Machine learning for ranking day-ahead decisions in the context of short-term operation planning

Abstract In operation planning, probabilistic reliability assessment consists in evaluating, for various candidate planning decisions, the induced probability of meeting a reliability target and the expected operating cost over a certain future time period. In this paper, we propose to exploit Monte-Carlo simulation and machine learning to predict operation costs for various day-ahead unit commitment and economic dispatch decisions and a range of realisations of uncertain loads and renewable generations over the next day. We describe how to generate a database, how to apply supervised machine learning to it, and how to use the learnt proxies to rank candidate day-ahead decisions in terms of the expected operating cost they induce over the next day. We illustrate the approach on the IEEE-RTS96 benchmark where we use the DC power-flow approximation and the N − 1 criterion to simulate real-time operation and to generate generation schedules in the day-ahead operation planning stage.

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