Learning the LMP-Load Coupling From Data: A Support Vector Machine Based Approach

This paper investigates the fundamental coupling between loads and locational marginal prices (LMPs) in security-constrained economic dispatch (SCED). Theoretical analysis based on multi-parametric programming theory points out the unique one-to-one mapping between load and LMP vectors. Such one-to-...

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Vydané v:	IEEE transactions on power systems Ročník 32; číslo 2; s. 1127 - 1138
Hlavní autori:	Geng, Xinbo, Xie, Le
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.03.2017
Predmet:	Couplings Economics Generators Locational marginal prices (LMPs) Programming security constrained economic dispatch (SCED) support vector machine (SVM) Support vector machines system pattern region Topology Transmission line matrix methods
ISSN:	0885-8950, 1558-0679
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Abstract	This paper investigates the fundamental coupling between loads and locational marginal prices (LMPs) in security-constrained economic dispatch (SCED). Theoretical analysis based on multi-parametric programming theory points out the unique one-to-one mapping between load and LMP vectors. Such one-to-one mapping is depicted by the concept of system pattern region (SPR) and identifying SPRs is the key to understanding the LMP-load coupling. Built upon the characteristics of SPRs, the SPR identification problem is modeled as a classification problem from a market participant's viewpoint, and a Support Vector Machine based data-driven approach is proposed. It is shown that even without the knowledge of system topology and parameters, the SPRs can be estimated by learning from historical load and price data. Visualization and illustration of the proposed data-driven approach are performed on a 3-bus system as well as the IEEE 118-bus system.
AbstractList	This paper investigates the fundamental coupling between loads and locational marginal prices (LMPs) in security-constrained economic dispatch (SCED). Theoretical analysis based on multi-parametric programming theory points out the unique one-to-one mapping between load and LMP vectors. Such one-to-one mapping is depicted by the concept of system pattern region (SPR) and identifying SPRs is the key to understanding the LMP-load coupling. Built upon the characteristics of SPRs, the SPR identification problem is modeled as a classification problem from a market participant's viewpoint, and a Support Vector Machine based data-driven approach is proposed. It is shown that even without the knowledge of system topology and parameters, the SPRs can be estimated by learning from historical load and price data. Visualization and illustration of the proposed data-driven approach are performed on a 3-bus system as well as the IEEE 118-bus system.
Author	Le Xie Xinbo Geng
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SubjectTerms	Couplings Economics Generators Locational marginal prices (LMPs) Programming security constrained economic dispatch (SCED) support vector machine (SVM) Support vector machines system pattern region Topology Transmission line matrix methods
Title	Learning the LMP-Load Coupling From Data: A Support Vector Machine Based Approach
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