An Online Optimal Dispatch Schedule for CCHP Microgrids Based on Model Predictive Control

Combined cooling, heating, and power (CCHP) systems have been widely applied in various kinds of buildings. Most operation strategies for CCHP microgrids are designed based on day-ahead profiles. However, prediction error for renewable energy resources (RES) and load leads to suboptimal operation in...

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Bibliographic Details
Published inIEEE transactions on smart grid Vol. 8; no. 5; pp. 2332 - 2342
Main Authors Gu, Wei, Wang, Zhihe, Wu, Zhi, Luo, Zhao, Tang, Yiyuan, Wang, Jun
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.09.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Combined cooling
Cooling
Cooling systems
Electric power grids
Energy sources
Error analysis
Error correction
Feedback
feedback correction
heating
Hierarchies
Kalman filters
Load modeling
Maintenance engineering
Mathematical models
Microgrids
model predictive control
online operation
Optimization
power (CCHP)
prediction error
Predictive control
Renewable energy
Scheduling
Time series
Upper bound
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Summary:Combined cooling, heating, and power (CCHP) systems have been widely applied in various kinds of buildings. Most operation strategies for CCHP microgrids are designed based on day-ahead profiles. However, prediction error for renewable energy resources (RES) and load leads to suboptimal operation in dispatch scheduling. In this paper, we propose an online optimal operation approach for CCHP microgrids based on model predictive control with feedback correction to compensate for prediction error. This approach includes two hierarchies: 1) rolling optimization; and 2) feedback correction. In the rolling part, a hybrid algorithm based on integrating time series analysis and Kalman filters is used to forecast the power for RES and load. A rolling optimization model is established to schedule operation according to the latest forecast information. The rolling dispatch scheduling is then adjusted based on ultra-short-term error prediction. The feedback correction model is applied to minimize the adjustments and to compensate for prediction error. A case study demonstrates the effectiveness of the proposed approach with better matching between demand and supply.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2016.2523504