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

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...

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Bibliographic Details
Published in:Energies (Basel) 2024-07, Vol.17 (13), p.3212
Main Authors: Li, Bin, Fesseha, Samrawit Bzayene, Chen, Songsong, Zhou, Ying
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Batteries
Data analysis
Decision making
demand response (DR)
Design
Efficiency
Electricity distribution
Energy management
Energy resources
Energy storage
Heat
Machine learning
model predictive control (MPC)
Neural networks
Optimization
Power plants
real-time scheduling
Renewable resources
Scheduling
Solar energy
solar power generation
Weather forecasting
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en17133212