Distributed stochastic security constrained unit commitment for coordinated operation of transmission and distribution system

With the high penetration of renewable energies in modern power systems, deterministic coordination algorithms are facing two major problems: one is degradation in accuracy if fewer scenarios are utilized for uncertainty evaluation while second is the high computational time if a high number of scen...

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Bibliographic Details
Published in:CSEE Journal of Power and Energy Systems Vol. 7; no. 4; pp. 708 - 718
Main Authors: Aamir Nawaz, Hongtao Wang
Format: Journal Article
Language:English
Published: Beijing Chinese Society for Electrical Engineering Journal of Power and Energy Systems 01.07.2021
China electric power research institute
Subjects:
Accuracy
Algorithms
Computational efficiency
Computing time
Coordination models
Cybersecurity
Probability theory
Renewable energy
Unit commitment
ISSN:2096-0042, 2096-0042
Online Access:Get full text
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Summary:With the high penetration of renewable energies in modern power systems, deterministic coordination algorithms are facing two major problems: one is degradation in accuracy if fewer scenarios are utilized for uncertainty evaluation while second is the high computational time if a high number of scenarios are considered for better accuracy. In both cases, the efficiency of the algorithm is degraded. To solve these problems in coupled transmission system and distribution systems (TSDS), probabilistic coordination algorithms are adopted to solve with less effort. In this paper, a TSDS probabilistic coordination model is proposed to solve the coordinated security-constrained unit commitment problem. A mean and standard deviation matching based probabilistic analytical target cascading algorithm has been utilized for evaluation of TSDS coordination problem. Instead of solving each scenario as a separate problem, the proposed algorithm considers a single coordination problem with probabilistic characteristics as shared variables and hence, achieves fast convergence. Different case studies are performed to prove the efficacy of the proposed algorithm. Results verify that the proposed algorithm reduces computational time and resources for large-scale systems.
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ISSN:2096-0042
2096-0042
DOI:10.17775/CSEEJPES.2020.02150