Spatio-temporal coupling suitability of solar energy resources and distributed photovoltaic power generation projects in Beijing
Vol 4, Issue 1, 2021
VIEWS - 1075 (Abstract) 339 (pdf)
Abstract
Distributed photovoltaic power generation projects contribute to the coordinated and sustainable development of “energy—economy—environment”. The spatio-temporal coupling relationship between regional solar resources and distributed photovoltaic power generation projects is studied. Taking Beijing area as the research object, a variety of spatial analysis methods are proposed to explore the relationship between solar resources and distributed photovoltaic power generation projects from a new perspective of spatial geography. The spatio-temporal coupling suitability of regional solar energy resources and distributed photovoltaic power generation project development area was scientifically evaluated. The research results can provide suggestions and decision support for optimizing the timing of regional development of photovoltaic power generation, and also provide reference for planning and layout optimization of photovoltaic power generation projects in the Beijing-Tianjin-Hebei region and other regions.
Keywords
Full Text:
pdfReferences
1. Zhang S, Fu H. Study on the incentive policy of distributed solar PV power in Beijing. Journal of North China Electric Power University (Social Science Edition) 2018; (6): 31–37.
2. Zhao Z, Fan W. Beijing shi kezaisheng nengyuan ziyuan Fengdu pingjia yu kongjian xiangguanxing fenxi (Chinese) [Abundance evaluation and spatial correlation analysis of renewable energy resources in Beijing]. Rural Electrification 2020; (6): 59–64.
3. Qi X, Wang M, An L, et al. Evaluation on development level of provincial renewable energy power generation projects in China. Renewable Energy Resources 2019; 37(6): 907–913.
4. Li G, Zhang Y, Xia L, et al. The feasibility study of distributed photovoltaic power generation system in Western Region of China. Advanced Materials Research 2015; 1070: 64–7.
5. Lu X, Lin D, Fan P, et al. Revenue budget of distributed PV power project. Solar Energy 2020; (3): 24–28.
6. Tran NH, Do CT, Hong CS, et al. Coordinated colocation datacenters for economic demand responce. ACM SIGMETRICS Performance Evaluation Review 2015; 43(3): 34–37.
7. Liu R, Liu Y, Jing Z. Impact of industrial virtual power plant on renewable energy integration. Global Energy Interconnection 2020; 3(6): 545–552.
8. Jo HC, Kim JY, Byeon G, et al. Optimal scheduling method of community microgrid with customer-owned distributed energy storage system. 2019 International Conference on Smart Energy Systems and Technologies (SEST); 2019 Sept 9–11; Porto, Portugal. IEEE; 2019. p. 1–6.
9. Zhao Z, Yuan S. Risk clustering analysis of regional wind power absorption based on spatial statistical model. Renewable Energy Resources 2020; 38(2): 225–232.
10. Jie C, Ren B, Wu K. The study of rainfall isosurface generation method. Electronic Design Engineering 2015; (16): 102–104.
DOI: https://doi.org/10.24294/nrcr.v4i1.1553
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 Zhenyu Zhao, Yujia Yang
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This site is licensed under a Creative Commons Attribution 4.0 International License.