Machine Learning for Electricity Market Clearing

TL;DR

This paper develops a machine learning-based digital twin for the optimal power flow problem in electricity markets, enabling faster and accurate market-clearing computations and scenario evaluations.

Contribution

It introduces a novel ML approach that approximates OPF solutions and locational marginal prices, reducing computational complexity while maintaining accuracy.

Findings

The ML model accurately predicts optimal dispatch and LMPs on IEEE test systems.

The approach significantly reduces computation time compared to traditional methods.

Trade-off analysis shows fewer samples needed for acceptable accuracy.

Abstract

This paper seeks to design a machine learning twin of the optimal power flow (OPF) optimization, which is used in market-clearing procedures by wholesale electricity markets. The motivation for the proposed approach stems from the need to obtain the digital twin, which is much faster than the original, while also being sufficiently accurate and producing consistent generation dispatches and locational marginal prices (LMPs), which are primal and dual solutions of the OPF optimization, respectively. Availability of market-clearing tools based on this approach will enable computationally tractable evaluation of multiple dispatch scenarios under a given unit commitment. Rather than direct solution of OPF, the Karush-Kuhn-Tucker (KKT) conditions for the OPF problem in question may be written, and in parallel the LMPs of generators and loads may be expressed in terms of the OPF Lagrangian multipliers. Also, taking advantage of the practical fact that many of the Lagrangian multipliers associated with lines will be zero (thermal limits are not binding), we build and train an ML scheme which maps flexible resources (loads and renewables) to the binding lines, and supplement it with an efficient power-grid aware linear map to optimal dispatch and LMPs. The scheme is validated and illustrated on IEEE models. We also report a trade of analysis between quality of the reconstruction and number of samples needed to train the model.