Renewable Energy Focus最新文献

{"title":"Control co-design under uncertainty for offshore wind farms: Optimizing grid integration, energy storage, and market participation","authors":"Himanshu Sharma,&nbsp;Wei Wang,&nbsp;Bowen Huang,&nbsp;Buxin She,&nbsp;Thiagarajan Ramachandran","doi":"10.1016/j.ref.2025.100806","DOIUrl":"10.1016/j.ref.2025.100806","url":null,"abstract":"<div><div>Offshore wind farms (OWFs) are crucial for decarbonization, but their grid integration is often planned using sequential optimization, which leads to suboptimal performance by overlooking resource and market uncertainties. To address this, we propose a novel control co-design framework that simultaneously optimizes OWF design, control, and market participation decisions under uncertainty. Our approach includes optimal sizing of onshore energy storage and considers participation in both energy and primary frequency markets. Applying this framework to five U.S. west-coast OWF locations identified by the Bureau of Ocean Energy Management (BOEM), our results show that the optimized co-design solution increases market revenue by 3.2% while enhancing flexibility against wind resource uncertainties.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"57 ","pages":"Article 100806"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976873","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":"BC-AFBTM: bubble-net communication optimization based adaptive fuzzy neural network with bidirectional long short-term memory for power quality improvement","authors":"Harshal Vitthalrao Takpire ,&nbsp;Mukesh Kumar ,&nbsp;Saurabh Sureshrao Jadhao","doi":"10.1016/j.ref.2026.100818","DOIUrl":"10.1016/j.ref.2026.100818","url":null,"abstract":"<div><div>Power quality (PQ) is mandatory to ensure the safety, energy efficiency, performance of the equipment, environmental standards and production targets. However, overheating and breakdown of machines reduce the PQ, which increases the maintenance costs and reduces the lifespan of the machine. Conventional control strategies had poor dynamic performance and limited adaptability, while considering complex and non-linear conditions. Therefore, the research work implements the Bubble-net Communication optimization based Adaptive Fuzzy Neural Network-Bidirectional Long Short-Term Memory (BC-AFBTM<strong>)</strong> Model on the Unified Power Quality Conditioner (UPQC) device, to mitigate the harmonic issues such as sag, swell, interruptions and harmonic fluctuations, which contribute to enhance the PQ and stability performance of the power system. The BC-AFBTM model provides a precise compensating current and voltage signal to inject into the series and shunt converter for PQ issues mitigation. The BC algorithm optimizes the gain parameters of the converters to enhance the model performance. Eventually, the BC-AFBTM model outperformed the efficient results of injected reactive power, injected voltage, load voltage, power and reactive power, which exhibit the values are 49.01kVAR, 0.9656V_pu, 0.9953V_pu, 202.32KW and 231.78kVAR in load side, respectively.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"57 ","pages":"Article 100818"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077740","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":"Sample-based robust unit commitment with scenario classification","authors":"Shuzhen Zheng ,&nbsp;Hanjiang Dong ,&nbsp;Jizhong Zhu ,&nbsp;Haosen Yang ,&nbsp;Shenglin Li","doi":"10.1016/j.ref.2026.100814","DOIUrl":"10.1016/j.ref.2026.100814","url":null,"abstract":"<div><div>This paper proposes a two-stage distributionally robust optimization (DRO) model for unit commitment (UC) that incorporates representative scenarios. By constructing an ambiguity set that considers representative scenarios, the model effectively addresses the challenges posed by uncertainties in both power generation and load demand in UC problems. First, we formulate a two-stage DRO mathematical model for UC based on the Wasserstein distance. Then, we construct a classification-based ambiguity set with representative scenarios. Subsequently, we apply linear decision rules (LDR) and duality theory to reformulate the original model into a tractable mixed-integer linear programming (MILP) problem. Finally, we conduct a comprehensive case study on 14-bus, 30-bus, 57-bus, and 118-bus test systems, evaluating the model from four different perspectives. The results demonstrate that the proposed model, incorporating representative scenarios, exhibits superior stability and robustness.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"57 ","pages":"Article 100814"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037585","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":"Decarbonizing maritime logistics through hydrogen-powered container ships","authors":"Reza Babaei,&nbsp;David S-K Ting,&nbsp;Rupp Carriveau","doi":"10.1016/j.ref.2026.100823","DOIUrl":"10.1016/j.ref.2026.100823","url":null,"abstract":"<div><div>Maritime shipping underpins global logistics but remains a source of greenhouse gas emissions, motivating the evaluation of low-carbon fuels and port-based supply infrastructure. This study assesses the techno-economic and environmental feasibility of hydrogen-powered small feeder container ships at the Port of Los Angeles (POLA) using a framework combining optimal sizing, techno-economic modeling, and sensitivity analysis. A hybrid hydrogen supply system is optimized, consisting of 500 kW PV, a 438 kW converter, a 7 MW electrolyzer, and a 300 kg H₂ tank, producing 716,538 kg H₂/yr to meet ship fueling demand. The optimal configuration achieves a levelized cost of hydrogen (LCOH) of $3.54/kg H₂, a 14-year payback period, and a net present cost (NPC) of $104.9M over a 30-year lifetime. Sensitivity analysis indicates that ship speed is the dominant operational driver: increasing speed from 8 to 24 knots raises hydrogen demand from 89,600 kg/y to 2.42 million kg/yr, while reducing LCOH from $11.99/kg H₂ to $1.33/kg H₂ due to higher utilization. Vessel weight further affects system performance, with favorable cost-power trade-offs observed near 8000–8500 t displacement. Solar resource availability also influences economics, with lower NPC and operating costs occurring near 5 kWh/m²/day. Environmental performance is evaluated using the normalized metric grams CO₂/TEU-nautical mile, highlighting scale-related efficiency effects across vessel classes: small feeder ships exhibit higher emissions intensity (153 g CO₂/TEU-n.mi) than large container ships (35 g CO₂/TEU-n.mi), despite substantially lower absolute hydrogen demand. A real-world POLA-Shanghai case study further demonstrates scalability requirements for long-distance routes and port-level hydrogen supply planning.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"57 ","pages":"Article 100823"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188277","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":"Enhancing photovoltaic system flexibility: a novel integrated converter with COA optimization approach","authors":"G. Madhusudanan ,&nbsp;S. Padhmanabhaiyappan","doi":"10.1016/j.ref.2025.100808","DOIUrl":"10.1016/j.ref.2025.100808","url":null,"abstract":"<div><div>This manuscript presents a Crayfish Optimization Algorithm (COA)-based strategy to enhance the performance of photovoltaic (PV) systems integrated with AC/DC microgrid converters. The proposed method adaptively tunes controller parameters to achieve high step-up voltage, reliable AC and DC outputs, and improved power quality. The COA efficiently optimizes the system, providing faster convergence compared to conventional approaches like Genetic Algorithm (GA) and Spotted Hyena Optimizer (SHO). Simulation outcomes establish that the proposed approach achieves the lowest error of 1.32 and THD of 2.56% outperforming existing approaches. These outcomes indicate that the proposed technique not only develops energy conversion efficiency but also ensures stable, reliable, and high-quality power delivery, which is crucial for modern microgrid operations. The improved performance is attributed to the COA’s adaptive parameter tuning and multi-objective optimization, which enable robust operation under variable conditions and enhance overall energy conversion. These results highlight the proposed method’s potential for efficient, reliable, and high-quality PV energy integration in modern distribution networks.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"57 ","pages":"Article 100808"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976874","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":"Comprehensive overview of photovoltaic plants operations and maintenance using UAV-based IR imaging and advanced image analysis techniques","authors":"Vítor Lopes ,&nbsp;Daniel Rocha ,&nbsp;João Alves ,&nbsp;Jennifer Teixeira ,&nbsp;Paulo Fernandes ,&nbsp;Mauro Costa ,&nbsp;Modesto Morais ,&nbsp;Pedro Salomé","doi":"10.1016/j.ref.2026.100825","DOIUrl":"10.1016/j.ref.2026.100825","url":null,"abstract":"<div><div>With the rapid expansion of photovoltaic (PV) energy production and the consequent growth of large-scale solar plants, traditional manual operation and maintenance (O&amp;M) practices for managing solar plants will become impractical due to their high cost and complexity. The effective management of these solar plants now demands the integration of advanced technologies to ensure enhanced efficiency, flexibility, and safety. In this paper, we review the integration of Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, equipped with thermal and infrared (IR) cameras, alongside sophisticated algorithms for data analysis. Individually, the domains of PV, UAVs, machine learning, and fault analysis exhibit significant growth potential. This study investigates how their convergence can revolutionize O&amp;M practices within the PV sector. By leveraging infrared cameras mounted on UAVs programmed to follow predefined flightpaths allowing for automated image recognition and failure detection, the complexity and resource requirements of maintaining large-scale solar plants can be substantially reduced. Our work encompasses a comprehensive overview of this interdisciplinary field, with a specific focus on UAVs and IR hardware, advanced algorithms, and the identification of common defects. Furthermore, we conduct a meticulous analysis of the existing literature to elucidate the untapped potential and synergies within this specialized domain.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"57 ","pages":"Article 100825"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188278","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":"Active power control for managing ramp events in NCR power plants within small-scale power systems","authors":"N.T. Senarathna ,&nbsp;S.P. Somathilaka ,&nbsp;H.M. Wijekoon Banda ,&nbsp;K.T.M.U. Hemapala","doi":"10.1016/j.ref.2025.100796","DOIUrl":"10.1016/j.ref.2025.100796","url":null,"abstract":"<div><div>Paper addresses the challenge of managing ramp events in non-conventional renewable (NCR) plants within small-scale, isolated power systems with high renewable energy penetration. Approach integrates real-time monitoring of generation, storage availability, and system dynamics to regulate power output effectively. Simulation-based methodology is employed to analyze system behavior during solar ramp events under varying NCR penetration levels. Results are used to determine the maximum ramp rate a power system can withstand, while maintaining operational margins. Novel Active Power Control (APC) strategy is proposed to mitigate power intermittency, enhance system stability and reliability, and achieve given ramp limits within system constraints, supported by storage sizing model based on 100 diverse generation scenarios. Findings demonstrate the effectiveness of the proposed APC system in managing ramp events in 94% of cases, with recommended storage capacity of 125% of plant’s rated output to cover 95% of typical generation patterns. Solution offers technically robust pathway for improving reliability of small-scale power systems with significant renewable integration.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"57 ","pages":"Article 100796"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693397","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":"Interdisciplinary optimization of potential analyses for renewable energies by integrating socio-psychological parameters","authors":"Julia Offermann ,&nbsp;Mona Frank ,&nbsp;Barbara S. Zaunbrecher ,&nbsp;Thorsten Reichartz ,&nbsp;Jannik Wendorff ,&nbsp;Lucas Blickwedel ,&nbsp;Laura Wendorff ,&nbsp;Christa Reicher ,&nbsp;Ralf Schelenz ,&nbsp;Martina Ziefle","doi":"10.1016/j.ref.2025.100747","DOIUrl":"10.1016/j.ref.2025.100747","url":null,"abstract":"<div><div>In view of climate change and recent political developments, the energy system transformation in Germany continues to gain in importance. Although the expansion of renewable energies is a national goal, implementation takes place at the local level in close interaction with society. Citizen action groups and court cases against renewable energy plants indicate that what is feasible from a technological and economic perspective might not correspond to what is socially accepted or tolerated. Reliable predictions about the feasible potential of renewable energies can thus only be achieved when political, economic, technological, and social requirements on a local scale are considered in a systematic and holistic way. To address this current research gap and support the stakeholders of the energy transition in their decision-making, this article proposes the idea of an interdisciplinary, cross-technological approach to developing a reliable perspective and an expansion concept for the successful implementation of the energy transition by considering parameters of acceptance already in the forecasting of the feasible potential. This approach aims at expanding existing techno-economical potential estimations for renewable energies by the societal dimension to approximate the realistic potential of specific sites and thus predict the feasibility of renewable energy projects more accurately. We exemplify how engineering, urban planning, and social science expertise can be integrated and combined for the forecasting of the feasible potential of renewable energies. Additionally, we identify the challenges that come with this interdisciplinary approach. The proposed approach serves three aims. First, it serves as an example of how interdisciplinary approaches to transformation processes, such as the energy system transformation, can have added value for forecasting and feasible potential analyses. Second, it exemplifies how an interdisciplinary approach to energy transformation can be achieved methodologically. Third, once executed and applied, it can outline the path toward a sustainable and accepted energy transformation.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100747"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933713","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":"Assessing financial feasibility and equity prospects in agrivoltaics: a case study of Hachinohe, Japan","authors":"Xiao Chen ,&nbsp;Vibhas Sukhwani ,&nbsp;Bijon Kumer Mitra ,&nbsp;Anudari Batsaikhan ,&nbsp;Rajib Shaw","doi":"10.1016/j.ref.2025.100751","DOIUrl":"10.1016/j.ref.2025.100751","url":null,"abstract":"<div><div>Agrivoltaics, which combines the use of land for both agriculture and photovoltaic energy production, is emerging as a promising solution to the land use conflicts between farming and renewable energy. By simultaneously boosting crop yields, enhancing solar panel efficiency and revitalising rural incomes, agrivoltaics is attracting farmers and solar developers, prompting innovation in technology and business models. Nevertheless, the key to scaling agrivoltaics depends on its commercial viability for different stakeholders, an area that still requires further exploration. To bridge this gap, this research examines the business model of an ongoing agrivoltaic project in the Hachinohe region of Aomori Prefecture, Japan, with a particular focus on financial feasibility and equitable distribution of benefits among stakeholders. The study applies multi-criteria decision-making method to assess the project’s overall financial feasibility using NPV, IRR, and payback periods, while also exploring the equity implications through Gini coefficients. Thereafter, a sensitivity analysis is conducted to offer policy suggestions such as revenue-sharing, better lease terms, and subsidies for farmers, with the purpose of enhancing rural economic revitalization and inform equitable business model design in Japan’s energy transition. Drawing on experiences from Europe and the United States, this research emphasizes the active engagement of farm owners in the development and implementation of agrivoltaic projects to enhance financial feasibility, equity and stakeholder participation.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100751"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106328","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":"Mitigating emissions: energy balancing in eco-industrial zones considering renewable energy and electric vehicle uncertainties","authors":"Aminabbas Golshanfard ,&nbsp;Younes Noorollahi ,&nbsp;Hamed Hashemi-Dezaki ,&nbsp;Henrik Lund","doi":"10.1016/j.ref.2025.100768","DOIUrl":"10.1016/j.ref.2025.100768","url":null,"abstract":"<div><div>Nowadays, the industrial sector stands as the major energy consumer globally, simultaneously holding a pivotal role as a significant contributor to greenhouse gas emissions. Therefore, energy system planning and management in these systems are under heightened scrutiny due to concerns over energy, economic, and environmental challenges. This study aims to develop a comprehensive optimal model that integrates renewable potential assessment and utilizes particle swarm optimization for accurate and cost-effective planning and operation of the energy system within an industrial zone. The research proposes a novel strategy for planning and operating industrial energy hubs, offering a robust and adaptable framework tailored to industrial zones. By integrating uncertain renewable energy sources and EVs, the framework effectively manages variability and uncertainty. It holistically connects electricity, heating, cooling, and transportation sectors, enabling cross-sectoral flexibility and enhancing system adaptability. The study compares four scenarios: BAU, BAU CO₂-Aware, CO₂-Blind, and CO₂-Aware, evaluating their impact on energy costs, investment, operational cost, and environmental benefits. The results show that the CO₂-Aware and CO₂-Blind scenarios reduce overall costs by approximately 15% and 10%, respectively, compared to the BAU. Additionally, the CO₂-Aware scenario achieves a 32% reduction in CO₂ emissions. Despite higher investment and operational costs, these alternative energy systems provide substantial economic and environmental advantages. Additionally, the implementation of this smart energy system within the industrial zone has addressed certain energy challenges in the studied region, such as mitigating electricity shortages during summer and alleviating natural gas shortages in winter.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100768"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266727","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}