Solving the Stochastic Generation and Transmission Capacity Planning Problem Applied to Large-Scale Power Systems Using Generalized Shift-Factors

Victor H. Hinojosa and Joaquín Sepúlveda
Additional contact information
Victor H. Hinojosa: Department of Electrical Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
Joaquín Sepúlveda: Department of Electrical Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile

Energies, 2020, vol. 13, issue 13, 1-15

Abstract: In this study, we successfully develop the transmission planning problem of large-scale power systems based on generalized shift-factors. These distribution factors produce a reduced solution space which does not need the voltage bus angles to model new transmission investments. The introduced formulation copes with the stochastic generation and transmission capacity expansion planning problem modeling the operational problem using a 24-hourly load behaviour. Results show that this formulation achieves an important reduction of decision variables and constraints in comparison with the classical disjunctive transmission planning methodology known as the Big M formulation without sacrificing optimality. We test both the introduced and the Big M formulations to find out convergence and time performance using a commercial solver. Finally, several test power systems and extensive computational experiments are conducted to assess the capacity planning methodology. Solving deterministic and stochastic problems, we demonstrate a prominent reduction in the solver simulation time especially with large-scale power systems.

Keywords: generalized distribution factors; stochastic programming; two-stage problem; hourly load modeling; large-scale power systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/13/3327/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/13/3327/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:13:p:3327-:d:378608

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().