Machine Learning for Electricity Market Clearing
This work addresses the need for computationally efficient market-clearing tools in wholesale electricity markets, enabling tractable evaluation of multiple dispatch scenarios, though it is incremental as it builds on existing OPF methods with a hybrid ML approach.
The paper tackled the problem of accelerating electricity market clearing by developing a machine learning twin of the optimal power flow (OPF) optimization, achieving faster computation while maintaining accuracy in generation dispatches and locational marginal prices (LMPs). It validated the approach on IEEE models and analyzed the trade-off between reconstruction quality and training sample size.
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.