# Machine Learning for Electricity Market Clearing

arXiv: https://arxiv.org/abs/2205.11641 · HTML: https://mlsanity.com/paper/2205.11641/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 12 of 12)

- [Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets](https://mlsanity.com/paper/1802.09639/) (2018)
- [DeepOPF: Deep Neural Network for DC Optimal Power Flow](https://mlsanity.com/paper/1905.04479/) (2019)
- [Learning for DC-OPF: Classifying active sets using neural nets](https://mlsanity.com/paper/1902.05607/) (2019)
- [Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch](https://mlsanity.com/paper/2112.13469/) (2021)
- [Leveraging power grid topology in machine learning assisted optimal power flow](https://mlsanity.com/paper/2110.00306/) (2021)
- [Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems](https://mlsanity.com/paper/2102.06349/) (2021)
- [Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach](https://mlsanity.com/paper/2006.00544/) (2020)
- [Machine Learning-Driven Virtual Bidding with Electricity Market Efficiency Analysis](https://mlsanity.com/paper/2104.02754/) (2021)
- [Embedding Power Flow into Machine Learning for Parameter and State Estimation](https://mlsanity.com/paper/2103.14251/) (2021)
- [A Chance-Constrained Stochastic Electricity Market](https://mlsanity.com/paper/1906.06963/) (2019)
- [A Decomposition Algorithm for Large-Scale Security-Constrained AC Optimal Power Flow](https://mlsanity.com/paper/2110.01737/) (2021)
- [Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information](https://mlsanity.com/paper/2112.11461/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study](https://mlsanity.com/paper/2304.00062/) (2023)

## Abstract (arXiv)

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.