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The Value of Stochastic Programming in Day-Ahead and Intraday Generation Unit Commitment

Tim Schulze (t.schulze-2***at***sms.ed.ac.uk)
Ken McKinnon (k.mckinnon***at***ed.ac.uk)

Abstract: The recent expansion of renewable energy supplies has prompted the development of a variety of efficient stochastic optimization models and solution techniques for hydro-thermal scheduling. However, little has been published about the added value of stochastic models over deterministic ones. In the context of day-ahead and intraday unit commitment under wind uncertainty, we compare two-stage and multi-stage stochastic models to deterministic ones and quantify their added value. We present a modification of the WILMAR scenario generation technique designed to match the properties of the errors in our wind forcasts, and show that this is needed to make the stochastic approach worthwhile. Our evaluation is done in a rolling horizon fashion over the course of two years, using a 2020 central scheduling model based on the British power system, with transmission constraints and a detailed model of pump storage operation and system-wide reserve and response provision. We show that in day-ahead scheduling the stochastic approach saves 0.3% of generation costs compared to the best deterministic approach, but the savings are less in intraday scheduling.

Keywords: Unit commitment, hydro-thermal scheduling, stochastic optimization, wind forecast uncertainty

Category 1: Applications -- OR and Management Sciences (Scheduling )

Category 2: Stochastic Programming

Citation: Technical report ERGO 15-010, July 2015, The University of Edinburgh, School of Mathematics, Peter Guthrie Tait Road, Edinburgh, EH9 3FD, United Kingdom

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Entry Submitted: 07/06/2015
Entry Accepted: 07/06/2015
Entry Last Modified: 09/17/2016

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