Pub Date : 2024-08-26DOI: 10.3389/fenrg.2024.1371616
Sidi Chen, Min Fan
This study explores the impact of ESG ratings on corporate performance, focusing on achieving sustainable development and corporate sustainability through innovation within the context of high-quality global economic growth. In recent years, ESG ratings have garnered significant attention in the financial sector, influencing corporate strategy and performance management. While some argue that ESG activities might detract from profitability, others highlight that firms with strong ESG performance can access low-cost capital, thereby enhancing overall performance. Using a sample of China’s A-share listed companies from 2009 to 2021, this research examines the influence and mechanisms of ESG ratings on corporate performance. The findings indicate a significant positive relationship between ESG ratings and corporate performance, which remains robust after rigorous testing. Mediation analysis reveals that ESG ratings improve corporate performance by alleviating financing constraints and enhancing corporate reputation. Furthermore, the performance-enhancing effects of ESG ratings are more pronounced in firms with robust internal controls and private enterprises. This research provides empirical evidence to support stronger ESG investment and the refinement of the ESG rating system.
{"title":"ESG ratings and corporate success: analyzing the environmental governance impact on Chinese companies’ performance","authors":"Sidi Chen, Min Fan","doi":"10.3389/fenrg.2024.1371616","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1371616","url":null,"abstract":"This study explores the impact of ESG ratings on corporate performance, focusing on achieving sustainable development and corporate sustainability through innovation within the context of high-quality global economic growth. In recent years, ESG ratings have garnered significant attention in the financial sector, influencing corporate strategy and performance management. While some argue that ESG activities might detract from profitability, others highlight that firms with strong ESG performance can access low-cost capital, thereby enhancing overall performance. Using a sample of China’s A-share listed companies from 2009 to 2021, this research examines the influence and mechanisms of ESG ratings on corporate performance. The findings indicate a significant positive relationship between ESG ratings and corporate performance, which remains robust after rigorous testing. Mediation analysis reveals that ESG ratings improve corporate performance by alleviating financing constraints and enhancing corporate reputation. Furthermore, the performance-enhancing effects of ESG ratings are more pronounced in firms with robust internal controls and private enterprises. This research provides empirical evidence to support stronger ESG investment and the refinement of the ESG rating system.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215132","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}
IntroductionAccurate prediction of line losses in distribution networks is crucial for optimizing power system planning and network restructuring, as these losses significantly impact grid operation quality. This paper proposes a novel approach that combines advanced feature selection techniques with Stacking ensemble learning to enhance the effectiveness of distribution network loss analysis and assessment.MethodsUtilizing data from 44 substations over an 18-month period, we integrated a Stacking ensemble learning model with multiple feature selection methods, including correlation coefficient, maximum information coefficient, and tree-based techniques. These methods were employed to identify the key predictors of power loss in the distribution network.ResultsThe proposed model achieved a Mean Absolute Percentage Error (MAPE) of 3.78% and a Root Mean Square Error (RMSE) of 1.53, demonstrating a substantial improvement over traditional linear regression-based prediction methods. The analysis revealed that historical line loss and line active power were the most influential predictive variables, while the inclusion of time-related features further refined the model's performance.DiscussionThis study highlights the efficacy of combining multiple feature selection methods with Stacking ensemble learning for predicting power loss in 10 kV distribution networks. The enhanced accuracy and reliability of the proposed model offer valuable insights for electrical engineering applications, potentially contributing to more efficient and sustainable energy distribution systems. Future research could explore the applicability of this approach to other distribution network voltage levels and investigate the incorporation of additional environmental and network-specific factors to further improve power loss prediction.
{"title":"Research on line loss prediction of distribution network based on ensemble learning and feature selection","authors":"Ke Zhang, Yongwang Zhang, Jian Li, Zetao Jiang, Yuxin Lu, Binghui Zhao","doi":"10.3389/fenrg.2024.1453039","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1453039","url":null,"abstract":"IntroductionAccurate prediction of line losses in distribution networks is crucial for optimizing power system planning and network restructuring, as these losses significantly impact grid operation quality. This paper proposes a novel approach that combines advanced feature selection techniques with Stacking ensemble learning to enhance the effectiveness of distribution network loss analysis and assessment.MethodsUtilizing data from 44 substations over an 18-month period, we integrated a Stacking ensemble learning model with multiple feature selection methods, including correlation coefficient, maximum information coefficient, and tree-based techniques. These methods were employed to identify the key predictors of power loss in the distribution network.ResultsThe proposed model achieved a Mean Absolute Percentage Error (MAPE) of 3.78% and a Root Mean Square Error (RMSE) of 1.53, demonstrating a substantial improvement over traditional linear regression-based prediction methods. The analysis revealed that historical line loss and line active power were the most influential predictive variables, while the inclusion of time-related features further refined the model's performance.DiscussionThis study highlights the efficacy of combining multiple feature selection methods with Stacking ensemble learning for predicting power loss in 10 kV distribution networks. The enhanced accuracy and reliability of the proposed model offer valuable insights for electrical engineering applications, potentially contributing to more efficient and sustainable energy distribution systems. Future research could explore the applicability of this approach to other distribution network voltage levels and investigate the incorporation of additional environmental and network-specific factors to further improve power loss prediction.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215136","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 : 2024-08-23DOI: 10.3389/fenrg.2024.1429118
Enoch I. Obanor, Joseph O. Dirisu, Oluwaseun O. Kilanko, Enesi Y. Salawu, Oluseyi O. Ajayi
Hydrogen is an abundant element and a flexible energy carrier, offering substantial potential as an environmentally friendly energy source to tackle global energy issues. When used as a fuel, hydrogen generates only water vapor upon combustion or in fuel cells, presenting a means to reduce carbon emissions in various sectors, including transportation, industry, and power generation. Nevertheless, conventional hydrogen production methods often depend on fossil fuels, leading to carbon emissions unless integrated with carbon capture and storage solutions. Conversely, green hydrogen is generated through electrolysis powered by renewable energy sources like solar and wind energy. This production method guarantees zero carbon emissions throughout the hydrogen’s lifecycle, positioning it as a critical component of global sustainable energy transitions. In Africa, where there are extensive renewable energy resources such as solar and wind power, green hydrogen is emerging as a viable solution to sustainably address the increasing energy demands. This research explores the influence of policy frameworks, technological innovations, and market forces in promoting green hydrogen adoption across Africa. Despite growing investments and favorable policies, challenges such as high production costs and inadequate infrastructure significantly hinder widespread adoption. To overcome these challenges and speed up the shift towards a sustainable hydrogen economy in Africa, strategic investments and collaborative efforts are essential. By harnessing its renewable energy potential and establishing strong policy frameworks, Africa can not only fulfill its energy requirements but also support global initiatives to mitigate climate change and achieve sustainable development objectives.
{"title":"Progress in green hydrogen adoption in the African context","authors":"Enoch I. Obanor, Joseph O. Dirisu, Oluwaseun O. Kilanko, Enesi Y. Salawu, Oluseyi O. Ajayi","doi":"10.3389/fenrg.2024.1429118","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1429118","url":null,"abstract":"Hydrogen is an abundant element and a flexible energy carrier, offering substantial potential as an environmentally friendly energy source to tackle global energy issues. When used as a fuel, hydrogen generates only water vapor upon combustion or in fuel cells, presenting a means to reduce carbon emissions in various sectors, including transportation, industry, and power generation. Nevertheless, conventional hydrogen production methods often depend on fossil fuels, leading to carbon emissions unless integrated with carbon capture and storage solutions. Conversely, green hydrogen is generated through electrolysis powered by renewable energy sources like solar and wind energy. This production method guarantees zero carbon emissions throughout the hydrogen’s lifecycle, positioning it as a critical component of global sustainable energy transitions. In Africa, where there are extensive renewable energy resources such as solar and wind power, green hydrogen is emerging as a viable solution to sustainably address the increasing energy demands. This research explores the influence of policy frameworks, technological innovations, and market forces in promoting green hydrogen adoption across Africa. Despite growing investments and favorable policies, challenges such as high production costs and inadequate infrastructure significantly hinder widespread adoption. To overcome these challenges and speed up the shift towards a sustainable hydrogen economy in Africa, strategic investments and collaborative efforts are essential. By harnessing its renewable energy potential and establishing strong policy frameworks, Africa can not only fulfill its energy requirements but also support global initiatives to mitigate climate change and achieve sustainable development objectives.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215135","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 : 2024-08-23DOI: 10.3389/fenrg.2024.1455576
Gaoqun Zhang, Dabo Duan, Jingcen Zhang, Junjie Hao, Zhanfeng Deng
Thermal storage ceramics using metals as phase change materials (PCMs) have both high thermal conductivity and high heat storage density. However, in the process of use is very easy to occur in the metal phase change material leakage, will seriously affect the service life of the thermal storage ceramics. In this study, ceramic composite phase change heat storage materials with Al-12Si alloy as phase change material were prepared. Firstly, Al-12Si was pretreated by sol-gel method and high temperature heat treatment to obtain the pretreated Al-12Si alloy powder with dense alumina shell layer. After that, the pretreated Al-12Si alloy powder was mixed and pressed with alumina, silicon dioxide, magnesium oxide, and mullite respectively, and sintered at 1,100°C, 1,200°C, or 1,300°C. The experimental results show that the metal phase change materials and the four ceramic materials show good chemical compatibility, and pretreated Al-12Si essentially retains its initial shape and is uniformly dispersed in the heat storage material. Among all the samples, the pre-treated Al-12Si/mullite ceramic thermal storage materials with a sintering temperature of 1,200°C showed the best thermal storage performance. The thermal conductivity of the samples was up to 17.94 W/(m·K). The latent heat storage value was 139.51 J/g before thermal cycling, 138.27 J/g after 100 thermal cycling, which was only decreased by 0.89%, and there was almost no alloy leakage. This study has successfully realized that the ceramic thermal storage material possesses high thermal conductivity, high thermal storage density and excellent thermal cycling performance at the same time, and provides a new method for the production and preparation of Al-12Si/ceramic heat storage materials, which has great potential for application in heat storage systems.
{"title":"Preparation and characterization of Al-12Si/ceramic composite phase change heat storage material","authors":"Gaoqun Zhang, Dabo Duan, Jingcen Zhang, Junjie Hao, Zhanfeng Deng","doi":"10.3389/fenrg.2024.1455576","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1455576","url":null,"abstract":"Thermal storage ceramics using metals as phase change materials (PCMs) have both high thermal conductivity and high heat storage density. However, in the process of use is very easy to occur in the metal phase change material leakage, will seriously affect the service life of the thermal storage ceramics. In this study, ceramic composite phase change heat storage materials with Al-12Si alloy as phase change material were prepared. Firstly, Al-12Si was pretreated by sol-gel method and high temperature heat treatment to obtain the pretreated Al-12Si alloy powder with dense alumina shell layer. After that, the pretreated Al-12Si alloy powder was mixed and pressed with alumina, silicon dioxide, magnesium oxide, and mullite respectively, and sintered at 1,100°C, 1,200°C, or 1,300°C. The experimental results show that the metal phase change materials and the four ceramic materials show good chemical compatibility, and pretreated Al-12Si essentially retains its initial shape and is uniformly dispersed in the heat storage material. Among all the samples, the pre-treated Al-12Si/mullite ceramic thermal storage materials with a sintering temperature of 1,200°C showed the best thermal storage performance. The thermal conductivity of the samples was up to 17.94 W/(m·K). The latent heat storage value was 139.51 J/g before thermal cycling, 138.27 J/g after 100 thermal cycling, which was only decreased by 0.89%, and there was almost no alloy leakage. This study has successfully realized that the ceramic thermal storage material possesses high thermal conductivity, high thermal storage density and excellent thermal cycling performance at the same time, and provides a new method for the production and preparation of Al-12Si/ceramic heat storage materials, which has great potential for application in heat storage systems.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215133","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}
IntroductionThe increasing global demand for sustainable energy solutions highlights the urgency of exploring renewable resources, particularly sunlight, which is abundant and virtually limitless. This study reviews innovative technologies like solar trees, wind trees, and hybrid solar-wind trees, which are emerging as efficient structures for harnessing renewable energy.MethodsA comprehensive SWOT analysis was conducted to evaluate the strengths, weaknesses, opportunities, and threats associated with solar, wind, and hybrid trees. The analysis also considered sustainability aspects, focusing on the efficiency and practicality of these technologies in various settings.ResultsSolar trees mimic natural foliage, utilizing solar modules to convert sunlight into electricity, while wind trees incorporate micro-wind turbines and solar panels, effectively harnessing both wind and solar energy. Hybrid solar-wind trees combine these technologies to provide a consistent energy supply. These structures are compact, cost-effective, and adaptable to urban landscapes.DiscussionChallenges such as land use, aesthetic considerations, and public perception were identified. The review emphasizes the need for future research to optimize configurations and address these challenges, ensuring the successful integration of these technologies into sustainable urban landscapes.ConclusionThis review provides critical insights for renewable energy researchers, particularly in the development of hybrid wind and solar power systems, promoting energy security and climate resilience.
{"title":"A Succinct review of strengths, weaknesses, opportunities, and threats (SWOT) analyses, challenges and prospects of solar and wind tree technologies for hybrid power generation","authors":"Kumaresen Mohanaravi, Mahendran Samykano, Adarsh Kumar Pandey, Muhamad Mat Noor, Kumaran Kadirgama","doi":"10.3389/fenrg.2024.1417511","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1417511","url":null,"abstract":"IntroductionThe increasing global demand for sustainable energy solutions highlights the urgency of exploring renewable resources, particularly sunlight, which is abundant and virtually limitless. This study reviews innovative technologies like solar trees, wind trees, and hybrid solar-wind trees, which are emerging as efficient structures for harnessing renewable energy.MethodsA comprehensive SWOT analysis was conducted to evaluate the strengths, weaknesses, opportunities, and threats associated with solar, wind, and hybrid trees. The analysis also considered sustainability aspects, focusing on the efficiency and practicality of these technologies in various settings.ResultsSolar trees mimic natural foliage, utilizing solar modules to convert sunlight into electricity, while wind trees incorporate micro-wind turbines and solar panels, effectively harnessing both wind and solar energy. Hybrid solar-wind trees combine these technologies to provide a consistent energy supply. These structures are compact, cost-effective, and adaptable to urban landscapes.DiscussionChallenges such as land use, aesthetic considerations, and public perception were identified. The review emphasizes the need for future research to optimize configurations and address these challenges, ensuring the successful integration of these technologies into sustainable urban landscapes.ConclusionThis review provides critical insights for renewable energy researchers, particularly in the development of hybrid wind and solar power systems, promoting energy security and climate resilience.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215134","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 : 2024-08-07DOI: 10.3389/fenrg.2024.1440487
Sanlei Dang, Jie Zhang, Tao Lu, Yongwang Zhang, Peng Song, Jun Zhang, Rirong Liu
To realize transparent monitoring and resilience improvement of low-voltage distribution network, both the data acquisition scope and frequency have been greatly expanded. Cloud-edge collaboration leverages the edge server’s real-time response capabilities and the cloud server’s robust data processing power to enhance the performance of high-frequency data acquisition processing. Nonetheless, it continues to confront challenges such as the entanglement of optimization variables, the presence of uncertain information, and a lack of awareness regarding acquisition frequencies. In this paper, we propose a machine learning-based cloud-edge collaborative data processing optimization algorithm to minimize the weighted sum of data processing delay and device energy consumption for distribution network resilience improvement. The joint optimization problem is decoupled into device-edge data offloading subproblem and edge-cloud data splitting subproblem, which are solved by the proposed upper confidence bound (UCB) based frequency-aware device-edge data offloading optimization algorithm and the exponential-weight algorithm for exploration and exploitation (EXP3) based edge-cloud data splitting optimization algorithm, respectively. Simulation results show that the proposed algorithm is superior to existing algorithms in performances of energy consumption and total processing delay.
{"title":"Cloud-edge collaborative high-frequency acquisition data processing for distribution network resilience improvement","authors":"Sanlei Dang, Jie Zhang, Tao Lu, Yongwang Zhang, Peng Song, Jun Zhang, Rirong Liu","doi":"10.3389/fenrg.2024.1440487","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1440487","url":null,"abstract":"To realize transparent monitoring and resilience improvement of low-voltage distribution network, both the data acquisition scope and frequency have been greatly expanded. Cloud-edge collaboration leverages the edge server’s real-time response capabilities and the cloud server’s robust data processing power to enhance the performance of high-frequency data acquisition processing. Nonetheless, it continues to confront challenges such as the entanglement of optimization variables, the presence of uncertain information, and a lack of awareness regarding acquisition frequencies. In this paper, we propose a machine learning-based cloud-edge collaborative data processing optimization algorithm to minimize the weighted sum of data processing delay and device energy consumption for distribution network resilience improvement. The joint optimization problem is decoupled into device-edge data offloading subproblem and edge-cloud data splitting subproblem, which are solved by the proposed upper confidence bound (UCB) based frequency-aware device-edge data offloading optimization algorithm and the exponential-weight algorithm for exploration and exploitation (EXP3) based edge-cloud data splitting optimization algorithm, respectively. Simulation results show that the proposed algorithm is superior to existing algorithms in performances of energy consumption and total processing delay.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943811","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 : 2024-08-07DOI: 10.3389/fenrg.2024.1371239
Xinwei Wei, Wanyu Tao, Xunbo Fu
In this article, a model predictive control (MPC) with common-mode voltage (CMV) suppression is proposed for single-phase cascaded H-bridge (CHB) inverters, which can also simultaneously achieve control objectives of grid-connected current tracking, voltages balancing of different H-bridge submodules on the DC-side and switching frequency reduction. To suppress high-frequency components of the common-mode voltage without additional switching devices, the algorithm proposed designs the predicted and reference values of the CMV and incorporates them in the cost function. At the same time, the capacitor voltages balancing control is integrated in the calculation of the optimal modulation function of the H-bridge, which reduces the complexity of control effectively. Besides, switching times of the MOSFETs are compared in two cycles. The cost function is constructed to represent comprehensive effect of the control. Finally, an experiment is performed on the hardware-in-the-loop experimental platform. The experimental results show that the proposed algorithm can offer a better voltage THD and reduce the times of switch action by nearly half while maintaining high-precision current tracking and maximum power point of photovoltaic modules, which alleviate the potential electromagnetic interference and cabling problem.
本文针对单相级联 H 桥 (CHB) 逆变器提出了一种具有共模电压 (CMV) 抑制功能的模型预测控制 (MPC)，它还能同时实现并网电流跟踪、直流侧不同 H 桥子模块的电压平衡以及降低开关频率等控制目标。为抑制共模电压的高频分量而无需额外的开关设备，所提出的算法设计了 CMV 的预测值和参考值，并将其纳入成本函数。同时，在计算 H 桥的最佳调制功能时，还纳入了电容器电压平衡控制，从而有效降低了控制的复杂性。此外，MOSFET 的开关时间在两个周期内进行比较。构建了成本函数来表示控制的综合效果。最后，在硬件在环实验平台上进行了实验。实验结果表明，所提出的算法可以提供更好的电压总谐波失真（THD），并将开关动作时间减少近一半，同时保持高精度的电流跟踪和光伏组件的最大功率点，从而缓解了潜在的电磁干扰和布线问题。
{"title":"Model predictive control for single-phase cascaded H-bridge photovoltaic inverter system considering common-mode voltage suppression","authors":"Xinwei Wei, Wanyu Tao, Xunbo Fu","doi":"10.3389/fenrg.2024.1371239","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1371239","url":null,"abstract":"In this article, a model predictive control (MPC) with common-mode voltage (CMV) suppression is proposed for single-phase cascaded H-bridge (CHB) inverters, which can also simultaneously achieve control objectives of grid-connected current tracking, voltages balancing of different H-bridge submodules on the DC-side and switching frequency reduction. To suppress high-frequency components of the common-mode voltage without additional switching devices, the algorithm proposed designs the predicted and reference values of the CMV and incorporates them in the cost function. At the same time, the capacitor voltages balancing control is integrated in the calculation of the optimal modulation function of the H-bridge, which reduces the complexity of control effectively. Besides, switching times of the MOSFETs are compared in two cycles. The cost function is constructed to represent comprehensive effect of the control. Finally, an experiment is performed on the hardware-in-the-loop experimental platform. The experimental results show that the proposed algorithm can offer a better voltage THD and reduce the times of switch action by nearly half while maintaining high-precision current tracking and maximum power point of photovoltaic modules, which alleviate the potential electromagnetic interference and cabling problem.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943853","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 : 2024-08-07DOI: 10.3389/fenrg.2024.1322047
Lyu-Guang Hua, S. Haseeb Ali Shah, Baheej Alghamdi, Ghulam Hafeez, Safeer Ullah, Sadia Murawwat, Sajjad Ali, Muhammad Iftikhar Khan
This study introduces a smart home load scheduling system that aims to address concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, which includes an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and the hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTPDR). The efficient integration of renewable energy into smart grids (SGs) is challenging due to its time-varying and intermittent nature. To address this, batteries were used in this study to mitigate the fluctuations in renewable generation. The simulation results validate the effectiveness of our proposed approach in optimally addressing the smart home load scheduling problem with photovoltaic generation and DR. The system achieves the minimization of utility bills, pollutant emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact.
{"title":"Smart home load scheduling system with solar photovoltaic generation and demand response in the smart grid","authors":"Lyu-Guang Hua, S. Haseeb Ali Shah, Baheej Alghamdi, Ghulam Hafeez, Safeer Ullah, Sadia Murawwat, Sajjad Ali, Muhammad Iftikhar Khan","doi":"10.3389/fenrg.2024.1322047","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1322047","url":null,"abstract":"This study introduces a smart home load scheduling system that aims to address concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, which includes an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and the hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTPDR). The efficient integration of renewable energy into smart grids (SGs) is challenging due to its time-varying and intermittent nature. To address this, batteries were used in this study to mitigate the fluctuations in renewable generation. The simulation results validate the effectiveness of our proposed approach in optimally addressing the smart home load scheduling problem with photovoltaic generation and DR. The system achieves the minimization of utility bills, pollutant emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943807","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}
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":null,"pages":null},"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 : 2024-08-07DOI: 10.3389/fenrg.2024.1437287
Yi Lu, Gang Shi, Qian Chen, Peng Qiu, Jianqiao Zhou, Renxin Yang, Jianwen Zhang
As the penetration of the integrated intermittent and fluctuating new energy (e.g., wind and photovoltaic power) increases, the conventional grid-following voltage source converter (VSC)-based high voltage direct current (HVDC) transmission system faces the problem of interactive instability with the grid. A novel grid-forming control strategy is proposed to overcome these issues, which adopts the dynamics of a DC capacitor to realize the function of self-synchronization with the grid. Moreover, the per-unit DC voltage can automatically track the grid frequency, acting as a phase-locked loop. Next, the small-signal model of the grid-forming VSC-HVDC system is established, and the stability of the system is analyzed using the eigenvalue analysis method and the complex power coefficient method. In addition, the stabilization controller is proposed for the grid-forming (GFM) control structure, which further enhances the grid-forming VSC-HVDC system’s stability and helps it operate stably under both stiff and weak grid conditions. Research results show that the VSC-HVDC system under the proposed grid-forming control can work stably in both stiff and weak grids. The grid-forming VSC-HVDC system is robust and can maintain stable operations with a large range variation of the parameters in the current and voltage control loop. Simulations are carried out on the PSCAD/EMTDC platform to verify the proposed grid-forming control strategy.
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