رویکرد مبتنی بر GIS برای تجزیه و تحلیل پتانسیل تولید PV خورشیدی در مقیاس منطقه ای : یک مطالعه موردی از استان فوجیان
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|18749||2013||12 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy Policy, Volume 58, July 2013, Pages 248–259
Spatial variation of solar energy is crucial for the estimation of the regional potential and selection of construction location. This paper presents a case study of using high resolution grid map of solar radiation combined with the other restriction factors to evaluate the comprehensive potential analysis of solar PV generation at the regional scale, in order to present a framework of decision support tool for solar energy management in a regional area. The cost of PV generation is calculated based on the geographical distribution of technical potential. Moreover, geospatial supply curve (GSC) is employed to portray the evolution of available potential of photovoltaics (PV) generation with the increase of the generation cost. By integrating the economic evaluation variables of net present value and simple payback period, grid-based economic feasibility of PV generation project is then carried out under two feed-in-tariff scenarios. Finally, total CO2 reduction potential and its spatial distribution in the study area are calculated. The results confirm that PV technology provides high potential for roof-top application and large-scale PV stations. Additionally, determining a reasonable feed-in tariff is essential for expanding the application of solar PV energy. The findings improve understanding of regional renewable energy strategies and the supply/demand assessment.
Most Chinese cities currently experience rapid urbanization and economic growth. Therefore, improvement in energy efficiency and promotion of clean and renewable energy development might play the most important role in energy conservation and greenhouse gas (GHG) reduction (Lin et al., 2010 and Xiao et al., 2011). Solar power is the conversion of sunlight into electricity, directly using PV, or indirectly using concentrated solar power (CSP). PV electricity is one of the best options for sustainable future energy requirements of the world. At present, the PV market is growing globally at an annual rate of 35–40%, with PV production around 10.66 GW in 2009 (Razykov et al., 2011). In the same year, China has newly installed PV capacity of 160 MW, and with the total installed capacity of 300 MW. China is about to raise its 2015 goal for solar photovoltaic (PV) power to 10 GW in the newly submitted “Development Plan for Renewable Energy during the 12th Five-Year Period” (Xu, 2011). If it is realized, China′ PV market will usher in an era of speeding up development. The market development of solar energy is strongly dependent on the policy, technology development and transfer, economics of solar energy products, and the local solar energy resource. It is necessary to integrate all these influencing factors to analyze the potential of solar energy as a source for producing electricity and plan the exploitation of solar energy in a given area. Spatial information technologies, particularly Geographical Information Systems (GIS), have been widely used in evaluating the feasibility of solar power stations in a given region and identifying optimizing locations. During the last decade, considerable effort has been expended to obtain Decisions Support Systems (DSS) tools in order to facilitate renewable energy at a regional scale (Domínguez and Amador, 2007). The main objectives of such studies were to evaluate the potential of renewable energy resources through integrating data of various constraints factors. For example, Hoogwijk (2004) presented a comprehensive analysis using a grid cell approach to assess the geographical, technical and economic potential of renewable energies at the regional and global scales. Clifton and Boruff (2010) integrated the local environmental variables and electricity infrastructure on a high resolution grid to identify the potential for CSP to generate electricity in a rural region of Western Australia. Charabi and Gastli (2011) assessed the land suitability for large PV farms implementation in Oman using GIS-based spatial fuzzy multi-criteria evaluation. Janke (2010) identified areas that are suitable for wind and solar farms using multi-criteria GIS modeling techniques in Colorado. Additionally, rooftop PV is a main application form of distributed solar generation in built-up area. In order to estimate the rooftop PV potential for a large-scale geographical region, various modeling technologies have been developed in recent studies. Wiginton et al. (2010) demonstrated techniques to merge the capabilities of GIS analysis and object-specific image recognition to determine the available rooftop area for PV deployment. Kabir et al. (2010) identified and calculated bright roof-tops of Dhaka Megacity from Quickbird high-resolution optical satellite imagery in order to assess power generation potential through solar photovoltaic applications. Liu et al. (2010) built a model with taking both natural and social restriction factors of solar resources into consideration to evaluate the available roof-mounted solar energy resource in Jiangsu Province. Due to the higher development cost of solar energy, economic feasibility is very critical for implementing regional solar energy projects. Sun et al. (2011) studied the economic and environmental benefits of the grid-connected PV power generation system in China’ 34 province capital cities using the net present value and the single factor sensitivity analysis tools. Ramadhan and Naseeb (2011) determined the economic feasibility and viability of implementing PV solar energy in the State of Kuwait. The cost analysis showed that when the value of saved energy resources was used in producing traditional electricity, and the cost of lowering CO2 emissions were accounted for, the true economic cost of the levelized cost of electricity (LCOE) of a PV system would decline significantly. Poullikkas (2009) carried out a feasibility study in order to investigate whether the installation of a parabolic trough solar thermal technology for power generation in the Mediterranean region was economically feasible. His case study took into account the available solar potential for Cyprus, as well as all available data concerning current renewable energy sources policy of the Cyprus Government. However, the study did not take the spatial variability of solar radiation into account. GIS is a power tool to perform spatial multi-criteria decision analysis integrating geographical spatial data for a comprehensive feasibility assessment of solar energy potential at the regional scale. The solar energy potential evaluation and economical feasibility analysis need to be evaluated together to identify the areas that have economically competitive renewable resources. Spatial explicit assessment of solar radiation is a key element of improved feasibility methodology framework present here. To integrate the potential evaluation and economical analysis for solar energy, this study developed a grid-based comprehensive potential analysis framework of solar energy at the regional scale for technical users and economic decision-makers. A case study of the approach is implemented for Fujian Province, China.
نتیجه گیری انگلیسی
This paper presented a computational procedure to derive a regional model of solar PV generation potential and its economic feasibility with the aid of the solar radiation analysis tool and map algebra functionality in the ArcGIS software. The work is innovative in integrating the physical and economical variables for a feasibility analysis on the regional scale. Meanwhile, numerous geographical factors, technology and cost data as well as the policy scenarios have been taken into account. This framework should be used by policy makers, investors, and maximum utilities of solar energy. The application of the methodology in the case study showed that very high technical potential of PV electricity generation is available in Fujian Province. Spatial variability of PV energy output is larger, and it is highly correlated with the local solar energy resource. From the technical application point of view, decentralized roof-top PV systems should be the main solar energy utility in the coastal regions of Fujian, while the regions of Zhangzhou and interior west of Fujian are suitable to construct large-scale grid-connected PV power plants. The improvement the efficiency of PV power system and raising the popularizing ratio of building roof PV utility could greatly increase the total technical potential of solar energy. The present unit cost of PV electricity generation is more than 0.16 $/kW h, which is far higher than the average tariff of Fujian. So the solar energy development still depends on the energy policy and financial subsidies to the great extent. The main investment risks for project investors come from the absence of incentive mechanism like no fixed FIT for PV generation in China or Fujian. The results from economic feasibility analysis showed: when the FIT of PV electricity is assumed to be 0.19 $/kW h, the extent of suitable land for PV utility is large enough to cover the present total electricity consumption for Fujian Province. Based on this assumed FIT, investors of PV electricity projection could have a reasonable benefit for some regions which have high solar energy resource. Therefore, our study suggests that the FIT of 0.19 $/kW h is an appropriate subsidy level at this stage in order to achieve the goal of installing 100MWp of PV capacity by the end of 2015. PV electricity also has the great potential to mitigate CO2 emissions as an alternative energy of conventional energy. In these terms, the economic and environmental benefits of PV electricity are significant for Fujian Province. The cost of PV system is continuing to fall, but requires policy and program support to assist it in bridging the gap between financial and infrastructure resource, to build a sustainable PV industry sector.