Pub Date : 2024-09-26DOI: 10.1016/j.apenergy.2024.124516
The self-powered technology of earthquake sensors and the seismic energy utilization have not been solved well up to now although earthquake includes mega energy. In view of this, a series of piezoelectric seismic energy harvesters (PSEHs) are developed, and their corresponding experiments and simulations about energy harvesting performance are conducted in the excitation of different seismic waves. The effects of some important design parameters on the output voltage and power of PSEHs are studied and discussed. The research results show that U-shaped PSEH has a good ability and ideal robustness in energy harvesting from different seismic waves. For example, the root mean square (RMS) voltages and RMS powers from U-shaped PSEH are 104 V and 11.1 mW for El-Centro wave with a peak ground acceleration (PGA) of 0.024 g, which is feasible to supply an earthquake sensor. Based on the experiment and simulation research, a series of theoretical models are derived to predict the output voltage and power of U-shaped PSEH with different design parameters and different PGAs, these theoretical models give reliable instructions for the design of U-shaped PSEH to match the earthquake sensors in the area authorized by different earthquake intensities.
虽然地震具有巨大的能量，但地震传感器的自供电技术和地震能量利用问题至今尚未得到很好的解决。有鉴于此，我们开发了一系列压电地震能量收集器（PSEHs），并对其在不同地震波激励下的能量收集性能进行了相应的实验和模拟。研究并讨论了一些重要设计参数对 PSEH 输出电压和功率的影响。研究结果表明，U 型 PSEH 在不同地震波的能量收集方面具有良好的能力和理想的鲁棒性。例如，对于峰值地面加速度（PGA）为 0.024 g 的 El-Centro 波，U 型 PSEH 的均方根电压和均方根功率分别为 104 V 和 11.1 mW，可以为地震传感器供电。在实验和仿真研究的基础上，推导出一系列理论模型来预测不同设计参数和不同 PGA 的 U 型 PSEH 的输出电压和功率，这些理论模型为 U 型 PSEH 的设计提供了可靠的指导，以匹配不同地震烈度授权区域的地震传感器。
{"title":"A study on the energy harvesting performance and corresponding theoretical models of piezoelectric seismic energy harvesters","authors":"","doi":"10.1016/j.apenergy.2024.124516","DOIUrl":"10.1016/j.apenergy.2024.124516","url":null,"abstract":"<div><div>The self-powered technology of earthquake sensors and the seismic energy utilization have not been solved well up to now although earthquake includes mega energy. In view of this, a series of piezoelectric seismic energy harvesters (PSEHs) are developed, and their corresponding experiments and simulations about energy harvesting performance are conducted in the excitation of different seismic waves. The effects of some important design parameters on the output voltage and power of PSEHs are studied and discussed. The research results show that U-shaped PSEH has a good ability and ideal robustness in energy harvesting from different seismic waves. For example, the root mean square (RMS) voltages and RMS powers from U-shaped PSEH are 104 V and 11.1 mW for El-Centro wave with a peak ground acceleration (PGA) of 0.024 g, which is feasible to supply an earthquake sensor. Based on the experiment and simulation research, a series of theoretical models are derived to predict the output voltage and power of U-shaped PSEH with different design parameters and different PGAs, these theoretical models give reliable instructions for the design of U-shaped PSEH to match the earthquake sensors in the area authorized by different earthquake intensities.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-09-26DOI: 10.1016/j.apenergy.2024.124528
The installation of photovoltaic (PV) systems has risen significantly as the global demand for renewable energy increases. Inequitable photovoltaic (PV) adoption exacerbates the energy burden on non-adopting households. Intervention in the PV deployment process is currently the dominant concept to address this challenge. Subsidizing low-income households to scale up PV adoption lacks sustainability, and curbing PV scale by reducing adopters’ benefits from PV has been shown to solidify this inequity. This study attempts to provide new ideas for solutions to eliminate the consequences of inequitable PV adoption without interfering with the scale of PV. We build a sequential game between a profit-maximizing power plant and a profit-maximizing electricity retailer to describe the optimal decisions of the relevant decision-makers in the face of unfair PV adoption. In addition, we construct an evolutionary game model of the electricity market to model the causes of and responses to long-term PV inequity. Our results show that PV results in a decrease in the wholesale price of electricity. However, the electricity retailer may not pass on the price decrease to households, which in turn leads to an increase in the cost of electricity for households that do not adopt PV, which is a new energy equity problem (i.e., price inequity). Subsequently, eliminating time-of-use tariff strategies for PV households and subsidizing the retailer in the early stages of PV deployment could decrease price inequity. In addition, we find that excessive adjustment decisions by the boundedly rational power plant and retailer can lead to electricity market instability and exacerbate the difficulty of decreasing long-run inequality while reducing their focus on the direction of profitability can help to eliminate long-run inequality.
{"title":"Shadows behind the sun: Inequity caused by rooftop solar and responses to it","authors":"","doi":"10.1016/j.apenergy.2024.124528","DOIUrl":"10.1016/j.apenergy.2024.124528","url":null,"abstract":"<div><div>The installation of photovoltaic (PV) systems has risen significantly as the global demand for renewable energy increases. Inequitable photovoltaic (PV) adoption exacerbates the energy burden on non-adopting households. Intervention in the PV deployment process is currently the dominant concept to address this challenge. Subsidizing low-income households to scale up PV adoption lacks sustainability, and curbing PV scale by reducing adopters’ benefits from PV has been shown to solidify this inequity. This study attempts to provide new ideas for solutions to eliminate the consequences of inequitable PV adoption without interfering with the scale of PV. We build a sequential game between a profit-maximizing power plant and a profit-maximizing electricity retailer to describe the optimal decisions of the relevant decision-makers in the face of unfair PV adoption. In addition, we construct an evolutionary game model of the electricity market to model the causes of and responses to long-term PV inequity. Our results show that PV results in a decrease in the wholesale price of electricity. However, the electricity retailer may not pass on the price decrease to households, which in turn leads to an increase in the cost of electricity for households that do not adopt PV, which is a new energy equity problem (i.e., price inequity). Subsequently, eliminating time-of-use tariff strategies for PV households and subsidizing the retailer in the early stages of PV deployment could decrease price inequity. In addition, we find that excessive adjustment decisions by the boundedly rational power plant and retailer can lead to electricity market instability and exacerbate the difficulty of decreasing long-run inequality while reducing their focus on the direction of profitability can help to eliminate long-run inequality.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-09-26DOI: 10.1016/j.apenergy.2024.124450
California has committed to ambitious decarbonization targets across multiple sectors, including decarbonizing the electrical grid by 2045. In addition, the medium- and heavy-duty truck fleets are expected to see rapid electrification over the next two decades. Considering these two pathways in tandem is critical for ensuring cost optimality and reliable power system operation. In particular, we examine the potential cost savings of electrical generation infrastructure by enabling flexible charging and bidirectional charging for these trucks. We also examine costs adjacent to enabling these services, such as charger upgrades and battery degradation. We deploy a large mixed-integer decarbonization planning model to quantify the costs associated with the electric generation decarbonization pathway. Example scenarios governing truck driving and charging behaviors are implemented to reveal the sensitivity of temporal driving patterns. Our experiments show that cost savings on the order of multiple billions of dollars are possible by enabling flexible and bidirectional charging in medium- and heavy-duty trucks in California.
{"title":"Impact of flexible and bidirectional charging in medium- and heavy-duty trucks on California’s decarbonization pathway","authors":"","doi":"10.1016/j.apenergy.2024.124450","DOIUrl":"10.1016/j.apenergy.2024.124450","url":null,"abstract":"<div><div>California has committed to ambitious decarbonization targets across multiple sectors, including decarbonizing the electrical grid by 2045. In addition, the medium- and heavy-duty truck fleets are expected to see rapid electrification over the next two decades. Considering these two pathways in tandem is critical for ensuring cost optimality and reliable power system operation. In particular, we examine the potential cost savings of electrical generation infrastructure by enabling flexible charging and bidirectional charging for these trucks. We also examine costs adjacent to enabling these services, such as charger upgrades and battery degradation. We deploy a large mixed-integer decarbonization planning model to quantify the costs associated with the electric generation decarbonization pathway. Example scenarios governing truck driving and charging behaviors are implemented to reveal the sensitivity of temporal driving patterns. Our experiments show that cost savings on the order of multiple billions of dollars are possible by enabling flexible and bidirectional charging in medium- and heavy-duty trucks in California.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.apenergy.2024.124519
Coordination between virtual power plants and active distribution networks is crucial as these plants increasingly aggregate distributed resources within the power system. This study introduces a bilevel optimization framework to coordinate the scheduling of multiple virtual power plants and an active distribution network using pricing strategies for energy and reserves. The upper-level optimization minimizes total operating costs by incorporating bidding plans of the active distribution network in various markets, its interactions with multiple virtual power plants, and operational costs. The lower-level optimization maximizes revenue for each virtual power plant, considering both battery capacity degradation costs and operational costs of various resources. To facilitate solutions, this research developed a nonlinear transformation method for modeling capacity degradation. Based on the dispatching strategy from the virtual power plant, this study uses the squared difference between energy consumption of equipment for controllable loads and the strategy as the optimization target to derive control strategies for two equipment types. Results show that the framework effectively integrates dispatch and control strategies without oversimplifying the system model, proving its applicability in various scenarios with diverse resource compositions.
{"title":"Co-optimization of virtual power plants and distribution grids: Emphasizing flexible resource aggregation and battery capacity degradation","authors":"","doi":"10.1016/j.apenergy.2024.124519","DOIUrl":"10.1016/j.apenergy.2024.124519","url":null,"abstract":"<div><div>Coordination between virtual power plants and active distribution networks is crucial as these plants increasingly aggregate distributed resources within the power system. This study introduces a bilevel optimization framework to coordinate the scheduling of multiple virtual power plants and an active distribution network using pricing strategies for energy and reserves. The upper-level optimization minimizes total operating costs by incorporating bidding plans of the active distribution network in various markets, its interactions with multiple virtual power plants, and operational costs. The lower-level optimization maximizes revenue for each virtual power plant, considering both battery capacity degradation costs and operational costs of various resources. To facilitate solutions, this research developed a nonlinear transformation method for modeling capacity degradation. Based on the dispatching strategy from the virtual power plant, this study uses the squared difference between energy consumption of equipment for controllable loads and the strategy as the optimization target to derive control strategies for two equipment types. Results show that the framework effectively integrates dispatch and control strategies without oversimplifying the system model, proving its applicability in various scenarios with diverse resource compositions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-09-26DOI: 10.1016/j.apenergy.2024.124476
With the escalating adoption of electric vehicles (EVs), the intricate interplay between power and traffic systems becomes increasingly pronounced. Understanding the distribution of charging loads and traffic flows are paramount for effective coordination. Traditionally, the distribution of EVs charging loads and traffic flows are obtained via solving the EVs traffic assignment problem with User Equilibrium (TAP-UE). Despite the general convexity of TAP-UE, the iterative nature of the prevailing solution process and the nonlinear objective function pose challenges, leading to prolonged solution times. This paper introduces a novel unsupervised learning-based framework aimed at efficiently distributing EVs charging loads and traffic flows without off-the-shelf solvers or a large dataset. Firstly, feasible paths are identified for each OD pair, eliminating the need for iterative procedures. Subsequently, the convexity-preserving reformulation of TAP-UE converts it into an unconstrained nonlinear optimization problem, leading to a properly designed loss function to guide neural networks in directly learning a legitimate OD demands-EVs loads-traffic flows mapping which satisfies the UE conditions. The incorporation of the Hessian matrix into the gradient update of network parameters, facilitated by the Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm, enhances the convergence speed of the unsupervised learning process. Case studies are conducted to demonstrate the efficacy of the proposed framework.
随着电动汽车（EV）的普及，电力和交通系统之间错综复杂的相互作用变得日益明显。了解充电负荷和交通流量的分布对于有效协调至关重要。传统上，电动汽车充电负荷和交通流量的分布是通过求解用户均衡的电动汽车交通分配问题（TAP-UE）获得的。尽管 TAP-UE 具有普遍的凸性，但普遍求解过程的迭代性和非线性目标函数带来了挑战，导致求解时间延长。本文介绍了一种基于无监督学习的新型框架，旨在无需现成的求解器或大型数据集，就能有效分配电动汽车充电负荷和交通流量。首先，为每个 OD 对确定可行路径，从而无需迭代程序。随后，通过对 TAP-UE 进行保留凸性的重新表述，将其转换为无约束非线性优化问题，从而设计出适当的损失函数，引导神经网络直接学习满足 UE 条件的合法 OD 需求-EV 负载-交通流映射。在有限记忆 Broyden-Fletcher-Goldfarb-Shanno 算法（L-BFGS）的帮助下，将 Hessian 矩阵纳入网络参数的梯度更新，提高了无监督学习过程的收敛速度。我们还进行了案例研究，以证明拟议框架的有效性。
{"title":"Unsupervised learning for efficiently distributing EVs charging loads and traffic flows in coupled power and transportation systems","authors":"","doi":"10.1016/j.apenergy.2024.124476","DOIUrl":"10.1016/j.apenergy.2024.124476","url":null,"abstract":"<div><div>With the escalating adoption of electric vehicles (EVs), the intricate interplay between power and traffic systems becomes increasingly pronounced. Understanding the distribution of charging loads and traffic flows are paramount for effective coordination. Traditionally, the distribution of EVs charging loads and traffic flows are obtained via solving the EVs traffic assignment problem with User Equilibrium (TAP-UE). Despite the general convexity of TAP-UE, the iterative nature of the prevailing solution process and the nonlinear objective function pose challenges, leading to prolonged solution times. This paper introduces a novel unsupervised learning-based framework aimed at efficiently distributing EVs charging loads and traffic flows without off-the-shelf solvers or a large dataset. Firstly, feasible paths are identified for each OD pair, eliminating the need for iterative procedures. Subsequently, the convexity-preserving reformulation of TAP-UE converts it into an unconstrained nonlinear optimization problem, leading to a properly designed loss function to guide neural networks in directly learning a legitimate OD demands-EVs loads-traffic flows mapping which satisfies the UE conditions. The incorporation of the Hessian matrix into the gradient update of network parameters, facilitated by the Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm, enhances the convergence speed of the unsupervised learning process. Case studies are conducted to demonstrate the efficacy of the proposed framework.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-09-26DOI: 10.1016/j.apenergy.2024.124496
The subject of battery sources and electrical energy accumulators is currently very topical. Moreover, the possibilities of reusing already discarded sources are being explored as so-called “second-life batteries”. This article is concerned with studying and modelling the behaviour of a battery in an electric aircraft in operation — the voltage during discharge. Outcomes from extensive experiments on real long-term stored batteries have provided statistically robust sets of data on both long-term stored and new batteries; some of the data, however, are truncated. A modern approach that neglects the truncated issues and is based on functional data analysis and modified with a specific time series is used to model the process. This suggested model is much more accurate than the model used previously as it can effectively process truncated data. It also allows a certain degree of generalization. The aim is to determine the probability density of the time when the battery reaches the critical value, including the numerical statistics, for both stored and new batteries. The results are compared using the specific statistical Kullback–Leibler divergence approach to determine the degree of difference. The proposed model applies to similar issues where battery voltage is modelled in a time domain while the data form is truncated. It is proved, however, that further use of the stored batteries does not disrupt the safe and reliable operation of an electric airplane in terms of their functionality.
{"title":"Perspective modelling and measuring discharge voltage on truncated data of long-term stored Li-ion batteries based on functional state space model","authors":"","doi":"10.1016/j.apenergy.2024.124496","DOIUrl":"10.1016/j.apenergy.2024.124496","url":null,"abstract":"<div><div>The subject of battery sources and electrical energy accumulators is currently very topical. Moreover, the possibilities of reusing already discarded sources are being explored as so-called “second-life batteries”. This article is concerned with studying and modelling the behaviour of a battery in an electric aircraft in operation — the voltage during discharge. Outcomes from extensive experiments on real long-term stored batteries have provided statistically robust sets of data on both long-term stored and new batteries; some of the data, however, are truncated. A modern approach that neglects the truncated issues and is based on functional data analysis and modified with a specific time series is used to model the process. This suggested model is much more accurate than the model used previously as it can effectively process truncated data. It also allows a certain degree of generalization. The aim is to determine the probability density of the time when the battery reaches the critical value, including the numerical statistics, for both stored and new batteries. The results are compared using the specific statistical Kullback–Leibler divergence approach to determine the degree of difference. The proposed model applies to similar issues where battery voltage is modelled in a time domain while the data form is truncated. It is proved, however, that further use of the stored batteries does not disrupt the safe and reliable operation of an electric airplane in terms of their functionality.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-09-26DOI: 10.1016/j.apenergy.2024.124479
Supermarkets are significant consumers of energy, primarily due to refrigeration systems, which also contribute to global climate change through hydrofluorocarbon refrigerants. The transition to CO2 refrigeration systems (CO2-RS) offers a low-environmental-impact alternative; however, malfunctions can undermine these benefits. To prevent CO2-RS from malfunctioning, fault detection and diagnostics (FDD) are commonly employed. This study presents an innovative approach to developing an efficient data-driven FDD model for CO2-RS, emphasizing cost-effective sensor utilization and model interpretability. This new method is essential due to the limitations of existing FDD techniques, which often lack cost-effective sensor solutions and model interpretability, thereby hindering their practical application and effectiveness in identifying and diagnosing faults in CO2-RS. The approach focuses on diagnosing common faults in CO2-RS by developing virtual sensors, employing tree-based machine learning algorithms (Random Forest, XGBoost, CatBoost, LightGBM), selecting an optimal sensor set, and using SHapley Additive exPlanations (SHAP) for interpretability. The integration of three developed virtual sensors with pre-installed physical sensors, derived from physical relationships and existing sensors, enhances access to cost-effective sensors and improves the performance of data-driven FDD models. These virtual sensors, as well as the physical sensors needed to develop them, are selected as the optimal sensor set. Additionally, the data-driven FDD model, utilizing the random forest (RF) algorithm and the optimal sensor set, is introduced as an efficient model capable of classifying faults in CO2-RS, achieving an accuracy of 99.48 %, with precision and recall of 99.57 %, and an F1-score of 99.42 %. The SHAP technique is employed to enhance model interpretability, ensuring practical deployment in supermarket settings.
{"title":"Enhancing energy efficiency in supermarkets: A data-driven approach for fault detection and diagnosis in CO2 refrigeration systems","authors":"","doi":"10.1016/j.apenergy.2024.124479","DOIUrl":"10.1016/j.apenergy.2024.124479","url":null,"abstract":"<div><div>Supermarkets are significant consumers of energy, primarily due to refrigeration systems, which also contribute to global climate change through hydrofluorocarbon refrigerants. The transition to CO<sub>2</sub> refrigeration systems (CO<sub>2</sub>-RS) offers a low-environmental-impact alternative; however, malfunctions can undermine these benefits. To prevent CO<sub>2</sub>-RS from malfunctioning, fault detection and diagnostics (FDD) are commonly employed. This study presents an innovative approach to developing an efficient data-driven FDD model for CO<sub>2</sub>-RS, emphasizing cost-effective sensor utilization and model interpretability. This new method is essential due to the limitations of existing FDD techniques, which often lack cost-effective sensor solutions and model interpretability, thereby hindering their practical application and effectiveness in identifying and diagnosing faults in CO<sub>2</sub>-RS. The approach focuses on diagnosing common faults in CO<sub>2</sub>-RS by developing virtual sensors, employing tree-based machine learning algorithms (Random Forest, XGBoost, CatBoost, LightGBM), selecting an optimal sensor set, and using SHapley Additive exPlanations (SHAP) for interpretability. The integration of three developed virtual sensors with pre-installed physical sensors, derived from physical relationships and existing sensors, enhances access to cost-effective sensors and improves the performance of data-driven FDD models. These virtual sensors, as well as the physical sensors needed to develop them, are selected as the optimal sensor set. Additionally, the data-driven FDD model, utilizing the random forest (RF) algorithm and the optimal sensor set, is introduced as an efficient model capable of classifying faults in CO<sub>2</sub>-RS, achieving an accuracy of 99.48 %, with precision and recall of 99.57 %, and an F1-score of 99.42 %. The SHAP technique is employed to enhance model interpretability, ensuring practical deployment in supermarket settings.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-09-26DOI: 10.1016/j.apenergy.2024.124521
Inaccurate edge detection is a common challenge in the segmentation of household rooftop photovoltaic (PV) systems from remote sensing images, which hinders the accurate retrieval of PV distribution information critical for planning and managing PV development. A widely adopted solution is to incorporate an additional edge detection task into a joint-task learning framework to enhance edge perception. However, existing joint-task learning methods often struggle to accurately detect PV edges and lack effective mechanisms for distinguishing PV edges from those of similar objects. To address the above challenges, we develop a novel joint-task learning framework. This framework introduces a Scale Adaptive Module (SAM) that dynamically adjusts the receptive field of edge features based on the PV actual size and shape, enabling precise detection of PV edges with varying shapes and sizes. In addition, a Position Guidance Module (PGM) is proposed based on the intrinsic relationship between the PV segmentation task and the edge detection task. The PGM not only guides the edge detection task to focus on identifying the semantic edges of PVs using the distribution information from the segmentation task but also enhances the ability of the segmentation task to accurately locate PVs in complex backgrounds by utilizing the backward gradient from the edge detection task. Multiple rounds of repeated experiments on the Duke and IGN datasets demonstrate the framework's superior performance. Compared to other models, the proposed framework significantly improves the detection accuracy of various PV edges, achieving the best performance in household rooftop PV segmentation with an Intersection over Union (IoU) of 77.4 %. This study provides valuable insights into the accurate acquisition of household rooftop PV information and offers a promising solution for object segmentation tasks facing the challenge of inaccurate edge extraction.
{"title":"Joint-task learning framework with scale adaptive and position guidance modules for improved household rooftop photovoltaic segmentation in remote sensing image","authors":"","doi":"10.1016/j.apenergy.2024.124521","DOIUrl":"10.1016/j.apenergy.2024.124521","url":null,"abstract":"<div><div>Inaccurate edge detection is a common challenge in the segmentation of household rooftop photovoltaic (PV) systems from remote sensing images, which hinders the accurate retrieval of PV distribution information critical for planning and managing PV development. A widely adopted solution is to incorporate an additional edge detection task into a joint-task learning framework to enhance edge perception. However, existing joint-task learning methods often struggle to accurately detect PV edges and lack effective mechanisms for distinguishing PV edges from those of similar objects. To address the above challenges, we develop a novel joint-task learning framework. This framework introduces a Scale Adaptive Module (SAM) that dynamically adjusts the receptive field of edge features based on the PV actual size and shape, enabling precise detection of PV edges with varying shapes and sizes. In addition, a Position Guidance Module (PGM) is proposed based on the intrinsic relationship between the PV segmentation task and the edge detection task. The PGM not only guides the edge detection task to focus on identifying the semantic edges of PVs using the distribution information from the segmentation task but also enhances the ability of the segmentation task to accurately locate PVs in complex backgrounds by utilizing the backward gradient from the edge detection task. Multiple rounds of repeated experiments on the Duke and IGN datasets demonstrate the framework's superior performance. Compared to other models, the proposed framework significantly improves the detection accuracy of various PV edges, achieving the best performance in household rooftop PV segmentation with an Intersection over Union (IoU) of 77.4 %. This study provides valuable insights into the accurate acquisition of household rooftop PV information and offers a promising solution for object segmentation tasks facing the challenge of inaccurate edge extraction.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-09-26DOI: 10.1016/j.apenergy.2024.124574
Solar Tower (ST) systems use heliostats to concentrate solar radiation onto a tower-mounted receiver. Optimizing the aiming strategy for these heliostats over the receiver remains a critical challenge due to the dynamic nature of solar radiation and the need to maximize energy capture while ensuring operational safety. This paper introduces a novel, model-free deep Reinforcement Learning (RL) approach to optimize heliostat aiming strategies, utilizing the Soft Actor–Critic (SAC) algorithm. This advanced RL method enhances the traditional Actor–Critic framework with two neural networks. The proposal dynamically adjusts the aiming points across the receiver surface in real time, trying to improve the overall performance of the ST plant. The strategy was simulated and evaluated over a full operational year and compared with traditional methods. The results show an increase of more than 8.8% in yearly absorbed power, a significant improvement that directly enhances performance and contributes to better economic outcomes for the technology. This technique also eliminates the need for constant human intervention and is applicable to both existing and future plants.
太阳能塔（ST）系统利用定日镜将太阳辐射集中到塔式接收器上。由于太阳辐射具有动态特性，而且需要在确保运行安全的同时最大限度地捕获能量，因此优化这些定日镜在接收器上的瞄准策略仍然是一项严峻的挑战。本文介绍了一种新颖、无模型的深度强化学习（RL）方法，利用软代理批评（SAC）算法优化定日镜瞄准策略。这种先进的强化学习（RL）方法利用两个神经网络增强了传统的行为批判框架。该建议可实时动态调整整个接收器表面的瞄准点，以提高 ST 设备的整体性能。对该策略进行了模拟，并对其全年运行情况进行了评估，同时与传统方法进行了比较。结果显示，每年的吸收功率提高了 8.8% 以上，这一显著改进直接提高了性能，有助于改善该技术的经济效益。该技术还无需持续的人工干预，适用于现有和未来的发电厂。
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Pub Date : 2024-09-26DOI: 10.1016/j.apenergy.2024.124562
With the increase of voltage level in energy-storage and power battery system, the electrical safety phenomenon of battery systems has received extensive attention. The issue of series arcs caused by electrical failure such as loose connections in battery systems has become increasingly serious. However, research on series arcs in battery systems is still in its early stages. Therefore, to investigate arc-related disasters in batteries, this study establishes an experimental platform to simulate series arc faults. Taking positive electrode terminal arcs as the focused point, this study explores the evolutionary patterns of battery-related arcs and under different conditions, and analyzes the hazardous effects on batteries. The results indicate that stable arcs can be generated in batteries with different states of charge (SOC) when the system voltage is 200 V and the circuit current is 2C. At the same time, the arc can melt the battery casing to form holes, leading to electrolyte leakage, and triggering battery short-circuit and open-circuit failures. The research findings of this study fill a gap in the field of battery system arc safety and are of vital importance for enhancing the safety performance of arc protection.
{"title":"Study on the evolution laws and induced failure of series arcs in cylindrical lithium-ion batteries","authors":"","doi":"10.1016/j.apenergy.2024.124562","DOIUrl":"10.1016/j.apenergy.2024.124562","url":null,"abstract":"<div><div>With the increase of voltage level in energy-storage and power battery system, the electrical safety phenomenon of battery systems has received extensive attention. The issue of series arcs caused by electrical failure such as loose connections in battery systems has become increasingly serious. However, research on series arcs in battery systems is still in its early stages. Therefore, to investigate arc-related disasters in batteries, this study establishes an experimental platform to simulate series arc faults. Taking positive electrode terminal arcs as the focused point, this study explores the evolutionary patterns of battery-related arcs and under different conditions, and analyzes the hazardous effects on batteries. The results indicate that stable arcs can be generated in batteries with different states of charge (SOC) when the system voltage is 200 V and the circuit current is 2C. At the same time, the arc can melt the battery casing to form holes, leading to electrolyte leakage, and triggering battery short-circuit and open-circuit failures. The research findings of this study fill a gap in the field of battery system arc safety and are of vital importance for enhancing the safety performance of arc protection.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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