Distributionally Robust Optimal Power Flow in Multi-Microgrids With Decomposition and Guaranteed Convergence

Multi-microgrids (MMGs) are emerging as a cost-effective solution to provide ancillary services. To reconcile external reserve provision and internal risk hedging for MMGs, a novel comprehensive multi-area dynamic optimal power flow (MADOPF) model is established, where energy-reserve co-optimization...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on smart grid 2021-01, Vol.12 (1), p.43-55
Main Authors: Huang, Wanjun, Zheng, Weiye, Hill, David J.
Format: Article
Language:English
Subjects:
Ancillary services
Convergence
Decomposition
Distributed generation
Distributed optimization
Electric power grids
Fuels
Generators
ISO
Load flow
optimal power flow
Optimization
Power flow
Predictive control
Robust control
Robustness
Stress concentration
unbalanced multi-microgrids
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Multi-microgrids (MMGs) are emerging as a cost-effective solution to provide ancillary services. To reconcile external reserve provision and internal risk hedging for MMGs, a novel comprehensive multi-area dynamic optimal power flow (MADOPF) model is established, where energy-reserve co-optimization, three-phase unbalanced network intrinsics and dual control time-scales are all addressed. To better hedge the uncertainties of distributed generation and loads, distributionally robust model predictive control (MPC) is applied to the MADOPF problem. To preserve operational independence and information privacy for each microgrid, decomposition of the nonconvex model is devised with guaranteed convergence. Numerical tests on a two-area system and a real large-scale 16-area system derived from Shandong Power Grid validate the effectiveness of the proposed method. The advantages are demonstrated by the comparison with the conventional MPC, stochastic and robust methods.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2020.3012025