Renewable Energy Focus最新文献

A review of data-driven deep learning models for solar and wind energy forecasting 数据驱动的太阳能和风能预测深度学习模型综述

IF 4.2

Renewable Energy Focus Pub Date : 2025-07-21 DOI: 10.1016/j.ref.2025.100739

Shubham Shringi , Lalit Mohan Saini , Sanjeev Kumar Aggarwal

{"title":"A review of data-driven deep learning models for solar and wind energy forecasting","authors":"Shubham Shringi , Lalit Mohan Saini , Sanjeev Kumar Aggarwal","doi":"10.1016/j.ref.2025.100739","DOIUrl":"10.1016/j.ref.2025.100739","url":null,"abstract":"<div><div>Numerous papers using advanced Artificial Intelligence (AI) based models - such as deep neural networks (DNN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks - have been reported for solar and wind forecasting. However, a systematic quantitative comparison of these diverse studies remains underexplored. This paper presents a comprehensive review and critical comparison of data-driven forecasting methods based on key parameters, including forecasting horizon, input features, geographical location, forecasting accuracy, training/testing period data length, pre-processing techniques, model architecture, activation functions, training algorithms, and the simulation platforms. Special emphasis is placed on data preparation strategies and model optimization techniques that significantly influence forecasting performance and model robustness. The scope is focused on purely data-driven AI and hybrid approaches, excluding physical and statistical models. An exploration of the strengths and weaknesses of these methods underscores the significance of hybrid models, particularly those combining DNN. A key contribution of this study lies in its structured synthesis of performance outcomes from various reported works, methodically arranged by increasing testing data duration. This organization aids in identifying consistently reliable and high-performing models. The findings highlight the superior accuracy and adaptability of hybrid AI models, offering practical guidance for researchers, developers, and stakeholders in renewable energy forecasting and planning.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100739"},"PeriodicalIF":4.2,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694322","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}

引用次数: 0

An adaptive robust planning method for data center micro-energy system towards flexibility enhancement 一种增强数据中心微能源系统灵活性的自适应鲁棒规划方法

IF 4.2

Renewable Energy Focus Pub Date : 2025-07-20 DOI: 10.1016/j.ref.2025.100742

Lijun Yang , Baiting Pan , Qinglong Duan

{"title":"An adaptive robust planning method for data center micro-energy system towards flexibility enhancement","authors":"Lijun Yang , Baiting Pan , Qinglong Duan","doi":"10.1016/j.ref.2025.100742","DOIUrl":"10.1016/j.ref.2025.100742","url":null,"abstract":"<div><div>Driven by the economic, flexibility, and sustainability requirements of data center (DC) development, a key challenge lies in optimizing the capacity planning of diverse energy devices within DC micro-energy systems. Thus, to further exploit the flexibility potential and ensure robustness, a two-stage adaptive robust methodology is proposed, based on an innovative architecture for DC micro-energy system. First, to achieve the efficient utilization of energy, dual-condition heat pumps is integrated into the system architecture with seasonal waste heat recovery (WHR) strategy. Second, a novel batch load demand response (DR) model with a differential compensation scheme is proposed, uniquely incorporating users’ fatigue effect and trust process, to incentivize load participation. Finally, a two-stage adaptive robust planning model that accounts for planning flexibility is developed, utilizing gaussian process regression (GPR) to capture key features of forecasted data. Case studies demonstrate that compared to conventional waste heat recovery strategies, carbon emission reductions increased by 79% and investment costs for photovoltaic, gas turbine and energy storage systems were reduced by 9.1%, 14.3% and 8.6%, respectively. And compared to scenarios that omit planning flexibility, the planning costs can be reduced by approximately 0.5% to 4.9%. Through the incorporation of resource and planning flexibility, alongside the refinement of the uncertainty set, the flexibility of the system is unlocked, while the stability and economy of the planning results are guaranteed.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100742"},"PeriodicalIF":4.2,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687428","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}

引用次数: 0

Engaging the Next Generation: the role of youth in promoting demand response participation in Ghana 参与下一代：青年在促进加纳需求响应参与中的作用

IF 4.2

Renewable Energy Focus Pub Date : 2025-07-19 DOI: 10.1016/j.ref.2025.100741

Timothy King Avordeh , Forson Peprah , Christopher Quaidoo , Rockson Opare-Boateng

{"title":"Engaging the Next Generation: the role of youth in promoting demand response participation in Ghana","authors":"Timothy King Avordeh , Forson Peprah , Christopher Quaidoo , Rockson Opare-Boateng","doi":"10.1016/j.ref.2025.100741","DOIUrl":"10.1016/j.ref.2025.100741","url":null,"abstract":"<div><div>Ghana’s energy sector struggles with power shortages (“dumsor”), increasing demand, and its reliance on fossil fuels. While Demand Response (DR) programs could improve grid stability and renewable integration, low participation persists due to awareness gaps and cultural barriers. This study examines youth (15-35 years) as potential DR catalysts, bridging digital literacy and traditional household decision-making. Through a mixed-methods approach (comprising 400 surveys, six focus group discussions, and 12 interviews), we found that urban youth had higher DR awareness (mean = 3.47) compared to rural youth (mean = 2.67). However, rural areas showed better energy-saving behaviors through community trust networks. Hybrid digital-community approaches (social media + radio) boosted engagement by 18-30%, outperforming top-down policies. Successful models from Kenya (Green Schools) and South Africa (#PowerShiftSA) demonstrate scalability. Key recommendations include integrating the DR curriculum, establishing youth task forces, and implementing mobile enrollment platforms. The research positions youth as active energy stakeholders, offering a framework for sustainable transitions in similar contexts through intergenerational engagement and culturally-adapted policy reforms.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100741"},"PeriodicalIF":4.2,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680333","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}

引用次数: 0

Optimizing biomass re-collection center locations for vietnam’s growing bioenergy industry: An integrated dematel and gra approach 优化越南不断发展的生物能源产业的生物质再收集中心位置：一个综合的dematel和gra方法

IF 4.2

Renewable Energy Focus Pub Date : 2025-07-16 DOI: 10.1016/j.ref.2025.100740

Thi Be Oanh Cao , Duc Duy Nguyen , Thi Diem Chau Le

{"title":"Optimizing biomass re-collection center locations for vietnam’s growing bioenergy industry: An integrated dematel and gra approach","authors":"Thi Be Oanh Cao , Duc Duy Nguyen , Thi Diem Chau Le","doi":"10.1016/j.ref.2025.100740","DOIUrl":"10.1016/j.ref.2025.100740","url":null,"abstract":"<div><div>This study introduces a process for selecting potential locations that not only supports better decisions by balancing multiple criteria such as economic, social, and environmental factors but also clarifies the relationships among these criteria and highlights the most influential ones. The objective is to help decision-makers accurately and comprehensively identify key criteria, understand their interrelationships, and quickly determine the best location choices. An integration of two techniques, including the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Grey Relational Analysis (GRA), is proposed. Particularly, DEMATEL is used to determine attributes’ weights and relationships. GRA supports decision-makers in choosing the best option. A case study, a re-collection center in 13 provinces in the South of Vietnam, under consideration of 15 criteria formed from three pillars of the sustainable supply chain, is to describe the proposed process. Data were gathered from interviews with nine experts and specialized departments, including the General Statistics Office of Vietnam, the People’s Committee of Can Tho City, the Department of Labor, Invalids and Social Affairs, and the Department of Planning and Investment. The results show that the local and government policy criteria have the highest weight, and Ben Tre province is the best location. These contribute to selecting optimal facilities with complex criteria quickly and effectively for other developing countries and promoting bioenergy production, such as in Vietnam.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100740"},"PeriodicalIF":4.2,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672203","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}

引用次数: 0

Modeling energy resilience and outage survivability of hybrid distributed energy resource system using REopt: A case study of a coastal institution 基于REopt的混合分布式能源系统能量弹性和停电生存能力建模——以某沿海机构为例

IF 4.2

Renewable Energy Focus Pub Date : 2025-07-10 DOI: 10.1016/j.ref.2025.100737

Md Mahmudul Hasan , Md Ezazul Kabir Shawon , Sayma Khandaker , Hadi Manap

{"title":"Modeling energy resilience and outage survivability of hybrid distributed energy resource system using REopt: A case study of a coastal institution","authors":"Md Mahmudul Hasan , Md Ezazul Kabir Shawon , Sayma Khandaker , Hadi Manap","doi":"10.1016/j.ref.2025.100737","DOIUrl":"10.1016/j.ref.2025.100737","url":null,"abstract":"<div><div>Energy systems must evolve to become more economical and resilient in response to the increasing frequency of climate-related disruptions. While several studies have presented renewable energy options, their resilience benefits, particularly in sensitive places, remain unexplored. This study analyzes the possibility of a hybrid distributed energy resource (DER) system to increase energy reliability and sustainability at the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), located in a coastal area with potential for flooding. The system configuration was evaluated using the REopt optimization software under an extended grid outage scenario utilizing a mixed-integer linear programming model. The results show that an efficient DER system could effectively maintain critical loads during long power outages while increasing energy autonomy. The findings additionally demonstrate that environmental performance and economic feasibility have significantly improved. Overall, this study indicates how hybrid DER systems can be used to improve energy resilience in vital infrastructures located in regions at risk of disasters.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100737"},"PeriodicalIF":4.2,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606033","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}

引用次数: 0

Cost optimization of electrolytic oxygen distribution: A mathematical framework with a green steel and hospital case study in Portugal 电解氧分配的成本优化：葡萄牙绿色钢铁和医院案例研究的数学框架

IF 4.2

Renewable Energy Focus Pub Date : 2025-06-30 DOI: 10.1016/j.ref.2025.100736

Florentin Eckl , Ana Moita , Rui Costa Neto

{"title":"Cost optimization of electrolytic oxygen distribution: A mathematical framework with a green steel and hospital case study in Portugal","authors":"Florentin Eckl , Ana Moita , Rui Costa Neto","doi":"10.1016/j.ref.2025.100736","DOIUrl":"10.1016/j.ref.2025.100736","url":null,"abstract":"<div><div>Decarbonizing the steel industry is crucial as it accounts for 7 % of global carbon emissions. Hydrogen-based direct reduced iron with electric arc furnace technology offers a sustainable solution. Both gases, hydrogen and oxygen, from electrolysis are necessary for steel production, however, in a different ratio than the electrolyzer delivers. This study evaluates the economic feasibility of distributing electrolytic excess oxygen from a green steel facility in Portugal to hospitals. Using a capacitated vehicle routing problem model, results show liquefaction (57 %), distribution (39.9 %) as key cost components, emphasizing the need for optimized logistics. Selling 6007 tons of excess oxygen yearly for 0.7 €/kg results in a net present value of €39.1 million over 20 years and a cost-benefit ratio of 8.25 and provides highly profitable application. The internal rate of return of 248 % and a discounted payback period of under half a year further underscore the project’s strong financial viability. This study confirms the economic benefits of valorizing electrolytic oxygen, providing a competitive alternative to conventional production while supporting steel decarbonization and medical oxygen supply.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100736"},"PeriodicalIF":4.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535763","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}

引用次数: 0

Techno-economic assessment of green hydrogen production via an optimized solar and wind system 通过优化的太阳能和风能系统进行绿色制氢的技术经济评估

IF 4.2

Renewable Energy Focus Pub Date : 2025-06-14 DOI: 10.1016/j.ref.2025.100726

Raj Sawhney , John Hearn , Ross Hibbett , Khaya Kingston , Makenna Parkinson , Mathias Zacarias , Joseph Majkut

{"title":"Techno-economic assessment of green hydrogen production via an optimized solar and wind system","authors":"Raj Sawhney , John Hearn , Ross Hibbett , Khaya Kingston , Makenna Parkinson , Mathias Zacarias , Joseph Majkut","doi":"10.1016/j.ref.2025.100726","DOIUrl":"10.1016/j.ref.2025.100726","url":null,"abstract":"<div><div>The economic feasibility of green hydrogen is integral to decarbonizing industries which are energy intensive and challenging to electrify via current power generation sectors. Cost-effective green hydrogen is dependent on the cost of both electrolyzer equipment and renewable energy production systems. In this study, the cost of producing green hydrogen was assessed worldwide. We produced a model using seven years of global solar and wind data to estimate the costs associated with off-grid, entirely emission-free, solar and wind hydrogen production facilities. These 6 costs were mapped across the world to visualize cost differences. We optimized for both the electrolyzer/renewable energy overfit and provide the option to combine solar and wind renewable systems, resulting in the lowest levelized cost of hydrogen at a given location. This procedure provides end users with the ability to toggle between wind, solar, and combined systems allowing for more realistic modeling and greater optionality. We incorporated country-level capital expenditure and operational expenditure data to more accurately model regional cost variations in global production costs. An analysis of these global costs resulted in a levelized cost of hydrogen range of $1 to > $5 per kilogram of hydrogen, in general agreement with public estimates.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100726"},"PeriodicalIF":4.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330420","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}

引用次数: 0

A global optimization framework for joint generation and transmission expansion planning with AC power flow representation 基于交流潮流表示的联产扩电规划全局优化框架

IF 4.2

Renewable Energy Focus Pub Date : 2025-06-14 DOI: 10.1016/j.ref.2025.100725

Ghazaleh Mozafari , Mahdi Mehrtash , Yankai Cao , Bhushan Gopaluni

{"title":"A global optimization framework for joint generation and transmission expansion planning with AC power flow representation","authors":"Ghazaleh Mozafari , Mahdi Mehrtash , Yankai Cao , Bhushan Gopaluni","doi":"10.1016/j.ref.2025.100725","DOIUrl":"10.1016/j.ref.2025.100725","url":null,"abstract":"<div><div>The integration of renewable energy generating units, often located in remote regions with limited grid connectivity, has created a pressing need for coordinated generation and transmission expansion planning (G&TEP). However, considering full AC network representation, the co-optimization of generation and transmission poses a challenging nonconvex mixed-integer problem that is prone to locally suboptimal solutions. In this study, we propose a tailored global optimization framework to identify the most cost-effective set of generating units and candidate transmission lines while satisfying operational and investment constraints. The proposed solver employs second-order cone relaxation, further enhanced through a set of relaxation-tightening constraints, along with feasibility-based and optimization-based bound tightening techniques to improve relaxation strength. A salient feature of the solver is the integration of a no-good cut technique, which enables efficient exploration of alternative candidate solutions within the feasible region. As demonstrated by numerical results, this technique is specifically tailored to the G&TEP problem and significantly improves solution quality while reducing the runtime required to achieve global optimality. A comparative performance analysis with state-of-the-art global MINLP solvers demonstrates that the proposed approach achieves tighter optimality gaps faster and exhibits superior flexibility and scalability.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100725"},"PeriodicalIF":4.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330419","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}

引用次数: 0

Two-stage stochastic robust optimization for the planning and operation of rural integrated energy systems considering source-load uncertainties 考虑源负荷不确定性的农村综合能源系统规划与运行两阶段随机鲁棒优化

IF 4.2

Renewable Energy Focus Pub Date : 2025-06-14 DOI: 10.1016/j.ref.2025.100735

Minghao Liu , Hongyang Huo , Yiding Xu , Zhonghe Han , Zhiquan Wu

{"title":"Two-stage stochastic robust optimization for the planning and operation of rural integrated energy systems considering source-load uncertainties","authors":"Minghao Liu , Hongyang Huo , Yiding Xu , Zhonghe Han , Zhiquan Wu","doi":"10.1016/j.ref.2025.100735","DOIUrl":"10.1016/j.ref.2025.100735","url":null,"abstract":"<div><div>The transition to renewable energy in rural areas is hindered by the inherent unpredictability of renewable resources and load demand. This study proposes a two-stage stochastic robust optimization (TS-SRO) framework to address the integrated planning and operation of rural energy systems under source-load uncertainties. The optimal capacity allocation for renewable energy generation, storage, and conversion devices is determined in the first stage, while the second stage optimizes multi-energy dispatch under uncertainty. Renewable energy fluctuations are modeled through data-driven stochastic scenarios derived from historical data using a 0–1 programming clustering method. In contrast, load uncertainties are characterized via confidence interval-based box-type uncertainty sets. A Nested Column-and-Constraint Generation (NCCG) algorithm combined with dynamic weighting is introduced to resolve the computational complexity arising from multi-objective optimization and uncertainty coupling. A case study in northern China demonstrates the model’s efficacy: compared to the baseline scenario, the TS-SRO approach reduces total energy costs by 37.62% and carbon emissions by 85.33%. Sensitivity analyses reveal that economic performance deteriorates with increasing uncertainty budgets and confidence intervals while robustness improves. Notably, biogas combined heat and power unit pricing significantly influences system economics, whereas solar water heaters and heat pumps show minimal impact.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100735"},"PeriodicalIF":4.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280506","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}

引用次数: 0

Future-ready power grids: From variability to predictability with scalable AI for PV energy integration 面向未来的电网：从可变性到可预测性与可扩展的人工智能光伏能源集成

IF 4.2

Renewable Energy Focus Pub Date : 2025-06-14 DOI: 10.1016/j.ref.2025.100721

Mariem Kammoun, Manef Bourogaoui

{"title":"Future-ready power grids: From variability to predictability with scalable AI for PV energy integration","authors":"Mariem Kammoun, Manef Bourogaoui","doi":"10.1016/j.ref.2025.100721","DOIUrl":"10.1016/j.ref.2025.100721","url":null,"abstract":"<div><div>The increasing integration of renewable energy, particularly solar power, presents challenges in maintaining grid stability due to fluctuations in power generation and voltage variations. This issue is especially important because solar energy is highly variable and depends on weather conditions, which makes it difficult to keep the power grid stable and reliable. Therefore, there is a strong need for accurate tools that can help predict these changes and improve the way the grid is managed. This study addresses these challenges by leveraging AI-based methods that combine climate data analysis and power grid simulations. The analysis relies on key environmental variables: solar irradiation, temperature, and wind speed to predict two critical outputs: power and voltage levels across the network. Among the tested models, Support Vector Regression (SVR) gave the best performance for power prediction. On the IEEE 123-bus Network, SVR achieved an RMSE of 183.07 and an MAE of 169.15, remaining well within the acceptable margin of 400 kW. For voltage prediction, the Long Short-Term Memory (LSTM) model performed best by capturing long-term time dependencies. On the IEEE 123-bus Network, LSTM achieved an RMSE of 0.0133 and an MAE of 0.0104 for Bus 64, staying well below the acceptable error threshold of 0.015 pu. Accordingly, through addressing a real-world challenge in electrical network operation, this study helps energy systems become more flexible and efficient. The proposed approach supports the transition toward a more stable, clean, and intelligent energy infrastructure.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100721"},"PeriodicalIF":4.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481270","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}

引用次数: 0