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A Control-variable Regression Monte Carlo Technique for Short-term Electricity Generation Planning

Perninge, Magnus LU (2015) In Optimization and Control (math.OC)
Abstract
In the day-to-day operation of a power system, the system operator repeatedly solves short-term generation planning problems. When formulating these problems the operators have to weigh the risk of costly failures against increased production costs. The resulting problems are often high-dimensional and various approximations have been suggested in the literature.



In this article we formulate the short-term planning problem as an optimal switching problem with delayed reaction. Furthermore, we proposed a control variable technique that can be used in Monte Carlo regression to obtain a computationally efficient numerical algorithm.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Working Paper
publication status
published
subject
in
Optimization and Control (math.OC)
issue
arXiv:1512.08880v1
publisher
arXiv.org
language
English
LU publication?
yes
id
460c1e66-7438-4bee-808d-7c8e889eb3a6 (old id 8518045)
alternative location
https://arxiv.org/abs/1512.08880
date added to LUP
2016-01-12 15:46:18
date last changed
2017-02-14 12:27:53
@misc{460c1e66-7438-4bee-808d-7c8e889eb3a6,
  abstract     = {In the day-to-day operation of a power system, the system operator repeatedly solves short-term generation planning problems. When formulating these problems the operators have to weigh the risk of costly failures against increased production costs. The resulting problems are often high-dimensional and various approximations have been suggested in the literature.<br/><br>
<br/><br>
In this article we formulate the short-term planning problem as an optimal switching problem with delayed reaction. Furthermore, we proposed a control variable technique that can be used in Monte Carlo regression to obtain a computationally efficient numerical algorithm.},
  author       = {Perninge, Magnus},
  language     = {eng},
  note         = {Working Paper},
  number       = { arXiv:1512.08880v1 },
  publisher    = {arXiv.org},
  series       = {Optimization and Control (math.OC)},
  title        = {A Control-variable Regression Monte Carlo Technique for Short-term Electricity Generation Planning},
  year         = {2015},
}