EconPapers    
Economics at your fingertips  
 

A MILP-Based Approach for Hydrothermal Scheduling

Dewan Fayzur Rahman (), Ana Viana () and João Pedro Pedroso ()
Additional contact information
Dewan Fayzur Rahman: INESC TEC (formerly INESC Porto)
Ana Viana: Polytechnic Institute of Porto and INESC TEC (formerly INESC Porto)
João Pedro Pedroso: University of Porto and INESC TEC (formerly INESC Porto)

A chapter in Operations Research Proceedings 2012, 2014, pp 157-162 from Springer

Abstract: Abstract This paper presents new solution approaches capable of finding optimal solutions for the Hydrothermal Scheduling Problem (HSP) in power generation planning. The problem has been proven to be NP-hard and no exact methods have been able to tackle it, for problem sizes of practical relevance. We explore three approaches. The first method is an iterative algorithm that has been successfully used previously to solve the thermal commitment problem. The two other methods are “Local Branching” and a hybridization of “Particle Swarm Optimization” with a general purpose solver. Computational experiments show that the iterative piecewise linear approximation method outperforms more elaborated approaches, indicating that recourse to matheuristics for solving this problem is not necessary.

Keywords: Local Branching; Power Generation Planning; MILP Solver; Mixed Integer Linear Programming (MILP); MILP Formulation (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:oprchp:978-3-319-00795-3_23

Ordering information: This item can be ordered from
http://www.springer.com/9783319007953

DOI: 10.1007/978-3-319-00795-3_23

Access Statistics for this chapter

More chapters in Operations Research Proceedings from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-05-18
Handle: RePEc:spr:oprchp:978-3-319-00795-3_23