Unit Commitment using Nearest Neighbor as a Short-Term Proxy

Abstract

We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be used as a proxy for quickly approximating outcomes of short-term decisions, to make tractable hierarchical long-term assessment and planning for large power systems. Experimental results on updated versions of IEEE-RTS79 and IEEE-RTS96 show high accuracy measured on operational cost, achieved in runtimes that are lower in several orders of magnitude than the traditional approach.

References in corpus (3)

• A Survey on Metric Learning for Feature Vectors and Structured Data
• Survey of Nearest Neighbor Techniques
• Hierarchical Decision Making In Electricity Grid Management

Cited by in corpus (2)

• Chance-Constrained Outage Scheduling using a Machine Learning Proxy
• Supervised Learning for Optimal Power Flow as a Real-Time Proxy