Are Optimal Day-Ahead Markets Able to Face RES Uncertainty?

Evaluating Perfect Stochastic Energy Planning Models

Other (2017)

Authors

G.A. Morales Espana Algorithmics - EEMCS
Benjamin F. Hobbs External organisation
Research Group
Algorithmics (EEMCS) (TU Delft)
Copyright: © 2017 G. Morales-Espana, Benjamin F. Hobbs
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Published Date

28-06-2017

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Algorithmics

Abstract

Approximations made in day-ahead markets can result in suboptimal or even infeasible schedules for generating units and inaccurate predictions of actual costs and wind curtailment. Here we compare different optimal models of day-ahead markets based on unit commitment (UC) formulations, especially energy- vs. power-based UC; excluding or including startup and shutdown trajectories; and deterministic vs. “ideal” stochastic models to face wind uncertainty. The day-ahead hourly schedules are then evaluated against actual wind and load profiles using a (5-min) real-time dispatch model. We find that each simplification usually causes expected generation costs to increase by several percentage points, and results in significant understatement of expected wind curtailment and, in some cases, load interruptions