基于随机模型预测控制的光伏系统最大功率点跟踪策略
Maximum Power Point Tracking Strategy for Photovoltaic System Based on Stochastic Model Predictive Control
摘要: 化石能源的储量有限且利用化石能源的过程会污染环境，太阳能作为一种清洁且大量存在的能量而广受关注。光伏系统由于受太阳辐照度、环境温度等随机因素的影响，往往很难实现最大功率点跟踪(maximum power point tracing, MPPT)。针对以上问题，本文提出了一种基于随机模型预测控制(SMPC)的光伏MPPT控制策略，以实现光伏系统在随机环境中的最大功率输出。首先建立了光伏系统受随机因素影响的非线性状态空间模型；然后提出了一种改进开普勒优化算法(IKOA)用于快速定位最大功率点；为应对太阳辐照度及环境温度对光伏系统带来的影响，本文设计了一种基于场景树的SMPC控制器：1) 提出一种场景树构建方法，以概率的方式表征太阳辐照度、环境温度等因素；2) 建立了马尔可夫跳变模型，提高场景构建及切换的准确性。基于场景的SMPC的光伏MPPT策略保证了光伏系统在太阳辐照度和环境温度随机扰动下能获得最大功率输出。仿真实验表明了所提控制策略的有效性和优越性。
Abstract: The limited reserves of fossil fuels and their environmental pollution potential during utilization have driven significant interest in solar energy as a clean and renewable alternative. However, the inherent stochastic characteristics of photovoltaic (PV) systems, particularly those influenced by solar irradiance fluctuations and ambient temperature variations, pose significant challenges to achieving reliable maximum power point tracking (MPPT). To address the above problems, this study proposes a PV MPPT control strategy based on stochastic model predictive control (SMPC) to realize the maximum power output of PV systems in stochastic environments. First, a nonlinear state-space model is formulated to describe the PV system affected by stochastic factors. Subsequently, an improved Kepler optimization algorithm (IKOA) is proposed for quickly locating the maximum power point; in order to cope with the effects of the solar irradiance and the ambient temperature on the PV system, this paper designs a scenario-tree-based SMPC controller: 1) A scenario tree-based modeling framework is proposed to probabilistically represent solar irradiance, temperature variations and other factors; 2) A Markov jump model is established to improve the accuracy of scenario construction and transition. The PV MPPT strategy based on scenario-based SMPC ensures that the PV system can obtain the maximum power output under the stochastic perturbations of solar irradiance and ambient temperature. Simulation experiments demonstrate the effectiveness and superiority of the proposed control strategy.
文章引用：张慧明, 张建华, 郭家旺. 基于随机模型预测控制的光伏系统最大功率点跟踪策略[J]. 电力与能源进展, 2025, 13(2): 77-92. https://doi.org/10.12677/aepe.2025.132010

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