Global warming, driven by human-induced disruptions to the natural carbon dioxide (CO2) cycle, is a pressing concern. To mitigate this, carbon capture and storage has emerged as a key strategy that enables the continued use of fossil fuels while transitioning to cleaner energy sources. Deep saline aquifers are of particular interest due to their substantial CO2 storage potential, often located near fossil fuel reservoirs. In this study, a deep saline aquifer model with a saline water production well was constructed to develop the optimization workflow. Due to the time-consuming nature of each realization of the numerical simulation, we introduce a surrogate aquifer model derived from extracted data. The novelty of our work lies in the pioneering of simultaneous optimization using machine learning within an integrated framework. Unlike previous studies, which typically focused on single-parameter optimization, our research addresses this gap by performing multi-objective optimization for CO2 storage and breakthrough time in deep saline aquifers using a data-driven model. Our methodology encompasses preprocessing and feature selection, identifying eight pivotal parameters. Evaluation metrics include root mean square error (RMSE), mean absolute percentage error (MAPE) and R2. In predicting CO2 storage values, RMSE, MAPE and R2 in test data were 2.07%, 1.52% and 0.99, respectively, while in blind data, they were 2.5%, 2.05% and 0.99. For the CO2 breakthrough time, RMSE, MAPE and R2 in the test data were 2.1%, 1.77% and 0.93, while in the blind data they were 2.8%, 2.23% and 0.92, respectively. In addressing the substantial computational demands and time-consuming nature of coupling a numerical simulator with an optimization algorithm, we have adopted a strategy in which the trained artificial neural network is seamlessly integrated with a multi-objective genetic algorithm. Within this framework, we conducted 5000 comprehensive experiments to rigorously validate the development of the Pareto front, highlighting the depth of our computational approach. The findings of the study promise insights into the interplay between CO2 breakthrough time and storage in aquifer-based carbon capture and storage processes within an integrated framework based on data-driven coupled multi-objective optimization.
{"title":"An application of a genetic algorithm in co-optimization of geological CO2 storage based on artificial neural networks","authors":"Pouya Vaziri, B. Sedaee","doi":"10.1093/ce/zkad077","DOIUrl":"https://doi.org/10.1093/ce/zkad077","url":null,"abstract":"\u0000 Global warming, driven by human-induced disruptions to the natural carbon dioxide (CO2) cycle, is a pressing concern. To mitigate this, carbon capture and storage has emerged as a key strategy that enables the continued use of fossil fuels while transitioning to cleaner energy sources. Deep saline aquifers are of particular interest due to their substantial CO2 storage potential, often located near fossil fuel reservoirs. In this study, a deep saline aquifer model with a saline water production well was constructed to develop the optimization workflow. Due to the time-consuming nature of each realization of the numerical simulation, we introduce a surrogate aquifer model derived from extracted data. The novelty of our work lies in the pioneering of simultaneous optimization using machine learning within an integrated framework. Unlike previous studies, which typically focused on single-parameter optimization, our research addresses this gap by performing multi-objective optimization for CO2 storage and breakthrough time in deep saline aquifers using a data-driven model. Our methodology encompasses preprocessing and feature selection, identifying eight pivotal parameters. Evaluation metrics include root mean square error (RMSE), mean absolute percentage error (MAPE) and R2. In predicting CO2 storage values, RMSE, MAPE and R2 in test data were 2.07%, 1.52% and 0.99, respectively, while in blind data, they were 2.5%, 2.05% and 0.99. For the CO2 breakthrough time, RMSE, MAPE and R2 in the test data were 2.1%, 1.77% and 0.93, while in the blind data they were 2.8%, 2.23% and 0.92, respectively. In addressing the substantial computational demands and time-consuming nature of coupling a numerical simulator with an optimization algorithm, we have adopted a strategy in which the trained artificial neural network is seamlessly integrated with a multi-objective genetic algorithm. Within this framework, we conducted 5000 comprehensive experiments to rigorously validate the development of the Pareto front, highlighting the depth of our computational approach. The findings of the study promise insights into the interplay between CO2 breakthrough time and storage in aquifer-based carbon capture and storage processes within an integrated framework based on data-driven coupled multi-objective optimization.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"57 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139441065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ameera M Almarzooqi, Maher Maalouf, Tarek H M El-Fouly, Vasileios E. Katzourakis, Mohamed S El Moursi, C. Chrysikopoulos
Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output. As the generation capacity within the electric grid increases, accurately predicting this output becomes increasingly essential, especially given the random and non-linear characteristics of solar irradiance under variable weather conditions. This study presents a novel prediction method for solar irradiance, which is directly in correlation with PV power output, targeting both short-term and medium-term forecast horizons. Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. The proposed method excels in forecasting solar irradiance, especially during highly intermittent weather periods. A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework. We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford, USA and compared it against three forecasting models: persistence, modified 24-hour persistence and least squares. Based on three widely accepted statistical performance metrics (root mean squared error, mean absolute error and coefficient of determination), our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.
{"title":"A hybrid machine-learning model for solar irradiance forecasting","authors":"Ameera M Almarzooqi, Maher Maalouf, Tarek H M El-Fouly, Vasileios E. Katzourakis, Mohamed S El Moursi, C. Chrysikopoulos","doi":"10.1093/ce/zkad075","DOIUrl":"https://doi.org/10.1093/ce/zkad075","url":null,"abstract":"\u0000 Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output. As the generation capacity within the electric grid increases, accurately predicting this output becomes increasingly essential, especially given the random and non-linear characteristics of solar irradiance under variable weather conditions. This study presents a novel prediction method for solar irradiance, which is directly in correlation with PV power output, targeting both short-term and medium-term forecast horizons. Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. The proposed method excels in forecasting solar irradiance, especially during highly intermittent weather periods. A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework. We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford, USA and compared it against three forecasting models: persistence, modified 24-hour persistence and least squares. Based on three widely accepted statistical performance metrics (root mean squared error, mean absolute error and coefficient of determination), our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"78 20","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xin Jin, Shi You, Marianne Petersen, Jonathan Riofrio, Soumya Thakur, C. Træholt, Zhijian Feng
The urgency for energy transition is evident through the increasing demand for new technologies such as water electrolysers (WEs), which have the potential to generate green hydrogen using renewable electricity. This paper aims to provide a comprehensive overview of the technical capabilities of commercially available WE system products. The analysis is based on publicly accessible data gathered from 28 WE manufacturers worldwide with a total of 186 products, focusing on technology types and various technical characteristics of each WE system, including capacity, footprint, hydrogen output pressure, hydrogen purity and conversion rate. The analysis reveals that the current WE system solutions in the market exhibit diverse and varied characteristics. Further, there is a lack of standardized product specifications adopted by manufacturers. This underscores the urgent need for the development of frameworks and standards. Implementing such standards is crucial for enhancing clarity and understanding, facilitating efficient comparisons and selection processes, and supporting the future advancement of WE technologies and WE-enabled Power-to-X applications on a global scale.
能源转型的紧迫性体现在对水电解槽（WE）等新技术日益增长的需求上，这些技术具有利用可再生电力生成绿色氢气的潜力。本文旨在全面概述市售水电解槽系统产品的技术能力。分析基于从全球 28 家水电解槽制造商收集到的公开数据，共计 186 种产品，重点关注每种水电解槽系统的技术类型和各种技术特征，包括容量、占地面积、氢气输出压力、氢气纯度和转换率。分析表明，目前市场上的 WE 系统解决方案呈现出多种多样的特点。此外，制造商还缺乏标准化的产品规格。这凸显了制定框架和标准的迫切性。实施此类标准对于提高清晰度和理解力、促进高效的比较和选择过程，以及支持未来全球范围内 WE 技术和支持 WE 的 Power-to-X 应用的发展至关重要。
{"title":"Exploring commercial water electrolyser systems: a data-based analysis of product characteristics","authors":"Xin Jin, Shi You, Marianne Petersen, Jonathan Riofrio, Soumya Thakur, C. Træholt, Zhijian Feng","doi":"10.1093/ce/zkad072","DOIUrl":"https://doi.org/10.1093/ce/zkad072","url":null,"abstract":"\u0000 The urgency for energy transition is evident through the increasing demand for new technologies such as water electrolysers (WEs), which have the potential to generate green hydrogen using renewable electricity. This paper aims to provide a comprehensive overview of the technical capabilities of commercially available WE system products. The analysis is based on publicly accessible data gathered from 28 WE manufacturers worldwide with a total of 186 products, focusing on technology types and various technical characteristics of each WE system, including capacity, footprint, hydrogen output pressure, hydrogen purity and conversion rate. The analysis reveals that the current WE system solutions in the market exhibit diverse and varied characteristics. Further, there is a lack of standardized product specifications adopted by manufacturers. This underscores the urgent need for the development of frameworks and standards. Implementing such standards is crucial for enhancing clarity and understanding, facilitating efficient comparisons and selection processes, and supporting the future advancement of WE technologies and WE-enabled Power-to-X applications on a global scale.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"10 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. K. Overen, Kechrist Obileke, Edson L Meyer, G. Makaka, Oliver O. Apeh
Solar home systems for rural electrification are often designed with a limited energy supply, which presents a drawback for the technology. Furthermore, uncontrolled livestock faeces in rural communities constitute environmental sanitation and health risks. Livestock excrement can be used through a biogas digester to supplement solar energy to provide adequate and sustainable electricity access to underserved rural communities while achieving waste management. Therefore, this study presents a hybrid solar–biogas system for a more dynamic energy supply and waste management for post-Covid recovery plans in rural communities. A parametric research approach that involves the use of the Integrated Environment Solution Virtual Environment software application and mathematical models to design the desired household load and the hybrid system sizing is used in the study. The findings show that the daily household energy consumption was 6.6 kWh, equivalent to 206.40 kWh/month. A 1.2-kWp and 1.2-m3 hybrid solar–biogas system was found to adequately power the house. Financially, the total initial investment cost of the system was $5777.20 with a net present value of $6566.78, net profit of $4443.6, a payback period of 14 years and 8 months, and a levelized cost of energy of $0.21/kWh; these include a 60% initial investment and maintenance costs subsidy. Energy performance contracting and energy-as-a-service were recommended to effectively run and operate the system. The study successfully revealed the design, specifications and upscaling mechanism of the proposed hybrid solar–biogas system. More research is required to unveil the efficacy of the system, the performance gap and the perception of the technology by the beneficiaries.
{"title":"A hybrid solar–biogas system for post-COVID-19 rural energy access","authors":"O. K. Overen, Kechrist Obileke, Edson L Meyer, G. Makaka, Oliver O. Apeh","doi":"10.1093/ce/zkad070","DOIUrl":"https://doi.org/10.1093/ce/zkad070","url":null,"abstract":"\u0000 Solar home systems for rural electrification are often designed with a limited energy supply, which presents a drawback for the technology. Furthermore, uncontrolled livestock faeces in rural communities constitute environmental sanitation and health risks. Livestock excrement can be used through a biogas digester to supplement solar energy to provide adequate and sustainable electricity access to underserved rural communities while achieving waste management. Therefore, this study presents a hybrid solar–biogas system for a more dynamic energy supply and waste management for post-Covid recovery plans in rural communities. A parametric research approach that involves the use of the Integrated Environment Solution Virtual Environment software application and mathematical models to design the desired household load and the hybrid system sizing is used in the study. The findings show that the daily household energy consumption was 6.6 kWh, equivalent to 206.40 kWh/month. A 1.2-kWp and 1.2-m3 hybrid solar–biogas system was found to adequately power the house. Financially, the total initial investment cost of the system was $5777.20 with a net present value of $6566.78, net profit of $4443.6, a payback period of 14 years and 8 months, and a levelized cost of energy of $0.21/kWh; these include a 60% initial investment and maintenance costs subsidy. Energy performance contracting and energy-as-a-service were recommended to effectively run and operate the system. The study successfully revealed the design, specifications and upscaling mechanism of the proposed hybrid solar–biogas system. More research is required to unveil the efficacy of the system, the performance gap and the perception of the technology by the beneficiaries.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"31 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139443518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main objective of this paper is to select the optimal model of a hybrid renewable-energy microgrid (MG) system for a village in India. The MG comprises solar photovoltaic (PV) modules, a wind turbine generator, a biomass generator, a battery bank, a diesel generator and an electric vehicle. The optimal model selection is based on technical, economic, environmental, social and reliability parameters. A novel spoonbill swarm optimization algorithm is proposed to select the best hybrid MG system. The optimization results are compared with particle swarm optimization, the genetic algorithm and the grasshopper optimization algorithm. The number or size of components of the optimized MG system is 215 PV modules, 92 kW of wind turbine generation, 25 kW of biomass generation, 267 batteries, 22 kW of electric vehicles and 30 kW of diesel generation. The optimized system was selected based on technical factors such as renewable dispersion (93.5%), the duty factor (5.85) and excess energy (15 975 kWh/year) as well as economic considerations including the net present cost (Rs. 34 686 622) and the cost of energy (9.3 Rs./kWh). Furthermore, environmental factors such as carbon emissions (396 348 kg/year) and atmospheric particulate matter (22.686 kg/year); social factors such as the human progress index (0.68411), the employment generation factor (0.0389) and local employment generation (15.64643); and reliability parameters including loss of power supply probability (0.01%) and availability index (99.99%) were considered during the selection process. The spoonbill swarm optimization algorithm has reduced the convergence time by 1.2 times and decreased the number of iterations by 0.83 times compared with other algorithms. The performance of the MG system is validated in the MATLAB® environment. The results show that the MG system is the optimal system considering technical, economic, environmental, social and reliability parameters. Additionally, the spoonbill swarm optimization algorithm is found to be more efficient than the other algorithms in terms of iteration time and convergence time.
{"title":"Optimal design of hybrid renewable-energy microgrid system: a techno–economic–environment–social–reliability perspective","authors":"Manoj Gupta, Annapurna Bhargava","doi":"10.1093/ce/zkad069","DOIUrl":"https://doi.org/10.1093/ce/zkad069","url":null,"abstract":"\u0000 The main objective of this paper is to select the optimal model of a hybrid renewable-energy microgrid (MG) system for a village in India. The MG comprises solar photovoltaic (PV) modules, a wind turbine generator, a biomass generator, a battery bank, a diesel generator and an electric vehicle. The optimal model selection is based on technical, economic, environmental, social and reliability parameters. A novel spoonbill swarm optimization algorithm is proposed to select the best hybrid MG system. The optimization results are compared with particle swarm optimization, the genetic algorithm and the grasshopper optimization algorithm. The number or size of components of the optimized MG system is 215 PV modules, 92 kW of wind turbine generation, 25 kW of biomass generation, 267 batteries, 22 kW of electric vehicles and 30 kW of diesel generation. The optimized system was selected based on technical factors such as renewable dispersion (93.5%), the duty factor (5.85) and excess energy (15 975 kWh/year) as well as economic considerations including the net present cost (Rs. 34 686 622) and the cost of energy (9.3 Rs./kWh). Furthermore, environmental factors such as carbon emissions (396 348 kg/year) and atmospheric particulate matter (22.686 kg/year); social factors such as the human progress index (0.68411), the employment generation factor (0.0389) and local employment generation (15.64643); and reliability parameters including loss of power supply probability (0.01%) and availability index (99.99%) were considered during the selection process. The spoonbill swarm optimization algorithm has reduced the convergence time by 1.2 times and decreased the number of iterations by 0.83 times compared with other algorithms. The performance of the MG system is validated in the MATLAB® environment. The results show that the MG system is the optimal system considering technical, economic, environmental, social and reliability parameters. Additionally, the spoonbill swarm optimization algorithm is found to be more efficient than the other algorithms in terms of iteration time and convergence time.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"46 7","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139442263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yida Qiu, Jingkun Wang, Jing Han, Yuzhu Chen, Jun Wang, Peter Lund
Absorption cooling technology is an environmentally friendly method to generate continuous chilled water making use of multiple thermal sources, such as waste heat and renewable thermal energy. In this study, two absorption chillers (nominal capacity of 400 kW) with series and parallel connections are evaluated. To research the ideal configuration of chillers after thermodynamic analysis, the structures of the chillers are optimized using the particle swarm optimization algorithm by considering the heat transfer area (HTA), exergy efficiency and total annual cost as single-objective functions. The impact of temperature differences between external and internal flows, heat exchanger efficiencies and the solution allocation ratio is estimated. The optimized HTA, coefficient of performance, exergy efficiency and total annual cost are 149.0 m2, 1.56, 29.44% and $229 119 for the series-connected chiller, and 146.7 m2, 1.59, 31.45% and $234 562 for the parallel-connected type, respectively. Under the lowest HTA condition, compared with the reference simulation results, the energy and exergy performances are improved, while the annual total cost is higher. The annual total cost is highest when maximizing the exergy efficiency, which is attributed to the increase in the HTA. The operating cost accounts for 27.42% (series type) and 26.54% (parallel type) when the annual cost is the lowest.
{"title":"Comparisons and optimization of two absorption chiller types by considering heat transfer area, exergy and economy as single-objective functions","authors":"Yida Qiu, Jingkun Wang, Jing Han, Yuzhu Chen, Jun Wang, Peter Lund","doi":"10.1093/ce/zkad086","DOIUrl":"https://doi.org/10.1093/ce/zkad086","url":null,"abstract":"\u0000 Absorption cooling technology is an environmentally friendly method to generate continuous chilled water making use of multiple thermal sources, such as waste heat and renewable thermal energy. In this study, two absorption chillers (nominal capacity of 400 kW) with series and parallel connections are evaluated. To research the ideal configuration of chillers after thermodynamic analysis, the structures of the chillers are optimized using the particle swarm optimization algorithm by considering the heat transfer area (HTA), exergy efficiency and total annual cost as single-objective functions. The impact of temperature differences between external and internal flows, heat exchanger efficiencies and the solution allocation ratio is estimated. The optimized HTA, coefficient of performance, exergy efficiency and total annual cost are 149.0 m2, 1.56, 29.44% and $229 119 for the series-connected chiller, and 146.7 m2, 1.59, 31.45% and $234 562 for the parallel-connected type, respectively. Under the lowest HTA condition, compared with the reference simulation results, the energy and exergy performances are improved, while the annual total cost is higher. The annual total cost is highest when maximizing the exergy efficiency, which is attributed to the increase in the HTA. The operating cost accounts for 27.42% (series type) and 26.54% (parallel type) when the annual cost is the lowest.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"10 33","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139380224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rabail Memon, M. A. Mahar, A. S. Larik, Syed Asif Ali shah
The enhanced power quality provided by multilevel inverters (MLIs) has made them more appropriate for medium- and high-power applications, including photovoltaic systems. Nevertheless, a prevalent limitation involves the necessity for numerous switches and increased voltage stress across these switches, consequently increasing the overall system cost. To address these challenges, a new 17-level asymmetrical MLI with fewer components and low voltage stress is proposed for the photovoltaic system. This innovative MLI configuration has four direct current (DC) sources and 10 switches. Based on the trinary sequence, the proposed topology uses photovoltaics with boost converters and fuzzy logic controllers as its DC sources. Mathematical equations are used to calculate crucial parameters for this proposed design, including total standing voltage per unit (TSVPU), cost function per level (CF/L), component count per level (CC/L) and voltage stress across the switches. The comparison is conducted by considering switches, DC sources, TSVPU, CF/L, gate driver circuits and CC/L with other existing MLI topologies. The analysis is carried out under various conditions, encompassing different levels of irradiance, variable loads and modulation indices. To reduce the total harmonic distortion of the suggested topology, the phase opposition disposition approach has been incorporated. The suggested framework is simulated in MATLAB®/Simulink®. The results indicate that the proposed topology achieves a well-distributed stress profile across the switches and has CC/L of 1.23, TSVPU of 5 and CF/L of 4.58 and 5.76 with weight coefficients of 0.5 and 1.5, respectively. These values are notably superior to those of existing MLI topologies. Simulation results demonstrate that the proposed topology maintains a consistent output at varying irradiance levels with FLCs and exhibits robust performance under variable loads and diverse modulation indices. Furthermore, the total harmonic distortion achieved with phase opposition disposition is 7.78%, outperforming alternative pulse width modulation techniques. In summary, it provides enhanced performance. Considering this, it is suitable for the photovoltaic system.
{"title":"An asymmetrical multilevel inverter with minimum voltage stress and fewer components for photovoltaic renewable-energy system","authors":"Rabail Memon, M. A. Mahar, A. S. Larik, Syed Asif Ali shah","doi":"10.1093/ce/zkad073","DOIUrl":"https://doi.org/10.1093/ce/zkad073","url":null,"abstract":"\u0000 The enhanced power quality provided by multilevel inverters (MLIs) has made them more appropriate for medium- and high-power applications, including photovoltaic systems. Nevertheless, a prevalent limitation involves the necessity for numerous switches and increased voltage stress across these switches, consequently increasing the overall system cost. To address these challenges, a new 17-level asymmetrical MLI with fewer components and low voltage stress is proposed for the photovoltaic system. This innovative MLI configuration has four direct current (DC) sources and 10 switches. Based on the trinary sequence, the proposed topology uses photovoltaics with boost converters and fuzzy logic controllers as its DC sources. Mathematical equations are used to calculate crucial parameters for this proposed design, including total standing voltage per unit (TSVPU), cost function per level (CF/L), component count per level (CC/L) and voltage stress across the switches. The comparison is conducted by considering switches, DC sources, TSVPU, CF/L, gate driver circuits and CC/L with other existing MLI topologies. The analysis is carried out under various conditions, encompassing different levels of irradiance, variable loads and modulation indices. To reduce the total harmonic distortion of the suggested topology, the phase opposition disposition approach has been incorporated. The suggested framework is simulated in MATLAB®/Simulink®. The results indicate that the proposed topology achieves a well-distributed stress profile across the switches and has CC/L of 1.23, TSVPU of 5 and CF/L of 4.58 and 5.76 with weight coefficients of 0.5 and 1.5, respectively. These values are notably superior to those of existing MLI topologies. Simulation results demonstrate that the proposed topology maintains a consistent output at varying irradiance levels with FLCs and exhibits robust performance under variable loads and diverse modulation indices. Furthermore, the total harmonic distortion achieved with phase opposition disposition is 7.78%, outperforming alternative pulse width modulation techniques. In summary, it provides enhanced performance. Considering this, it is suitable for the photovoltaic system.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"133 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Demand-side management (DSM) schemes play a crucial role in managing renewable energy generation and load fluctuations by utilizing demand–response programmes (DRPs). This paper aims to provide a detailed overview of DRPs that help microgrid operators to keep costs and reliability within acceptable ranges. Additionally, this review paper provides a detailed economic load model for DRPs based on initial load, demand–response (DR) incentive, DR penalty and elasticity coefficients. This article also aims to guide researchers in identifying research gaps in DSM applications in microgrids by comparing various DSM schemes from different countries and regions in terms of DSM strategies, objective functions and optimization techniques. Furthermore, this study analyses the impact of DRPs on microgrid configuration from the perspective of utilities and customers, considering technical and economic performance metrics. As a result, it can be concluded that none of the studied cases provides models or guidelines for choosing appropriate DSM schemes that consider different consumer interests or load-type features. Furthermore, a few researchers have addressed the features of a modern price-based DR strategy, renewable generation-based dynamic pricing DR, which offers higher customer satisfaction than traditional DRPs.
{"title":"Impacts of multiple demand-side management strategies on microgrids planning: a literature survey","authors":"Rasha Elazab, Ahmed T. Abdelnaby, A. A. Ali","doi":"10.1093/ce/zkad057","DOIUrl":"https://doi.org/10.1093/ce/zkad057","url":null,"abstract":"\u0000 Demand-side management (DSM) schemes play a crucial role in managing renewable energy generation and load fluctuations by utilizing demand–response programmes (DRPs). This paper aims to provide a detailed overview of DRPs that help microgrid operators to keep costs and reliability within acceptable ranges. Additionally, this review paper provides a detailed economic load model for DRPs based on initial load, demand–response (DR) incentive, DR penalty and elasticity coefficients. This article also aims to guide researchers in identifying research gaps in DSM applications in microgrids by comparing various DSM schemes from different countries and regions in terms of DSM strategies, objective functions and optimization techniques. Furthermore, this study analyses the impact of DRPs on microgrid configuration from the perspective of utilities and customers, considering technical and economic performance metrics. As a result, it can be concluded that none of the studied cases provides models or guidelines for choosing appropriate DSM schemes that consider different consumer interests or load-type features. Furthermore, a few researchers have addressed the features of a modern price-based DR strategy, renewable generation-based dynamic pricing DR, which offers higher customer satisfaction than traditional DRPs.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"75 10","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139390048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Solar power is mostly influenced by solar irradiation, weather conditions, solar array mismatches and partial shading conditions. Therefore, before installing solar arrays, it is necessary to simulate and determine the possible power generated. Maximum power point tracking is needed in order to make sure that, at any time, the maximum power will be extracted from the photovoltaic system. However, maximum power point tracking is not a suitable solution for mismatches and partial shading conditions. To overcome the drawbacks of maximum power point tracking due to mismatches and shadows, distributed maximum power point tracking is utilized in this paper. The solar farm can be distributed in different ways, including one DC–DC converter per group of modules or per module. In this paper, distributed maximum power point tracking per module is implemented, which has the highest efficiency. This technology is applied to electric vehicles (EVs) that can be charged with a Level 3 charging station in <1 hour. However, the problem is that charging an EV in <1 hour puts a lot of stress on the power grid, and there is not always enough peak power reserve in the existing power grid to charge EVs at that rate. Therefore, a Level 3 (fast DC) EV charging station using a solar farm by implementing distributed maximum power point tracking is utilized to address this issue. Finally, the simulation result is reported using MATLAB®, LTSPICE and the System Advisor Model. Simulation results show that the proposed 1-MW solar system will provide 5 MWh of power each day, which is enough to fully charge ~120 EVs each day. Additionally, the use of the proposed photovoltaic system benefits the environment by removing a huge amount of greenhouse gases and hazardous pollutants. For example, instead of supplying EVs with power from coal-fired power plants, 1989 pounds of CO2 will be eliminated from the air per hour.
{"title":"Design of a Level-3 electric vehicle charging station using a 1-MW solar system via the distributed maximum power point tracking technique","authors":"Afshin Balal, Michael Giesselmann","doi":"10.1093/ce/zkad084","DOIUrl":"https://doi.org/10.1093/ce/zkad084","url":null,"abstract":"\u0000 Solar power is mostly influenced by solar irradiation, weather conditions, solar array mismatches and partial shading conditions. Therefore, before installing solar arrays, it is necessary to simulate and determine the possible power generated. Maximum power point tracking is needed in order to make sure that, at any time, the maximum power will be extracted from the photovoltaic system. However, maximum power point tracking is not a suitable solution for mismatches and partial shading conditions. To overcome the drawbacks of maximum power point tracking due to mismatches and shadows, distributed maximum power point tracking is utilized in this paper. The solar farm can be distributed in different ways, including one DC–DC converter per group of modules or per module. In this paper, distributed maximum power point tracking per module is implemented, which has the highest efficiency. This technology is applied to electric vehicles (EVs) that can be charged with a Level 3 charging station in <1 hour. However, the problem is that charging an EV in <1 hour puts a lot of stress on the power grid, and there is not always enough peak power reserve in the existing power grid to charge EVs at that rate. Therefore, a Level 3 (fast DC) EV charging station using a solar farm by implementing distributed maximum power point tracking is utilized to address this issue. Finally, the simulation result is reported using MATLAB®, LTSPICE and the System Advisor Model. Simulation results show that the proposed 1-MW solar system will provide 5 MWh of power each day, which is enough to fully charge ~120 EVs each day. Additionally, the use of the proposed photovoltaic system benefits the environment by removing a huge amount of greenhouse gases and hazardous pollutants. For example, instead of supplying EVs with power from coal-fired power plants, 1989 pounds of CO2 will be eliminated from the air per hour.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"6 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139452378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sayeed Hasan, Md. Rifat Hazari, Effat Jahan, Mohammad Abdul Mannan
Growing energy demand, diminishing fossil fuel reserves and geopolitical tensions are serious concerns for any country’s energy strategy and security. These factors have a greater impact on developing countries, as many of them rely largely on traditional energy resources. Cleaner energy generation is the viable alternative for mitigating these problems, as well as achieving energy independence and tackling climate change. The article discusses planning and design optimization of a residential community microgrid based on multiple renewable resources. In particular, the design and techno-economic assessment of a grid-tied hybrid microgrid for meeting the electricity demand of an alluvial region, Urir Char, located in southern Bangladesh, was addressed. Hybrid Optimization of Multiple Energy Resources is used for the evaluation and it is supplemented by a fuzzy-logic-based load profile design strategy. In addition to the analysis, a predictive load-shifting-based demand management is also introduced. Several cases were considered for the studies and, after considering several criteria, a grid-tied system comprising a photovoltaic array, wind turbine and energy storage system was found to be the best fit for powering the loads. The suggested system reduces the life-cycle cost by 18.3%, the levelized cost of energy by 61.9% and emissions by 77.2% when compared with the grid-only option. Along with the microgrid design, cooking emissions and energy categorization were also discussed.
{"title":"Design optimization of a grid-tied microgrid for a residential community in southern Bangladesh","authors":"Sayeed Hasan, Md. Rifat Hazari, Effat Jahan, Mohammad Abdul Mannan","doi":"10.1093/ce/zkad056","DOIUrl":"https://doi.org/10.1093/ce/zkad056","url":null,"abstract":"Growing energy demand, diminishing fossil fuel reserves and geopolitical tensions are serious concerns for any country’s energy strategy and security. These factors have a greater impact on developing countries, as many of them rely largely on traditional energy resources. Cleaner energy generation is the viable alternative for mitigating these problems, as well as achieving energy independence and tackling climate change. The article discusses planning and design optimization of a residential community microgrid based on multiple renewable resources. In particular, the design and techno-economic assessment of a grid-tied hybrid microgrid for meeting the electricity demand of an alluvial region, Urir Char, located in southern Bangladesh, was addressed. Hybrid Optimization of Multiple Energy Resources is used for the evaluation and it is supplemented by a fuzzy-logic-based load profile design strategy. In addition to the analysis, a predictive load-shifting-based demand management is also introduced. Several cases were considered for the studies and, after considering several criteria, a grid-tied system comprising a photovoltaic array, wind turbine and energy storage system was found to be the best fit for powering the loads. The suggested system reduces the life-cycle cost by 18.3%, the levelized cost of energy by 61.9% and emissions by 77.2% when compared with the grid-only option. Along with the microgrid design, cooking emissions and energy categorization were also discussed.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"25 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139255009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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