With the increasing application of battery energy storage in buildings, networks and transportation, an emerging challenge to overall system resilience is in understanding the constituent asset health. Current battery energy storage considerations focus on adhering to the technical specification of the service in the short term, rather than the long-term consequences to battery health. However, accurately determining battery health generally requires invasive measurements or computationally expensive physicsbased models which do not scale up to a fleet of assets cost-effectively. This paper alternatively proposes capturing cumulative maloperation through a physics model-free proxy for cell health, articulated via the strong influence misuse has on the internal chemical state. A Hidden Markov Chain approach is used to automatically recognize violations of chemistry specific usage preferences from sequences of observed charging actions. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios.
This version is available at https://strathprints.strath.ac.uk/61467/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.Energy storage day-ahead scheduling to reduce grid energy export and increase self-consumption for micro-grid and small power park applications. AbstractDevelopments in energy storage technology will start to play a prominent role in overcoming the problems of generation intermittency by providing the ability to shift demand to times when generation is available. However, exploiting the potential of this technology requires the design of an optimal charging and discharging schedule to allow its integration with the energy network that brings maximum advantage to both the system and the user. This paper introduces a mathematical model for generation and demand forecasting with energy storage scheduling that can be used for micro-grid and small power park applications. The proposed solution models the physical limitations associated with the energy storage technology used, which will constrain charge and discharge schedules beyond what can be forecast for them. A case study of a community feeder with large PV installations is presented to demonstrate the effectiveness of the model. Day-ahead charge and discharge schedules were produced that increased self-consumption within the system and reduced energy export to the grid. The main contribution of this work is the design of a generic parametrized forecasting and energy storage scheduling tool that will be a platform for further development to specialized storage technology and its potential scalability.
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