Generation Expansion Planning Considering the Output and Flexibility Requirement of Renewable Energy: The Case of Jiangsu Province

Lv, Tao; Yang, Qi; Deng, Xu; Xu, Jie; Gao, Jianliang

doi:10.3389/fenrg.2020.00039

Cited by 13 publications

(3 citation statements)

References 22 publications

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“…Table 2 summarizes the articles found and characterizes them according to the method used on the GEP problem, the externalities considered, and the approach used for the inclusion of these externalities. Externalities can be considered in the model as a restriction [59,75,79], or they can be included in the cost function to be optimized (e.g., [40,63]). In other cases, a mixed approach is used and some externalities are addressed as restrictions, and others are included in the objective function [58,66].…”

Section: Resultsmentioning

confidence: 99%

A Review on the Internalization of Externalities in Electricity Generation Expansion Planning

Costa

Ferreira

2023

Energies

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This work addresses the internalization of externalities in energy decision making and in generation expansion planning (GEP). Although the linkage between externalities and energy is well recognized, the issue of the internalization in GEP models and from a sustainability perspective is still far from being fully explored. A critical literature review is presented, including scientific articles published in the period from 2011 to 2021 and selected from scientific databases according to a set of pre-defined keywords. The literature is vast and quite heterogeneous in the models and methods used to deal with these externalities, and therefore a categorization of these studies was attempted. This categorization was based on the methods used, the geographical scope, the externalities included in the planning model and the strategies for their inclusion. As a result, it was possible to perceive that most studies tend to focus on the internalization of externalities related to CO2 and equivalent emissions from a national perspective. Departing from the critical analysis, pathways for future research were presented, highlighting the need to improve the internalization of social externalities to overcome environmental and economic bias, and also highlighting the importance of recognizing regional specificities and development priorities.

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Section: Resultsmentioning

confidence: 99%

A Review on the Internalization of Externalities in Electricity Generation Expansion Planning

Costa

Ferreira

2023

Energies

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“…Owing to the declining costs of solar photovoltaic (PV) modules and government support, solar PV has experienced rapid growth in recent decades [3], reaching a global installed capacity of approximately 1062 GW by the end of 2022 [4]. In this context, accurate information on the location, shape, and size of PV arrays is meaningful and essential for optimizing power system planning [5],…”

Section: Introductionmentioning

confidence: 99%

Impact of Deep Convolutional Neural Network Structure on Photovoltaic Array Extraction from High Spatial Resolution Remote Sensing Images

Li,

Lu,

Jiang

et al. 2023

Remote Sensing

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Accurate information on the location, shape, and size of photovoltaic (PV) arrays is essential for optimal power system planning and energy system development. In this study, we explore the potential of deep convolutional neural networks (DCNNs) for extracting PV arrays from high spatial resolution remote sensing (HSRRS) images. While previous research has mainly focused on the application of DCNNs, little attention has been paid to investigating the influence of different DCNN structures on the accuracy of PV array extraction. To address this gap, we compare the performance of seven popular DCNNs—AlexNet, VGG16, ResNet50, ResNeXt50, Xception, DenseNet121, and EfficientNetB6—based on a PV array dataset containing 2072 images of 1024 × 1024 size. We evaluate their intersection over union (IoU) values and highlight four DCNNs (EfficientNetB6, Xception, ResNeXt50, and VGG16) that consistently achieve IoU values above 94%. Furthermore, through analyzing the difference in the structure and features of these four DCNNs, we identify structural factors that contribute to the extraction of low-level spatial features (LFs) and high-level semantic features (HFs) of PV arrays. We find that the first feature extraction block without downsampling enhances the LFs’ extraction capability of the DCNNs, resulting in an increase in IoU values of approximately 0.25%. In addition, the use of separable convolution and attention mechanisms plays a crucial role in improving the HFs’ extraction, resulting in a 0.7% and 0.4% increase in IoU values, respectively. Overall, our study provides valuable insights into the impact of DCNN structures on the extraction of PV arrays from HSRRS images. These findings have significant implications for the selection of appropriate DCNNs and the design of robust DCNNs tailored for the accurate and efficient extraction of PV arrays.

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“…Consequently, the ability to accurately and efficiently acquire the installation locations, distribution, and total area of distributed photovoltaic power stations over a wide range is of importance to energy companies, governmental departments, and investors. For example, obtaining information of distributed photovoltaic power stations can help optimize power system planning [12]. The information of distributed photovoltaic power stations and solar irradiance data of building surfaces can be combined to predict the power generation potential [13].…”

Section: Introductionmentioning

confidence: 99%

Combined Multi-Layer Feature Fusion and Edge Detection Method for Distributed Photovoltaic Power Station Identification

Jie

Yue

et al. 2020

Energies

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Distributed photovoltaic power stations are an effective way to develop and utilize solar energy resources. Using high-resolution remote sensing images to obtain the locations, distribution, and areas of distributed photovoltaic power stations over a large region is important to energy companies, government departments, and investors. In this paper, a deep convolutional neural network was used to extract distributed photovoltaic power stations from high-resolution remote sensing images automatically, accurately, and efficiently. Based on a semantic segmentation model with an encoder-decoder structure, a gated fusion module was introduced to address the problem that small photovoltaic panels are difficult to identify. Further, to solve the problems of blurred edges in the segmentation results and that adjacent photovoltaic panels can easily be adhered, this work combines an edge detection network and a semantic segmentation network for multi-task learning to extract the boundaries of photovoltaic panels in a refined manner. Comparative experiments conducted on the Duke California Solar Array data set and a self-constructed Shanghai Distributed Photovoltaic Power Station data set show that, compared with SegNet, LinkNet, UNet, and FPN, the proposed method obtained the highest identification accuracy on both data sets, and its F1-scores reached 84.79% and 94.03%, respectively. These results indicate that effectively combining multi-layer features with a gated fusion module and introducing an edge detection network to refine the segmentation improves the accuracy of distributed photovoltaic power station identification.

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Generation Expansion Planning Considering the Output and Flexibility Requirement of Renewable Energy: The Case of Jiangsu Province

Cited by 13 publications

References 22 publications

A Review on the Internalization of Externalities in Electricity Generation Expansion Planning

A Review on the Internalization of Externalities in Electricity Generation Expansion Planning

Impact of Deep Convolutional Neural Network Structure on Photovoltaic Array Extraction from High Spatial Resolution Remote Sensing Images

Combined Multi-Layer Feature Fusion and Edge Detection Method for Distributed Photovoltaic Power Station Identification

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