Real-Time Solar Power Generation Scheduling for Maintenance and Suboptimally Performing Equipment Using Demand Response Unified with Model Predictive Control

Bin Li, Samrawit Bzayene Fesseha (), Songsong Chen and Ying Zhou
Additional contact information
Bin Li: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Samrawit Bzayene Fesseha: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Songsong Chen: Beijing Key Laboratory of Demand Side Multi-Energy Complementary Optimization and Supply-Demand Interaction Technology, China Electric Power Research Institute Co., Ltd., Beijing 100035, China
Ying Zhou: Beijing Key Laboratory of Demand Side Multi-Energy Complementary Optimization and Supply-Demand Interaction Technology, China Electric Power Research Institute Co., Ltd., Beijing 100035, China

Energies, 2024, vol. 17, issue 13, 1-14

Abstract: This paper proposes a novel approach that unifies a demand response (DR) with a master plan of the model predictive control method focusing on scheduling maintenance and replacement for suboptimal equipment in real-time solar power plants. By leveraging DR mechanisms and MPC algorithms, our proposed framework starts with understanding the correlation between solar module temperature, surrounding temperature, and irradiation—essential for predicting and optimizing the performance of solar energy installations. It extends to evaluate the DC to AC conversion ratio, which is an indicator of the efficiency of the inverters. This integration enables proactive decisions for repair, maintenance, or replacement of equipment. Through exploratory data analysis using Python, we establish the efficiency and benefits of our anticipated approach in identifying the relationship between the factors that affect solar power generation.

Keywords: demand response (DR); model predictive control (MPC); solar power generation; real-time scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/13/3212/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/13/3212/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:13:p:3212-:d:1425895

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().