El Mahfoud Boulaoutaq, Asma Aziz, Abdelmounime El Magri, Ahmed Abbou, Mohamed Ajaamoum, Azeddine Rachdy
Abstract Disconnections due to voltage drops in the grid cannot be permitted if wind turbines (WTs) contribute significantly to electricity production, as this increases the risk of production loss and destabilizes the grid. To mitigate the negative effects of these occurrences, WTs must be able to ride through the low-voltage conditions and inject reactive current to provide dynamic voltage support. This paper investigates the low-voltage ride-through (LVRT) capability enhancement of a Type-3 WT utilizing a dynamic voltage restorer (DVR). During the grid voltage drop, the DVR quickly injects a compensating voltage to keep the stator voltage constant. This paper proposes an active disturbance rejection control (ADRC) scheme to control the rotor-side, grid-side and DVR-side converters in a wind–DVR integrated network. The performance of the Type-3 WT with DVR topology is evaluated under various test conditions using MATLAB®/Simulink®. These simulation results are also compared with the experimental results for the LVRT capability performed on a WT emulator equipped with a crowbar and direct current (DC) chopper. The simulation results demonstrate a favourable transient and steady-state response of the Type-3 wind turbine quantities defined by the LVRT codes, as well as improved reactive power support under balanced fault conditions. Under the most severe voltage drop of 95%, the stator currents, rotor currents and DC bus voltage are 1.25 pu, 1.40 pu and 1.09 UDC, respectively, conforming to the values of the LVRT codes. DVR controlled by the ADRC technique significantly increases the LVRT capabilities of a Type-3 doubly-fed induction generator-based WT under symmetrical voltage dip events. Although setting up ADRC controllers might be challenging, the proposed method has been shown to be extremely effective in reducing all kinds of internal and external disturbances.
{"title":"Low-voltage ride-through capability improvement of Type-3 wind turbine through active disturbance rejection feedback control-based dynamic voltage restorer","authors":"El Mahfoud Boulaoutaq, Asma Aziz, Abdelmounime El Magri, Ahmed Abbou, Mohamed Ajaamoum, Azeddine Rachdy","doi":"10.1093/ce/zkad050","DOIUrl":"https://doi.org/10.1093/ce/zkad050","url":null,"abstract":"Abstract Disconnections due to voltage drops in the grid cannot be permitted if wind turbines (WTs) contribute significantly to electricity production, as this increases the risk of production loss and destabilizes the grid. To mitigate the negative effects of these occurrences, WTs must be able to ride through the low-voltage conditions and inject reactive current to provide dynamic voltage support. This paper investigates the low-voltage ride-through (LVRT) capability enhancement of a Type-3 WT utilizing a dynamic voltage restorer (DVR). During the grid voltage drop, the DVR quickly injects a compensating voltage to keep the stator voltage constant. This paper proposes an active disturbance rejection control (ADRC) scheme to control the rotor-side, grid-side and DVR-side converters in a wind–DVR integrated network. The performance of the Type-3 WT with DVR topology is evaluated under various test conditions using MATLAB®/Simulink®. These simulation results are also compared with the experimental results for the LVRT capability performed on a WT emulator equipped with a crowbar and direct current (DC) chopper. The simulation results demonstrate a favourable transient and steady-state response of the Type-3 wind turbine quantities defined by the LVRT codes, as well as improved reactive power support under balanced fault conditions. Under the most severe voltage drop of 95%, the stator currents, rotor currents and DC bus voltage are 1.25 pu, 1.40 pu and 1.09 UDC, respectively, conforming to the values of the LVRT codes. DVR controlled by the ADRC technique significantly increases the LVRT capabilities of a Type-3 doubly-fed induction generator-based WT under symmetrical voltage dip events. Although setting up ADRC controllers might be challenging, the proposed method has been shown to be extremely effective in reducing all kinds of internal and external disturbances.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135811182","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 main hindrances to the large-scale development of renewable-energy projects are the lack of bankability and the inability to align investments and investors with suitable financial instruments or robust policy measures. To illustrate a bankable project, this paper presents a research-based case study on the installation of solar photovoltaic panels on the rooftops of 195 trains of the Indian Railways. Detailed information on the annual running hours, exposure to sunlight, efficiency of solar photovoltaic generation and electrical power demands of each rail coach is considered to conduct a quantitative measure of the tentative amount of fossil fuel savings. The purpose is to provide insight into the types of renewable-energy projects that can be highly attractive to financial institutions and promoters due to their lucrative internal return on investment. As seen in this case study, there are annual savings in diesel of 12 323 088 litres and a CO2 reduction of 32 755 tonnes, with return on investment of 1.3 years. Furthermore, this study conducts a comprehensive analysis of the limitations of existing renewable-energy project financing mechanisms in India. Subsequently, three policy measures are recommended to develop a robust financial mechanism that can effectively meet the needs of investors and investors. These measures include increasing equity injection through a buy-and-hold strategy, providing direct tax benefits to promoters and financing through real-estate investment trusts. The findings are highly relevant to address the challenges associated with bridging the financial gap between access to finance and capital investment in the renewable-energy sector, especially for Asian countries.
{"title":"Strategic recommendations for financing green and sustainable energy projects","authors":"Arindam Dutta, Akash Samanta","doi":"10.1093/ce/zkad052","DOIUrl":"https://doi.org/10.1093/ce/zkad052","url":null,"abstract":"Abstract The main hindrances to the large-scale development of renewable-energy projects are the lack of bankability and the inability to align investments and investors with suitable financial instruments or robust policy measures. To illustrate a bankable project, this paper presents a research-based case study on the installation of solar photovoltaic panels on the rooftops of 195 trains of the Indian Railways. Detailed information on the annual running hours, exposure to sunlight, efficiency of solar photovoltaic generation and electrical power demands of each rail coach is considered to conduct a quantitative measure of the tentative amount of fossil fuel savings. The purpose is to provide insight into the types of renewable-energy projects that can be highly attractive to financial institutions and promoters due to their lucrative internal return on investment. As seen in this case study, there are annual savings in diesel of 12 323 088 litres and a CO2 reduction of 32 755 tonnes, with return on investment of 1.3 years. Furthermore, this study conducts a comprehensive analysis of the limitations of existing renewable-energy project financing mechanisms in India. Subsequently, three policy measures are recommended to develop a robust financial mechanism that can effectively meet the needs of investors and investors. These measures include increasing equity injection through a buy-and-hold strategy, providing direct tax benefits to promoters and financing through real-estate investment trusts. The findings are highly relevant to address the challenges associated with bridging the financial gap between access to finance and capital investment in the renewable-energy sector, especially for Asian countries.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135605565","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 Detailed description is given for a hypothetical US hydrogen economy with solar and wind energy supplying virtually all current energy needs and with electrolytic hydrogen the energy carrier and storage medium. Fossil fuels provide nonfuel products (plastics, chemicals, cement and asphalt). Only current technologies are considered and hydrogen storage accommodates generation intermittency and variability, using pit storage of high-pressure vessels in open air, yielding daily storage round-trip energy installation costs of 722 and 538 $/kWh for electric and thermal, respectively; and for power, 2351 and 2240 $/kW for electric and thermal, respectively. For long-duration storage, the costs are 94.1 and 23.8 $/kWh and 937 and 845 $/kW, respectively. Increased energy generation 20% over baseline accommodates low-season generation, obviates much required storage and ensures that reserves are topped off; 96% of US 2022 total energy consumption is provided for. In the default scenario (demand energy portions: half photovoltaic, quarter onshore wind and quarter offshore wind), the surface area for the farms (including offshore surface) requires ~4.6% of the US 48-state land area. About 350 pit storage sites provide both daily and long-duration storage, with the latter accounting for complete loss of generation for 4 days over a quarter of the nation. Hydrogen pipelines and a renewed electric grid transmit and distribute energy. The installation cost of the public infrastructure is ~$27.8 trillion for the default scenario. Alternative scenarios show significant infrastructure and cost savings when batteries are used for transportation and/or utility storage, provided current insufficiencies can be overcome. Broadly, cost levels in money, surface and infrastructure are within existing levels already achieved in historical events and modern living.
{"title":"What would a US green hydrogen energy economy look like?","authors":"Thomas Tonon","doi":"10.1093/ce/zkad047","DOIUrl":"https://doi.org/10.1093/ce/zkad047","url":null,"abstract":"Abstract Detailed description is given for a hypothetical US hydrogen economy with solar and wind energy supplying virtually all current energy needs and with electrolytic hydrogen the energy carrier and storage medium. Fossil fuels provide nonfuel products (plastics, chemicals, cement and asphalt). Only current technologies are considered and hydrogen storage accommodates generation intermittency and variability, using pit storage of high-pressure vessels in open air, yielding daily storage round-trip energy installation costs of 722 and 538 $/kWh for electric and thermal, respectively; and for power, 2351 and 2240 $/kW for electric and thermal, respectively. For long-duration storage, the costs are 94.1 and 23.8 $/kWh and 937 and 845 $/kW, respectively. Increased energy generation 20% over baseline accommodates low-season generation, obviates much required storage and ensures that reserves are topped off; 96% of US 2022 total energy consumption is provided for. In the default scenario (demand energy portions: half photovoltaic, quarter onshore wind and quarter offshore wind), the surface area for the farms (including offshore surface) requires ~4.6% of the US 48-state land area. About 350 pit storage sites provide both daily and long-duration storage, with the latter accounting for complete loss of generation for 4 days over a quarter of the nation. Hydrogen pipelines and a renewed electric grid transmit and distribute energy. The installation cost of the public infrastructure is ~$27.8 trillion for the default scenario. Alternative scenarios show significant infrastructure and cost savings when batteries are used for transportation and/or utility storage, provided current insufficiencies can be overcome. Broadly, cost levels in money, surface and infrastructure are within existing levels already achieved in historical events and modern living.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136094872","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 Hybrid solar-based integrated systems represent a viable solution for countries with abundant solar radiation, as they provide energy needs in an environmentally friendly way, offering a sustainable and economically advantageous energy solution that utilizes a free source of energy. Therefore, this research offers a thermodynamic evaluation of a novel integrated system driven by solar energy that aims to produce power, heating and freshwater. The integrated system consists of a parabolic trough collector that uses CO2 as its working fluid and implements the supercritical carbon dioxide cycle to generate power and heating. The integrated system also includes an adsorption desalination system with heat recovery between the condenser and evaporator, which employs a cutting-edge material called an aluminium fumarate metal–organic framework to produce fresh water. For the modelling of a novel system, an engineering equation solver, which is considered a reliable tool for thermodynamic investigations, is employed. The effectiveness of an integrated system is evaluated using a mathematical model and different varying parameters are examined to ascertain their influence on thermal and exergy efficiency, specific daily water production and gained output ratio. The results revealed that the parabolic trough collector achieved a thermal efficiency of 67.2% and an exergy efficiency of 41.2% under certain conditions. Additionally, the thermal efficiencies for electrical and heating were obtained 24.68% and 9.85%, respectively. Finally, the specific daily water production was calculated, showing promising results and an increase from 7.1 to 12.5 m3/ton/day, while the gain output ratio increased from 0.395 to 0.62 when the temperature of hot water increased from 65°C to 85°C, under the selected conditions.
{"title":"Development and assessment of a novel integrated system powered by parabolic trough collectors for combined power, heating and freshwater production","authors":"Mohd Asjad Siddiqui, Eydhah Almatrafi","doi":"10.1093/ce/zkad051","DOIUrl":"https://doi.org/10.1093/ce/zkad051","url":null,"abstract":"Abstract Hybrid solar-based integrated systems represent a viable solution for countries with abundant solar radiation, as they provide energy needs in an environmentally friendly way, offering a sustainable and economically advantageous energy solution that utilizes a free source of energy. Therefore, this research offers a thermodynamic evaluation of a novel integrated system driven by solar energy that aims to produce power, heating and freshwater. The integrated system consists of a parabolic trough collector that uses CO2 as its working fluid and implements the supercritical carbon dioxide cycle to generate power and heating. The integrated system also includes an adsorption desalination system with heat recovery between the condenser and evaporator, which employs a cutting-edge material called an aluminium fumarate metal–organic framework to produce fresh water. For the modelling of a novel system, an engineering equation solver, which is considered a reliable tool for thermodynamic investigations, is employed. The effectiveness of an integrated system is evaluated using a mathematical model and different varying parameters are examined to ascertain their influence on thermal and exergy efficiency, specific daily water production and gained output ratio. The results revealed that the parabolic trough collector achieved a thermal efficiency of 67.2% and an exergy efficiency of 41.2% under certain conditions. Additionally, the thermal efficiencies for electrical and heating were obtained 24.68% and 9.85%, respectively. Finally, the specific daily water production was calculated, showing promising results and an increase from 7.1 to 12.5 m3/ton/day, while the gain output ratio increased from 0.395 to 0.62 when the temperature of hot water increased from 65°C to 85°C, under the selected conditions.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136054529","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}
Abdelaziz El Aoumari, Hamid Ouadi, Jamal El-Bakkouri, Fouad Giri
Abstract This paper develops an adaptive neural network (NN) observer for proton-exchange membrane fuel cells (PEMFCs). Indeed, information on the oxygen excess ratio (OER) value is crucial to ensure optimal management of the durability and reliability of the PEMFC. The OER indicator is computed from the mass of oxygen and nitrogen inside the PEMFC cathode. Unfortunately, the measurement process of both these masses is difficult and costly. To solve this problem, the design of a PEMFC state observer is attractive. However, the behaviour of the fuel cell system is highly non-linear and its modelling is complex. Due to this constraint, a multilayer perceptron neural network (MLPNN)-based observer is proposed in this paper to estimate the oxygen and nitrogen masses. One notable advantage of the suggested MLPNN observer is that it does not require a database to train the NN. Indeed, the weights of the NN are updated in real time using the output error. In addition, the observer parameters, namely the learning rate and the damping factor, are online adapted using the optimization tools of extremum seeking. Moreover, the proposed observer stability analysis is performed using the Lyapunov theory. The observer performances are validated by simulation under MATLAB®/Simulink®. The supremacy of the proposed adaptive MLPNN observer is highlighted by comparison with a fixed-parameter MLPNN observer and a classical high-gain observer (HGO). The mean relative error value of the excess oxygen rate is considered the performance index, which is equal to 1.01% for an adaptive MLPNN and 3.95% and 9.95% for a fixed MLPNN and HGO, respectively. Finally, a robustness test of the proposed observer with respect to measurement noise is performed.
{"title":"Adaptive neural network observer for proton-exchange membrane fuel cell system","authors":"Abdelaziz El Aoumari, Hamid Ouadi, Jamal El-Bakkouri, Fouad Giri","doi":"10.1093/ce/zkad048","DOIUrl":"https://doi.org/10.1093/ce/zkad048","url":null,"abstract":"Abstract This paper develops an adaptive neural network (NN) observer for proton-exchange membrane fuel cells (PEMFCs). Indeed, information on the oxygen excess ratio (OER) value is crucial to ensure optimal management of the durability and reliability of the PEMFC. The OER indicator is computed from the mass of oxygen and nitrogen inside the PEMFC cathode. Unfortunately, the measurement process of both these masses is difficult and costly. To solve this problem, the design of a PEMFC state observer is attractive. However, the behaviour of the fuel cell system is highly non-linear and its modelling is complex. Due to this constraint, a multilayer perceptron neural network (MLPNN)-based observer is proposed in this paper to estimate the oxygen and nitrogen masses. One notable advantage of the suggested MLPNN observer is that it does not require a database to train the NN. Indeed, the weights of the NN are updated in real time using the output error. In addition, the observer parameters, namely the learning rate and the damping factor, are online adapted using the optimization tools of extremum seeking. Moreover, the proposed observer stability analysis is performed using the Lyapunov theory. The observer performances are validated by simulation under MATLAB®/Simulink®. The supremacy of the proposed adaptive MLPNN observer is highlighted by comparison with a fixed-parameter MLPNN observer and a classical high-gain observer (HGO). The mean relative error value of the excess oxygen rate is considered the performance index, which is equal to 1.01% for an adaptive MLPNN and 3.95% and 9.95% for a fixed MLPNN and HGO, respectively. Finally, a robustness test of the proposed observer with respect to measurement noise is performed.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135811180","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 This article discusses the implementation of a hybrid renewable-energy system to satisfy the electricity requirements of a building. The analysis is based on optimization calculations performed using HOMER software. The components of the simulated hybrid renewable-energy system include photovoltaics, generators powered by biogas, converters and a grid. The input data utilized by the HOMER software are derived from measurements and surveys. The electric load curve is obtained through measurements at the location of the case study. Through surveys, parameters pertaining to the components of the hybrid renewable-energy system were gathered. The analysis was carried out using two sensitivity variables, namely electricity price and grid reliability. On the basis of these two sensitivity variables, optimal system configuration, net present cost, energy cost, return on investment, internal rate of return and payback period were analysed. The results of the analysis indicated that reducing subsidies, which results in higher electricity prices, provided opportunities for economically competitive hybrid renewable-energy systems. With electricity prices of US$0.094/kWh, the return of investment and the internal rate of return increased to 15% and 19%, respectively, and the payback period decreased to 5.3 years. When a hybrid renewable-energy system is implemented in regions with low grid reliability, the same phenomenon occurs.
{"title":"An analysis of the implementation of a hybrid renewable-energy system in a building by considering the reduction in electricity price subsidies and the reliability of the grid","authors":"Rahmat Adiprasetya Al Hasibi, Abdul Haris","doi":"10.1093/ce/zkad053","DOIUrl":"https://doi.org/10.1093/ce/zkad053","url":null,"abstract":"Abstract This article discusses the implementation of a hybrid renewable-energy system to satisfy the electricity requirements of a building. The analysis is based on optimization calculations performed using HOMER software. The components of the simulated hybrid renewable-energy system include photovoltaics, generators powered by biogas, converters and a grid. The input data utilized by the HOMER software are derived from measurements and surveys. The electric load curve is obtained through measurements at the location of the case study. Through surveys, parameters pertaining to the components of the hybrid renewable-energy system were gathered. The analysis was carried out using two sensitivity variables, namely electricity price and grid reliability. On the basis of these two sensitivity variables, optimal system configuration, net present cost, energy cost, return on investment, internal rate of return and payback period were analysed. The results of the analysis indicated that reducing subsidies, which results in higher electricity prices, provided opportunities for economically competitive hybrid renewable-energy systems. With electricity prices of US$0.094/kWh, the return of investment and the internal rate of return increased to 15% and 19%, respectively, and the payback period decreased to 5.3 years. When a hybrid renewable-energy system is implemented in regions with low grid reliability, the same phenomenon occurs.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136054530","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 cofiring of biomass and coal may be one of the most effective methods to improve energy utilization efficiency and reduce greenhouse gas emissions. This study aims to investigate combustion performance, interaction and synergistic effects in the cofiring of coal and three types of biomass. Blended fuel consisting of coal and three types of biomass such as sawdust, rice husk and solid recovery fuel was selected as the research object. Ultimate and proximate analysis and differential thermogravimetric analysis with heating rates of between 10°C and 40°C/minute are used to analyse combustion characteristics. Simulation of combustion in a 600-MWe steam power plant with a Carolina-type boiler is also carried out with the help of computational fluid dynamic (CFD) analysis to see the effect of the interaction and synergy of the mixed fuel on the performance of the steam generator. The effect on the combustion process in the combustion chamber of a steam power plant is also simulated. Based on the analysis of several test results of parameters such as ignition temperature, burnout temperature, calorific value of the fuel mixtures as well as CFD simulation, the results of the study show a strong indication of a positive synergy in mixing some of these biomasses as compared with a fuel mixture consisting only of coal and one type of biomass. Practically no power derating of the boiler occurs until the biomass content in the fuel mixture is ~30% on a mass basis. The reduction in greenhouse gas emissions also appears significant from the results of the CFD simulation of this study, which is characterized by a decrease in the fraction of CO2 in flue gas from 21.5% for coal alone as fuel to 15.9% in the case of cofiring excluding the CO2 attributed to the biomass.
{"title":"Impact of different kinds of biomass mixtures on combustion performance, interaction and synergistic effects in cofiring of coal and biomass in steam power plants","authors":"Mochamad Soleh, Azaria Haykal Ahmad, Firman Bagja Juangsa, Prihadi Setyo Darmanto, Ari Darmawan Pasek","doi":"10.1093/ce/zkad049","DOIUrl":"https://doi.org/10.1093/ce/zkad049","url":null,"abstract":"Abstract The cofiring of biomass and coal may be one of the most effective methods to improve energy utilization efficiency and reduce greenhouse gas emissions. This study aims to investigate combustion performance, interaction and synergistic effects in the cofiring of coal and three types of biomass. Blended fuel consisting of coal and three types of biomass such as sawdust, rice husk and solid recovery fuel was selected as the research object. Ultimate and proximate analysis and differential thermogravimetric analysis with heating rates of between 10°C and 40°C/minute are used to analyse combustion characteristics. Simulation of combustion in a 600-MWe steam power plant with a Carolina-type boiler is also carried out with the help of computational fluid dynamic (CFD) analysis to see the effect of the interaction and synergy of the mixed fuel on the performance of the steam generator. The effect on the combustion process in the combustion chamber of a steam power plant is also simulated. Based on the analysis of several test results of parameters such as ignition temperature, burnout temperature, calorific value of the fuel mixtures as well as CFD simulation, the results of the study show a strong indication of a positive synergy in mixing some of these biomasses as compared with a fuel mixture consisting only of coal and one type of biomass. Practically no power derating of the boiler occurs until the biomass content in the fuel mixture is ~30% on a mass basis. The reduction in greenhouse gas emissions also appears significant from the results of the CFD simulation of this study, which is characterized by a decrease in the fraction of CO2 in flue gas from 21.5% for coal alone as fuel to 15.9% in the case of cofiring excluding the CO2 attributed to the biomass.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136093637","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 This paper explores and theoretically reports the effects of different magnet structures on the cogging torque and the total harmonic distortion of the output waveforms from a permanent magnet generator. The generator is a radial flux machine and four different structures are considered for the magnet arrangement in the rotor component and modelled in the Ansys/Maxwell electromagnetic simulation program. This three-phase machine exhibits different behaviours towards various magnet structures, i.e. rectangular, inclined slotted rectangular, skewed double rectangular and inclined slotted skewed double rectangular, respectively. It has been proven by finite element analysis and Fourier analysis that both the cogging and total harmonic distortion values vary significantly for all models. The cogging torque values change in the range of 89.95 to 436.75 mNm and the lowest cogging torque is measured for the inclined slotted skewed double rectangular magnet geometry, while the conventional rectangular magnet geometry yields the worst value with 436.75 mNm. Furthermore, the total harmonic distortion values varies between 1.63 and 3.55 for different magnetic orientations. While the worst total harmonic distortion value is obtained from the inclined slotted rectangular magnet, the best total harmonic distortion is acquired from the skewed double rectangular magnet. All these results will provide scientists and engineers with important information in order to obtain more efficient machines.
{"title":"Effects of magnet shapes on total harmonic distortion and cogging torque in a permanent magnet synchronous generator for wind turbines","authors":"Erol Kurt, Adem Dalcalı","doi":"10.1093/ce/zkad046","DOIUrl":"https://doi.org/10.1093/ce/zkad046","url":null,"abstract":"Abstract This paper explores and theoretically reports the effects of different magnet structures on the cogging torque and the total harmonic distortion of the output waveforms from a permanent magnet generator. The generator is a radial flux machine and four different structures are considered for the magnet arrangement in the rotor component and modelled in the Ansys/Maxwell electromagnetic simulation program. This three-phase machine exhibits different behaviours towards various magnet structures, i.e. rectangular, inclined slotted rectangular, skewed double rectangular and inclined slotted skewed double rectangular, respectively. It has been proven by finite element analysis and Fourier analysis that both the cogging and total harmonic distortion values vary significantly for all models. The cogging torque values change in the range of 89.95 to 436.75 mNm and the lowest cogging torque is measured for the inclined slotted skewed double rectangular magnet geometry, while the conventional rectangular magnet geometry yields the worst value with 436.75 mNm. Furthermore, the total harmonic distortion values varies between 1.63 and 3.55 for different magnetic orientations. While the worst total harmonic distortion value is obtained from the inclined slotted rectangular magnet, the best total harmonic distortion is acquired from the skewed double rectangular magnet. All these results will provide scientists and engineers with important information in order to obtain more efficient machines.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135719008","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}
Zhijian Qu, Xinxing Hou, Wenbo Hu, Rentao Yang, Chao Ju
Abstract Due to the significant intermittent, stochastic and non-stationary nature of wind power generation, it is difficult to achieve the desired prediction accuracy. Therefore, a wind power prediction method based on improved variational modal decomposition with permutation entropy is proposed. First, based on the meteorological data of wind farms, the Spearman correlation coefficient method is used to filter the meteorological data that are strongly correlated with the wind power to establish the wind power prediction model data set; then the original wind power is decomposed using the improved variational modal decomposition technique to eliminate the noise in the data, and the decomposed wind power is reconstructed into a new subsequence by using the permutation entropy; with the meteorological data and the new subsequence as input variables, a stacking deeply integrated prediction model is developed; and finally the prediction results are obtained by optimizing the hyperparameters of the model algorithm through a genetic algorithm. The validity of the model is verified using a real data set from a wind farm in north-west China. The results show that the mean absolute error, root mean square error and mean absolute percentage error are improved by at least 33.1%, 56.1% and 54.2% compared with the autoregressive integrated moving average model, the support vector machine, long short-term memory, extreme gradient enhancement and convolutional neural networks and long short-term memory models, indicating that the method has higher prediction accuracy.
{"title":"Wind power forecasting based on improved variational mode decomposition and permutation entropy","authors":"Zhijian Qu, Xinxing Hou, Wenbo Hu, Rentao Yang, Chao Ju","doi":"10.1093/ce/zkad043","DOIUrl":"https://doi.org/10.1093/ce/zkad043","url":null,"abstract":"Abstract Due to the significant intermittent, stochastic and non-stationary nature of wind power generation, it is difficult to achieve the desired prediction accuracy. Therefore, a wind power prediction method based on improved variational modal decomposition with permutation entropy is proposed. First, based on the meteorological data of wind farms, the Spearman correlation coefficient method is used to filter the meteorological data that are strongly correlated with the wind power to establish the wind power prediction model data set; then the original wind power is decomposed using the improved variational modal decomposition technique to eliminate the noise in the data, and the decomposed wind power is reconstructed into a new subsequence by using the permutation entropy; with the meteorological data and the new subsequence as input variables, a stacking deeply integrated prediction model is developed; and finally the prediction results are obtained by optimizing the hyperparameters of the model algorithm through a genetic algorithm. The validity of the model is verified using a real data set from a wind farm in north-west China. The results show that the mean absolute error, root mean square error and mean absolute percentage error are improved by at least 33.1%, 56.1% and 54.2% compared with the autoregressive integrated moving average model, the support vector machine, long short-term memory, extreme gradient enhancement and convolutional neural networks and long short-term memory models, indicating that the method has higher prediction accuracy.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"37 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":"136375717","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 development of microgrids is progressing due to intelligent load demands, clean energy, batteries and electric vehicles. The presence of such systems in microgrids causes power balance inconsistency, leading to increased power losses and deviation in voltage. In this paper, a mixed-integer non-linear programming model is proposed for modelling island microgrid energy management considering smart loads, clean energy resources, electric vehicles and batteries. Similarly, a flexible distributed AC transmission system device is proposed to prevent voltage deviation and reduce power losses. A scenario-based multi-objective function has been proposed to decrease energy losses and voltage deviations and energy outages of clean energy resources, reduce emissions from fossil-fired distributed generation and finally decrease load outages to reduce the vulnerability of the islanded microgrid. Regarding the proposed mixed-integer non-linear model and the high number of variables and constraints, a modified evolutionary algorithm based on particle swarm optimization has been proposed to solve the proposed model, which can be more efficient than other algorithms to achieve global optimal solutions. The model presented is implemented on a 33-node island microgrid and the results illustrate that the proposed algorithm and model are effective in reducing energy losses and voltage deviation, as well as reducing the vulnerability of the microgrid. The simulation results demonstrate that the proposed approach can lead to significant improvements in the performance of the microgrid. Specifically, the approach can result in a 27% reduction in losses, a 6% reduction in pollution and a 31% improvement in voltage. Additionally, the approach allows maximum utilization of renewable energy sources, making it a promising solution for sustainable energy management.
{"title":"Multi-objective energy management of island microgrids with D-FACTS devices considering clean energy, storage systems and electric vehicles","authors":"Mahyar Moradi, Mohamad Hoseini Abardeh, Mojtaba Vahedi, Nasrin Salehi, Azita Azarfar","doi":"10.1093/ce/zkad045","DOIUrl":"https://doi.org/10.1093/ce/zkad045","url":null,"abstract":"Abstract The development of microgrids is progressing due to intelligent load demands, clean energy, batteries and electric vehicles. The presence of such systems in microgrids causes power balance inconsistency, leading to increased power losses and deviation in voltage. In this paper, a mixed-integer non-linear programming model is proposed for modelling island microgrid energy management considering smart loads, clean energy resources, electric vehicles and batteries. Similarly, a flexible distributed AC transmission system device is proposed to prevent voltage deviation and reduce power losses. A scenario-based multi-objective function has been proposed to decrease energy losses and voltage deviations and energy outages of clean energy resources, reduce emissions from fossil-fired distributed generation and finally decrease load outages to reduce the vulnerability of the islanded microgrid. Regarding the proposed mixed-integer non-linear model and the high number of variables and constraints, a modified evolutionary algorithm based on particle swarm optimization has been proposed to solve the proposed model, which can be more efficient than other algorithms to achieve global optimal solutions. The model presented is implemented on a 33-node island microgrid and the results illustrate that the proposed algorithm and model are effective in reducing energy losses and voltage deviation, as well as reducing the vulnerability of the microgrid. The simulation results demonstrate that the proposed approach can lead to significant improvements in the performance of the microgrid. Specifically, the approach can result in a 27% reduction in losses, a 6% reduction in pollution and a 31% improvement in voltage. Additionally, the approach allows maximum utilization of renewable energy sources, making it a promising solution for sustainable energy management.","PeriodicalId":36703,"journal":{"name":"Clean Energy","volume":"24 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":"136375716","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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