Renewable Energy Focus最新文献

{"title":"A review of micro-hybrid energy systems for rural electrification, challenges and probable interventions","authors":"Likonge Makai,&nbsp;Olawale Popoola","doi":"10.1016/j.ref.2025.100687","DOIUrl":"10.1016/j.ref.2025.100687","url":null,"abstract":"<div><div>Micro-hybrid energy systems (MHES) are essential for meeting the energy needs of end users. However, their implementation in mitigating energy deficit, especially in Sub-Saharan African rural, remains low. To substantially achieve sustainable development goal seven 7 (SDG7), Sub-Saharan African (SSA) countries must tackle the various risks, challenges, and barriers that hinder the rapid adoption and implementation of MHES. This research comprehensively reviews MHESs in SSA through the lenses of twenty selected countries while zeroing in on six countries, specifically focusing on rural Zambia using an elimination approach. The research analyzed micro-grids in these countries, drew lessons, addressed barriers and limitations, and provided possible mitigation strategies for the challenges. The study findings showed an insufficient focus on load assessment, prioritization, and behavioral change in rural areas to enhance the implementation and utilization of micro-grid renewable energy systems (MHRES). Another finding was the intermittent supply from one energy source, especially solar and wind; combining more than one energy source gives reliable, affordable, and sustainable energy to meet end users’ energy demands. These deductions are crucial for developing countries’ rural areas, particularly in SSA, where most of the population resides, lack access to electricity, and the low-key rural activities that impact economic development (gross domestic product -GDP). Integrating load assessment, prioritization, and behavioral tendencies for energy utilization of MHRES can lead to cost-effective implementation and utilization of renewable energy resources in rural areas. This research is vital for supporting sustainable energy access. Adopting mitigation strategies will guide addressing the challenges associated with sustainable MHRES implementation and the strategic planning level for rural electrification in Sub-Saharan African countries.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100687"},"PeriodicalIF":4.2,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grey Wolf Algorithm-Enhanced Sensor-Less Integral Sliding Mode Control of DFIG on Wind Turbine Systems under Real Variable Speeds using ANN/MRAS","authors":"Lakhdar Saihi,&nbsp;Fateh Ferroudji,&nbsp;Khayra Roummani,&nbsp;Khaled Koussa","doi":"10.1016/j.ref.2025.100688","DOIUrl":"10.1016/j.ref.2025.100688","url":null,"abstract":"<div><div>This study presents a robust Sensor-Less First Order Integral Sliding Mode (SL/FOISM) strategy, incorporating an innovative observer known as Artificial Neural Network with Model Reference Adaptive System/Adaptive (ANN/MRAS), specifically designed for wind turbine systems. The proposed model is implemented on a Doubly Fed Induction Generator (DFIG) operating under real variable speed conditions in the Adrar region of Algeria. The primary control objective is to independently regulate the reactive and active power of the DFIG stator. This is achieved through decoupling using the field-oriented control technique and control application via FOISM/C. An interesting feature of this methodology is the reduction in both the cost of the control scheme and the size of the DFIG by eliminating the need for a speed sensor. To enhance the Model Reference Adaptive System with Proportional-Integral (MRAS/PI), an ANN is introduced to replace the conventional PI controller in the adaptation mechanism of MRAS. The rotor position estimation is thoroughly examined across various load conditions, encompassing low, zero, and high-speed regions. The optimal parameters for the controller are determined through the application of Grey Wolf Optimization (GWO). The simulation results demonstrate the compelling performance of the proposed observer (ANN/MRAS), with rotor speed estimation errors reduced to less than 0.05% across all speed regions. The methodology ensures finite-time convergence, robust tracking of rotor speed with high accuracy, and resilience against parameter variations and load disturbances. Furthermore, the proposed control scheme achieves stable operation under variable speed conditions, showcasing adaptability and improved performance compared to the conventional MRAS/PI. Consequently, the estimated rotor speed converges to its actual value, demonstrating the capability to accurately estimate position across different speed regions (low/zero/high) while maintaining a maximum estimation error below acceptable thresholds.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100688"},"PeriodicalIF":4.2,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444468","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 novel deep learning based digital twin model for mitigating wake effects in wind farms","authors":"Abdollah Kavousi-Fard ,&nbsp;Morteza Dabbaghjamanesh ,&nbsp;Morteza Sheikh ,&nbsp;Tao Jin","doi":"10.1016/j.ref.2025.100686","DOIUrl":"10.1016/j.ref.2025.100686","url":null,"abstract":"<div><div>Wind energy plays a significant role in sustainable power generation in power systems such as energy hubs, microgrids, smart grids and smart cities. On the other hand, some challenges such as wake effects in wind farms can lead to reduced efficiency and increased maintenance costs for the wind farms. This paper presents a cutting-edge approach to tackle these challenges through the development of a novel deep learning-based digital twin model. The proposed model integrates advanced deep learning algorithms with digital twin technology to accurately simulate and predict wake effects within wind farms. By leveraging data from various sensors and weather forecasts, the model can dynamically adjust turbine settings and optimize energy production in real-time. Key features of the digital twin include a convolutional neural network (CNN) for spatial analysis of wake patterns, a recurrent neural network (RNN) for temporal modelling of wind behaviour, and a reinforcement learning (RL) framework for autonomous decision-making. Through extensive simulations and validation against field data, the model demonstrates superior performance in mitigating wake effects and improving overall wind farm efficiency. This research contributes to the advancement of renewable energy technologies by providing a robust and scalable solution for optimizing wind farm operations and maximizing energy output.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100686"},"PeriodicalIF":4.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421871","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":"Optimal control strategy for enhancing energy efficiency of Pelamis wave energy converter: a Simulink-based simulation approach","authors":"Alireza Vakili ,&nbsp;Ali Pourzangbar ,&nbsp;Mir Mohammad Ettefagh ,&nbsp;Maghsoud Abdollahi Haghghi","doi":"10.1016/j.ref.2025.100685","DOIUrl":"10.1016/j.ref.2025.100685","url":null,"abstract":"<div><div>Wave energy is a promising renewable resource due to its predictability, consistency, and low environmental impact, making it an efficient solution for electricity generation in marine environments. Among various wave energy converters, the Pelamis stands out for its simplicity and scalability; however, its energy conversion efficiency can be further improved through advanced control strategies. This research aims to enhance the energy extraction efficiency of a Pelamis wave energy converter by implementing an optimal control strategy to regulate the production torque within the power take-off (PTO) system between the Pelamis cylinders. A dynamic model of the system interacting with regular waves is developed, and optimal control theory is applied to compute the PTO torques in real-time, maximizing the energy captured. The Pelamis energy converter and its control system were simulated in MATLAB’s Simulink environment. The results indicate that applying the optimal control method leads to a threefold increase in energy capture compared to the Proportional-Integral-Derivative (PID) control approach and a tenfold increase compared to the uncontrolled system. Additionally, frequency analysis of the average power output demonstrates that the energy gain with the optimal controller is achieved across all wave frequencies.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100685"},"PeriodicalIF":4.2,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Management and control strategy of multiple frequency powers in multifrequency microgrid","authors":"Rajdip Dey,&nbsp;Shabari Nath","doi":"10.1016/j.ref.2025.100681","DOIUrl":"10.1016/j.ref.2025.100681","url":null,"abstract":"<div><div>Multifrequency microgrid (MFMG) is a unique microgrid which has more than one frequency component superimposed on the bus is examined in this paper. There are three basic ideas behind MFMG which are orthogonal power flow theory, superposition theorem, and frequency selectivity criteria. It overcomes various disadvantages of traditional AC and DC microgrids and has many new features.</div><div>In MFMG, several frequency currents and voltages are superimposed on the multifrequency (MF) bus. The customers can select any available frequency currents at the load side. In MFMG, power is absorbed in different frequencies at load side and it creates different active and reactive power imbalance situations in MFMG. In existing literature, there is no analysis of the power imbalance of MFMG and the existing power control methods of microgrids cannot solve this problem. This paper bridges the gap by analyzing different power imbalance cases due to frequency selectivity criteria and proposes new control strategies to balance different frequency active and reactive powers in islanded and grid connected modes. The power balancing strategies are verified with 7 bus primitive MFMG structures in the Matlab Simulink environment.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100681"},"PeriodicalIF":4.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387346","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":"Emerging Trends in Active Distribution Network Fault Detection","authors":"Veronica A. Rosero-Morillo ,&nbsp;F. Gonzalez-Longatt ,&nbsp;Juan C. Quispe ,&nbsp;Eduardo J. Salazar ,&nbsp;Eduardo Orduña ,&nbsp;Mauricio E. Samper","doi":"10.1016/j.ref.2025.100684","DOIUrl":"10.1016/j.ref.2025.100684","url":null,"abstract":"<div><div>Electrical systems are constantly transforming to achieve global decarbonization and address the climate emergency. This process involves a substantial modernization of the distribution network that includes the integration of distributed energy resources, particularly those using inverter interfaces. Given the inevitability of faults, it is crucial to strengthen the infrastructure of protection systems so they can handle the new challenges imposed by this evolution. This article explores the challenges associated with protecting active distribution networks, caused by the incorporation of technologies such as rotary machines and power electronic converters. Special attention is given to critical issues such as changes in short-circuit currents, the bidirectional flow of currents, and the response times of protection relays. Current practical solutions are examined, and their limitations identified, highlighting the urgent need to develop more sophisticated and tailored protection schemes for the particularities of these networks. Additionally, the fault detection process is described in detail, breaking down the stages of parameter acquisition, signal processing, and fault classification, based on recent research. Finally, future trends in protection schemes are discussed, emphasizing the importance of continuously adapting and optimizing protection strategies in response to the dynamic evolution of electrical networks.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100684"},"PeriodicalIF":4.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181118","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":"Advanced control strategies for grid-following inverter fault response: Implementation and analysis in MATLAB for protection studies in medium voltage distribution networks","authors":"Veronica A. Rosero-Morillo ,&nbsp;Le Nam Hai Pham ,&nbsp;F. Gonzalez-Longatt ,&nbsp;Eduardo Orduña","doi":"10.1016/j.ref.2025.100683","DOIUrl":"10.1016/j.ref.2025.100683","url":null,"abstract":"<div><div>The growing integration of Inverter-Based Distributed Generation (IIDG) in distribution networks poses significant challenges for protection systems, as it alters the usual short-circuit patterns and impacts their effectiveness. International standards such as IEEE 1547-2018 and the German network code VDE-AR-N 410 for distribution networks, along with the IEEE 2800-2021 standard for transmission systems, have set criteria for the connection of IIDGs and their behavior under fault conditions, including the injection of reactive current and current limiting. These standards have driven the development of new control models for fault response: the conventional model, according to IEEE 1547-2018, requires IIDGs to inject only balanced positive sequence currents to provide voltage support to the network, while the advanced model, in accordance with VDE-AR-N 410 and IEEE 2800-2021, demands the injection of both positive and negative sequence currents to enhance voltage support during unbalanced faults. This article explores how these fault response models affect the efficiency of traditional protection schemes, including overcurrent and directional elements, and develops a methodology for modeling the inverter’s response to faults. This approach enables the replication and application of international standards for the design of new protection schemes, facilitating their adoption by researchers in the field.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100683"},"PeriodicalIF":4.2,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181120","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":"Exploring deep learning methods for solar photovoltaic power output forecasting: A review","authors":"Dheeraj Kumar Dhaked ,&nbsp;V.L. Narayanan ,&nbsp;Ram Gopal ,&nbsp;Omveer Sharma ,&nbsp;Sagar Bhattarai ,&nbsp;S.K. Dwivedy","doi":"10.1016/j.ref.2025.100682","DOIUrl":"10.1016/j.ref.2025.100682","url":null,"abstract":"<div><div>The rise of distributed energy resources stems from reliance on carbon-intensive energy and climate concerns. While photovoltaic solar energy leads in modern grids, its intermittent nature and weather variability challenge reliability and efficiency. Photovoltaic power output forecasting ensures a stable power supply by mitigating weather-induced disruptions. Thus, this review paper investigates the transformative impact of Deep Learning (DL) on photovoltaic power output forecasting. Leveraging the extensive data generated by smart meters, DL has shown unprecedented potential to outperform traditional forecasting models. The primary purpose of this research is to systematically analyze and compare mainstream DL-based forecasting techniques, uncovering their respective strengths and limitations. Least explored techniques such as deep transfer learning, big data DL, federated learning, probabilistic models, deterministic models, and hybrid architectures in forecasting are explored which have distinct advantages in processing large-scale multi-source data to deliver more accuracy. Covering research from 2019 to 2023, this study aims to capture the latest developments and ensure relevance to ongoing trends. Nearly 200 journals were acquired for this review paper using a systematic protocol. Among the DL methods, Autoencoder-Long Short-Term Memory outperformed its counterparts, achieving an impressive R² score of 99.98%. Moreover, the major conclusion underscores that DL offers a promising pathway for advancing PV forecasting, with future opportunities to address identified gaps and emerging challenges. This analysis serves as a comprehensive guide to stakeholders, illuminating the unique capabilities of DL in driving the next generation of solar power forecasting solutions.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100682"},"PeriodicalIF":4.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181148","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":"Risk-based optimal management of a multi-energy community integrated with P2X-based vector-bridging systems considering natural gas/hydrogen refueling and electric vehicle charging stations","authors":"Zahra Moshaver Shoja ,&nbsp;Ali Bohluli Oskouei ,&nbsp;Morteza Nazari-Heris","doi":"10.1016/j.ref.2025.100680","DOIUrl":"10.1016/j.ref.2025.100680","url":null,"abstract":"<div><div>Growing environmental concerns have increased interest in renewable energy-powered natural gas/hydrogen refueling (NGHR) and electric charging (EC) stations, driving the adoption of advanced energy resources like power-to-X (P2X) technologies in energy systems. This paper introduces vector-bridging systems (VBSs). In this concept, P2X technologies coupled with energy storage form a bridge across multiple energy vectors, such as electricity, gas, heat, and hydrogen, to enhance flexibility in community-integrated energy systems (CIESs). We propose a risk-based optimal energy management framework that integrates P2X-based VBSs to optimize participation in multi-energy markets while meeting power, gas, heat, and hydrogen demands from NGHR and EC stations at minimum cost. An incentive-based integrated demand response (IDR) model is also incorporated to reduce daily operation costs for power and heat demands. To manage uncertainties, a hybrid multi-objective info-gap decision theory (MOIGDT)/stochastic programming approach is used, adapting to the nature and knowledge of uncertain parameters. The multi-objective problem is solved using the augmented ε-constraint method, with the best solution selected through fuzzy decision-making and the min-max approach. Numerical results demonstrate that the combined use of P2X-based VBSs and IDR lowers daily operating costs by up to 8.36% and reduces risk levels in short-term CIES scheduling by 11.3%, underscoring the effectiveness of VBSs in achieving cost-efficient, resilient energy management.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100680"},"PeriodicalIF":4.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182113","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":"Optimizing transparent photovoltaic integration with battery energy storage systems in greenhouse: a daily light integral-constrained economic analysis considering BESS degradation","authors":"Mohammadreza Gholami ,&nbsp;A. Arefi ,&nbsp;M.E.H. Chowdhury ,&nbsp;L. Ben-Brahim ,&nbsp;S.M. Muyeen ,&nbsp;IEEE Fellow Member","doi":"10.1016/j.ref.2025.100679","DOIUrl":"10.1016/j.ref.2025.100679","url":null,"abstract":"<div><div>Greenhouses provide controlled environments for crop cultivation, and integrating semi transparent photovoltaic (STPV) panels offers the dual benefits of generating renewable energy while facilitating natural light penetration for photosynthesis. This study conducts a feasibility analysis of integrating Battery Energy Storage Systems (BESSs) with STPV systems in greenhouse agriculture, considering the Daily Light Integral (DLI) requirement for different crops as the primary constraint. Employing an enhanced Firefly Algorithm (FA) to optimize the PV cover ratio and BESS capacity, the analysis aims to maximize the Net Present Value (NPV) over a 25-year period, serving as the primary economic parameter. By incorporating DLI requirements for various crop types, the study ensures optimal crop growth while maximizing electricity generation. To ensure realistic long-term projections, the analysis incorporates BESS degradation over the 25-year period, accounting for capacity loss and efficiency reduction in energy storage. The results reveal the significant impact of crop type, with various required DLI , and transparency factor on optimized BESS and consequently the NPV of the project. Simulation results show that for crops with high DLI requirements, the feasible range of PVR% in the greenhouse varies from 42 % to 91 %, depending on the STPV’s transmittance factor. Additionally, the study reveals that initial negative revenue is common across all cases, with the highest NPV achieved at $1,331,340 for crops with low DLI requirements and a BESS capacity of 216 kW.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100679"},"PeriodicalIF":4.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}