This paper proposes a dynamic programming (DP)-based stochastic model predictive control (SMPC) method for the economic operation of solar PV-powered ice-storage air-conditioning (PIAC) systems. The forecast data of PV generation and building cooling load are considered as stochastic variables in this paper. To deal with the uncertainties of the day-ahead forecast data, Latin hypercube sequential sampling, Cholesky decomposition and Simultaneous backward reduction are adopted to provide representative scenarios for SMPC. The value function matrix is employed to solve the receding-horizon optimization problem formulated by DP. With updated short-term forecast information, SMPC is able to reduce the impact of inaccurate forecasts on the operation of PIAC systems. A study of typical operation cases demonstrates the effectiveness of the proposed method, which ensures the satisfaction of cooling supply and yields solutions closer to the global optimality than the traditional MPC method.

中文翻译：

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考虑预测不确定性的太阳能光伏冰蓄冷空调系统模型预测控制

本文提出了一种基于动态规划 (DP) 的随机模型预测控制 (SMPC) 方法，用于太阳能光伏供电的冰蓄冷空调 (PIAC) 系统的经济运行。光伏发电和建筑冷负荷的预测数据在本文中被视为随机变量。针对日前预报数据的不确定性，采用拉丁超立方序列采样、Cholesky分解和同步向后缩减等方法为SMPC提供具有代表性的场景。价值函数矩阵被用来解决由DP制定的后退水平优化问题。通过更新的短期预测信息，SMPC 能够减少不准确预测对 PIAC 系统运行的影响。