| 基于地理探测器的广州市分行业月度用电特点及优化管理 |
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| 引用本文： | 张云涛,蔡国田,柯尚军,王文秀. 基于地理探测器的广州市分行业月度用电特点及优化管理[J]. 中国电力, 2021, 54(9): 125-134. DOI: 10.11930/j.issn.1004-9649.202004118 |
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| 作者姓名： | 张云涛 蔡国田 柯尚军 王文秀 |
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| 作者单位： | 1. 中国科学院 广州能源研究所，广东 广州 510640;2. 中国科学院 可再生能源重点实验室，广东 广州 510640;3. 中国科学院大学，北京 100049;4. 中国科学技术大学 纳米科学技术学院，江苏 苏州 215123 |
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| 基金项目： | 广东省软科学研究计划项目(2016A080803002)；广东省科技计划资助项目(2018A050501011)；广州市基础与应用基础研究项目(202002030189) |
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| 摘 要： | 为将广州市电力需求侧管理落实到具体行业,使用地理探测器、用电互补性模型分析2013—2017年共60个月各行业用电特点,在此基础上,通过情景分析方法,从行业、时间角度探索行业用电优化管理路径.基于广州市分行业月度用电特点,抓住行业用电量风向标子行业,利用存在可相互抵消用电波动的用电组合,研究发现:在2017年广州市用电...
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| 关 键 词： | 行业月度用电量 地理探测器 用电互补性 情景分析 优化管理 |
| 收稿时间： | 2020-04-15 |
| 修稿时间： | 2020-10-22 |
Monthly Electricity Consumption Characteristics of Industries in Guangzhou and Optimized Electricity Management Based on Geographical Detector |
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| ZHANG Yuntao,CAI Guotian,KE Shangjun,WANG Wenxiu. Monthly Electricity Consumption Characteristics of Industries in Guangzhou and Optimized Electricity Management Based on Geographical Detector[J]. Electric Power, 2021, 54(9): 125-134. DOI: 10.11930/j.issn.1004-9649.202004118 |
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| Authors: | ZHANG Yuntao CAI Guotian KE Shangjun WANG Wenxiu |
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| Affiliation: | 1. Guangzhou Institute of Energy Conversion, CAS, Guangzhou 510640, China;2. Key Laboratory of Renewable Energy, CAS, Guangzhou 510640, China;3. University of Chinese Academy of Sciences, Beijing 100049, China;4. Nano Science and Technology Institute, University of Science and Technology of China, Suzhou 215123, China |
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| Abstract: | To implement demand-side management (DSM) to specific industries, this paper analyzed electricity consumption characteristics of each industry within 60 months from 2013 to 2017, depending on the geographical detector and the electricity consumption complementarity model. On this basis, it explored the optimal management path of industry electricity consumption from the perspective of industry and time through scenario analysis. In light of the monthly electricity consumption characteristics of different industries in Guangzhou, the main driving sub-industries for electricity consumption in the industry were identified, and the electricity consumption combinations that can offset the fluctuation in electricity consumption were employed. The research finds that optimizing the industrial electricity consumption structure and upgrading the consumption time management considering the electricity consumption level of Guangzhou in 2017 can reduce the monthly peak–valley difference of Guangzhou electricity consumption by about 225 million kW·h. In the future, the monthly electricity consumption forecast of the industry, combined with the analysis of electricity complementarity, can be used to calculate the industry’s electricity dispatching potential, so as to provide data support for the improvement in demand-side management of electricity. |
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| Keywords: | monthly electricity consumption of industry geographical detector electricity complementarity scenario analysis optimal management |
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