Assessment of compressor control in photovoltaic-driven air conditioner based on dynamic programing

Abstract

The demand for energy efficient and low carbon operation of air conditioning systems has increased significantly in recent years, driven by both rising global temperatures and the growing energy consumption of buildings. Photovoltaic-driven air conditioner (PVAC) systems utilize solar energy to power air conditioning, providing a sustainable solution to reduce building energy consumption and carbon emissions. In this study, a PVAC model, a resistance and capacitance building model, an air conditioning model and evaluators model are established and integrated into a comprehensive simulation framework. A dynamic programming (DP) strategy is developed for optimizing the operation of a photovoltaic-driven air conditioner, focusing on compressor speed scheduling to balance multiple objectives: minimizing operational costs and carbon emissions, maximizing PV self-consumption, and maintaining indoor thermal comfort. The DP algorithm successfully optimizes PVAC systems with the performance significantly influenced by evaluation indicators. Additionally, integrating photovoltaic generation with outdoor condition predictions further enhances PVAC system efficiency. The research highlights the capacity of DP-based compressor speed scheduling to achieve multiple objectives. The findings contribute to advancing predictive scheduling approaches for energy-efficient, sustainable building operations.