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

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.

Keywords

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