Zero energy potential of photovoltaic direct-driven air conditioners with considering the load flexibility of air conditioners

Sihui Li, Jinqing Peng*, Bin Zou, Bojia Li, Chujie Lu, Jingyu Cao, Yimo Luo, Tao Ma

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

17 Citations (Scopus)

Abstract

The real-time energy matching between building load and PV generation is low in actual applications of photovoltaic direct-driven air conditioners (PVACs). The indoor thermal comfort temperature range (TCTR) can enhance the load flexibility of PVACs to improve the real-time zero energy probability. Therefore, a real-time zero-energy potential evaluation method with the adoption of TCTR for PVACs was proposed. The indoor temperatures are mainly determined by real-time PV generation, which minimizes the power taken from the grid and batteries. The indoor temperature, conditioned by PVACs under varying operating conditions, is predicted using machine learning models at a one-minute time resolution. The real-time energy matching performances of PVACs are evaluated by key indicators, including zero-energy probability, real-time zero-energy probability, complete overcooling rate, and complete overheating rate, for different climatic regions. With a fixed indoor setting temperature in summer, the real-time zero-energy probabilities in Shanghai, Guangzhou, and Beijing are 1.89%, 2.45%, and 2.11%, respectively. While adopting the TCTR with the similar PV capacity, the corresponding values in Shanghai, Guangzhou, and Beijing reach 53.13%, 49.48%, and 87.11%, respectively. In addition, the real-time zero-energy points frequently appear when large cooling demands are needed. Finally, an optimization of PV capacity is conducted. The optimal PV capacity was found to be at the point where seasonal PV generation is 1.3 times the dominant seasonal energy consumption. PVACs with TCTR can utilize the PV generation and load flexibility to the greatest extent and the evaluation method for PVACs is beneficial for designing a more practical and economical system.

Original languageEnglish
Article number117821
JournalApplied Energy
Volume304
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • Machine learning models
  • Photovoltaic direct-driven air conditioners
  • PV systems
  • Zero energy

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