Chance-Constrained Ancillary Service Specification for Heterogeneous Storage Devices

Conference paper (2019)

Authors

Michael Evans Imperial College London
David Angeli Imperial College London
Goran Strbac Imperial College London
Simon H. Tindemans Intelligent Electrical Power Grids - EEMCS
Research Group
Intelligent Electrical Power Grids (EEMCS) (TU Delft)
Copyright: © 2019 Michael Evans, David Angeli, Goran Strbac, Simon H. Tindemans
DOI: https://doi.org/10.1109/ISGTEurope.2019.8905723
Optimal control Energy storage systems Ancillary service Chance-constrained optimisation
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Published Date

22-05-2019

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Electrical Sustainable Energy

Research Group

Intelligent Electrical Power Grids

Abstract

We present a method to find the maximum magnitude of any supply-shortfall service that an aggregator of energy storage devices is able to sell to a grid operator. This is first demonstrated in deterministic settings, then applied to scenarios in which device availabilities are stochastic. In this case we implement chance constraints on the inability to deliver as promised. We show a significant computational improvement in using our method in place of straightforward scenario simulation. As an extension, we present an approximation to this method which allows the determined fleet capability to be applied to any chosen service, rather than having to re-solve the chance-constrained optimisation each time.