Fugui Dong, Zihang Meng, Laihao Chi, Xiaofeng Wang
The response potential of demand-side resources is becoming increasingly significant in integrated energy system (IES) operations. In addition, to ensure the effective participation of system devices, their actual responsiveness at different timescales should be considered. Based on these considerations, this paper proposes an IES multi-timescale operation optimization strategy that incorporates multiple forms of integrated demand response (IDR) and considers the response characteristics of the equipment. First, the multi-timescale characteristics of IDR are analyzed. Moreover, a multi-timescale operation model of IES that comprises day-ahead, intraday, and real-time stages is further established. In the day-ahead dispatch, a low-carbon economic scheduling model is developed by considering the shifting demand response (DR) and the cost of carbon emissions. In the intraday scheduling, noting that cooling and heat energy transmission possess slow dynamic characteristics, a rolling optimization model for cooling/heating coupled equipment considering load shedding and substituting DR is established. In real-time scheduling, the output of electric/gas coupled equipment is adjusted. Finally, an industrial park-type IES in northern China was selected as an example for a case study. The results show that (1) the IDR multi-timescale response strategy can exploit different types of demand-side flexibility resources. After implementing the shifting DR, the peak-to-valley difference of the electric load curve was reduced by 20%, and the total system cost was reduced by 2.3%. After implementing load shedding, the maximum load differences per unit period of the electric, heat, and cooling load curves decreased by 18.7%, 40.0%, and 68.9%, respectively. (2) By refining the timescale of IES optimization, the proposed model can effectively ensure the energy supply and demand balance of the system under different load scenarios and reduce the system operation cost. After applying the model to simulation in three typical days (transition season, summer, and winter), the penalty costs of lost loads reduce by ¥3650, ¥3807, and ¥3599, respectively, and the total system costs decrease by 17.4%, 16.1%, and 16.2%, respectively.
{"title":"A multi-timescale optimal operation strategy for an integrated energy system considering integrated demand response and equipment response time","authors":"Fugui Dong, Zihang Meng, Laihao Chi, Xiaofeng Wang","doi":"10.1063/5.0159626","DOIUrl":"https://doi.org/10.1063/5.0159626","url":null,"abstract":"The response potential of demand-side resources is becoming increasingly significant in integrated energy system (IES) operations. In addition, to ensure the effective participation of system devices, their actual responsiveness at different timescales should be considered. Based on these considerations, this paper proposes an IES multi-timescale operation optimization strategy that incorporates multiple forms of integrated demand response (IDR) and considers the response characteristics of the equipment. First, the multi-timescale characteristics of IDR are analyzed. Moreover, a multi-timescale operation model of IES that comprises day-ahead, intraday, and real-time stages is further established. In the day-ahead dispatch, a low-carbon economic scheduling model is developed by considering the shifting demand response (DR) and the cost of carbon emissions. In the intraday scheduling, noting that cooling and heat energy transmission possess slow dynamic characteristics, a rolling optimization model for cooling/heating coupled equipment considering load shedding and substituting DR is established. In real-time scheduling, the output of electric/gas coupled equipment is adjusted. Finally, an industrial park-type IES in northern China was selected as an example for a case study. The results show that (1) the IDR multi-timescale response strategy can exploit different types of demand-side flexibility resources. After implementing the shifting DR, the peak-to-valley difference of the electric load curve was reduced by 20%, and the total system cost was reduced by 2.3%. After implementing load shedding, the maximum load differences per unit period of the electric, heat, and cooling load curves decreased by 18.7%, 40.0%, and 68.9%, respectively. (2) By refining the timescale of IES optimization, the proposed model can effectively ensure the energy supply and demand balance of the system under different load scenarios and reduce the system operation cost. After applying the model to simulation in three typical days (transition season, summer, and winter), the penalty costs of lost loads reduce by ¥3650, ¥3807, and ¥3599, respectively, and the total system costs decrease by 17.4%, 16.1%, and 16.2%, respectively.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45636060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Benitez, Jessa A. Ibañez, Cenon D. Lumabad, Jayson M. Cañete, F. N. De los Reyes, J. Principe
This study proposes a modified gaps filling method, expanding the column mean imputation method and evaluated using randomly generated missing values comprising 5%, 10%, 15%, and 20% of the original data on power output. The XGBoost algorithm was implemented as a forecasting model using the original and processed datasets and two sources of solar radiation data, namely, Shortwave Radiation (SWR) from Advanced Himawari Imager 8 (AHI-8) and Surface Solar Radiation Downward (SSRD) from ERA5 global reanalysis data. The accuracy of the two sets of forecasted power output was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that by applying the proposed gap filling method and using SWR in forecasting solar photovoltaic (PV) output, the improvement in the RMSE and MAE values range from 12.52% to 24.30% and from 21.10% to 31.31%, respectively. Meanwhile, using SSRD, the improvement in the RMSE values range from 14.01% to 28.54% and MAE values from 22.39% to 35.53%. To further evaluate the accuracy of the proposed gap-filling method, the proposed method could be validated using different datasets and other forecasting methods. Future studies could also consider applying the said method to datasets with data gaps higher than 20%.
{"title":"A novel data gaps filling method for solar PV output forecasting","authors":"I. Benitez, Jessa A. Ibañez, Cenon D. Lumabad, Jayson M. Cañete, F. N. De los Reyes, J. Principe","doi":"10.1063/5.0157570","DOIUrl":"https://doi.org/10.1063/5.0157570","url":null,"abstract":"This study proposes a modified gaps filling method, expanding the column mean imputation method and evaluated using randomly generated missing values comprising 5%, 10%, 15%, and 20% of the original data on power output. The XGBoost algorithm was implemented as a forecasting model using the original and processed datasets and two sources of solar radiation data, namely, Shortwave Radiation (SWR) from Advanced Himawari Imager 8 (AHI-8) and Surface Solar Radiation Downward (SSRD) from ERA5 global reanalysis data. The accuracy of the two sets of forecasted power output was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that by applying the proposed gap filling method and using SWR in forecasting solar photovoltaic (PV) output, the improvement in the RMSE and MAE values range from 12.52% to 24.30% and from 21.10% to 31.31%, respectively. Meanwhile, using SSRD, the improvement in the RMSE values range from 14.01% to 28.54% and MAE values from 22.39% to 35.53%. To further evaluate the accuracy of the proposed gap-filling method, the proposed method could be validated using different datasets and other forecasting methods. Future studies could also consider applying the said method to datasets with data gaps higher than 20%.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46404440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To promote the efficient use of energy storage and renewable energy consumption in the integrated energy system (IES), an economic dispatch strategy for the concentrating solar power (CSP)-IES with generalized energy storage and a conditional value-at-risk (CVaR) model is proposed. First, considering the characteristics of energy storage and distributed power supply timing, a CSP-IES configuration is established by using a CSP plant to achieve thermal decoupling of the combined heat and power unit and by defining the thermal storage system of the CSP plant and the battery as the actual energy storage. Second, the fuzzy response of the logistic function is used to optimize the time-of-use tariff to guide load shifting, and the load shifting is defined as virtual energy storage. Third, the CSP-IES economic dispatch model is established to consider the carbon emission allowance model. Finally, considering the system uncertainty, a fuzzy chance constraint is used to relax the system power balance constraint, and then the trapezoidal fuzzy number is transformed into a deterministic equivalence class, and the CVaR model is used as a risk assessment index to quantify the risk cost of the system due to uncertainty. The CSP-IES economic dispatch model with CVaR is constructed. The feasibility and effectiveness of the proposed optimization model are verified by comparing various scenarios.
{"title":"CSP-IES economic dispatch strategy with generalized energy storage and a conditional value-at-risk model","authors":"W. Chen, Haonan Lu, Zhanhong Wei","doi":"10.1063/5.0161850","DOIUrl":"https://doi.org/10.1063/5.0161850","url":null,"abstract":"To promote the efficient use of energy storage and renewable energy consumption in the integrated energy system (IES), an economic dispatch strategy for the concentrating solar power (CSP)-IES with generalized energy storage and a conditional value-at-risk (CVaR) model is proposed. First, considering the characteristics of energy storage and distributed power supply timing, a CSP-IES configuration is established by using a CSP plant to achieve thermal decoupling of the combined heat and power unit and by defining the thermal storage system of the CSP plant and the battery as the actual energy storage. Second, the fuzzy response of the logistic function is used to optimize the time-of-use tariff to guide load shifting, and the load shifting is defined as virtual energy storage. Third, the CSP-IES economic dispatch model is established to consider the carbon emission allowance model. Finally, considering the system uncertainty, a fuzzy chance constraint is used to relax the system power balance constraint, and then the trapezoidal fuzzy number is transformed into a deterministic equivalence class, and the CVaR model is used as a risk assessment index to quantify the risk cost of the system due to uncertainty. The CSP-IES economic dispatch model with CVaR is constructed. The feasibility and effectiveness of the proposed optimization model are verified by comparing various scenarios.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46465682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xinhao Liang, Feihu Hu, X. Li, Lin Zhang, Xuan Feng, Mohammad Abu Gunmi
To maintain power system stability, accurate wind speed prediction is essential. Taking into account the temporal and spatial characteristics of wind speed in an integrated manner can improve the accuracy of wind speed prediction. Considering complex nonlinear spatial factors such as wake effects in wind farms, a deep residual network is valuable in predicting wind speed with a high degree of accuracy. Wind speed data are typically a time series that requires feature extraction and attribute modeling, while maintaining signal integrity. In order to measure the importance of different temporal attributes and effectively aggregate temporal and spatial features, we used a parameter fusion matrix. We introduce a deep spatial-temporal residual network (DST-ResNet) for wind speed prediction that extracts the spatial-temporal characteristics, which can forecast the future wind speed of a multi-site wind farm in a particular region. In this model, wind speed data's nearby property and periodic property are separately modeled using a residual network. The outputs of the two temporal components are dynamically aggregated using a parameter fusion matrix and then fused with additional meteorological features to achieve wind speed prediction. Based on wind data from the National Renewable Energy Laboratory, our experiments show that the proposed DST-ResNet improves prediction accuracy by 8.90%.
{"title":"Ultra-short-term wind speed prediction based on deep spatial-temporal residual network","authors":"Xinhao Liang, Feihu Hu, X. Li, Lin Zhang, Xuan Feng, Mohammad Abu Gunmi","doi":"10.1063/5.0153298","DOIUrl":"https://doi.org/10.1063/5.0153298","url":null,"abstract":"To maintain power system stability, accurate wind speed prediction is essential. Taking into account the temporal and spatial characteristics of wind speed in an integrated manner can improve the accuracy of wind speed prediction. Considering complex nonlinear spatial factors such as wake effects in wind farms, a deep residual network is valuable in predicting wind speed with a high degree of accuracy. Wind speed data are typically a time series that requires feature extraction and attribute modeling, while maintaining signal integrity. In order to measure the importance of different temporal attributes and effectively aggregate temporal and spatial features, we used a parameter fusion matrix. We introduce a deep spatial-temporal residual network (DST-ResNet) for wind speed prediction that extracts the spatial-temporal characteristics, which can forecast the future wind speed of a multi-site wind farm in a particular region. In this model, wind speed data's nearby property and periodic property are separately modeled using a residual network. The outputs of the two temporal components are dynamically aggregated using a parameter fusion matrix and then fused with additional meteorological features to achieve wind speed prediction. Based on wind data from the National Renewable Energy Laboratory, our experiments show that the proposed DST-ResNet improves prediction accuracy by 8.90%.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45177879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The simplest aerodynamic model of horizontal-axis wind turbines is the blade element momentum theory, which assumes that the blades behave as airfoils, but a correct two-dimensional representation is an infinite cascade of lifting bodies. This study analyzes the conventional and impulse forms of the forces on cascades of airfoils at spacings and pitch angles typical of wind turbine applications. OpenFOAM software was used to simulate steady, incompressible flow at a Reynolds number of 6×106 through cascades of NACA 0012 airfoils. The force equations agree well (less than 1% error) with the forces determined directly from OpenFOAM for four spacing ratios. We concentrate on the “wake vorticity” term, which is ignored in blade element momentum analysis. At a pitch angle of 90°, this term balances the viscous drag when the angle of attack is zero. At zero pitch, which models the outer region of a wind turbine blade at a high tip speed ratio, the term can account for 27% of the axial thrust when the angle of attack is about 4°. The normal force equation, like the angular momentum equation for wind turbines, has no viscous term, which forces the body drag to contribute to the circulation in the wake. It is shown that the airfoil assumption is conservative in that cascade elements have higher lift-to-drag ratios than airfoils at the same angle of attack. An associated result is that separation occurs at higher angles of attack on a cascade element compared to an airfoil.
{"title":"Investigating horizontal-axis wind turbine aerodynamics using cascade flows","authors":"Narges Golmirzaee, D. Wood","doi":"10.1063/5.0147946","DOIUrl":"https://doi.org/10.1063/5.0147946","url":null,"abstract":"The simplest aerodynamic model of horizontal-axis wind turbines is the blade element momentum theory, which assumes that the blades behave as airfoils, but a correct two-dimensional representation is an infinite cascade of lifting bodies. This study analyzes the conventional and impulse forms of the forces on cascades of airfoils at spacings and pitch angles typical of wind turbine applications. OpenFOAM software was used to simulate steady, incompressible flow at a Reynolds number of 6×106 through cascades of NACA 0012 airfoils. The force equations agree well (less than 1% error) with the forces determined directly from OpenFOAM for four spacing ratios. We concentrate on the “wake vorticity” term, which is ignored in blade element momentum analysis. At a pitch angle of 90°, this term balances the viscous drag when the angle of attack is zero. At zero pitch, which models the outer region of a wind turbine blade at a high tip speed ratio, the term can account for 27% of the axial thrust when the angle of attack is about 4°. The normal force equation, like the angular momentum equation for wind turbines, has no viscous term, which forces the body drag to contribute to the circulation in the wake. It is shown that the airfoil assumption is conservative in that cascade elements have higher lift-to-drag ratios than airfoils at the same angle of attack. An associated result is that separation occurs at higher angles of attack on a cascade element compared to an airfoil.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46579250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ning Yin, Y. Song, Lei Wu, P. Dong, C. Wang, Jun Zhou, Xinwei Zhang
Low-rank coal (LRC) pyrolysis assisted by biomass can realize the clean and efficient conversion utilization of LRC. The gas and tar characteristics obtained from co-pyrolysis of apple branch (AB) and LRC at different stages were studied with TG-FTIR and Py-GC/MS. It was found that the co-pyrolysis process could be divided into four stages, and the weight loss rate of AB+LRC was 24.03% in the second stage (194.60–404.63 °C), lower than the calculated value. However, the third stage (404.63–594.33 °C) weight loss rate was 13.33%, higher than the calculated value. The content of volatile products increased during co-pyrolysis, resulting in a higher total weight loss rate than the calculated value. There was a synergistic effect between AB and LRC. Aromatic hydrocarbon release intensity in co-pyrolysis products was significantly enhanced in the second and third stages, and it was stronger than that of pyrolysis alone; in contrast, the release intensity of gaseous products was weaker than that of pyrolysis alone. In co-pyrolysis tar, the content of monocyclic and bicyclic aromatic hydrocarbons was increased. The C<10 component was 86.48%, higher than the calculated value of 12.68%. The proportion of aromatic hydrocarbons and phenols increased significantly compared with the calculated value.
{"title":"Analysis of tar and pyrolysis gas from low-rank coal pyrolysis assisted by apple branch","authors":"Ning Yin, Y. Song, Lei Wu, P. Dong, C. Wang, Jun Zhou, Xinwei Zhang","doi":"10.1063/5.0156660","DOIUrl":"https://doi.org/10.1063/5.0156660","url":null,"abstract":"Low-rank coal (LRC) pyrolysis assisted by biomass can realize the clean and efficient conversion utilization of LRC. The gas and tar characteristics obtained from co-pyrolysis of apple branch (AB) and LRC at different stages were studied with TG-FTIR and Py-GC/MS. It was found that the co-pyrolysis process could be divided into four stages, and the weight loss rate of AB+LRC was 24.03% in the second stage (194.60–404.63 °C), lower than the calculated value. However, the third stage (404.63–594.33 °C) weight loss rate was 13.33%, higher than the calculated value. The content of volatile products increased during co-pyrolysis, resulting in a higher total weight loss rate than the calculated value. There was a synergistic effect between AB and LRC. Aromatic hydrocarbon release intensity in co-pyrolysis products was significantly enhanced in the second and third stages, and it was stronger than that of pyrolysis alone; in contrast, the release intensity of gaseous products was weaker than that of pyrolysis alone. In co-pyrolysis tar, the content of monocyclic and bicyclic aromatic hydrocarbons was increased. The C<10 component was 86.48%, higher than the calculated value of 12.68%. The proportion of aromatic hydrocarbons and phenols increased significantly compared with the calculated value.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46627691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shengmao Lin, Jing Wang, Xuefang Xu, Hang Tan, Peiming Shi, Ruixiong Li
Accurate ultra-short-term wind speed forecasting is great significance to ensure large scale integration of wind power into the power grid, but the randomness, instability, and non-linear nature of wind speed make it very difficult to be predicted accurately. To solve this problem, shifted window stationary attention transformer (SWSA transformer) is proposed based on a global attention mechanism for ultra-short-term forecasting of wind speed. SWSA transformer can sufficiently extract these complicated features of wind speed to improve the prediction accuracy of wind speed. First, positional embedding and temporal embedding are added at the bottom of the proposed method structure to mark wind speed series, which enables complicated global features of wind speed to be more effectively extracted by attention. Second, a shifted window is utilized to enhance the ability of attention to capture features from the edge sequences. Third, a stationary attention mechanism is applied to not only extract features of wind speed but also optimize the encoder-decoder network for smoothing wind speed sequences. Finally, the predicted values of wind speed are obtained using the calculation in the decoder network. To verify the proposed method, tests are performed utilizing data from an real offshore wind farm. The results show that the proposed method outperforms many popular models evaluated by many indexes including gated recurrent unit, Gaussian process regression, long-short term memory, shared weight long short-term memory network, and shared weight long short-term memory network -Gaussian process regression, in terms of mean absolute error, mean square error (MSE), root mean square error, mean absolute percentage error, mean square percentage error, and coefficient of determination (R2).
{"title":"SWSA transformer: A forecasting method of ultra-short-term wind speed from an offshore wind farm using global attention mechanism","authors":"Shengmao Lin, Jing Wang, Xuefang Xu, Hang Tan, Peiming Shi, Ruixiong Li","doi":"10.1063/5.0153511","DOIUrl":"https://doi.org/10.1063/5.0153511","url":null,"abstract":"Accurate ultra-short-term wind speed forecasting is great significance to ensure large scale integration of wind power into the power grid, but the randomness, instability, and non-linear nature of wind speed make it very difficult to be predicted accurately. To solve this problem, shifted window stationary attention transformer (SWSA transformer) is proposed based on a global attention mechanism for ultra-short-term forecasting of wind speed. SWSA transformer can sufficiently extract these complicated features of wind speed to improve the prediction accuracy of wind speed. First, positional embedding and temporal embedding are added at the bottom of the proposed method structure to mark wind speed series, which enables complicated global features of wind speed to be more effectively extracted by attention. Second, a shifted window is utilized to enhance the ability of attention to capture features from the edge sequences. Third, a stationary attention mechanism is applied to not only extract features of wind speed but also optimize the encoder-decoder network for smoothing wind speed sequences. Finally, the predicted values of wind speed are obtained using the calculation in the decoder network. To verify the proposed method, tests are performed utilizing data from an real offshore wind farm. The results show that the proposed method outperforms many popular models evaluated by many indexes including gated recurrent unit, Gaussian process regression, long-short term memory, shared weight long short-term memory network, and shared weight long short-term memory network -Gaussian process regression, in terms of mean absolute error, mean square error (MSE), root mean square error, mean absolute percentage error, mean square percentage error, and coefficient of determination (R2).","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49240814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nina Liu, Hong Wang, Dangsheng Zhou, He Shi, Zhe Chen
Recently, the large-scale integration of power electronic-based renewable energy power plants has changed the operation and response mechanism of the power system, resulting in several emerging oscillation issues that have seriously been threatening the system's stability. It helps us to recognize the similarities and differences among the triggering causes and formation mechanisms of oscillation scenarios. Following several typical oscillation events in the real world and the timescale decomposition method, this paper comprehensively reviews the wide-bandwidth oscillation study from the aspects of the analysis methods, possible cause, mechanism, and mitigation solution. The paper provides a perspective to classify the oscillations in the modern power systems on the basis of the oscillation frequency and the main oscillation module. This classification framework involves not only emerging oscillations in the power system with large-scale renewable energy sources integration but also includes typical oscillations in traditional power systems. It also systematically presents the relative relationship, development process, and inner influence between emerging oscillations and typical oscillations. Based on this review, the future research is suggested to focus on the relationship between different analytical methods or oscillation mechanisms, as well as the stability risk assessment of hybrid alternating current and direct current power systems.
{"title":"Comprehensive review of power system oscillations in large-scale power electronic-based renewable energy power plants","authors":"Nina Liu, Hong Wang, Dangsheng Zhou, He Shi, Zhe Chen","doi":"10.1063/5.0148188","DOIUrl":"https://doi.org/10.1063/5.0148188","url":null,"abstract":"Recently, the large-scale integration of power electronic-based renewable energy power plants has changed the operation and response mechanism of the power system, resulting in several emerging oscillation issues that have seriously been threatening the system's stability. It helps us to recognize the similarities and differences among the triggering causes and formation mechanisms of oscillation scenarios. Following several typical oscillation events in the real world and the timescale decomposition method, this paper comprehensively reviews the wide-bandwidth oscillation study from the aspects of the analysis methods, possible cause, mechanism, and mitigation solution. The paper provides a perspective to classify the oscillations in the modern power systems on the basis of the oscillation frequency and the main oscillation module. This classification framework involves not only emerging oscillations in the power system with large-scale renewable energy sources integration but also includes typical oscillations in traditional power systems. It also systematically presents the relative relationship, development process, and inner influence between emerging oscillations and typical oscillations. Based on this review, the future research is suggested to focus on the relationship between different analytical methods or oscillation mechanisms, as well as the stability risk assessment of hybrid alternating current and direct current power systems.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45700195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Climate change and land use conflicts represent two of the greatest challenges worldwide. One possible solution are agrivoltaic (APV) systems, in which agricultural production is combined with a photovoltaic (PV) system in the same area. However, there is insufficient information on the environmental impacts of this technology. Therefore, the goal of this study was to evaluate the environmental impacts of an agrivoltaic system with conventional potato production using life cycle assessment (LCA). For this purpose, three scenarios were developed and compared in terms of their environmental impact: An APV system with combined potato and electricity production (scenario 1), a system with spatially separated potato and photovoltaic (PV) electricity production (scenario 2), and a potato scenario in which the electricity purchase was covered by the German electricity mix (scenario 3). The APV system (scenario 1) and the system with ground-mounted PV modules (scenario 2) performed better than scenario 3. In the Land Use category, scenario 1 caused the lowest environmental impact. Comparing the PV scenarios, scenario 2 had lower impacts in 12 of the 17 impact categories due to lower steel consumption. Also, comparing scenario 1 with scenario 3, lower impacts of the APV system were observed in 13 categories. The impacts of APV systems are generally similar to those of ground mounted PV systems, and impacts of both PV systems are lower than the existing, conventional systems of separate energy and crop production. However, due to ongoing advances in system design, materials used for the mounting structures and in the development of solar modules, it can be expected that the impact of APV will be significantly reduced in the future.
{"title":"Life cycle assessment of an agrivoltaic system with conventional potato production","authors":"Christin Busch, K. Wydra","doi":"10.1063/5.0156779","DOIUrl":"https://doi.org/10.1063/5.0156779","url":null,"abstract":"Climate change and land use conflicts represent two of the greatest challenges worldwide. One possible solution are agrivoltaic (APV) systems, in which agricultural production is combined with a photovoltaic (PV) system in the same area. However, there is insufficient information on the environmental impacts of this technology. Therefore, the goal of this study was to evaluate the environmental impacts of an agrivoltaic system with conventional potato production using life cycle assessment (LCA). For this purpose, three scenarios were developed and compared in terms of their environmental impact: An APV system with combined potato and electricity production (scenario 1), a system with spatially separated potato and photovoltaic (PV) electricity production (scenario 2), and a potato scenario in which the electricity purchase was covered by the German electricity mix (scenario 3). The APV system (scenario 1) and the system with ground-mounted PV modules (scenario 2) performed better than scenario 3. In the Land Use category, scenario 1 caused the lowest environmental impact. Comparing the PV scenarios, scenario 2 had lower impacts in 12 of the 17 impact categories due to lower steel consumption. Also, comparing scenario 1 with scenario 3, lower impacts of the APV system were observed in 13 categories. The impacts of APV systems are generally similar to those of ground mounted PV systems, and impacts of both PV systems are lower than the existing, conventional systems of separate energy and crop production. However, due to ongoing advances in system design, materials used for the mounting structures and in the development of solar modules, it can be expected that the impact of APV will be significantly reduced in the future.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45238903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to factors such as meteorology and geography, the generated power of wind turbines fluctuates frequently. In this way, power changes should be predicted in grid connection to take control measures in time. In this paper, an operation and maintenance data-driven LSTM-SA (long short-term memory with self-attention) prediction algorithm is designed to predict the ultra-short-term power of wind turbines. First, the wind turbine operation and maintenance data, including wind speed, blade deflection angle, yaw angle, humidity, and temperature, are subjected to feature selection by using the Pearson correlation coefficient method and the Lasso algorithm, thereby establishing the correlation between wind speed, blade deflection angle, and out power. Then, full-connect neural network is trained to establish a mapping model of wind speed, blade deflection angle, and out power. The power change rate k is calculated by the derivative of output power to wind speed. Finally, based on the historical power data and the power change rate k, the LSTM neural network power prediction model is trained to calculate the output power prediction value. In order to increase the training efficiency and reduce the delay, the self-attention mechanism is used to optimize the hidden layer of the LSTM model. The test results show that, compared with similar prediction algorithms, this algorithm has higher prediction accuracy, faster convergence speed, and better stability, which can solve the problem of accurately predicting ultra-short-term power when wind power training data is inadequate.
{"title":"A design of ultra-short-term power prediction algorithm driven by wind turbine operation and maintenance data for LSTM-SA neural network","authors":"Hong-Qiang You, Renyuan Jia, Xiaolei Chen, Lingxiang Huang","doi":"10.1063/5.0159574","DOIUrl":"https://doi.org/10.1063/5.0159574","url":null,"abstract":"Due to factors such as meteorology and geography, the generated power of wind turbines fluctuates frequently. In this way, power changes should be predicted in grid connection to take control measures in time. In this paper, an operation and maintenance data-driven LSTM-SA (long short-term memory with self-attention) prediction algorithm is designed to predict the ultra-short-term power of wind turbines. First, the wind turbine operation and maintenance data, including wind speed, blade deflection angle, yaw angle, humidity, and temperature, are subjected to feature selection by using the Pearson correlation coefficient method and the Lasso algorithm, thereby establishing the correlation between wind speed, blade deflection angle, and out power. Then, full-connect neural network is trained to establish a mapping model of wind speed, blade deflection angle, and out power. The power change rate k is calculated by the derivative of output power to wind speed. Finally, based on the historical power data and the power change rate k, the LSTM neural network power prediction model is trained to calculate the output power prediction value. In order to increase the training efficiency and reduce the delay, the self-attention mechanism is used to optimize the hidden layer of the LSTM model. The test results show that, compared with similar prediction algorithms, this algorithm has higher prediction accuracy, faster convergence speed, and better stability, which can solve the problem of accurately predicting ultra-short-term power when wind power training data is inadequate.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42168830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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