Pub Date : 2024-12-01Epub Date: 2024-11-19DOI: 10.1016/j.cles.2024.100158
Caifei Luo, Keyu Zhang
This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal).
< This article has been retracted at the request of the Authors.
Post-publication, the authors found that using these data, which are not based on officially published documents, as a foundation for scenario setting could lead to significant deviations in the final simulation results. Thus, the authors feel that the findings of the manuscript cannot be relied upon and that the article needs to be retracted.
The authors would like to apologize for any inconvenience caused to the readers.>
{"title":"Retraction notice to “Elasticity of substitution between clean energy and non-clean energy: Evidence from the Chinese electricity industry” [Cleaner Energy Systems 8 (2024) 100117]","authors":"Caifei Luo, Keyu Zhang","doi":"10.1016/j.cles.2024.100158","DOIUrl":"10.1016/j.cles.2024.100158","url":null,"abstract":"<div><div>This article has been retracted: please see Elsevier Policy on Article Withdrawal (<span><span>https://www.elsevier.com/about/policies/article-withdrawal</span><svg><path></path></svg></span>).</div><div>< This article has been retracted at the request of the Authors.</div><div>Post-publication, the authors found that using these data, which are not based on officially published documents, as a foundation for scenario setting could lead to significant deviations in the final simulation results. Thus, the authors feel that the findings of the manuscript cannot be relied upon and that the article needs to be retracted.</div><div>The authors would like to apologize for any inconvenience caused to the readers.></div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100158"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103035","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}
Pub Date : 2024-12-01Epub Date: 2024-09-26DOI: 10.1016/j.cles.2024.100148
Jordi F.P. Cornette, Julien Blondeau
The decarbonisation of the building heating sector requires a shift from decentralised fossil fuel heating appliances to systems converting energy carriers with low greenhouse gas (GHG) emissions. However, for certain energy carriers, a considerable portion of GHG emissions arises upstream during production, processing and transportation, rather than during energy conversion. Accurately quantifying these indirect GHG emissions typically requires life cycle assessments, which are often resource-intensive and impractical during the early stages of energy system design. This study introduces operational GHG emissions as a pragmatic metric for the preliminary assessment of energy carrier environmental impact in building heating applications. These operational GHG emissions include both direct CO emissions and indirect CO, CH and NO emissions. Based on a comprehensive literature analysis, average estimates are proposed for the operational GHG emissions of various energy carriers within a European context, including natural gas, oil, coal and wood, as well as the average European and Belgian electricity grid, and hydrogen from various production methods. The findings underscore the significant contribution of indirect GHG emissions, as the selection of the energy carrier with the lowest environmental impact hinges on whether direct emissions alone or the broader operational GHG emissions are considered. By integrating operational GHG emissions into the early design stages of energy systems, stakeholders can make more informed decisions about which energy systems warrant further investigation, thereby facilitating more sustainable energy system development from the outset.
{"title":"Operational greenhouse gas emissions of various energy carriers for building heating","authors":"Jordi F.P. Cornette, Julien Blondeau","doi":"10.1016/j.cles.2024.100148","DOIUrl":"10.1016/j.cles.2024.100148","url":null,"abstract":"<div><div>The decarbonisation of the building heating sector requires a shift from decentralised fossil fuel heating appliances to systems converting energy carriers with low greenhouse gas (GHG) emissions. However, for certain energy carriers, a considerable portion of GHG emissions arises upstream during production, processing and transportation, rather than during energy conversion. Accurately quantifying these indirect GHG emissions typically requires life cycle assessments, which are often resource-intensive and impractical during the early stages of energy system design. This study introduces operational GHG emissions as a pragmatic metric for the preliminary assessment of energy carrier environmental impact in building heating applications. These operational GHG emissions include both direct CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions and indirect CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>, CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> and N<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O emissions. Based on a comprehensive literature analysis, average estimates are proposed for the operational GHG emissions of various energy carriers within a European context, including natural gas, oil, coal and wood, as well as the average European and Belgian electricity grid, and hydrogen from various production methods. The findings underscore the significant contribution of indirect GHG emissions, as the selection of the energy carrier with the lowest environmental impact hinges on whether direct emissions alone or the broader operational GHG emissions are considered. By integrating operational GHG emissions into the early design stages of energy systems, stakeholders can make more informed decisions about which energy systems warrant further investigation, thereby facilitating more sustainable energy system development from the outset.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100148"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327142","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}
Pub Date : 2024-12-01Epub Date: 2024-11-19DOI: 10.1016/j.cles.2024.100162
Md Tasbirul Islam , Sikandar Abdul Qadir , Amjad Ali , Muhammad Waseem Khan
This review article critically examines papers on renewable energy integration (REI), with a specific focus on the economic and environmental impact assessments across multiple sectors, including agriculture, transportation, electricity production, buildings, and biofuel production. A total of 111 articles from the Web of Science Core Collection database were reviewed using a systematic literature review methodology and content analysis techniques. The results indicate that evaluation-type studies, particularly those employing optimization and simulation-based methods, such as techno-economic analysis (TEA) (28 papers) and life cycle assessment (LCA) (20 papers), were the most prominent approaches used for economic and environmental analyses. Optimization techniques such as mixed-integer linear programming (6 papers), genetic algorithms (GA) (5 papers), and particle swarm optimization (PSO) (4 papers) were widely applied. The quantitative analysis of impact assessment indicators shows that REI has yielded significant long-term positive results across multiple RE sources, sectors, and regions. A detailed examination of mathematical models (e.g., optimization techniques) and simulation modeling combined with LCA will assist future researchers in optimizing energy systems and enhancing sustainability in sectors such as agriculture and water desalination. The conceptual inclusion of circular economy within the research field needs to be more present among researchers, and most of the studies focused on technical aspects of RE integration and assessing impacts rather than identifying a systemic change across the sectors. Several future research directions have been identified across sectors, offering opportunities to advance the field. Policymakers will find this paper valuable for informed decision-making and the development of robust policy frameworks.
{"title":"Economic and environmental impact assessment of renewable energy integration: A review and future research directions","authors":"Md Tasbirul Islam , Sikandar Abdul Qadir , Amjad Ali , Muhammad Waseem Khan","doi":"10.1016/j.cles.2024.100162","DOIUrl":"10.1016/j.cles.2024.100162","url":null,"abstract":"<div><div>This review article critically examines papers on renewable energy integration (REI), with a specific focus on the economic and environmental impact assessments across multiple sectors, including agriculture, transportation, electricity production, buildings, and biofuel production. A total of 111 articles from the Web of Science Core Collection database were reviewed using a systematic literature review methodology and content analysis techniques. The results indicate that evaluation-type studies, particularly those employing optimization and simulation-based methods, such as techno-economic analysis (TEA) (28 papers) and life cycle assessment (LCA) (20 papers), were the most prominent approaches used for economic and environmental analyses. Optimization techniques such as mixed-integer linear programming (6 papers), genetic algorithms (GA) (5 papers), and particle swarm optimization (PSO) (4 papers) were widely applied. The quantitative analysis of impact assessment indicators shows that REI has yielded significant long-term positive results across multiple RE sources, sectors, and regions. A detailed examination of mathematical models (e.g., optimization techniques) and simulation modeling combined with LCA will assist future researchers in optimizing energy systems and enhancing sustainability in sectors such as agriculture and water desalination. The conceptual inclusion of circular economy within the research field needs to be more present among researchers, and most of the studies focused on technical aspects of RE integration and assessing impacts rather than identifying a systemic change across the sectors. Several future research directions have been identified across sectors, offering opportunities to advance the field. Policymakers will find this paper valuable for informed decision-making and the development of robust policy frameworks.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100162"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707013","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}
Pub Date : 2024-12-01Epub Date: 2024-11-14DOI: 10.1016/j.cles.2024.100160
Juliet Attah , Latifatu Mohammed , Andrew Nyamful , Paulina Donkor , Anita Asamoah , Mohammed Nafiu Zainudeen , John Adjah , Charles K. Klutse , Sylvester Attakorah Birikorang , Frederick Agyemang , Owiredu Gyampo
The urgent need to address climate change has prompted researchers to explore sustainable power generation methods using low or net-zero fuels and energy storage. Historically, gases derived from acetylene or LPG have been used for welding in factories. Despite its negative effects on the environment and human health, acetylene gas remains widely used. Examples of pollutants released from acetylene gas include carbon dioxide and carbon monoxide, both of which contribute to the greenhouse effect and global warming. There is a need for an alternative gas that is environmentally friendly, economically viable, and readily available. Hydrogen gas is currently used across various industries and is increasingly considered a potential primary fuel source for the future. In this study, a hydrogen fuel cell was used to produce HHO (brown) gas as a replacement for acetylene through electrolysis. The HHO gas was used to weld a randomly selected test piece, which was then evaluated alongside an acetylene-welded test piece. The integrity of both welds was assessed using dye-penetrant and radiographic testing, showing that welds from both gases were strong. Welding with HHO gas, followed by non-destructive inspection, also proved effective, with any defects attributed to inexperience in welding. The adoption of HHO gas in the welding industry is recommended due to its potential socio-economic benefits, health advantages, and environmental friendliness. Challenges related to initial investment costs may be mitigated as technology advances. Further research should focus on qualitative weld testing, economic and environmental impact assessments, and developing a business model for HHO systems.
{"title":"Oxy-hydrogen gas as a sustainable fuel for the welding industry: Alternative for oxy-acetylene gas","authors":"Juliet Attah , Latifatu Mohammed , Andrew Nyamful , Paulina Donkor , Anita Asamoah , Mohammed Nafiu Zainudeen , John Adjah , Charles K. Klutse , Sylvester Attakorah Birikorang , Frederick Agyemang , Owiredu Gyampo","doi":"10.1016/j.cles.2024.100160","DOIUrl":"10.1016/j.cles.2024.100160","url":null,"abstract":"<div><div>The urgent need to address climate change has prompted researchers to explore sustainable power generation methods using low or net-zero fuels and energy storage. Historically, gases derived from acetylene or LPG have been used for welding in factories. Despite its negative effects on the environment and human health, acetylene gas remains widely used. Examples of pollutants released from acetylene gas include carbon dioxide and carbon monoxide, both of which contribute to the greenhouse effect and global warming. There is a need for an alternative gas that is environmentally friendly, economically viable, and readily available. Hydrogen gas is currently used across various industries and is increasingly considered a potential primary fuel source for the future. In this study, a hydrogen fuel cell was used to produce HHO (brown) gas as a replacement for acetylene through electrolysis. The HHO gas was used to weld a randomly selected test piece, which was then evaluated alongside an acetylene-welded test piece. The integrity of both welds was assessed using dye-penetrant and radiographic testing, showing that welds from both gases were strong. Welding with HHO gas, followed by non-destructive inspection, also proved effective, with any defects attributed to inexperience in welding. The adoption of HHO gas in the welding industry is recommended due to its potential socio-economic benefits, health advantages, and environmental friendliness. Challenges related to initial investment costs may be mitigated as technology advances. Further research should focus on qualitative weld testing, economic and environmental impact assessments, and developing a business model for HHO systems.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100160"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757001","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}
Pub Date : 2024-12-01Epub Date: 2024-11-22DOI: 10.1016/j.cles.2024.100161
Erik López-Basto , Gijsbert Korevaar , Samantha Eleanor Tanzer , Andrea Ramírez Ramírez
<div><div>This paper evaluates the potential impacts of introducing low-carbon intensity hydrogen technologies in two oil refineries with different complexity levels, emphasizing the role of hydrogen production in reducing CO<sub>2</sub> emissions. The novelty of this work lies in three key aspects: Comprehensive system analysis of refinery complexity using real site data, integration of low-carbon Hydrogen technologies, long-term and short-term strategies. Two Colombian refineries serve as case studies, with technological solutions adapted to their complexity levels. The methodology involves evaluating different options for hydrogen production, accounting for improvement in technological efficiency over time.</div><div>The refinery systems were evaluated in a cost-optimization model built in Linny-r. Three different scenarios were considered, Business-As-Usual (BAU), high, and low-ambitions decarbonization scenarios, focusing on the time horizons of 2030 and 2050.</div><div>When comparing the two case studies, the preferred decarbonization strategy for both facilities involves the substitution of SMR technology with water electrolyzers powered by renewable electricity. Post-2030, biomass-based hydrogen technology is still a costly alternative; however, to achieve CO<sub>2</sub> neutrality, negative emissions storage of biogenic CO<sub>2</sub> emerges as an achievable alternative.</div><div>Our results indicate the achievability of CO<sub>2</sub> reduction objectives in both refineries. Our results show that achieving long-term CO<sub>2</sub> neutrality requires both refineries to increase renewable electricity production by 5 to 6 times for powering water electrolyzers, steam production by 2 to 2.5 times for CO<sub>2</sub> capture, and supply of dry biomass by 2.6 to 4.5 kt/d.</div><div>The two most significant factors influencing the refining net margin in the decarbonization scenarios are primarily the CO<sub>2</sub> and the renewable electricity prices. The short-term horizon emerges as the pivotal period, particularly within the high-ambition decarbonization scenarios. In this context, the medium complexity refinery demonstrates economic viability until a CO<sub>2</sub> price of 140 €/t CO<sub>2</sub>, while the high complexity refinery endures up to 205 €/t CO<sub>2</sub>.</div><div>The high complexity refinery is better prepared to face the challenges of decarbonization and the impacts generated on the refining margin. Compared to the BAU scenario, the high complexity refinery shows a negative impact on the net margin that corresponds to a 40 % and 5 % reduction in the short and long term, respectively. Meanwhile, for the medium complexity refinery, the impact on net margin amounts to a 52 % reduction in the short term and a 27 % improvement in the long term.</div><div>Furthermore, our research highlights the significant potential for reducing CO<sub>2</sub> emissions by fully eliminating the use of refinery gas as fuel, providing alternat
{"title":"Assessing the impacts of low-carbon intensity hydrogen integration in oil refineries","authors":"Erik López-Basto , Gijsbert Korevaar , Samantha Eleanor Tanzer , Andrea Ramírez Ramírez","doi":"10.1016/j.cles.2024.100161","DOIUrl":"10.1016/j.cles.2024.100161","url":null,"abstract":"<div><div>This paper evaluates the potential impacts of introducing low-carbon intensity hydrogen technologies in two oil refineries with different complexity levels, emphasizing the role of hydrogen production in reducing CO<sub>2</sub> emissions. The novelty of this work lies in three key aspects: Comprehensive system analysis of refinery complexity using real site data, integration of low-carbon Hydrogen technologies, long-term and short-term strategies. Two Colombian refineries serve as case studies, with technological solutions adapted to their complexity levels. The methodology involves evaluating different options for hydrogen production, accounting for improvement in technological efficiency over time.</div><div>The refinery systems were evaluated in a cost-optimization model built in Linny-r. Three different scenarios were considered, Business-As-Usual (BAU), high, and low-ambitions decarbonization scenarios, focusing on the time horizons of 2030 and 2050.</div><div>When comparing the two case studies, the preferred decarbonization strategy for both facilities involves the substitution of SMR technology with water electrolyzers powered by renewable electricity. Post-2030, biomass-based hydrogen technology is still a costly alternative; however, to achieve CO<sub>2</sub> neutrality, negative emissions storage of biogenic CO<sub>2</sub> emerges as an achievable alternative.</div><div>Our results indicate the achievability of CO<sub>2</sub> reduction objectives in both refineries. Our results show that achieving long-term CO<sub>2</sub> neutrality requires both refineries to increase renewable electricity production by 5 to 6 times for powering water electrolyzers, steam production by 2 to 2.5 times for CO<sub>2</sub> capture, and supply of dry biomass by 2.6 to 4.5 kt/d.</div><div>The two most significant factors influencing the refining net margin in the decarbonization scenarios are primarily the CO<sub>2</sub> and the renewable electricity prices. The short-term horizon emerges as the pivotal period, particularly within the high-ambition decarbonization scenarios. In this context, the medium complexity refinery demonstrates economic viability until a CO<sub>2</sub> price of 140 €/t CO<sub>2</sub>, while the high complexity refinery endures up to 205 €/t CO<sub>2</sub>.</div><div>The high complexity refinery is better prepared to face the challenges of decarbonization and the impacts generated on the refining margin. Compared to the BAU scenario, the high complexity refinery shows a negative impact on the net margin that corresponds to a 40 % and 5 % reduction in the short and long term, respectively. Meanwhile, for the medium complexity refinery, the impact on net margin amounts to a 52 % reduction in the short term and a 27 % improvement in the long term.</div><div>Furthermore, our research highlights the significant potential for reducing CO<sub>2</sub> emissions by fully eliminating the use of refinery gas as fuel, providing alternat","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100161"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743834","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}
Pub Date : 2024-12-01Epub Date: 2024-10-10DOI: 10.1016/j.cles.2024.100155
Muhammad Rifansyah, Dzikri Firmansyah Hakam
The utilization of solar energy is crucial for the advancement of sustainable power generation on a worldwide scale, driven by environmental concerns and the depletion of fossil fuels. Indonesia's goal is to achieve carbon neutrality by 2060 and it is aggressively advocating for solar energy, which includes the implementation of new methods such as floating photovoltaic (PV) systems. This study evaluates the Techno-Economic Feasibility of Indonesia's Cirata 145 MW floating solar PV project by employing RETScreen technology. The objective is to improve the long-term financial stability, decrease greenhouse gas emissions, and suggest viable choices for improvement. Examining three scenarios that involve alterations in carbon emissions, energy pricing, and loan interest rates demonstrates different levels of project feasibility. The introduction of carbon tax emission pricing has a substantial impact on the feasibility of projects. This study provides useful insights into doing techno-economic feasibility assessments using RETScreen for floating photovoltaic (PV) systems. It demonstrates how modifying parameters can effectively mitigate project risks.
{"title":"Techno economic study of floating solar photovoltaic project in Indonesia using RETscreen","authors":"Muhammad Rifansyah, Dzikri Firmansyah Hakam","doi":"10.1016/j.cles.2024.100155","DOIUrl":"10.1016/j.cles.2024.100155","url":null,"abstract":"<div><div>The utilization of solar energy is crucial for the advancement of sustainable power generation on a worldwide scale, driven by environmental concerns and the depletion of fossil fuels. Indonesia's goal is to achieve carbon neutrality by 2060 and it is aggressively advocating for solar energy, which includes the implementation of new methods such as floating photovoltaic (PV) systems. This study evaluates the Techno-Economic Feasibility of Indonesia's Cirata 145 MW floating solar PV project by employing RETScreen technology. The objective is to improve the long-term financial stability, decrease greenhouse gas emissions, and suggest viable choices for improvement. Examining three scenarios that involve alterations in carbon emissions, energy pricing, and loan interest rates demonstrates different levels of project feasibility. The introduction of carbon tax emission pricing has a substantial impact on the feasibility of projects. This study provides useful insights into doing techno-economic feasibility assessments using RETScreen for floating photovoltaic (PV) systems. It demonstrates how modifying parameters can effectively mitigate project risks.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553029","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}
Pub Date : 2024-12-01Epub Date: 2024-10-24DOI: 10.1016/j.cles.2024.100157
Shree Om Bade, Olusegun Stanley Tomomewo
This paper investigates the optimal design of a hybrid renewable energy system, integrating wind turbines, solar photovoltaic systems, biomass, and battery and hydrogen storage to ensure a reliable energy supply at the lowest annual cost for a residential load in Kern County, USA. The hybrid generic algorithm particle swarm optimization (GAPSO) algorithm was adopted to determine the optimal configuration of parameters and cost-effectiveness, considering technical, economic, environmental, and social performance indicators. The generic algorithm (GA) and particle swarm optimization (PSO) validate the effectiveness of the proposed technique, showcasing its efficiency in system optimization. The findings indicate that GAPSO outperforms GA and PSO due to its rapid convergence, lowest final fitness value, and stable optimization process. The hybrid GAPSO's performance, combined with the different capacities of wind turbines (4,561 kW), solar PV (8,480 kW), biomass (2,261 kW), battery banks (8,000 kWh), and fuel cells (2,392 kW), resulted in an annual cost of $6,239,193; energy cost and net present value of $0.48/kWh and $101,333,937. The system maintained a supply loss of 0.8 %, achieved an availability index of 99.2 %, a renewable energy fraction of 88.87 %, GHGs emission of 953,615 kg, land use of 3,842,875 m2, and water consumption 528,678 L respectively. GAPSO achieved a 2.17 % and 0.01 % improvement in cost-effectiveness and 11.11 % increase in reliability compared to GA and PSO.
{"title":"Optimizing a hybrid wind-solar-biomass system with battery and hydrogen storage using generic algorithm-particle swarm optimization for performance assessment","authors":"Shree Om Bade, Olusegun Stanley Tomomewo","doi":"10.1016/j.cles.2024.100157","DOIUrl":"10.1016/j.cles.2024.100157","url":null,"abstract":"<div><div>This paper investigates the optimal design of a hybrid renewable energy system, integrating wind turbines, solar photovoltaic systems, biomass, and battery and hydrogen storage to ensure a reliable energy supply at the lowest annual cost for a residential load in Kern County, USA. The hybrid generic algorithm particle swarm optimization (GAPSO) algorithm was adopted to determine the optimal configuration of parameters and cost-effectiveness, considering technical, economic, environmental, and social performance indicators. The generic algorithm (GA) and particle swarm optimization (PSO) validate the effectiveness of the proposed technique, showcasing its efficiency in system optimization. The findings indicate that GAPSO outperforms GA and PSO due to its rapid convergence, lowest final fitness value, and stable optimization process. The hybrid GAPSO's performance, combined with the different capacities of wind turbines (4,561 kW), solar PV (8,480 kW), biomass (2,261 kW), battery banks (8,000 kWh), and fuel cells (2,392 kW), resulted in an annual cost of $6,239,193; energy cost and net present value of $0.48/kWh and $101,333,937. The system maintained a supply loss of 0.8 %, achieved an availability index of 99.2 %, a renewable energy fraction of 88.87 %, GHGs emission of 953,615 kg, land use of 3,842,875 m<sup>2</sup>, and water consumption 528,678 L respectively. GAPSO achieved a 2.17 % and 0.01 % improvement in cost-effectiveness and 11.11 % increase in reliability compared to GA and PSO.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100157"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573530","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}
This manuscript introduces an innovative Maximum Power Point Tracking (MPPT) strategy to improve the efficiency of Wind Energy Conversion Systems (WECS) equipped with Permanent Magnet Synchronous Generators (PMSG) under variable wind conditions. The proposed approach integrates Active Disturbance Rejection Control (ADRC) with the Perturb and Observe (P&O) algorithm, effectively addressing challenges such as external disturbances and fluctuating wind environments. By combining ADRC with P&O control, the system achieves enhanced tracking performance and adaptability.To validate the added value of this approach, we compare it with a traditional P&O strategy combined with Proportional Integral (PI) control. For the PI-based method, controller parameters Kp and Ki are optimized using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) to enhance control precision. The Integrated Time Absolute Error (ITAE) objective function is employed to fine-tune these parameters, further optimizing system performance. Our analysis underscores the superiority of ADRC in disturbance rejection and quick adaptability over the PI approach.The proposed strategy is tested under two distinct wind speed profiles—constant and fluctuating—through time-domain simulations in MATLAB/Simulink. Simulation results confirm the superior performance of the ADRC-P&O method, highlighting its effectiveness in maximizing power extraction from wind energy and proving its potential for real-world applications. This study offers a significant advancement in wind energy technology by providing a robust and efficient solution for MPPT in WECS.
{"title":"Enhancing Wind Energy Conversion Efficiency: A Novel MPPT Approach Using P&O with ADRC Controllers versus PI Controllers with Kp and Ki Optimization via Genetic Algorithm and Ant Colony Optimization","authors":"Najoua Mrabet , Chirine Benzazah , Chakib Mohssine , El akkary Ahmed , Khouili Driss , Rerhrhaye Badr , Lahlouh Ilyas","doi":"10.1016/j.cles.2024.100159","DOIUrl":"10.1016/j.cles.2024.100159","url":null,"abstract":"<div><div>This manuscript introduces an innovative Maximum Power Point Tracking (MPPT) strategy to improve the efficiency of Wind Energy Conversion Systems (WECS) equipped with Permanent Magnet Synchronous Generators (PMSG) under variable wind conditions. The proposed approach integrates Active Disturbance Rejection Control (ADRC) with the Perturb and Observe (P&O) algorithm, effectively addressing challenges such as external disturbances and fluctuating wind environments. By combining ADRC with P&O control, the system achieves enhanced tracking performance and adaptability.To validate the added value of this approach, we compare it with a traditional P&O strategy combined with Proportional Integral (PI) control. For the PI-based method, controller parameters Kp and Ki are optimized using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) to enhance control precision. The Integrated Time Absolute Error (ITAE) objective function is employed to fine-tune these parameters, further optimizing system performance. Our analysis underscores the superiority of ADRC in disturbance rejection and quick adaptability over the PI approach.The proposed strategy is tested under two distinct wind speed profiles—constant and fluctuating—through time-domain simulations in MATLAB/Simulink. Simulation results confirm the superior performance of the ADRC-P&O method, highlighting its effectiveness in maximizing power extraction from wind energy and proving its potential for real-world applications. This study offers a significant advancement in wind energy technology by providing a robust and efficient solution for MPPT in WECS.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100159"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743833","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}
Pub Date : 2024-12-01Epub Date: 2024-10-01DOI: 10.1016/j.cles.2024.100152
Yubao Wang, Huiyuan Pan, Junjie Zhen, Boyang Xu
This paper quantitatively examines the substitution effects within China's clean energy sector, focusing on the hydropower and new energy generation sectors across the top 14 hydropower-producing provinces, which collectively contribute to over 80 % of the country's total hydropower output. To provide a comprehensive analysis of regions that significantly influence national trends, the study utilizes the Cross-Price Elasticity (CPE) and Morishima Elasticity of Substitution (MES). CPE measures how the quantity demanded of one energy source responds to a change in the price of another, while MES assesses the sensitivity of the ratio between two energy inputs to price changes. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model is employed to forecast energy substitution dynamics, offering robust predictive accuracy. The average MES between clean energy and thermal power is 0.663, indicating a moderate substitution relationship, with the effect more pronounced in summer. Additionally, the mean MES between hydropower and new energy generation is 2.067, reflecting a strong substitution effect between these two clean energy forms. Furthermore, the SARIMA model shows a mean squared error (MSE) as low as 0.0006 in some cases, demonstrating its robust predictive accuracy in forecasting energy substitution dynamics. These results offer empirical support for policies aimed at reducing reliance on thermal power and promoting clean energy development in key provinces.
本文定量研究了中国清洁能源行业的替代效应，重点关注水电产量最高的 14 个省份的水电和新能源发电行业，这 14 个省份的水电产量合计占全国水电总产量的 80% 以上。为了全面分析对全国趋势有重大影响的地区，研究采用了交叉价格弹性 (CPE) 和森岛替代弹性 (MES)。CPE 衡量一种能源的需求量如何对另一种能源的价格变化做出反应，而 MES 则评估两种能源投入之间的比率对价格变化的敏感性。采用季节自回归综合移动平均（SARIMA）模型来预测能源替代动态，具有很高的预测准确性。清洁能源与火力发电之间的平均 MES 为 0.663，表明两者之间存在适度的替代关系，夏季的替代效应更为明显。此外，水力发电与新能源发电之间的平均 MES 为 2.067，反映出这两种清洁能源形式之间存在较强的替代效应。此外，SARIMA 模型在某些情况下的均方误差（MSE）低至 0.0006，这表明该模型在预测能源替代动态方面具有很强的预测准确性。这些结果为重点省份减少对火电的依赖、促进清洁能源发展的政策提供了经验支持。
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Pub Date : 2024-12-01Epub Date: 2024-07-24DOI: 10.1016/j.cles.2024.100130
Ambuj, Rajendra Machavaram
The accurate energy consumption prediction in Agricultural Ground Vehicles (AGVs) holds immense potential for optimizing operational efficiency and minimizing environmental impact. However, existing optimization methods for such prediction tasks often suffer from high computational demands, hindering their practical implementation. This paper introduces a ground-breaking approach that overcomes this limitation by leveraging the potent computational power of Graphics Processing Units (GPUs) to accelerate the optimization process dramatically. We propose a novel adaptation of the Particle Swarm Optimization (PSO) algorithm, specifically tailored to the intricate multi-objective challenges of AGV energy prediction. This framework harnesses the strengths of a multi-objective approach, enabling the simultaneous optimization of prediction accuracy and model complexity. To further enhance efficiency, we seamlessly integrate GPU parallelization techniques, significantly expediting both the optimization process and the training of Artificial Neural Networks (ANNs) employed for prediction. Preliminary results demonstrate a remarkable improvement in the accuracy of AGV energy consumption predictions, directly attributed to the synergistic effect of optimizing the ANN architecture and parameters through our proposed PSO framework. This tailored PSO adaptation distinguishes itself by its ability to tackle the complex multi-objective nature of AGV energy prediction with enhanced efficiency and precision. It thus emerges as a compelling and novel solution within the realm of Machine Learning and heuristic methods for agricultural robotics, paving the way for sustainable and optimal AGV operations.
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