基于天气分型的区域风光出力互补规律评价与并网容量优化方法

Evaluation of regional wind and photovoltaic power output complementarity and grid-connected capacity optimization method based on weather type classification

摘要

【目的】风光互补发电存在波动性和间歇性，给系统运行的稳定性和效率带来巨大挑战。现有研究多单一分析风光出力或容量配置，缺乏基于天气分型的综合风光出力互补规律评价与并网容量优化的研究。【方法】针对此问题，研究基于青海省2020年气象数据，计算并归一化处理风光出力并构建风光出力矩阵。采用K-means算法划分天气类型，通过波动互补率和爬坡互补率指标，分别从波动性和爬坡性两个方面评估不同天气类型下的风光出力互补特性。此外，从多个时间尺度对区域的逐日、逐月风光互补特性开展定量评价。最后，建立风光并网容量比例优化模型，采用蛇鹫优化算法和枚举法相互参证的方式对波动互补率和爬坡互补率进行优化，以确定最佳风光并网容量比例。【结果】结果表明：(1)青海地区的风光出力季节差异显著，夏秋季节两者之间具有良好的互补性，可以形成有效的互补发电系统。(2)不同天气类型对风光互补性影响显著，晴朗天气下风光互补性较弱，多云或突变天气下互补性相对较强。(3)所有天气类型的爬坡互补率均值为20.74%,远高于波动互补率的均值1.84%,表明风光联合出力可以有效降低出力爬坡率，增强系统的可靠性。(4)不同天气类型下，优化风光并网容量比例可以最大化互补率指标，实现最佳互补效果。【结论】研究结果能够有效揭示不同天气类型下的风光出力互补特性，并确定最佳风光并网容量比例，为风光互补发电系统的规划、建设和运行提供科学依据。

Abstract

[Objective] Wind and photovoltaic(PV) complementary power generation is characterized by volatility and intermittency, which poses significant challenges to the stability and efficiency of system operation. Existing research often focuses on the analysis of wind and PV power output or capacity configuration in isolation, and there is a lack of research on the evaluation of the complementarity of wind and PV power output and the optimization of grid-connected capacity based on weather type classification. [Methods] To address this issue, wind and PV power outputs were calculated and normalized using meteorological data from Qinghai Province in 2020, and the wind and PV power output matrix was constructed. The K-means clustering algorithm was employed to classify weather types, and the indicators of complementary volatility rate and ramp rate were used to assess the complementary characteristics of wind and PV power output under different weather types from the aspects of volatility and ramping. In addition, the daily and monthly wind and PV complementary characteristics of the region were quantitatively evaluated from multiple time scales. Finally, the optimization model for the wind and PV grid-connected capacity ratio was established. The secretary bird optimization algorithm and enumeration method were used to optimize the complementary volatility rate and ramp rate to determine the optimal wind and PV grid-connected capacity ratio. [Results] The result showed that:(1) there was a significant seasonal difference in wind and PV power output in Qinghai, with good complementarity between the two in summer and autumn, making it possible to form an effective complementary power generation system.(2) Different weather types had a significant impact on the complementarity of wind and PV power, with weaker complementarity in sunny weather and relatively stronger complementarity in cloudy weather or abrupt weather change.(3) The average complementary ramp rate of all weather types was 20.74 %, much higher than the average complementary volatility rate of 1.84 %, indicating that the combined output of wind and PV power could effectively reduce the power ramp rate and enhance the reliability of the system.(4) Under different weather types, optimizing the wind and PV grid-connected capacity ratio could maximize the indicator of complementary rate and achieve the best complementary effect. [Conclusion] The research findings effectively reveal the complementary characteristics of wind and PV power output under different weather types and determine the optimal wind and PV grid-connected capacity ratio, which provides a scientific basis for the planning, construction, and operation of wind and PV complementary power generation system.

关键词

风光出力互补特性 / 天气分型 / 风光并网容量比例优化 / K-means聚类 / 蛇鹫优化算法 / 影响因素

Key words

complementary characteristics of wind and photovoltaic power output / weather classification / wind and photovoltaic grid-connected capacity ratio optimization / K-means clustering / secretary bird optimization algorithm / influencing factors

基金资助

中国电力建设股份有限公司科技项目(DJ-ZDXM-2022-10)

中央高校基本科研业务费专项资金资助(B240201123)

国家自然科学基金(52009029)