Renewable Energy Focus最新文献

{"title":"Corrigendum to “Detection of microcracks in silicon solar cells using Otsu-Canny edge detection algorithm” [Renew. Energy Focus 43 (2022) 183–190]","authors":"D. Manimegalai , S. Gayathri Monicka , M. Karthikeyan","doi":"10.1016/j.ref.2024.100678","DOIUrl":"10.1016/j.ref.2024.100678","url":null,"abstract":"","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100678"},"PeriodicalIF":4.2,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609181","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":"Chance-constrained co-optimization of demand response and Volt/Var under Gaussian mixture model uncertainty","authors":"Soroush Najafi,&nbsp;Hanif Livani","doi":"10.1016/j.ref.2024.100674","DOIUrl":"10.1016/j.ref.2024.100674","url":null,"abstract":"<div><div>Managing voltage and active load in distribution networks is an increasingly challenging task with the integration of volatile distributed energy resources (DERs) and flexible demands. This paper proposes a two-stage chance-constrained co-optimization framework using a Gaussian mixture model (GMM) to address Volt-VAR optimization (VVO) and demand response programs (DRP). The utilization of GMM in chance constrained optimization CCO (GMM-CCO) approach handles non-Gaussian forecast errors, ensuring network resilience with manageable computational demands. In the first stage, flexible demands, inverters’ reactive power, capacitor bank switching, and battery states of charge are co-scheduled, focusing on minimizing energy loss, reducing grid operational costs, and managing voltage deviations over a four-hour ahead schedule with hourly intervals. The second stage involves intra-hour, near-real-time optimization for VVO to respond to real-time disturbances. Simulations on a modified unbalanced three-phase IEEE 37-node system validate the framework’s effectiveness, comparing it to traditional chance-constrained optimization methods. Additionally, the proposed framework is implemented on the IEEE 69-node system to analyze its scalability and robustness under different levels of uncertainty and varying penetration levels.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100674"},"PeriodicalIF":4.2,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182112","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 machine learning-supported framework for predicting Nigeria’s optimal energy storage and emission reduction potentials","authors":"Stanley Aimhanesi Eshiemogie ,&nbsp;Peace Precious Aielumoh ,&nbsp;Tobechukwu Okamkpa ,&nbsp;Miracle Chinonso Jude ,&nbsp;Lois Efe ,&nbsp;Andrew Nosakhare Amenaghawon ,&nbsp;Handoko Darmokoesoemo ,&nbsp;Heri Septya Kusuma","doi":"10.1016/j.ref.2024.100677","DOIUrl":"10.1016/j.ref.2024.100677","url":null,"abstract":"<div><div>Energy sufficiency and the need to reduce carbon emissions have always been at the forefront of global efforts in recent times. This is the motivation of this study which seeks to reduce carbon emissions through the integration of renewable energy sources, by comparing two electricity scenarios for Nigeria by 2050, focusing on the inclusion and exclusion of electricity storage technologies, using a machine learning-supported approach. A Central Composite Design (CCD) was used to generate a design matrix for data collection, with EnergyPLAN software used to create energy system simulations on the CCD data for four outputs: total annual cost, CO₂ emissions, critical excess electricity production (CEEP), and electricity import. Three machine learning (ML) algorithms— multi-layer perceptron (MLP), extreme gradient boosting (XGBoost), and support vector regression (SVR)—were tuned using Bayesian optimization to develop models mapping the inputs to outputs. A genetic algorithm was used for optimization to determine the optimal input capacities that minimize the outputs. Results indicated that incorporating electricity storage technologies (EST) leads to a 37% increase in renewable electricity sources (RES) share, resulting in a 19.14% reduction in CO₂ emissions. EST such as battery energy storage systems (BESS), vehicle-to-grid (V2G) storage, and pumped hydro storage (PHS), allow for the storage of the critical excess electricity that comes with increasing RES share. Integrating EST in Nigeria’s 2050 energy landscape is crucial for incorporating more renewable electricity sources into the energy mix – thereby reducing CO₂ emissions – and managing excess electricity production. This study outlines a plan for optimal electricity production to meet Nigeria’s 2050 demand, highlighting the need for a balanced approach that combines fossil fuels, renewable energy, nuclear power, and advanced storage solutions to achieve a sustainable and efficient electricity system.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100677"},"PeriodicalIF":4.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182050","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 power flow and grid frequency control of conventional and renewable energy source using evolutionary algorithm based FOPID controller","authors":"Debodyuti Upadhaya ,&nbsp;Soumen Biswas ,&nbsp;Susanta Dutta ,&nbsp;Anagha Bhattacharya","doi":"10.1016/j.ref.2024.100676","DOIUrl":"10.1016/j.ref.2024.100676","url":null,"abstract":"<div><div>The primary objective of optimal power flow (OPF) in power systems is to minimize fuel expenses while simultaneously addressing several critical factors,including reducing transmission losses, minimizing voltage variations, and enhancing overall system stability. As the energy landscape evolves, the integration of renewable energy sources (RES) into the power grid has become increasingly important. In this research article, a study of Automatic Generation Control including RES to achieve cost optimization highlighting the advantages of GZA algorithm through a comprehensive study with other two evolutionary algorithm has been done. The research focuses on a three-area system integrating renewable energy sources – specifically solar, wind, and electric vehicles (EVs) – within a deregulated environment. While these sources can significantly reduce fuel costs associated with thermal power plants, they also introduce new challenges. Specifically, the variability and unpredictability of renewable energy can lead to increased frequency deviations due to changes in load inertia. This frequency deviation can disrupt the synchronization of the power system, potentially compromising stability and reliability. Detail study has been done in the simulation results for frequency deviation to achieve LFC, emphasizing performance metrics like overshoot, undershoot, and steady-state stability. Both traditional PID and FOPID controllers were evaluated for their effectiveness in managing frequency deviations.LFC ensures that the frequency of the power system remains within acceptable limits, particularly in a multi-area system where different regions may experience varying loads and generation capabilities. Effective frequency control is essential for maintaining the balance between generation and consumption, which is vital for the smooth operation of the grid. This innovative approach aims to enhance frequency regulation by effectively managing the dynamics introduced by the incorporation of renewable energy sources alongside traditional thermal power generation. The findings aim to demonstrate the effectiveness of the evolutionary algorithm GZA in enhancing the overall performance of multi-area power systems with diverse generation sources. By providing insights into the benefits of advanced control strategies, this study has been introduced a novel approach to simultaneously minimize costs and manage frequency deviations, marking a significant advancement in the field.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100676"},"PeriodicalIF":4.2,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182049","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 the impacts of irrigation loads and capital subsidies on minigrids: A case study of Kenya","authors":"Fhazhil Wamalwa ,&nbsp;Reagan Wafula ,&nbsp;Charles Kagiri","doi":"10.1016/j.ref.2024.100675","DOIUrl":"10.1016/j.ref.2024.100675","url":null,"abstract":"<div><div>Minigrids offer a promising electrification solution for rural communities beyond the grid in developing countries in Sub-Saharan Africa (SSA). However, their economic viability is hindered by low electricity demand which results in high minigrid tariffs as compared to centralized utilities. This underscores the need to explore technical and policy measures to achieve grid parity tariffs and hence energy access equity as well as accelerating rural electrification. Productive use of electricity (PUE) has potential to mitigate the low demand barrier and enhance minigrid viability. In this paper, we present an integrated modeling framework for determining the optimal subsidy needed to achieve grid parity for irrigation-anchored minigrids in SSA, with Kenya as a case study. We focus on irrigation due to the economic importance of agriculture in SSA as well as the high prevalence of farming activities in rural SSA. We estimate irrigation energy demand using projections from the Global Change Assessment Model (GCAM) for 2020–2045 and formulate the minigrid model as a constrained optimization problem to minimize daily energy costs over a year with hourly resolution. The results from our techno-economic assessments show that incorporating irrigation loads in the minigrid operation can reduce their tariffs by up to 41%, with final results dependent on geographical location and the forecasted climate future scenarios. Sensitivity analysis indicates that a 50% subsidy is required to achieve grid parity in irrigation-anchored minigrids, while communal models (without irrigation as a PUE) require an estimated 75% capital subsidy to realize grid parity tariff. Our model and its results can be used as a high-level framework of reference when planning minigrids with irrigation loads in developing countries.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100675"},"PeriodicalIF":4.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181497","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":"A review of modelling tools for net-zero emission energy systems, based on model capabilities, modelling criteria and model availability","authors":"Suleshini L. Samarasinghe ,&nbsp;Mojtaba Moghimi ,&nbsp;Prasad Kaparaju","doi":"10.1016/j.ref.2024.100659","DOIUrl":"10.1016/j.ref.2024.100659","url":null,"abstract":"<div><div>Transformation of the energy sector to a safer, cleaner, and more economical business is increasingly becoming important, in an era in which many countries have pledged to meet net-zero emissions energy. The best possible strategy for this transformation of production, transportation and consumption of energy can be found by modelling the energy system using capable modelling tools and envisioning future needs ahead of time. There is great interest, but lack of information about these tools and trends in the literature. To fill this gap, the paper systematically overviews modelling capabilities, technical criteria and the usability of thirty energy modelling tools that are currently available. Results show that, selected tools cover satisfactory ranges of modelling resolution in time and space. Nevertheless, no single tool covers all, short-term, medium-term and long-term planning horizons over a local geographical area to global level. Moreover, state-of-the-art energy system modelling and insights on future energy modelling needs are also elaborated on in the paper. The challenges of cross-sector and cross-border modelling, uncertainty modelling and forward market modelling and plausible solutions for them are discussed. The paper can be used in aid of selecting a suitable tool for a specific energy modelling purpose and attaining insights on future modelling needs that are required to obtain carbon neutrality by 2050.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100659"},"PeriodicalIF":4.2,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181117","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":"Multi-objective optimization of a biogas-fired gas turbine incorporated with closed Brayton and ejector power/cooling co-generation cycles","authors":"M. Zare,&nbsp;V. Zare,&nbsp;F. Talati","doi":"10.1016/j.ref.2024.100658","DOIUrl":"10.1016/j.ref.2024.100658","url":null,"abstract":"<div><div>Fossil fuels have long been the primary source of energy for human consumption. However, with increasing population growth and industrialization, electricity demand continues to rise, necessitating a sustainable and clean energy supply to mitigate environmental damage and support global development. This research proposes a gas turbine-based power plant that utilizes renewable biogas as its fuel source. To enhance the plant’s efficiency, the gas turbine is integrated with a closed Brayton cycle, complemented by compressor intake cooling. This cooling process is achieved through a combined power and ejector refrigeration unit, which recovers waste heat from the gas turbine. The energy, exergy, and economic performance of the proposed plant are thoroughly analyzed, with exergy efficiency and unit product cost serving as the objective functions for multi-criteria optimization. The results demonstrate that compressor intake cooling improves both thermodynamic and economic performance under all operating conditions. At the optimal design point, the system with intake cooling achieves an exergy efficiency of 39.38%, compared to 33.64% for the system without it. Additionally, while the system with intake cooling requires higher initial investment, it offers lower unit product costs, making it a more economically viable option.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"52 ","pages":"Article 100658"},"PeriodicalIF":4.2,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744162","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":"PyPSA-BD: A customized model to explore decarbonized energy transition for developing country","authors":"Firuz Ahamed Nahid ,&nbsp;Joyashree Roy","doi":"10.1016/j.ref.2024.100655","DOIUrl":"10.1016/j.ref.2024.100655","url":null,"abstract":"<div><div>This article provides high-resolution, evidence-based insights towards power sector planning for a developing country. We consider the PyPSA-BD model as a cutting-edge contribution as it’s a fully customized adaptation of PyPSA-Earth for Bangladesh to identify challenges and opportunities for transitioning to a decarbonized power system through counterfactual validation of inputs from national official statistics with a spatial resolution of 30km x 30km and an hourly temporal resolution. Its open-source framework is helpful for future researchers and decision-makers in developing countries like Bangladesh to develop more scenarios to answer any policy-relevant questions as per national need. With 2019 as a reference year, scenarios for 2030, 2041, and 2050 align with national renewable energy integration and decarbonization targets revealing cost-effective generation expansions, diversification of installed capacity through renewable energy penetration, net employment generation, additional land and investment requirement. Model results show that the 2019 installed capacity of 18.94 GW will grow to 61.45 GW by 2030, 102.36 GW by 2041, and 281.52 GW by 2050. By 2050, a storage capacity of 28.5 GW will be required to maintain grid stability. This transition could create approximately 6.7 million jobs and reduce generation costs to 7.63 BDT/kWh by 2050, requiring 3690.85 sq.km of land. Achieving these outcomes will demand an annual investment of approximately 1.99% of Bangladesh’s 2023 GDP from 2025, underscoring the need for national and international finance mobilization. The results guide policymakers to develop sustainable energy transition strategies for Bangladesh that provide power supply security at both spatial and temporal scale.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"52 ","pages":"Article 100655"},"PeriodicalIF":4.2,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757408","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":"Transition to a 100% renewable grid for a remote island: A case study of Tsushima Island, Japan","authors":"Alireza Tavana ,&nbsp;Tatchaphon Leelaprachakul ,&nbsp;Soo-Bin Kim ,&nbsp;Daisuke Tokuda","doi":"10.1016/j.ref.2024.100657","DOIUrl":"10.1016/j.ref.2024.100657","url":null,"abstract":"<div><div>Remote islands face unique challenges in achieving carbon neutrality. These islands typically depend on imported fossil fuels, operate independently of larger grids, and experience high electricity costs. Furthermore, limited access to reliable data complicates efforts to design effective energy solutions. Tsushima Island, Japan, exemplifies these challenges. Through a comprehensive analysis informed by a site visit, this study explores the island’s energy landscape and identifies viable renewable resources. Two transition scenarios are proposed to achieve a 100% renewable energy system, addressing key issues such as energy security and sustainability. The findings demonstrate that a complete renewable energy transition is possible, providing a replicable model for other remote islands worldwide.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"52 ","pages":"Article 100657"},"PeriodicalIF":4.2,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744061","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":"Renewable hydrogen systems for a sustainable heavy-duty mobility: The Italian case","authors":"Daniele Daminelli,&nbsp;Luciano Masotti,&nbsp;Silvia Corigliano","doi":"10.1016/j.ref.2024.100656","DOIUrl":"10.1016/j.ref.2024.100656","url":null,"abstract":"<div><div>This study offers a detailed economic and technical assessment of renewable hydrogen production, storage, and utilisation for heavy-duty transport in Italy. To achieve the initial targets set by the national hydrogen strategy for 2030, the heavy-duty fleet will require 3.6 GW of variable renewables, 1 GW of electrolyzers. The findings reveal that the Levelized Cost of renewable hydrogen varies based on system configurations, the mix of variable renewables, and electricity market zones. Specifically, hydrogen cost ranges from 7.5 €/kgH2 for grid-connected plants in market zones with high renewable electricity share, to 18.6 €/kgH2 for hydrogen plants connected to solar photovoltaic in the Northern zone. The South of Italy, Sicily, and Sardinia are the most suitable and cost effective zones for hydrogen production. However, low local demand projection necessitate network infrastructure investments to enable zonal transfer and prevent congestion risks.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"52 ","pages":"Article 100656"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703911","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}