Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,ru...Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.展开更多
Green roofs play a vital role in promoting sustainable urban development and achieving carbon neutrality by enhancing carbon sequestration, oxygen release, and efficiency of land use. Despite these benefits, living ro...Green roofs play a vital role in promoting sustainable urban development and achieving carbon neutrality by enhancing carbon sequestration, oxygen release, and efficiency of land use. Despite these benefits, living roof coverage in China remains limited. To address the challenges in policy formulation, operational monitoring, and the absence of multi-scale retrofit strategies supported by robust assessment methods, this study develops a comprehensive evaluation framework. The framework integrates vector data, building age information, and point-of-interest(POI) data, and applies an optimized Prophet model to classify six major climate zones. This approach facilitates the selection of appropriate plant species and substrates while quantifying the potential for carbon sequestration and oxygen release. An assessment of 90 cities reveals approximately 1.3861 billion square meters of rooftop area suitable for green roof implementation, with an estimated annual carbon sequestration potential of 67.30 million tons and oxygen release of 30.36 million tons. Commercial buildings contribute significantly, comprising 65% of the total suitable area. Climate zones 2 and 3 exhibit the most favorable outcomes. The current study provides a reliable quantitative reference for evaluating the carbon sequestration and oxygen release capacities of green roofs and supports the formulation of effective retrofit policies.展开更多
基金supported by the State Grid Science&Technology Project of China(5400-202224153A-1-1-ZN).
文摘Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.
文摘Green roofs play a vital role in promoting sustainable urban development and achieving carbon neutrality by enhancing carbon sequestration, oxygen release, and efficiency of land use. Despite these benefits, living roof coverage in China remains limited. To address the challenges in policy formulation, operational monitoring, and the absence of multi-scale retrofit strategies supported by robust assessment methods, this study develops a comprehensive evaluation framework. The framework integrates vector data, building age information, and point-of-interest(POI) data, and applies an optimized Prophet model to classify six major climate zones. This approach facilitates the selection of appropriate plant species and substrates while quantifying the potential for carbon sequestration and oxygen release. An assessment of 90 cities reveals approximately 1.3861 billion square meters of rooftop area suitable for green roof implementation, with an estimated annual carbon sequestration potential of 67.30 million tons and oxygen release of 30.36 million tons. Commercial buildings contribute significantly, comprising 65% of the total suitable area. Climate zones 2 and 3 exhibit the most favorable outcomes. The current study provides a reliable quantitative reference for evaluating the carbon sequestration and oxygen release capacities of green roofs and supports the formulation of effective retrofit policies.
