A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study

TL;DR

This paper introduces PIMA-AS-OPF, a physics-informed neural network approach that efficiently solves real-time electricity market clearing problems by incorporating physical constraints and market properties, tested on a large NYISO system.

Contribution

It presents a novel physics-informed machine learning method that accounts for renewable curtailment and market constraints, improving efficiency and feasibility in power system optimization.

Findings

Accurately predicts market clearing outcomes on a large NYISO system.

Reduces complex optimization to linear equations for efficiency.

Handles renewable curtailment and load shedding scenarios.

Abstract

This paper addresses the challenge of efficiently solving the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the active set learning technique and expands its capabilities to account for curtailment in load or renewable power generation, which is a common challenge in real-world power systems. The core of PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. These outputs allow for reducing the original market-clearing optimization to a system of linear equations, which can be solved efficiently and yield both the dispatch decisions and the locational marginal prices (LMPs). The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market-clearing results. The accuracy and scalability of the proposed method is tested on a realistic 1814-bus NYISO system with current and future renewable energy penetration levels.