A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem

The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power systems. One way to tackle this challenge is through a stochastic security constraint un...

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Vydáno v:Energies (Basel) Ročník 13; číslo 15; s. 3777
Hlavní autoři: Marín-Cano, Cristian Camilo, Sierra-Aguilar, Juan Esteban, López-Lezama, Jesús M., Jaramillo-Duque, Álvaro, Villegas, Juan G.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.08.2020
Témata:
Decision making
Decomposition
Integer programming
Optimization
power system optimization
progressive hedging algorithm
Security-Constraint Unit Commitment
Variables
ISSN:1996-1073, 1996-1073
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Shrnutí:The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power systems. One way to tackle this challenge is through a stochastic security constraint unit commitment (SSCUC) model. However, the SSCUC is a mixed-integer linear programming problem with high computational and dimensional complexity in large-scale power systems. This feature hinders the reaction times required for decision making to ensure a proper operation of the system. As an alternative, this paper presents a joint strategy to efficiently solve a SSCUC model. The solution strategy combines the use of linear sensitivity factors (LSF) to compute power flows in a quick and reliable way and a method, which dynamically identifies and adds as user cuts those active security constraints N − 1 that establish the feasible region of the model. These two components are embedded within a progressive hedging algorithm (PHA), which breaks down the SSCUC problem into computationally more tractable subproblems by relaxing the coupling constraints between scenarios. The numerical results on the IEEE RTS-96 system show that the proposed strategy provides high quality solutions, up to 50 times faster compared to the extensive formulation (EF) of the SSCUC. Additionally, the solution strategy identifies the most affected (overloaded) lines before contingencies, as well as the most critical contingencies in the system. Two metrics that provide valuable information for decision making during transmission system expansion are studied.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en13153777