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Multi-Time Scale Operation and Simulation Strategy of the Park Based on Model Predictive Control

Jun Zhao*, Chaoying Yang, Ran Li, Jinge Song

Grid Center, State Grid Shanxi Electric Power Company Electric Power Science Research Institute, Taiyuan, 030000, China

* Corresponding Author: Jun Zhao. Email: email

Energy Engineering 2024, 121(3), 747-767. https://doi.org/10.32604/ee.2023.042806

Abstract

Due to the impact of source-load prediction power errors and uncertainties, the actual operation of the park will have a wide range of fluctuations compared with the expected state, resulting in its inability to achieve the expected economy. This paper constructs an operating simulation model of the park power grid operation considering demand response and proposes a multi-time scale operating simulation method that combines day-ahead optimization and model predictive control (MPC). In the day-ahead stage, an operating simulation plan that comprehensively considers the user’s side comfort and operating costs is proposed with a long-term time scale of 15 min. In order to cope with power fluctuations of photovoltaic, wind turbine and conventional load, MPC is used to track and roll correct the day-ahead operating simulation plan in the intra-day stage to meet the actual operating operation status of the park. Finally, the validity and economy of the operating simulation strategy are verified through the analysis of arithmetic examples.

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APA Style
Zhao, J., Yang, C., Li, R., Song, J. (2024). Multi-time scale operation and simulation strategy of the park based on model predictive control. Energy Engineering, 121(3), 747-767. https://doi.org/10.32604/ee.2023.042806
Vancouver Style
Zhao J, Yang C, Li R, Song J. Multi-time scale operation and simulation strategy of the park based on model predictive control. Energ Eng. 2024;121(3):747-767 https://doi.org/10.32604/ee.2023.042806
IEEE Style
J. Zhao, C. Yang, R. Li, and J. Song, “Multi-Time Scale Operation and Simulation Strategy of the Park Based on Model Predictive Control,” Energ. Eng., vol. 121, no. 3, pp. 747-767, 2024. https://doi.org/10.32604/ee.2023.042806



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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