LAPSE:2023.9102
Published Article
LAPSE:2023.9102
Islanded Microgrid Restoration Studies with Graph-Based Analysis
February 27, 2023
Abstract
The need to restore and keep the grid running or fast restoration during emergencies such as extreme weather conditions is quite apparent given the reliance of other infrastructure on electricity. One promising approach to electricity restoration is the use of locally available energy resources to restore the system to form isolated microgrids. In this paper, we present a black start restoration method that forms islanded microgrids after a blackout. The master DGs in the formed microgrids are coordinated to work together through droop control. Several constraints, including incentive-based demand response (DR) with direct load control (DLC) and distributed generator (DG) operation constraints, were formulated and linearized to realize a mixed-integer linear programming (MILP) restoration model. To improve compactness and to ensure that the model is neither under-sized nor over-sized, a pre-processing graph analysis approach was introduced which helps to characterize the least number of restoration steps needed to optimally restore the microgrid. Studies were performed on a modified IEEE 123 node test feeder to evaluate the effects of demand response, non-dispatchable DGs, and choice of restoration steps on the quality of the restoration solution. When possible, the proposed method yields an interconnected multi-master microgrid with improved redundancy.
Keywords
black start, demand response, droop, graph analysis, island, linear power flow, microgrid, MILP, restoration
Suggested Citation
Bassey O, Butler-Purry KL. Islanded Microgrid Restoration Studies with Graph-Based Analysis. (2023). LAPSE:2023.9102
Author Affiliations
Bassey O: Open Systems International (OSI) Inc. (AspenTech), Reading RG2 6DT, UK [ORCID]
Butler-Purry KL: Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX 77843, USA [ORCID]
Journal Name
Energies
Volume
15
Issue
19
First Page
6979
Year
2022
Publication Date
2022-09-23
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en15196979, Publication Type: Journal Article
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LAPSE:2023.9102
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https://doi.org/10.3390/en15196979
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