Study on the Simulation Method for Photovoltaic Power Output Series Based on Headroom Model

Hong Dong¹Yuqun Gao¹Liujun Hu¹Yanna Gao¹Yue Xing^2*

• ¹Guangzhou Power Supply Bureau, Guangdong Power Grid Co., LTD., Guangdong, China
• ²Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu, China

Existing photovoltaic (PV) output simulation methods often rely on artificial neural networks for short-term forecasting, or the struggle to capture long-term patterns and stochastic fluctuations when using Markov Chain Monte Carlo (MCMC) techniques. To address these limitations, this paper proposes an improved headroom model-based approach that enhances traditional methods in three key aspects. First, unlike traditional headroom models that ignore temporal dependencies in output fluctuations, the approach integrates probabilistic distributions with soft sequential constraints to preserve time-dependent patterns. Second, whereas previous studies often overlook seasonal weather variations, PV output curves are classified into representative weather types and construct seasonally adaptive Markov chains to model radiation dynamics and transition probabilities. Third, to address the oversimplification of sunrise and sunset transitions, method introduces a specialized statistical correction tailored to these critical periods. The method accurately models PV output patterns and fluctuations, demonstrating <1% deviation in annual duration (4121h) and utilization (1297h), with 7.80-14.59% lower RMSE and 10.27-14.07% reduced MAE versus conventional methods. It efficiently generates realistic long-term sequences from limited data, enhancing the accuracy and efficiency of PV power sequence simulation.