uncertainty estimation, validating the prediction interval reliability with a Prediction Interval Coverage Probability (PICP) of 93.72%. Finally, the optimal energy procurement decision was formulated using a Robust Optimization model to guarantee load supply under the 90% lower bound of PV generation (<span><math><msubsup><mi>P</mi><mrow><mn>5</mn><mo>%</mo></mrow><mrow><mi>PV</mi></mrow></msubsup></math></span>​). The results show that adopting the robust decision, assuming day-ahead market prices (C^DA=50 UP/kWh) and imbalance settlement (C^RT=70 UP/kWh), leads to a total operational cost of 68.03 million UP over a one-year testing period. Compared to the ideal (deterministic) model, this cost includes a risk cost of 5,546,926.32 UP (equivalent to 7.53% of the deterministic model’s cost). This additional cost quantifies the economic value of uncertainty information and ensures the system’s 93.84% stability in meeting demand. The findings suggest that the proposed framework not only offers technical solutions but can also provide effective guidance for operators and policymakers in electricity markets.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100774"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}