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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Bin Li & Samrawit Bzayene Fesseha & Songsong Chen & Ying Zhou, 2024. "Real-Time Solar Power Generation Scheduling for Maintenance and Suboptimally Performing Equipment Using Demand Response Unified with Model Predictive Control," Energies, MDPI, vol. 17(13), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3212-:d:1425895
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    References listed on IDEAS

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    1. Almaita, Eyad & Abdelsalam, Emad & Almomani, Fares & Nawafah, Hamza & Kassem, Fadwa & Alshkoor, Saleh & Shloul, Maan, 2023. "Impact study of integrating solar double chimney power plant into electrical grid," Energy, Elsevier, vol. 265(C).
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