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

TL;DR

This paper introduces a data-driven method using Support Vector Machines to identify system pattern regions, revealing the load-LMP coupling in power markets without needing system topology details.

Contribution

It proposes a novel SVM-based classification approach to estimate system pattern regions from historical data, enhancing understanding of load and LMP relationships.

Findings

Successfully applied to 3-bus and IEEE 118-bus systems

Can estimate SPRs without system topology knowledge

Provides visual insights into load-LMP coupling

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.