Continuous Switch Model and Heuristics for Mixed-Integer Nonlinear Problems in Power Systems

Many power systems operation and planning computations (e.g., transmission and generation switching and placement) solve a mixed-integer nonlinear problem (MINLP) with binary variables representing the decision to connect devices to the grid. Binary variables with nonlinear AC network constraints ma...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on power systems Ročník 39; číslo 4; s. 5780 - 5791
Hlavní autori: Agarwal, Aayushya, Pandey, Amritanshu, Pileggi, Larry
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Continuity (mathematics)
continuous switch
Damping
Equivalent circuits
Integer programming
Integrated circuit modeling
Mixed integer
Mixed-integer optimization
Nonlinear control
Optimization
Power systems
Power transmission lines
Robustness (mathematics)
Switches
Switching circuits
transmission line switching
unit commitment
ISSN:0885-8950, 1558-0679
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Many power systems operation and planning computations (e.g., transmission and generation switching and placement) solve a mixed-integer nonlinear problem (MINLP) with binary variables representing the decision to connect devices to the grid. Binary variables with nonlinear AC network constraints make this problem NP-hard. For large real-world networks, obtaining an AC feasible optimum solution for these problems is computationally challenging and often unattainable with state-of-the-art tools today. In this work, we map the MINLP decision problem into a set of equivalent circuits by representing binary variables with a circuit-based continuous switch model. We characterize the continuous switch model by a controlled nonlinear impedance that more closely mimics the physical behavior of a real-world switch. This mapping effectively transforms the MINLP problem into an NLP problem. We mathematically show that this transformation is a tight relaxation of the MINLP problem. For fast and robust convergence, we develop physics-driven homotopy and Newton-Raphson damping methods. To validate this approach, we empirically show robust convergences for large, realistic systems (<inline-formula><tex-math notation="LaTeX">></tex-math></inline-formula>70,000 buses) in a practical wall-clock time to an AC-feasible optimum. We compare our results and show improvement over industry-standard tools and other binary relaxation methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2023.3340113