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首页> 外文期刊>Energy Reports >The 6th International Conference on Power and Energy Systems Engineering (CPESE 2019), 20–23 September 2019, Okinawa, Japan Reproducing solar curtailment with Fourier analysis using Japan dataset
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The 6th International Conference on Power and Energy Systems Engineering (CPESE 2019), 20–23 September 2019, Okinawa, Japan Reproducing solar curtailment with Fourier analysis using Japan dataset

机译:第六次国际电力和能源系统会议(CPESE 2019),2019年9月20日至23日,日本冲绳,使用日本数据集再生傅里叶分析太阳缩减

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摘要

Curtailment of variable renewable energy increases the Levelized Cost of Energy (LCOE), which is the tool often used to compare its profitability against traditional energy sources. Recently, the Kyushu Region of Japan had to curtail some of its solar production to meet energy balance. As many countries increase their solar energy production, curtailment will be inevitable. It is therefore important to develop methodologies to calculate it. In the case of Japan, curtailment can easily be estimated using hourly data. However, such data is unavailable in other countries. In this study, a methodology to reproduce curtailment using known periodicity and statistical data is presented. Insights were initially generated by simulating future curtailment scenarios of Kyushu to extract the factors that affect curtailment. Fourier analysis was used to identify the periodicity of demand and solar production. The Fourier representation was simplified using the identified factors. Along with statistical data, the demand and solar data were approximated and the curtailment was reproduced. Results show that curtailment can be closely reproduced using the proposed methodology on a yearly and monthly level. Further research is necessary to test the methodology for other conditions like having different climate, varying daily fluctuations, and other human-related fluctuations.
机译:可变可再生能源的缩减会增加能量(LCoE)的均衡成本,该工具通常用于比较其对传统能源的盈利能力的工具。最近,日本九州地区不得不削减一些太阳能生产来满足能源平衡。由于许多国家增加了太阳能生产,缩减将是不可避免的。因此,制定方法计算方法是很重要的。在日本的情况下,可以使用每小时数据轻松估算缩减。但是,在其他国家/地区,此类数据不可用。在本研究中,呈现了一种使用已知周期性和统计数据再现缩减的方法。最初通过模拟九州的未来削减方案来提取影响影响缩减的因素来生成洞察力。傅立叶分析用于确定需求和太阳能生产的周期性。使用所识别的因素简化了傅立叶表示。随着统计数据,需求和太阳能数据近似,再现缩减。结果表明,可以在每月和每月水平使用所提出的方法密切复制缩减。进一步的研究是为了测试具有不同气候,不同每日波动和其他与人类相关波动的其他条件的方法。

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