Learning the LMP-Load Coupling From Data: A Support Vector Machine Based Approach
Published in IEEE Transactions on Power Systems ( Volume: 32, Issue: 2, March 2017), 2017
Recommended citation: [bib file] Geng, Xinbo, and Le Xie. "Learning the LMP-load coupling from data: A support vector machine based approach." IEEE Transactions on Power Systems 32, no. 2 (2017): 1127-1138. https://ieeexplore.ieee.org/abstract/document/7478156/
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.
Recommended citation: bib.