English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Machine Learning for Electricity Market Clearing