Two-Time-Scale Energy Management for Microgrids With Data-Based Day-Ahead Distributionally Robust Chance-Constrained Scheduling

The uncertainties arising from both renewable generation and load demand have brought challenges to the reliable and efficient operation of power systems. This paper presents a two-time-scale (i.e., day-ahead and intraday) microgrid energy management model for scheduling with low operational costs a...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2021-11, Vol.12 (6), p.4778-4787
Main Authors: Yuan, Zhi-Peng, Xia, Jing, Li, Peng
Format: Article
Language:English
Subjects:
Constraints
Distributed generation
Distributionally robust chance-constrained (DRCC) optimization
Economic models
Energy management
Load modeling
Mathematical model
microgrid energy management
Microgrids
model predictive control (MPC) rolling optimization
Optimization
Predictive control
Predictive models
Probability distribution
Scheduling
two-time-scale
Uncertainty
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Summary:The uncertainties arising from both renewable generation and load demand have brought challenges to the reliable and efficient operation of power systems. This paper presents a two-time-scale (i.e., day-ahead and intraday) microgrid energy management model for scheduling with low operational costs and high reliability against uncertainties. For the day-ahead scheduling, we propose a data-based distributionally robust chance-constrained (DRCC) energy dispatch model for grid-connected microgrids, to trade off the economic efficiency and operational risk. This model gains a robust and low conservative day-ahead scheduling solution against uncertainties by formulating the chance-constraint based on Wasserstein ambiguity set into a tractable convex constraint with conditional value-at-risk (CVaR) approximation. For the intraday scheduling, we blend the shorter-time scale prediction with a robust day-ahead scheduling plan as well as the model predictive control (MPC) rolling optimization method. This ensures accurate intraday dispatch solution and balanced supply-demand. Finally, the effectiveness and performance of the proposed method are verified via case studies.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2021.3092371