Preprint Article Version 1 This version not peer reviewed

Data-Driven Model Based Charging Profile Prediction for Energy Storage Systems

Version 1 : Received: 17 October 2017 / Approved: 17 October 2017 / Online: 17 October 2017 (04:29:19 CEST)

How to cite: Kandasamy, N.K.; Tjandra, R. Data-Driven Model Based Charging Profile Prediction for Energy Storage Systems. Preprints 2017, 2017100111 (doi: 10.20944/preprints201710.0111.v1). Kandasamy, N.K.; Tjandra, R. Data-Driven Model Based Charging Profile Prediction for Energy Storage Systems. Preprints 2017, 2017100111 (doi: 10.20944/preprints201710.0111.v1).

Abstract

Energy storage systems (ESS) are penetrating into various sections of power system through different applications. ESS can be used either as a buffer for intermittent renewable energy sources or as a stand-alone distributed storage for load shifting. ESS use different types of storage devices such as lead-acid batteries, lithium ion batteries, flow batteries, and super-capacitors. Hybrid ESS consisting of few types of storage devices are also common in practice. Determining the load demand of such ESSs at various instances (charging profile) accurately is indispensable in most of the cases. Capacity loss is common phenomenon that occurs in all types of storage devices because of ageing. Capacity loss has to be accounted while determining the charging profile of storage devices for better accuracy. Data-driven modeling is an attractive approach for determining the load demand of ESS due to the availability of valuable data from smart grid technologies. In this paper, the application of different types of data-driven models to predict the current charging profile of the ESS based on previous charging profiles is examined. The proposed method can leverage on the existing data from smart grid and is a black box modeling approach.

Subject Areas

energy storage systems; charging profile; capacity loss; data-driven modeling

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