Study on a simulation method for photovoltaic power output series based on the headroom model

Existing photovoltaic (PV) output simulation methods often rely on artificial neural networks for short-term forecasting, and there has been a struggle to capture long-term patterns and stochastic fluctuations when using Markov Chain Monte Carlo 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 overlooked seasonal weather variations, here PV output curves are classified into representative weather types and seasonally adaptive Markov chains are constructed to model radiation dynamics and transition probabilities. Third, to address the oversimplification of sunrise and sunset transitions, the 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 (4,121 h) and utilization (1,297 h), with a 7.80%−14.59% lower root mean square error and 10.27%−14.07% reduced mean absolute error vs. conventional methods. It efficiently generates realistic long-term sequences from limited data, enhancing the accuracy and efficiency of PV power sequence simulation.