Data-Driven Unit Commitment Refinement - a Scalable Approach for Complex Modern Power Grids
Informazioni aggiuntive
Autori
Holt T. A. B.,
Abhyankar S. .,
Kuruganti T.,
Schenk O.,
Peles S.
Tipo
Contributo in atti di conferenza
Anno
2024
Lingua
Inglese
Abstract
Integration of renewable generation, which is often intermittent and decentralized, substantially
increases the stochasticity and complexity of power grid operations. Future power systems planning will require significant computational capability to evaluate balance between demand and supply under varying conditions, both temporally and spatially. The standard approach for generation unit commitment is to use mixed-integer linear programming to find the optimal generation schedule considering ramping and generator constraints. In the future grid this poses computational scalability challenges because generation and demand are not known with certainty due to stochasticity in weather and complexity of the grid. To address this challenge, we present a data-driven unit commitment approach that can efficiently include stochastic weather impacts and contingency considerations to improve unit commitment. Our approach uses graph-based
data analytics techniques on solutions to the security constrained (and possibly stochastic) economic dispatch problem to identify potential improvements to a given
unit commitment. Recent breakthroughs in fully-parallel stochastic economic dispatch software allow this approach to be scalably deployed. Simulations on synthetic South Carolina and Texas grids show this method can improve grid reliability with security constraints over a set of contingencies, while also meaningfully lowering total generation cost.
Atti di conferenza
HICSS 2024
Mese
gennaio
Nome conferenza
The Hawaii International Conference on System Sciences
Luogo conferenza
University of Hawaii
Data conferenza
January 2-6, 2024
Parole chiave
Unit Commitment Scheduling, Economic Dispatch, Renewable Energy, Stochastic Grid Analysis
