In this paper, we present a Model Predictive Control (MPC) based energy management method for isolated microgrid systems. An elastic reserve strategy is proposed to handle the probabilistic predictions of loads and Renewable Energy Sources (RES) given in the form of intervals so that the state information of systems can be better utilized in the scheduling. Scenario-selected optimizers apply the MPC framework and the mixed-integer linear programming (MILP) model to find optimal dispatches for isolated microgrids. Simulations are conducted to analyze the economic and stability performances of our proposed method and make comparisons with other methods. Numerical results show that the proposed approach is more adaptive and has better performance in terms of both economic efficiency and stability.
|Original language||English (US)|
|Title of host publication||2020 Asia Energy and Electrical Engineering Symposium, AEEES 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - May 2020|
|Event||2020 Asia Energy and Electrical Engineering Symposium, AEEES 2020 - Chengdu, China|
Duration: May 28 2020 → May 31 2020
|Name||2020 Asia Energy and Electrical Engineering Symposium, AEEES 2020|
|Conference||2020 Asia Energy and Electrical Engineering Symposium, AEEES 2020|
|Period||5/28/20 → 5/31/20|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported in part by Shenzhen Fundamental Research Fund under Grant No. JCYJ20170411102217994 and Guangdong province under grant No. 2017ZT07X152.
© 2020 IEEE.
- Model Predictive Control (MPC)
- interval predictions
- isolated microgrid system
- microgrid energy management
- probabilistic control