A GIS-based assessment of large-scale PV potential in China

Huang, Tianyue; Wang, Saige; Yang, Qing; Li, Jiashuo

doi:10.1016/j.egypro.2018.09.126

Cited by 38 publications

(13 citation statements)

References 10 publications

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“…Literature describing PV potential assessment based on remote sensing images has previously been published. Joshi et al [78] presented a high-resolution global assessment of rooftop solar photovoltaics potential using big data, machine learning and geospatial analysis; Huang et al [79] proposed a GIS-based approach for the assessment of large-scale PV potential in China; Hou et al [80] proposed a deep learning framework named SolarNet, designed to perform semantic segmentation on large-scale satellite imagery data for the development of solar farms; Plakman et al [81] developed an object-based random forest (RF) classification approach using public satellite images to develop large-scale solar parks. However, all of the above studies were quantitative assessments for low-resolution and large-scale areas, and do not reflect the actual PV carbon reduction potential of a specific region in any detailed manner.…”

Section: The Difference Between Existing Work and Our Approachmentioning

confidence: 99%

Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

et al. 2022

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Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Effective assessment of carbon emission reduction has become an urgent challenge for the government and for business enterprises. In this study, we propose a method to assess accurately the potential reduction of long-term carbon emission by installing solar PV on rooftops. This is achieved using the joint action of GF-2 satellite images, Point of Interest (POI) data, and meteorological data. Firstly, we introduce a building extraction method that extends the DeepLabv3+ by fusing the contextual information of building rooftops in GF-2 images through multi-sensory fields. Secondly, a ridgeline detection algorithm for rooftop classification is proposed, based on the Hough transform and Canny edge detection. POI semantic information is used to calculate the usable area under different subsidy policies. Finally, a multilayer perceptron (MLP) is constructed for long-term PV electricity generation series with regional meteorological data, and carbon emission reduction is estimated for three scenarios: the best, the general, and the worst. Experiments were conducted with GF-2 satellite images collected in Daxing District, Beijing, China in 2021. Final results showed that: (1) The building rooftop recognition method achieved overall accuracy of 95.56%; (2) The best, the general and the worst amount of annual carbon emission reductions in the study area were 7,705,100 tons, 6,031,400 tons, and 632,300 tons, respectively; (3) Multi-source data, such as POIs and climate factors play an indispensable role for long-term estimation of carbon emission reduction. The method and conclusions provide a feasible approach for quantitative assessment of carbon reduction and policy evaluation.

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Section: The Difference Between Existing Work and Our Approachmentioning

confidence: 99%

Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

et al. 2022

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“…The major technical element used in this study's geographical analysis is sunlight hours, also known as solar radiance. The amount of sunlight hours is the most important factor in determining whether sites will get adequate solar radiation for a power plant [19]. The yearly distribution of sunlight hours and average sunshine…”

Section: Figure 1 Criteria Of Site Selectionmentioning

confidence: 99%

Determining Optimal Solar Power Plant Location in Melaka, Malaysia: A GIS-Based Solutions

Zulkifly¹,

Said²

2022

IOP Conf. Ser.: Earth Environ. Sci.

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The energy demand has risen dramatically in recent years as a result of population growth and the fast expansion of the global economy. Due to rising carbon dioxide (CO2) emissions and increasing energy needs, Malaysia has recently intensified its efforts to encourage the development of renewable energy supplies. Despite the abundance of solar resources, the PV growth in underdeveloped countries such as Malaysia is moving slower. Malaysia has the advantage in solar energy generation since it is geographically close to the equator and has a significant solar generation potential due to its hot and sunny weather all year. Therefore the question here is, where is the most suitable place to build more solar power plants? What methods will we be using to find the most suitable place to build the solar power plant? In picking a place for such development, several factors must be examined, such as how effective the PV power station site is and how to decrease the overall cost of the project by minimising proximity to existing infrastructures while improving solar panel power production. A geographic information system (GIS) is a tool that can help to solve this challenge. In this case, we integrate economic, environmental, and technical variables such as solar radiation intensity, local physical terrain, environment, climate, and placement criteria such as distance from roads and rivers. As additional input factors, other geographical information data were used (solar radiation, digital elevation models (DEM), land cover, and temperature). Further analysis using the Analytic Hierarchy Process (AHP) based Multi-Criteria Decision Making (MCDM) was then applied to this study to get the suitable location based on the importance of the criteria. In order to build a cost-effective and high-performing solar project, a complete solar site assessment is required. The results of the study should give a decision support system model and map on determining the optimal locations of solar power plants in Melaka.

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“…The connection cost is calculated based on the proximity to the major road and grid, which is around USD 208,000 per km for grid connection [36] and USD 196,000 per km for road connection [37]. CRF is the Capital Recovery Factor, which converts the current total investment cost to the equal annual cost during the period of a lifetime [18], which can be estimated as follows:…”

Section: Economic Assessment Factorsmentioning

confidence: 99%

“…Therefore, the use of geographical information systems (GIS) to aid the development of the solar energy sector has attracted increasing attention as the GIS can be used to perform inexpensive site suitability analysis [6], required for the determination of the best location for a PV farm installation via common analytic hierarchy process (AHP) algorithms [7,8], and multi-decision-criteria analysis (MDCA) techniques [9,10], considering largely diverse climatological, topographic, and societal conditioning factors, as seen in the arid and semi-arid regions of Iran [11] and Saudi Arabia [12], or the area of Cartagena-Murcia in the southwest region of Spain [13], or the city of Oujda at the Eastern region of Morocco [14], the city of Rethimno at the north coast of the Greek island of Crete [15], and the Karapinar region of Konya in Turkey [16]. Likewise, these techniques have been shown to be highly successful in the integration of economic factors for suitability studies of the large-scale development and utilization of solar energy resources, where optimal locations can also be found by using GIS, AHP, and MDCA techniques adapted to the specific conditions (criteria) of countries such as China [17][18][19], where it has been recently found that the province of Xinjiang is the most optimal site for large-scale photovoltaic station construction according to their calculated Levelized Cost of Energy (LCOE) [20], or the positive LCOE trends found at the sovereign state of Bahrain in the Persian Gulf, which indicates that large-scale photovoltaics in this region is a viable alternative for meeting their future electricity demand [21]. A similar trend can be found in the 2014 study for the technical and economic potential of solar energy at Indonesia, which, at the time, predicted a payback period of 11 to 17 years [22], similar to what was predicted for the province of Elazig in Turkey [23].…”

Section: Introductionmentioning

confidence: 99%

GIS-Based Assessment of the Technical and Economic Feasibility of Utility-Scale Solar PV Plants: Case Study in West Kalimantan Province

et al. 2020

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This paper presents a technical and economic feasibility assessment of utility-scale solar photovoltaic (PV) plants in the West Kalimantan Province of Borneo, which is essential for boosting the development of solar PV plants in Indonesia. The assessment was performed based on a previously developed geographical information systems (GIS) package that integrates satellite-derived data of solar irradiation with locally obtained data such as land usage, topography, road lines, and an electrical network. For the evaluation of the technical and economic feasibility, annual energy production and electrical cost were calculated using an analysis tool that was integrated into a GIS package. The results show that more than 96% of the exploitable land that covers the area of 49,859 km2 is available for the development of solar PV plants, with an annual energy production higher than 180 GWh/km2 and an electricity cost lower than 0.05 USD/kWh, indicating the attractiveness of utility-scale solar PV plant development in West Kalimantan Province. A further detailed assessment of optimal sites shows that the selected sites are technically and economically feasible for the development of utility-scale solar PV plants. The approaches and results of this research should be valuable for energy planners, developers, and policy makers to set the strategies for promoting the development of utility-scale solar PV plants in pro of the sustainable development of Indonesia.

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A GIS-based assessment of large-scale PV potential in China

Cited by 38 publications

References 10 publications

Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

Determining Optimal Solar Power Plant Location in Melaka, Malaysia: A GIS-Based Solutions

GIS-Based Assessment of the Technical and Economic Feasibility of Utility-Scale Solar PV Plants: Case Study in West Kalimantan Province

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