Vijaya Krishna Rayi, Ranjeeta Bisoi, S P Mishra, P K Dash
Abstract Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric conditions. Although there are several intelligent techniques in the literature for wind speed prediction, their accuracies are not yet very reliable. Therefore, in this paper, a new hybrid intelligent technique named the deep mixed kernel random vector functional-link network auto-encoder (AE) is proposed for wind speed prediction. The proposed method eliminates manual tuning of hidden nodes with random weights and biases, providing prediction model generalization and representation learning. This reduces reconstruction error due to the exact inversion of the kernel matrix, unlike the pseudo-inverse in a random vector functional-link network, and shortens the execution time. Furthermore, the presence of a direct link from the input to the output reduces the complexity of the prediction model and improves the prediction accuracy. The kernel parameters and coefficients of the mixed kernel system are optimized using a new chaotic sine–cosine Levy flight optimization technique. The lowest errors in terms of mean absolute error (0.4139), mean absolute percentage error (4.0081), root mean square error (0.4843), standard deviation error (1.1431) and index of agreement (0.9733) prove the efficiency of the proposed model in comparison with other deep learning models such as deep AEs, deep kernel extreme learning machine AEs, deep kernel random vector functional-link network AEs, benchmark models such as least square support vector machine, autoregressive integrated moving average, extreme learning machines and their hybrid models along with different state-of-the-art methods.
{"title":"Improved deep mixed kernel randomized network for wind speed prediction","authors":"Vijaya Krishna Rayi, Ranjeeta Bisoi, S P Mishra, P K Dash","doi":"10.1093/ce/zkad042","DOIUrl":"https://doi.org/10.1093/ce/zkad042","url":null,"abstract":"Abstract Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric conditions. Although there are several intelligent techniques in the literature for wind speed prediction, their accuracies are not yet very reliable. Therefore, in this paper, a new hybrid intelligent technique named the deep mixed kernel random vector functional-link network auto-encoder (AE) is proposed for wind speed prediction. The proposed method eliminates manual tuning of hidden nodes with random weights and biases, providing prediction model generalization and representation learning. This reduces reconstruction error due to the exact inversion of the kernel matrix, unlike the pseudo-inverse in a random vector functional-link network, and shortens the execution time. Furthermore, the presence of a direct link from the input to the output reduces the complexity of the prediction model and improves the prediction accuracy. The kernel parameters and coefficients of the mixed kernel system are optimized using a new chaotic sine–cosine Levy flight optimization technique. The lowest errors in terms of mean absolute error (0.4139), mean absolute percentage error (4.0081), root mean square error (0.4843), standard deviation error (1.1431) and index of agreement (0.9733) prove the efficiency of the proposed model in comparison with other deep learning models such as deep AEs, deep kernel extreme learning machine AEs, deep kernel random vector functional-link network AEs, benchmark models such as least square support vector machine, autoregressive integrated moving average, extreme learning machines and their hybrid models along with different state-of-the-art methods.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136375729","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}
Abstract The star-labelling programme for residential buildings was introduced by India in 2020 and applies to all residential buildings with no lower limit on the built-up area or electrical demand. The energy-star label for a residential building is awarded against the notified standard by the regulatory body and electric vehicles (EVs) have not been accommodated as a load for residential buildings. The energy consumption of an existing residential building is taken from a study already carried out and compared with the requirement of the Indian residential star-labelling programme with an EV as a plugged-in load. An annual energy gap of 6060 kWh for the existing residential buildings considered in this study for five-star building energy labels increases to 7784 kWh if the EV load is added to the building load. The residential building will lose two energy stars if it caters to the EV load and, to bridge this energy gap, the replacement of existing electrical appliances with five-star-rated energy appliances, employing grid-connected rooftop solar photovoltaics (PV) and retrofit of the building envelope are considered. The techno-economic potential of rooftop solar PV and building envelope retrofitting for existing residential buildings is explored using RETScreen® and eQUEST software, respectively. The study establishes that the installation of rooftop solar PV can accommodate the additional load of EVs and can bridge half and three-quarters of the energy gap to achieve five energy stars for an existing building with and without EVs, respectively. It is the most economical option among the options explored in this study. The target Energy Performance Index is achievable by high-end energy consumers (12 000 kWh/year) by additional measures, the replacement of inefficient electrical appliances and building envelope retrofitting in addition to the installation of rooftop solar PV.
{"title":"Mitigation of the impacts of electric vehicle charging on energy-star ratings for residential buildings in India","authors":"Rakesh Dalal, Devender Kumar Saini","doi":"10.1093/ce/zkad041","DOIUrl":"https://doi.org/10.1093/ce/zkad041","url":null,"abstract":"Abstract The star-labelling programme for residential buildings was introduced by India in 2020 and applies to all residential buildings with no lower limit on the built-up area or electrical demand. The energy-star label for a residential building is awarded against the notified standard by the regulatory body and electric vehicles (EVs) have not been accommodated as a load for residential buildings. The energy consumption of an existing residential building is taken from a study already carried out and compared with the requirement of the Indian residential star-labelling programme with an EV as a plugged-in load. An annual energy gap of 6060 kWh for the existing residential buildings considered in this study for five-star building energy labels increases to 7784 kWh if the EV load is added to the building load. The residential building will lose two energy stars if it caters to the EV load and, to bridge this energy gap, the replacement of existing electrical appliances with five-star-rated energy appliances, employing grid-connected rooftop solar photovoltaics (PV) and retrofit of the building envelope are considered. The techno-economic potential of rooftop solar PV and building envelope retrofitting for existing residential buildings is explored using RETScreen® and eQUEST software, respectively. The study establishes that the installation of rooftop solar PV can accommodate the additional load of EVs and can bridge half and three-quarters of the energy gap to achieve five energy stars for an existing building with and without EVs, respectively. It is the most economical option among the options explored in this study. The target Energy Performance Index is achievable by high-end energy consumers (12 000 kWh/year) by additional measures, the replacement of inefficient electrical appliances and building envelope retrofitting in addition to the installation of rooftop solar PV.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135734196","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}
Tarig Z Ahmed, Ayah Mohamed, Mawahib Eltayeb Ahmed, Ahmed Osman Elamin Abdalgader, Mohamed G Hassan-Sayed
Abstract Grid-connected rooftop solar photovoltaic (PV) systems can reduce the energy demand from the grid and significantly increase the power available to it. However, rooftop solar PV has not yet been widely adopted in many sub-Saharan African countries, such as Sudan, although they are endowed with high solar radiation and in dire need of additional power. This paper investigates risks and policies to increase grid-connected rooftop solar PV adoption in Sudan. A simplified United Nations Development Program Derisking Renewable Energy Investment framework is adopted to investigate this over three stages. For Stage 1, a list of risks and barriers was produced based on a literature review of solar PV studies in Sudan and interviews with nine stakeholders. Affordability was the risk most often mentioned (eight times from nine interviewees), followed by concerns about poor utility grid infrastructure. For Stage 2, policy de-risking instruments and financial de-risking instruments were listed to overcome the barriers. These include the introduction of net metering, the use of a third-party organization to monitor policy implementation, upgrade of the grid infrastructure, public awareness campaigns and energy-saving schemes. For Stage 3, the levelized cost of electricity was estimated for a typical 2-kW rooftop PV system without policies (0.11 $/kWh) and with a net-metering policy (0.07 $/kWh).
{"title":"Investigating energy policies to boost grid-connected rooftop solar PV in Sudan","authors":"Tarig Z Ahmed, Ayah Mohamed, Mawahib Eltayeb Ahmed, Ahmed Osman Elamin Abdalgader, Mohamed G Hassan-Sayed","doi":"10.1093/ce/zkad044","DOIUrl":"https://doi.org/10.1093/ce/zkad044","url":null,"abstract":"Abstract Grid-connected rooftop solar photovoltaic (PV) systems can reduce the energy demand from the grid and significantly increase the power available to it. However, rooftop solar PV has not yet been widely adopted in many sub-Saharan African countries, such as Sudan, although they are endowed with high solar radiation and in dire need of additional power. This paper investigates risks and policies to increase grid-connected rooftop solar PV adoption in Sudan. A simplified United Nations Development Program Derisking Renewable Energy Investment framework is adopted to investigate this over three stages. For Stage 1, a list of risks and barriers was produced based on a literature review of solar PV studies in Sudan and interviews with nine stakeholders. Affordability was the risk most often mentioned (eight times from nine interviewees), followed by concerns about poor utility grid infrastructure. For Stage 2, policy de-risking instruments and financial de-risking instruments were listed to overcome the barriers. These include the introduction of net metering, the use of a third-party organization to monitor policy implementation, upgrade of the grid infrastructure, public awareness campaigns and energy-saving schemes. For Stage 3, the levelized cost of electricity was estimated for a typical 2-kW rooftop PV system without policies (0.11 $/kWh) and with a net-metering policy (0.07 $/kWh).","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135734185","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}
Abstract Green hydrogen is considered one of the key technologies of the energy transition, as it can be used to store surpluses from renewable energies in times of high solar radiation or wind speed for use in dark lulls. This paper examines the decarbonization potential of hydrogen for the heating industry. Worldwide, 99% of hydrogen is produced from fossil fuels, because hydrogen derived from renewable energy sources remains prohibitively expensive compared with its conventional counterpart. However, due to the expansion of renewable energy sources and the current energy crisis of conventional energy sources, hydrogen from renewable energy sources is becoming more and more economical. To optimize the efficiency of green hydrogen production and make it more price-competitive, the author simulates a hydrogen production plant consisting of a photovoltaic plant, a power grid, hydrogen storage, an electrolyser, a natural gas purchase option, a district heating plant and households. Using the deep deterministic policy gradient algorithm from deep reinforcement learning, the plant is designed to optimize itself by simulating different production scenarios and deriving strategies. The connected district heating plant is used to map how hydrogen can be optimally used for heat supply. A demonstrable outcome of this paper is that the utilization of deep deterministic policy gradient, over the course of a full year, can result in a competitive production of hydrogen derived from renewable or stored energy sources for the heating industry as a natural gas substitute.
{"title":"Optimal control of a hybrid microgrid for hydrogen-based heat supply using deep reinforcement learning","authors":"Robin Heckmann","doi":"10.1093/ce/zkad038","DOIUrl":"https://doi.org/10.1093/ce/zkad038","url":null,"abstract":"Abstract Green hydrogen is considered one of the key technologies of the energy transition, as it can be used to store surpluses from renewable energies in times of high solar radiation or wind speed for use in dark lulls. This paper examines the decarbonization potential of hydrogen for the heating industry. Worldwide, 99% of hydrogen is produced from fossil fuels, because hydrogen derived from renewable energy sources remains prohibitively expensive compared with its conventional counterpart. However, due to the expansion of renewable energy sources and the current energy crisis of conventional energy sources, hydrogen from renewable energy sources is becoming more and more economical. To optimize the efficiency of green hydrogen production and make it more price-competitive, the author simulates a hydrogen production plant consisting of a photovoltaic plant, a power grid, hydrogen storage, an electrolyser, a natural gas purchase option, a district heating plant and households. Using the deep deterministic policy gradient algorithm from deep reinforcement learning, the plant is designed to optimize itself by simulating different production scenarios and deriving strategies. The connected district heating plant is used to map how hydrogen can be optimally used for heat supply. A demonstrable outcome of this paper is that the utilization of deep deterministic policy gradient, over the course of a full year, can result in a competitive production of hydrogen derived from renewable or stored energy sources for the heating industry as a natural gas substitute.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135734244","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}
Ammar Hummieda, Ali Bouabid, Karim Moawad, Ahmad Mayyas
Abstract In recent years, the United Arab Emirates (UAE) has developed strategies to increase renewable power generation and reduce emissions to net zero by 2050. Electricity generation and energy-intensive industries (EII) have the largest potentials for emission reduction. Therefore, an up-to-date inventory of greenhouse gas emissions and a study of the pathways to achieving the 2050 targets are essential. This study focuses on power production and EII (aluminium, steel and cement). The structure of these sectors is modelled and simulated up to 2050 using a system dynamics (SD) methodology. The SD model is validated to reflect the real-world state of the system using the emissions inventory projections as reference modes. Nineteen mitigation policies are considered in the selected sectors and four policy scenarios were simulated. The results show that implementing the Energy Strategy 2050 in the power sector can result in a reduction of 63.5% in emissions in that sector, which translates into a reduction of 33.5% overall by 2050. Additionally, implementing all identified mitigation strategies to full utilization in EII yields a 94% reduction in that sector, which translates into a 78% reduction overall. Decarbonizing the aluminium industry yields the highest emissions reductions, followed by power production, then cement and finally steel. In the best-case scenario, 22.1% of the business-as-usual emissions are still released and further decarbonization—mainly in the power sector—will be required. This is achievable given the trajectory of the UAE’s successful nuclear energy programme and the prospect of utilizing carbon capture, utilization and storage even further.
{"title":"The UAE’s energy system and GHG emissions: pathways to achieving national goals by 2050","authors":"Ammar Hummieda, Ali Bouabid, Karim Moawad, Ahmad Mayyas","doi":"10.1093/ce/zkad040","DOIUrl":"https://doi.org/10.1093/ce/zkad040","url":null,"abstract":"Abstract In recent years, the United Arab Emirates (UAE) has developed strategies to increase renewable power generation and reduce emissions to net zero by 2050. Electricity generation and energy-intensive industries (EII) have the largest potentials for emission reduction. Therefore, an up-to-date inventory of greenhouse gas emissions and a study of the pathways to achieving the 2050 targets are essential. This study focuses on power production and EII (aluminium, steel and cement). The structure of these sectors is modelled and simulated up to 2050 using a system dynamics (SD) methodology. The SD model is validated to reflect the real-world state of the system using the emissions inventory projections as reference modes. Nineteen mitigation policies are considered in the selected sectors and four policy scenarios were simulated. The results show that implementing the Energy Strategy 2050 in the power sector can result in a reduction of 63.5% in emissions in that sector, which translates into a reduction of 33.5% overall by 2050. Additionally, implementing all identified mitigation strategies to full utilization in EII yields a 94% reduction in that sector, which translates into a 78% reduction overall. Decarbonizing the aluminium industry yields the highest emissions reductions, followed by power production, then cement and finally steel. In the best-case scenario, 22.1% of the business-as-usual emissions are still released and further decarbonization—mainly in the power sector—will be required. This is achievable given the trajectory of the UAE’s successful nuclear energy programme and the prospect of utilizing carbon capture, utilization and storage even further.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135734580","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}
Abstract To provide clean energy at a lower cost to their citizens, all nations of the world are striving to increase their energy production in an environmentally friendly way. Benin has also joined this dynamic by considerably increasing its green energy production efforts in recent years. The country has a huge undeveloped renewable-energy (RE) potential that can contribute considerably to its national energy production capacity. This paper summarizes the current RE situation in Benin and examines its future prospects. The current energy situation of the country is discussed, followed by an examination of its electricity demand-and-supply situation. The country has been found to depend heavily on natural gas and petroleum products from neighbouring countries and has ~41% of national electricity access. However, the government is taking considerable steps to implement RE projects in the country. The study analyzes government targets in the energy sector with existing policies and institutional frameworks. Recommendations are made for the benefit of the government, the private sector and other actors in order to developing the RE potential of Benin.
{"title":"Renewable energy in Benin: current situation and future prospects","authors":"Romain Akpahou, Lena D Mensah, David A Quansah","doi":"10.1093/ce/zkad039","DOIUrl":"https://doi.org/10.1093/ce/zkad039","url":null,"abstract":"Abstract To provide clean energy at a lower cost to their citizens, all nations of the world are striving to increase their energy production in an environmentally friendly way. Benin has also joined this dynamic by considerably increasing its green energy production efforts in recent years. The country has a huge undeveloped renewable-energy (RE) potential that can contribute considerably to its national energy production capacity. This paper summarizes the current RE situation in Benin and examines its future prospects. The current energy situation of the country is discussed, followed by an examination of its electricity demand-and-supply situation. The country has been found to depend heavily on natural gas and petroleum products from neighbouring countries and has ~41% of national electricity access. However, the government is taking considerable steps to implement RE projects in the country. The study analyzes government targets in the energy sector with existing policies and institutional frameworks. Recommendations are made for the benefit of the government, the private sector and other actors in order to developing the RE potential of Benin.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135734183","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}
Mohammed A Qasim, Vladimir I Velkin, Sergey E Shcheklein
Abstract The concept of employing thermoelectric generators (TEGs) to recover energy from waste heat has gained popularity, with applications that range from milliwatt to kilowatt levels of output power. In this study, a hybrid photovoltaic panel and thermoelectric generator (HPVTEG) system consisting of an integrated heat exchanger, a commercial polycrystalline silicon photovoltaic (PV) panel and a commercial bismuth telluride TEG was proposed. Here, TE components can be used to cool PV modules, increasing their output power via the Seebeck effect. The main finding is that the hybrid system has a reduced average temperature of 16.01°C. The average power of the stand-alone PV panel is 28.06 W, but that of the HPVTEG system is 32.76 W, which is an increase of 4.7 W. The conversion efficiency and power of the hybrid system increased by 16.7% and 16.4%, respectively, compared with a stand-alone PV panel. The HPVTEG system achieved an average exergy efficiency of 12.79% compared with 10.98% for a stand-alone PV panel. According to the calculation results, the levelized cost of energy (LCOE) of the stand-alone PV panel can range from 0.06741 to 0.10251 US$/kWh depending on how many days it is in operation, while the LCOE of the HPVTEG system can range from 0.06681 to 0.10160 US$/kWh.
{"title":"Experimental study on hybridization of a PV–TEG system for electrical performance enhancement using heat exchangers, energy, exergy and economic levelized cost of energy (LCOE) analysis","authors":"Mohammed A Qasim, Vladimir I Velkin, Sergey E Shcheklein","doi":"10.1093/ce/zkad023","DOIUrl":"https://doi.org/10.1093/ce/zkad023","url":null,"abstract":"Abstract The concept of employing thermoelectric generators (TEGs) to recover energy from waste heat has gained popularity, with applications that range from milliwatt to kilowatt levels of output power. In this study, a hybrid photovoltaic panel and thermoelectric generator (HPVTEG) system consisting of an integrated heat exchanger, a commercial polycrystalline silicon photovoltaic (PV) panel and a commercial bismuth telluride TEG was proposed. Here, TE components can be used to cool PV modules, increasing their output power via the Seebeck effect. The main finding is that the hybrid system has a reduced average temperature of 16.01°C. The average power of the stand-alone PV panel is 28.06 W, but that of the HPVTEG system is 32.76 W, which is an increase of 4.7 W. The conversion efficiency and power of the hybrid system increased by 16.7% and 16.4%, respectively, compared with a stand-alone PV panel. The HPVTEG system achieved an average exergy efficiency of 12.79% compared with 10.98% for a stand-alone PV panel. According to the calculation results, the levelized cost of energy (LCOE) of the stand-alone PV panel can range from 0.06741 to 0.10251 US$/kWh depending on how many days it is in operation, while the LCOE of the HPVTEG system can range from 0.06681 to 0.10160 US$/kWh.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135930826","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}
Abstract The intense increase in the installed capacity of wind farms has required a computationally efficient dynamic equivalent model of wind farms. Various types of wind-farm modelling aim to identify the accuracy and simulation time in the presence of the power system. In this study, dynamic simulation of equivalent models of a sample wind farm, including single-turbine representation, multiple-turbine representation, quasi-multiple-turbine representation and full-turbine representation models, are performed using a doubly-fed induction generator wind turbine model developed in DIgSILENT software. The developed doubly-fed induction generator model in DIgSILENT is intended to simulate inflow wind turbulence for more accurate performance. The wake effects between wind turbines for the full-turbine representation and multiple-turbine representation models have been considered using the Jensen method. The developed model improves the extraction power of the turbine according to the layout of the wind farm. The accuracy of the mentioned methods is evaluated by calculating the output parameters of the wind farm, including active and reactive powers, voltage and instantaneous flicker intensity. The study was carried out on a sample wind farm, which included 39 wind turbines. The simulation results confirm that the computational loads of the single-turbine representation (STR), the multiple-turbine representation and the quasi-multiple-turbine representation are 1/39, 1/8 and 1/8 times the full-turbine representation model, respectively. On the other hand, the error of active power (voltage) with respect to the full-turbine representation model is 74.59% (1.31%), 43.29% (0.31%) and 7.19% (0.11%) for the STR, the multiple-turbine representation and the quasi-multiple representation, respectively.
{"title":"Power quality assessment in different wind power plant models considering wind turbine wake effects","authors":"Mohsen Khatamiaghda, Saeed Bahraminejad, Roohollah Fadaeinedjad","doi":"10.1093/ce/zkad033","DOIUrl":"https://doi.org/10.1093/ce/zkad033","url":null,"abstract":"Abstract The intense increase in the installed capacity of wind farms has required a computationally efficient dynamic equivalent model of wind farms. Various types of wind-farm modelling aim to identify the accuracy and simulation time in the presence of the power system. In this study, dynamic simulation of equivalent models of a sample wind farm, including single-turbine representation, multiple-turbine representation, quasi-multiple-turbine representation and full-turbine representation models, are performed using a doubly-fed induction generator wind turbine model developed in DIgSILENT software. The developed doubly-fed induction generator model in DIgSILENT is intended to simulate inflow wind turbulence for more accurate performance. The wake effects between wind turbines for the full-turbine representation and multiple-turbine representation models have been considered using the Jensen method. The developed model improves the extraction power of the turbine according to the layout of the wind farm. The accuracy of the mentioned methods is evaluated by calculating the output parameters of the wind farm, including active and reactive powers, voltage and instantaneous flicker intensity. The study was carried out on a sample wind farm, which included 39 wind turbines. The simulation results confirm that the computational loads of the single-turbine representation (STR), the multiple-turbine representation and the quasi-multiple-turbine representation are 1/39, 1/8 and 1/8 times the full-turbine representation model, respectively. On the other hand, the error of active power (voltage) with respect to the full-turbine representation model is 74.59% (1.31%), 43.29% (0.31%) and 7.19% (0.11%) for the STR, the multiple-turbine representation and the quasi-multiple representation, respectively.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136022069","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}
Ranga Rao Chegudi, Balamurugan Ramadoss, Ramakoteswara Rao Alla
Abstract This study suggests an optimal renewable energy source (RES) allocation and distribution-static synchronous compensator (D-STATCOM) and passive power filters (PPFs) for an electrical distribution network (EDN) to improve its performance and power quality (PQ). First, the latest metaheuristic artificial rabbits optimization (ARO) is used to locate and size solar photovoltaic (PV), wind turbine (WT) and D-STATCOM units. In the second stage, ratings of single-tuned PPFs and D-STATCOMs at the RESs are determined, considering non-linear loads in the network. The multi-objective function reduces power loss, improves the voltage stability index (VSI) and limits total harmonic distortion. Simulations using the IEEE 33-bus EDN compared the ARO results with those of previous studies. In the first scenario, ideally integrated D-STATCOMs, PVs and WTs reduced losses by 34.79%, 64.74% and 94.15%, respectively. VSI increases from 0.6965 to 0.7749, 0.8804 and 0.967. The optimal WT integration of the first scenario outperformed the PVs and D-STATCOMs. The second step optimizes the WTs and PQ devices for non-linear loads. WTs and D-STATCOMs reduce the maximum total harmonic distortion of the voltage waveform by 5.21% with non-linear loads to 3.23%, while WTs and PPFs reduce it to 4.39%. These scenarios demonstrate how WTs and D-STATCOMs can improve network performance and PQ. The computational efficiency of ARO is compared to that of the pathfinder algorithm, future search algorithm, butterfly optimization algorithm and coyote optimization algorithm. ARO speeds up convergence and improves solution quality and comprehension.
{"title":"Simultaneous allocation of renewable energy sources and custom power quality devices in electrical distribution networks using artificial rabbits optimization","authors":"Ranga Rao Chegudi, Balamurugan Ramadoss, Ramakoteswara Rao Alla","doi":"10.1093/ce/zkad019","DOIUrl":"https://doi.org/10.1093/ce/zkad019","url":null,"abstract":"Abstract This study suggests an optimal renewable energy source (RES) allocation and distribution-static synchronous compensator (D-STATCOM) and passive power filters (PPFs) for an electrical distribution network (EDN) to improve its performance and power quality (PQ). First, the latest metaheuristic artificial rabbits optimization (ARO) is used to locate and size solar photovoltaic (PV), wind turbine (WT) and D-STATCOM units. In the second stage, ratings of single-tuned PPFs and D-STATCOMs at the RESs are determined, considering non-linear loads in the network. The multi-objective function reduces power loss, improves the voltage stability index (VSI) and limits total harmonic distortion. Simulations using the IEEE 33-bus EDN compared the ARO results with those of previous studies. In the first scenario, ideally integrated D-STATCOMs, PVs and WTs reduced losses by 34.79%, 64.74% and 94.15%, respectively. VSI increases from 0.6965 to 0.7749, 0.8804 and 0.967. The optimal WT integration of the first scenario outperformed the PVs and D-STATCOMs. The second step optimizes the WTs and PQ devices for non-linear loads. WTs and D-STATCOMs reduce the maximum total harmonic distortion of the voltage waveform by 5.21% with non-linear loads to 3.23%, while WTs and PPFs reduce it to 4.39%. These scenarios demonstrate how WTs and D-STATCOMs can improve network performance and PQ. The computational efficiency of ARO is compared to that of the pathfinder algorithm, future search algorithm, butterfly optimization algorithm and coyote optimization algorithm. ARO speeds up convergence and improves solution quality and comprehension.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135264274","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}
Abstract Catalytic co-pyrolysis of biomass and plastic is an effective method to improve bio-oil produced by biomass pyrolysis. To further exploit the synergistic mechanism between biomass and plastic, co-pyrolysis of polypropylene (PP) and deuterated glucose (G) (1:1 wt%) over mesoporous catalysts MCM-41 (M) and Al-MCM-41(Al) was studied using a thermal gravimetric analyser (TGA) and pyrolysis–gas chromatography–mass spectrometry. The findings show that M and Al overlap the decomposition of PP and G, making synergy possible. With catalysts M and Al, the yield of olefins increases sharply to 36.75% and 13.66% more than the calculated value. Additionally, hydrogen transfers from G to 4C–13C olefins and aromatic products are influenced by the catalysts. Without a catalyst, there is no deuterium in all the co-pyrolytic products. However, catalysts M and Al can help transfer one to four deuterium atoms from G to the products. M and Al provide the pool for the intermediates of PP and G to form synergetic products. Additionally, Al helps break the carbon chain and transfer more deuterium into the products by reducing carbon atoms.
{"title":"Origin of hydrogen in aromatic and olefin products derived from (Al-) MCM-41 catalysed co-pyrolysis of glucose and polypropylene via isotopic labelling","authors":"Junjie Xue, Jiankun Zhuo, Yifan Wu, Mingnuo Jin, Mufei Sun, Qiang Yao","doi":"10.1093/ce/zkac059","DOIUrl":"https://doi.org/10.1093/ce/zkac059","url":null,"abstract":"Abstract Catalytic co-pyrolysis of biomass and plastic is an effective method to improve bio-oil produced by biomass pyrolysis. To further exploit the synergistic mechanism between biomass and plastic, co-pyrolysis of polypropylene (PP) and deuterated glucose (G) (1:1 wt%) over mesoporous catalysts MCM-41 (M) and Al-MCM-41(Al) was studied using a thermal gravimetric analyser (TGA) and pyrolysis–gas chromatography–mass spectrometry. The findings show that M and Al overlap the decomposition of PP and G, making synergy possible. With catalysts M and Al, the yield of olefins increases sharply to 36.75% and 13.66% more than the calculated value. Additionally, hydrogen transfers from G to 4C–13C olefins and aromatic products are influenced by the catalysts. Without a catalyst, there is no deuterium in all the co-pyrolytic products. However, catalysts M and Al can help transfer one to four deuterium atoms from G to the products. M and Al provide the pool for the intermediates of PP and G to form synergetic products. Additionally, Al helps break the carbon chain and transfer more deuterium into the products by reducing carbon atoms.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136166927","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：