An importance sampling technique for probabilistic security assessment in power systems with large amounts of wind power
(2016) In Electric Power Systems Research 131. p.11-18- Abstract
- Larger amounts of variable renewable energy sources bring about larger amounts of uncertainty in the form of forecast errors. When taking operational and planning decisions under uncertainty, a tradeoff between risk and costs must be made. Today's deterministic operational tools, such as N-1-based methods, cannot directly account for the underlying risk due to uncertainties. Instead, several definitions of operating risks, which are probabilistic indicators, have been proposed in the literature. Estimating these risks require estimating very low probabilities of violations of operating constraints. Crude Monte-Carlo simulations are very computationally demanding for estimating very low probabilities. In this paper, an importance sampling... (More)
- Larger amounts of variable renewable energy sources bring about larger amounts of uncertainty in the form of forecast errors. When taking operational and planning decisions under uncertainty, a tradeoff between risk and costs must be made. Today's deterministic operational tools, such as N-1-based methods, cannot directly account for the underlying risk due to uncertainties. Instead, several definitions of operating risks, which are probabilistic indicators, have been proposed in the literature. Estimating these risks require estimating very low probabilities of violations of operating constraints. Crude Monte-Carlo simulations are very computationally demanding for estimating very low probabilities. In this paper, an importance sampling technique from mathematical finance is adapted to estimate very low operating risks in power systems given probabilistic forecasts for the wind power and the load. Case studies in the IEEE 39 and 118 bus systems show a decrease in computational demand of two to three orders of magnitude. (C) 2015 Elsevier B.V. All rights reserved. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8539891
- author
- Hamon, Camille ; Perninge, Magnus LU and Soder, Lennart
- organization
- publishing date
- 2016
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Risk-based operation, Monte-Carlo simulations, Importance sampling, Wind, power, N-1 criterion, Stability boundary
- in
- Electric Power Systems Research
- volume
- 131
- pages
- 11 - 18
- publisher
- Elsevier
- external identifiers
-
- wos:000367126900002
- scopus:84944351188
- ISSN
- 1873-2046
- DOI
- 10.1016/j.epsr.2015.09.016
- project
- Stochastic Control Approach to Optimal Power System Operation
- language
- English
- LU publication?
- yes
- id
- 3305bb9a-e02e-4a30-9515-ac82b7b75d60 (old id 8539891)
- date added to LUP
- 2016-04-01 09:49:51
- date last changed
- 2022-04-11 23:17:16
@article{3305bb9a-e02e-4a30-9515-ac82b7b75d60,
abstract = {{Larger amounts of variable renewable energy sources bring about larger amounts of uncertainty in the form of forecast errors. When taking operational and planning decisions under uncertainty, a tradeoff between risk and costs must be made. Today's deterministic operational tools, such as N-1-based methods, cannot directly account for the underlying risk due to uncertainties. Instead, several definitions of operating risks, which are probabilistic indicators, have been proposed in the literature. Estimating these risks require estimating very low probabilities of violations of operating constraints. Crude Monte-Carlo simulations are very computationally demanding for estimating very low probabilities. In this paper, an importance sampling technique from mathematical finance is adapted to estimate very low operating risks in power systems given probabilistic forecasts for the wind power and the load. Case studies in the IEEE 39 and 118 bus systems show a decrease in computational demand of two to three orders of magnitude. (C) 2015 Elsevier B.V. All rights reserved.}},
author = {{Hamon, Camille and Perninge, Magnus and Soder, Lennart}},
issn = {{1873-2046}},
keywords = {{Risk-based operation; Monte-Carlo simulations; Importance sampling; Wind; power; N-1 criterion; Stability boundary}},
language = {{eng}},
pages = {{11--18}},
publisher = {{Elsevier}},
series = {{Electric Power Systems Research}},
title = {{An importance sampling technique for probabilistic security assessment in power systems with large amounts of wind power}},
url = {{http://dx.doi.org/10.1016/j.epsr.2015.09.016}},
doi = {{10.1016/j.epsr.2015.09.016}},
volume = {{131}},
year = {{2016}},
}