Summary:
Profitability and operational control of residential lithium-ion-battery storage systems coupled to local PV generation have been studied intensively in the last decade [1, 2]. Lesser attention has been paid to the analysis of industry and utility scale storage systems. We foresee, that the landscape of applications to be served with large scale storage is rapidly changing in forthcoming years. This is e.g. due to increasing shares of variable renewable energies in power grids, altered energy market designs and through perturbations caused by the COVID-19 pandemic:
In this virtual slide-deck we take a closer look at the current trends for grid scale battery storage: lithium-ion battery technology developments as well as regulatory and applicational framework with particular focus of Germany are being looked at. [3]
We take a focus at the energy arbitrage trading on short term markets as a potential revenue stream for battery storage. While the revenue potential is promising already to date, a challenge remains to serve this application with lithium-ion battery storage: high charge/discharge rates and over a thousand full equivalent cycles per year will lead to significant degradation. We propose an integrated optimization framework that allows aging aware battery dispatch in arbitrage markets based on an electro-thermal cell model. We combine the performance, aging and thermal models parametrized for a commercially available state of art lithium-iron phosphate battery cell. For subsequent optimization we derive how the dynamic cell and system behavior can be captured through a three-dimensional parameter space: requested power, temperature, and state of charge. The aging and efficiency aware optimization allows to increase battery lifetime by a factor of 1.4 whilst maintaining ~80% of attainable revenues – an overall 25% profit increase is demonstrated [4].
Lastly, we give a brief overview how the presented optimization tool is part of a larger framework of open-source modelling code available free of charge for all researchers. Model validation and aging analysis are being conducted through the time-series modelling tool SimSES [5].
References
[1] S. Comello and S. Reichelstein, “The emergence of cost effective battery storage,” Nature Communications, vol. 10, no. 1, p. 2038, 2019, doi: 10.1038/s41467-019-09988-z.
[2] H. Hesse, R. Martins, P. Musilek, M. Naumann, C. Truong, and A. Jossen, “Economic Optimization of Component Sizing for Residential Battery Storage Systems,” Energies, vol. 10, no. 7, p. 835, 2017, doi: 10.3390/en10070835.
[3] B. Tepe, N. Collath, H. Hesse, M. Rosenthal, and U. Windelen, “Stationäre Batteriespeicher in Deutschland: Aktuelle Entwicklungen und Trends in 2021,” Energiewirtschaftliche Tagesfragen, vol. 71, no. 3, pp. 23–27, 2021.
[4] V. Kumtepeli, H. Hesse, M. Schimpe, A. Tripathi, Y. Wang, and A. Jossen, “Energy Arbitrage Optimization with Battery Storage: 3D-MILP for Electro-thermal Performance and Semi-empirical Aging Models,” IEEE Access, vol. 2021. [Online]. Available: accepted for publication
[5] M. Möller et al., “SimSES: A holistic simulation framework for modeling and analyzing stationary energy storage systems,” [Online]. Available: Manuscript in preparation
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