论文标题
产生长期连续的多类产生曲线
Generating Long-term Continuous Multi-type Generation Profiles
论文作者
论文摘要
如今，采用新技术已大大提高了电力系统动态。大多数公用事业公司基于离散功率水平（例如峰值或平均值）进行的传统长期计划研究不能反映系统动态，并且通常无法准确预测系统的可靠性缺陷。结果，需要长期的未来连续概况，例如8760小时概况，以实现基于时间序列的长期计划研究。但是，与用于操作研究的短期概况不同，可以产生可以反映历史时变特征和未来预期功率幅度的长期连续概况非常具有挑战性。当前的方法（例如平均分析）具有主要缺点。为了解决这一挑战，本文提出了一种完全新颖的方法，以生成多种一代类型的此类概况。提出了一个多级轮廓合成过程，以捕获不同时间水平的时变特征。根据公共数据集对所提出的方法进行了评估，并证明了长期连续多类型生成概况的良好性能和应用价值。
Today, the adoption of new technologies has increased power system dynamics significantly. Traditional long-term planning studies that most utility companies perform based on discrete power levels such as peak or average values cannot reflect system dynamics and often fail to accurately predict system reliability deficiencies. As a result, long-term future continuous profiles such as the 8760 hourly profiles are required to enable time-series based long-term planning studies. However, unlike short-term profiles used for operation studies, generating long-term continuous profiles that can reflect both historical time-varying characteristics and future expected power magnitude is very challenging. Current methods such as average profiling have major drawbacks. To solve this challenge, this paper proposes a completely novel approach to generate such profiles for multiple generation types. A multi-level profile synthesis process is proposed to capture time-varying characteristics at different time levels. The proposed approach was evaluated based on a public dataset and demonstrated great performance and application value for generating long-term continuous multi-type generation profiles.
