Existing feeder block division methods fail to consider the complementary characteristics and uncertainty between power sources and loads, which result in excessive feeder blocks, low inter-block balance, and significant disparity in net load peak-valley difference. To address these issues, a medium-voltage feeder block division method that considers the uncertainty and complementary characteristics of sources and loads is proposed. Firstly, based on the probability density characteristics of sources and loads, an uncertainty model of DG output and load demand is established. Secondly, considering the constraints of block maximum load rate and feeder non-crossing, a feeder block division model is established. Additionally, a set of center circles is defined, and based on this, an improved K-means clustering algorithm is proposed. The initial clustering centers based on the center circles is set, and the clustering centers based on the arcs of the center circles corrected. And the weighted distances between power sources and clustering centers are calculated. An algorithm flow for improved K-means clustering feeder block division is designed accordingly. Finally, the case studies show that the result of block division is improved.
{"title":"Medium-voltage feeder blocks division method considering source-load uncertainty and characteristics complementary clustering","authors":"Jieyun Zheng, Zhanghuang Zhang, Ying Shi, Zhuolin Chen","doi":"10.3389/fenrg.2024.1452011","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1452011","url":null,"abstract":"Existing feeder block division methods fail to consider the complementary characteristics and uncertainty between power sources and loads, which result in excessive feeder blocks, low inter-block balance, and significant disparity in net load peak-valley difference. To address these issues, a medium-voltage feeder block division method that considers the uncertainty and complementary characteristics of sources and loads is proposed. Firstly, based on the probability density characteristics of sources and loads, an uncertainty model of DG output and load demand is established. Secondly, considering the constraints of block maximum load rate and feeder non-crossing, a feeder block division model is established. Additionally, a set of center circles is defined, and based on this, an improved K-means clustering algorithm is proposed. The initial clustering centers based on the center circles is set, and the clustering centers based on the arcs of the center circles corrected. And the weighted distances between power sources and clustering centers are calculated. An algorithm flow for improved K-means clustering feeder block division is designed accordingly. Finally, the case studies show that the result of block division is improved.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":"44 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943810","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}
Pub Date : 2024-08-06DOI: 10.3389/fenrg.2024.1442299
Wang Wenhua, Cui Rui, Chen Yu, Zhao Xu, Xue Yongbing
To meet the growing demand for integrated monitoring of complex power grid equipment, it is necessary to improve the situational awareness model of power transformers. The model is expected to assist monitoring personnel in timely identifying transformers with deteriorating trends among massive and discrete monitoring information, and to make responses in advance. However, the current transformer state awareness technology generally has the problem of single data source and poor timeliness, and still requires monitoring personnel to make artificial analysis and prediction in combination with telemetry information, which cannot fully meet the requirements of power grid equipment monitoring. This paper is based on multi-source data fusion technology, through associating and mining transformer alarm information, equipment maintenance records and power transmission and transformation online monitoring data, to extract the dimension features of transformer operation situation assessment. By constructing a multi-layer perceptron model, a transformer state transition model based on the principle of Markov chain is established, which can predict possible defects 2 h in advance and achieve good results, and determine the transformer state early warning index, providing sufficient time for monitoring personnel to deploy transformer operation and maintenance work in advance. Finally, the effectiveness of the method proposed in this paper is proved by the case of transformer crisis state in a city substation, and the method proposed in this paper has important significance for transformer state early warning.
{"title":"Research on standardization of power transformer monitoring and early warning based on multi-source data","authors":"Wang Wenhua, Cui Rui, Chen Yu, Zhao Xu, Xue Yongbing","doi":"10.3389/fenrg.2024.1442299","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1442299","url":null,"abstract":"To meet the growing demand for integrated monitoring of complex power grid equipment, it is necessary to improve the situational awareness model of power transformers. The model is expected to assist monitoring personnel in timely identifying transformers with deteriorating trends among massive and discrete monitoring information, and to make responses in advance. However, the current transformer state awareness technology generally has the problem of single data source and poor timeliness, and still requires monitoring personnel to make artificial analysis and prediction in combination with telemetry information, which cannot fully meet the requirements of power grid equipment monitoring. This paper is based on multi-source data fusion technology, through associating and mining transformer alarm information, equipment maintenance records and power transmission and transformation online monitoring data, to extract the dimension features of transformer operation situation assessment. By constructing a multi-layer perceptron model, a transformer state transition model based on the principle of Markov chain is established, which can predict possible defects 2 h in advance and achieve good results, and determine the transformer state early warning index, providing sufficient time for monitoring personnel to deploy transformer operation and maintenance work in advance. Finally, the effectiveness of the method proposed in this paper is proved by the case of transformer crisis state in a city substation, and the method proposed in this paper has important significance for transformer state early warning.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":"26 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943854","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 fluctuations brought by the renewable energy access to the distribution network make it difficult to accurately describe the state space model of the distribution network’s dynamic process, which is the basis of the existing dynamic state estimation methods such as the Kalman filter. The inaccurate state space model directly causes an error of dynamic state estimation results. This paper proposed a new dynamic state estimation method which can mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Firstly, the proposed method mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Secondly, SVSF filter is introduced to achieve more accurate estimation under noise. The case study and evaluations are carried out based on MATLAB simulation. The results prove that the smooth variable structure filter with photovoltaic power prediction has a better dynamic state estimation effect under the fluctuation of the distribution network compared with the existing Kalman filter.
{"title":"A new dynamic state estimation method for distribution networks based on modified SVSF considering photovoltaic power prediction","authors":"Huiqiang Zhi, Xiao Chang, Jinhao Wang, Rui Mao, Rui Fan, Tengxin Wang, Jinge Song, Guisheng Xiao","doi":"10.3389/fenrg.2024.1421555","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1421555","url":null,"abstract":"The fluctuations brought by the renewable energy access to the distribution network make it difficult to accurately describe the state space model of the distribution network’s dynamic process, which is the basis of the existing dynamic state estimation methods such as the Kalman filter. The inaccurate state space model directly causes an error of dynamic state estimation results. This paper proposed a new dynamic state estimation method which can mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Firstly, the proposed method mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Secondly, SVSF filter is introduced to achieve more accurate estimation under noise. The case study and evaluations are carried out based on MATLAB simulation. The results prove that the smooth variable structure filter with photovoltaic power prediction has a better dynamic state estimation effect under the fluctuation of the distribution network compared with the existing Kalman filter.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":"74 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943855","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}
Introduction: Amidst escalating global temperatures, increasing climate change, and rapid urbanization, addressing urban heat islands and improving outdoor thermal comfort is paramount for sustainable urban development. Green walls offer a promising strategy by effectively lowering ambient air temperatures in urban environments. While previous studies have explored their impact in various climates, their effectiveness in humid climates remains underexplored.Methods: This research investigates the cooling effect of a green wall during summer in a humid climate, employing two approaches: Field Measurement-Based Analysis (SC 1: FMA) and Deep Learning Model (SC 2: DLM). In SC 1: FMA, experiments utilized data loggers at varying distances from the green wall to capture real-time conditions. SC 2: DLM utilized a deep learning model to predict the green wall’s performance over time.Results: Results indicate a significant reduction in air temperature, with a 1.5°C (6%) decrease compared to real-time conditions. Long-term analysis identified specific distances (A, B, C, and D) contributing to temperature reductions ranging from 1.5°C to 2.5°C, highlighting optimal distances for green wall efficacy.Discussion: This study contributes novel insights by determining effective distances for green wall systems to mitigate ambient temperatures, addressing a critical gap in current literature. The integration of a deep learning model enhances analytical precision and forecasts future outcomes. Despite limitations related to a single case study and limited timeframe, this research offers practical benefits in urban heat island mitigation, enhancing outdoor comfort, and fostering sustainable and climate-resilient urban environments.
{"title":"An experimental analysis and deep learning model to assess the cooling performance of green walls in humid climates","authors":"Abdollah Baghaei Daemei, Tomasz Bradecki, Alina Pancewicz, Amirali Razzaghipour, Asma Jamali, Seyedeh Maryam Abbaszadegan, Reza Askarizad, Mostafa Kazemi, Ayyoob Sharifi","doi":"10.3389/fenrg.2024.1447655","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1447655","url":null,"abstract":"Introduction: Amidst escalating global temperatures, increasing climate change, and rapid urbanization, addressing urban heat islands and improving outdoor thermal comfort is paramount for sustainable urban development. Green walls offer a promising strategy by effectively lowering ambient air temperatures in urban environments. While previous studies have explored their impact in various climates, their effectiveness in humid climates remains underexplored.Methods: This research investigates the cooling effect of a green wall during summer in a humid climate, employing two approaches: Field Measurement-Based Analysis (SC 1: FMA) and Deep Learning Model (SC 2: DLM). In SC 1: FMA, experiments utilized data loggers at varying distances from the green wall to capture real-time conditions. SC 2: DLM utilized a deep learning model to predict the green wall’s performance over time.Results: Results indicate a significant reduction in air temperature, with a 1.5°C (6%) decrease compared to real-time conditions. Long-term analysis identified specific distances (A, B, C, and D) contributing to temperature reductions ranging from 1.5°C to 2.5°C, highlighting optimal distances for green wall efficacy.Discussion: This study contributes novel insights by determining effective distances for green wall systems to mitigate ambient temperatures, addressing a critical gap in current literature. The integration of a deep learning model enhances analytical precision and forecasts future outcomes. Despite limitations related to a single case study and limited timeframe, this research offers practical benefits in urban heat island mitigation, enhancing outdoor comfort, and fostering sustainable and climate-resilient urban environments.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":"76 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943857","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}
Pub Date : 2024-08-05DOI: 10.3389/fenrg.2024.1415430
Eva Rößler, Tim Schmeckel, Ute Kesselheim, Katrin Arning
The transportation sector is a significant contributor to CO2 emissions, necessitating the adoption of alternative drive technologies to achieve decarbonization. This study investigates public perceptions of fossil fuels, e-fuels, and electric drives, with the aim of identifying factors influencing risk perceptions, perceived efficacy in combating climate change, and readiness to use or purchase cars with these technologies. Therefore, a quantitative study using a questionnaire (N = 141) was conducted. The results indicate that e-fuels and electric drives are perceived more positively than fossil fuels. E-fuels were found to have the lowest risk perceptions. Differences in cognitive and affective risk perceptions, as well as in financial, environmental, and health-related risks, were observed across drive types. Car affinity was found to correlate positively with risk perceptions of e-fuels and fossil fuels, but negatively with electric drives. The risk perception of global warming showed an inverse relationship. Regarding the prediction of readiness, differences were found between e-fuels and electric drives in terms of the influencing factors on readiness. The study contributes to the understanding of public perceptions by providing a comparison between different drive technologies and offers valuable insights for developing targeted communication strategies.
{"title":"Driving towards sustainability: exploring risk perceptions of fossil fuels, e-fuels, and electric drives in individual transport","authors":"Eva Rößler, Tim Schmeckel, Ute Kesselheim, Katrin Arning","doi":"10.3389/fenrg.2024.1415430","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1415430","url":null,"abstract":"The transportation sector is a significant contributor to CO<jats:sub>2</jats:sub> emissions, necessitating the adoption of alternative drive technologies to achieve decarbonization. This study investigates public perceptions of fossil fuels, e-fuels, and electric drives, with the aim of identifying factors influencing risk perceptions, perceived efficacy in combating climate change, and readiness to use or purchase cars with these technologies. Therefore, a quantitative study using a questionnaire (N = 141) was conducted. The results indicate that e-fuels and electric drives are perceived more positively than fossil fuels. E-fuels were found to have the lowest risk perceptions. Differences in cognitive and affective risk perceptions, as well as in financial, environmental, and health-related risks, were observed across drive types. Car affinity was found to correlate positively with risk perceptions of e-fuels and fossil fuels, but negatively with electric drives. The risk perception of global warming showed an inverse relationship. Regarding the prediction of readiness, differences were found between e-fuels and electric drives in terms of the influencing factors on readiness. The study contributes to the understanding of public perceptions by providing a comparison between different drive technologies and offers valuable insights for developing targeted communication strategies.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":"10 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943856","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}
Pub Date : 2024-08-05DOI: 10.3389/fenrg.2024.1452173
Ning Zhou, Bo-wen Shang, Jin-shuai Zhang, Ming-ming Xu
Accurate prediction of photovoltaic power generation is of great significance to stable operation of power system. To improve the prediction accuracy of photovoltaic power, a photovoltaic power generation prediction machine learning model based on Transformer model is proposed in this paper. In this paper, the basic principle of Transformer model is introduced. Correlation analysis tools such as Pearson correlation coefficient and Spearman correlation coefficient are introduced to analyze the correlation between various factors and power generation in the photovoltaic power generation process. Then, the prediction results of traditional machine learning models and the Transformer model proposed in this paper were compared and analyzed for errors. The results show that: for long-term prediction tasks such as photovoltaic power generation prediction, Transformer model has higher prediction accuracy than traditional machine learning models. Moreover, compared with BP, LSTM and Bi-LSTM models, the Mean Square Error (MSE) of Transformer model decreases by 70.16%, 69.32% and 62.88% respectively in short-term prediction, and the Mean Square Error (MSE) of Transformer model decreases by 63.58%, 51.02% and 38.3% respectively in long-term prediction, which has good prediction effect. In addition, compared with the long-term prediction effect of Informer model, Transformer model has higher prediction accuracy.
{"title":"Research on prediction method of photovoltaic power generation based on transformer model","authors":"Ning Zhou, Bo-wen Shang, Jin-shuai Zhang, Ming-ming Xu","doi":"10.3389/fenrg.2024.1452173","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1452173","url":null,"abstract":"Accurate prediction of photovoltaic power generation is of great significance to stable operation of power system. To improve the prediction accuracy of photovoltaic power, a photovoltaic power generation prediction machine learning model based on Transformer model is proposed in this paper. In this paper, the basic principle of Transformer model is introduced. Correlation analysis tools such as Pearson correlation coefficient and Spearman correlation coefficient are introduced to analyze the correlation between various factors and power generation in the photovoltaic power generation process. Then, the prediction results of traditional machine learning models and the Transformer model proposed in this paper were compared and analyzed for errors. The results show that: for long-term prediction tasks such as photovoltaic power generation prediction, Transformer model has higher prediction accuracy than traditional machine learning models. Moreover, compared with BP, LSTM and Bi-LSTM models, the Mean Square Error (MSE) of Transformer model decreases by 70.16%, 69.32% and 62.88% respectively in short-term prediction, and the Mean Square Error (MSE) of Transformer model decreases by 63.58%, 51.02% and 38.3% respectively in long-term prediction, which has good prediction effect. In addition, compared with the long-term prediction effect of Informer model, Transformer model has higher prediction accuracy.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":"44 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943859","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}
Pub Date : 2024-08-05DOI: 10.3389/fenrg.2024.1404811
Ayman Aljarbouh, Dmytro Zubov, Issam A. R. Moghrabi
The Rockaways Peninsula faces issues related to congestion and power outages during times of peak usage. Additionally, it is susceptible to disruptions caused by disasters such as hurricanes and storms. In this paper, we propose a new methodology that employs multi-paradigm modelling and control for the design and implementation of interconnected microgrid systems in the Rockaways. Microgrids are small-scale power networks that incorporate renewable energy technologies for power generation and distribution to enhance the control of energy supply and demand. Multi-paradigm modelling is employed to describe microgrids’ dynamic behavior more accurately by integrating system dynamics, agent-based modelling, as well as discrete event and continuous time simulation. We use agent-based models to describe the behavior of separate microgrid elements and the microgrid as a whole. Discrete event/continuous time simulation is used to analyze real-time operation of electrical parameters, such as voltage, current and frequency. Thus, the design, analysis and performance of microgrids are improved. Also, control strategies are used for the purpose of enabling the microgrids to operate effectively by responding to changes in power supply and demand and minimizing the effects of disturbances. The findings of this study demonstrate the feasibility and resilience benefits of incorporating multi-paradigm modelling and control in the design and management of microgrid systems in the Rockaways, which can result in the development of more durable, efficient, and sustainable energy systems in the region.
{"title":"Multi-paradigm modelling and control of microgrid systems for better power stability in the Rockaways","authors":"Ayman Aljarbouh, Dmytro Zubov, Issam A. R. Moghrabi","doi":"10.3389/fenrg.2024.1404811","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1404811","url":null,"abstract":"The Rockaways Peninsula faces issues related to congestion and power outages during times of peak usage. Additionally, it is susceptible to disruptions caused by disasters such as hurricanes and storms. In this paper, we propose a new methodology that employs multi-paradigm modelling and control for the design and implementation of interconnected microgrid systems in the Rockaways. Microgrids are small-scale power networks that incorporate renewable energy technologies for power generation and distribution to enhance the control of energy supply and demand. Multi-paradigm modelling is employed to describe microgrids’ dynamic behavior more accurately by integrating system dynamics, agent-based modelling, as well as discrete event and continuous time simulation. We use agent-based models to describe the behavior of separate microgrid elements and the microgrid as a whole. Discrete event/continuous time simulation is used to analyze real-time operation of electrical parameters, such as voltage, current and frequency. Thus, the design, analysis and performance of microgrids are improved. Also, control strategies are used for the purpose of enabling the microgrids to operate effectively by responding to changes in power supply and demand and minimizing the effects of disturbances. The findings of this study demonstrate the feasibility and resilience benefits of incorporating multi-paradigm modelling and control in the design and management of microgrid systems in the Rockaways, which can result in the development of more durable, efficient, and sustainable energy systems in the region.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":"142 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943861","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}
Pub Date : 2024-08-05DOI: 10.3389/fenrg.2024.1435310
Wenping Zhang, Yiming Wang, Po Xu, Donghui Li, Baosong Liu
Depending on the PV power, load power, and battery status, the system may operate in different modes. The control loop may have to switch between operating modes. In practice, it is difficult to implement control loop switching because the transition and dynamic process are difficult to control. As a result, this paper presents a generalized mode control method that avoids loop switching across modes. First, system structure and topology are introduced. The operating conditions for both grid-connected and off-grid modes are then divided into six sub-cases. Furthermore, the control architecture, control loop, and reference transition for various scenarios are described. Finally, an experimental platform is built, and the results are presented to verify the proposed method.
{"title":"An operating mode control method for photovoltaic (PV) battery hybrid systems","authors":"Wenping Zhang, Yiming Wang, Po Xu, Donghui Li, Baosong Liu","doi":"10.3389/fenrg.2024.1435310","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1435310","url":null,"abstract":"Depending on the PV power, load power, and battery status, the system may operate in different modes. The control loop may have to switch between operating modes. In practice, it is difficult to implement control loop switching because the transition and dynamic process are difficult to control. As a result, this paper presents a generalized mode control method that avoids loop switching across modes. First, system structure and topology are introduced. The operating conditions for both grid-connected and off-grid modes are then divided into six sub-cases. Furthermore, the control architecture, control loop, and reference transition for various scenarios are described. Finally, an experimental platform is built, and the results are presented to verify the proposed method.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":"10 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943860","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}
Pub Date : 2024-08-05DOI: 10.3389/fenrg.2024.1434695
Borhen Torchani, Ahmad Taher Azar, Saim Ahmed, Ahmed Redha Mahlous, Ibraheem Kasim Ibraheem
This article presents a proportional-integral sliding mode control (PI-SMC) approach for a two-mass variable speed wind turbine (VSWT) system. Most studies on wind turbines typically focus mainly on the electromagnetic part of the generators, or even on the high-speed part, considering the shaft stiffness as negligible. However, the generator torque is actually driven by the aerodynamic torque, and a two-mass system like the one studied here plays the role of a transmission element for this power. To address this challenge, the problem of low power generation resulting from wind speed variability is tackled by designing a PI-SMC control law, capable of controlling the mechanical turbine model that optimizes power and torque by tracking the maximum power point (MPPT) for rotational speed and aerodynamic power. To validate the developed theoretical results, an application of the wind turbine system is simulated in Matlab/Simulink, for a particular case. The control used is capable of satisfying the dynamic performance of the systems.
{"title":"Sliding mode control based on maximum power point tracking for dynamics of wind turbine system","authors":"Borhen Torchani, Ahmad Taher Azar, Saim Ahmed, Ahmed Redha Mahlous, Ibraheem Kasim Ibraheem","doi":"10.3389/fenrg.2024.1434695","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1434695","url":null,"abstract":"This article presents a proportional-integral sliding mode control (PI-SMC) approach for a two-mass variable speed wind turbine (VSWT) system. Most studies on wind turbines typically focus mainly on the electromagnetic part of the generators, or even on the high-speed part, considering the shaft stiffness as negligible. However, the generator torque is actually driven by the aerodynamic torque, and a two-mass system like the one studied here plays the role of a transmission element for this power. To address this challenge, the problem of low power generation resulting from wind speed variability is tackled by designing a PI-SMC control law, capable of controlling the mechanical turbine model that optimizes power and torque by tracking the maximum power point (MPPT) for rotational speed and aerodynamic power. To validate the developed theoretical results, an application of the wind turbine system is simulated in Matlab/Simulink, for a particular case. The control used is capable of satisfying the dynamic performance of the systems.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":"5 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943858","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}
Pub Date : 2024-08-02DOI: 10.3389/fenrg.2024.1400905
Abdullah M. Al-Qahtani, Abdullah M. Al-Shaalan, Waheed A. Al-Masry, Hassan M. Hussein Farh
The future electric loads in the Kingdom of Saudi Arabia (KSA) are increasing significantly, particularly in the Eastern Province of the KSA. These high-rise loads are primarily driven by the operational needs of the Saudi Arabian oil company Aramco, including oil refineries, and the infrastructures of the Saudi Basic Industries Corporation (SABIC) factories. This study aims to construct a nuclear power plant in that area to supplement and support the baseload currently covered by conventional generation units powered by fossil fuels within the Saudi Electricity Company (SEC) operations. The objective function is to minimize the operational costs of the power systems to the greatest extent possible. This paper describes a case study conducted using the IBM CPLEX Optimizer software to compare the operational costs of KSA’s power systems for a 24-h period. Two scenarios were considered and addressed: the first scenario without the inclusion of a nuclear power plant (NPP) and the second scenario with the inclusion of the NPP. The unit commitment problem was modeled for both scenarios. The obtained results revealed that the second scenario, which involved the penetration of the NPP, offered an optimal economic solution for operating KSA’s power systems. By employing the CPLEX Optimizer software and analyzing the unit commitment problem, this study provides valuable insights into the economic advantages of integrating the NPP into the power systems of the Kingdom of Saudi Arabia. The NPP shows viability in terms of minimizing the operational costs to 32.10 $/MWh compared to the first scenario where the operational costs were 42.10 $/MWh and resulted in almost 24% reduction in operational costs. In addition, the NPP is deemed as an optimal technology to contribute to the net zero goal by 2060, where it can reduce the reliance on fossil fuel power plants and contribute to the reduction of CO2 emissions.
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