Data-driven power system operation: Exploring the balance between cost and risk

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Title: Data-driven power system operation: Exploring the balance between cost and risk
Authors: Cremer, J
Konstantelos, I
Tindemans, S
Strbac, G
Item Type: Journal Article
Abstract: Supervised machine learning has been successfully used in the past to infer a system's security boundary by training classifiers (also referred to as security rules) on a large number of simulated operating conditions. Although significant research has been carried out on using classifiers for the detection of critical operating points, using classifiers for the subsequent identification of suitable preventive/corrective control actions remains underdeveloped. This paper focuses on addressing the challenges that arise when utilizing security rules for control purposes. The inherent trade-off between operating cost and security risk is explored in detail. To optimally navigate this trade-off, a novel approach is proposed that uses an ensemble learning method (AdaBoost) to infer a probabilistic description of a system's security boundary and Platt Calibration to correct the introduced bias. Subsequently, a general-purpose framework for building probabilistic and disjunctive security rules of a system's secure operating domain is developed that can be embedded within classic operation formulations. Through case studies on the IEEE 39-bus system, it is showcased how security rules can be efficiently utilized to optimally operate the system under multiple uncertainties while respecting a user-defined cost-risk balance. This is a fundamental step towards embedding data-driven models within classic optimisation approaches.
Issue Date: 1-Jan-2019
Date of Acceptance: 19-Aug-2018
ISSN: 0885-8950
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 791
End Page: 801
Journal / Book Title: IEEE Transactions on Power Systems
Volume: 34
Issue: 1
Copyright Statement: © 2018 The Author(s). This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see
Sponsor/Funder: Engineering & Physical Science Research Council (E
Funder's Grant Number: R96051 - EP/K036173/1
Keywords: Science & Technology
Engineering, Electrical & Electronic
Supervised machine learning
power systems operation
security rules
dynamic stability
0906 Electrical And Electronic Engineering
Publication Status: Published
Online Publication Date: 2018-08-27
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering
Centre for Environmental Policy
Faculty of Natural Sciences

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