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On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages
Publikationstyp
Journal Article
Date Issued
2018-01-15
Sprache
English
Author(s)
Journal
Volume
210
Start Page
1207
End Page
1218
Citation
Applied Energy 210: 1207-1218 (2018)
Publisher DOI
Scopus ID
Publisher
Elsevier
Electric energy generation from renewable energy sources is generally non-dispatchable due to its intrinsic volatility. Therefore, its integration into electricity markets and in power system operation is often based on volatility-compensating energy storage systems. Scheduling and control of this kind of coupled systems is usually based on hierarchical control and optimization. On the upper level, one solves an optimization problem to compute a dispatch schedule and a coherent allocation of energy reserves. On the lower level, one performs online adjustments of the dispatch schedule using, for example, model predictive control. In the present paper, we propose a formulation of the upper level optimization based on data-driven probabilistic forecasts of the power and energy output of the uncontrollable loads and generators dependent on renewable energy sources. Specifically, relying on probabilistic forecasts of both power and energy profiles of the uncertain demand/generation, we propose a novel framework to ensure the online feasibility of the dispatch schedule with a given security level. The efficacy of the proposed scheme is illustrated by simulations based on real household production and consumption data.
Subjects
Chance constraints
Dispatch schedule optimization
Energy storage system
Model predictive control
Probabilistic forecasting
Renewable energy
DDC Class
004: Computer Sciences
333.7: Natural Resources, Energy and Environment
510: Mathematics