Development of Physics-Based Models of Lithium-ion Battery Energy Storage for Power System Techno-Economic Studies
Date
2023-09-21
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Abstract
The pathway to achieving a sustainable, low-carbon power system includes the widespread integration of energy storage to tackle intermittency of renewable energy sources and provide stability to the grid through various grid services. Among the wide range of stationary energy storage technologies available, the lithium-ion battery dominates the growth in installations throughout the world. Although lithium-ion battery energy storage systems are complex grid assets with nonlinear characteristics and lifespans that depend on operating conditions, the majority of economic assessments are conducted using a simple energy reservoir model that does not consider the physical processes occurring inside the lithium-ion battery storage. This thesis focuses on the development of physics-based models for lithium-ion battery energy storage in power system techno-economic studies. The aim of this work is to assist developers and investors in making better-informed decisions. In this work, modelling approaches used to represent lithium-ion battery energy storage in power system operation and planning studies are reviewed. The role of advanced models in enhancing the accuracy of economic evaluations and producing feasible schedules for battery storage providing transmission-level services is discussed. More importantly, this work proposes three physics-based mixed-integer models for battery energy storage for use in power system operation research studies. The first model is based on the single particle model and replicates the nonlinear operational characteristics of the battery. This model can be used for short-term operation studies. The second proposed model combines the widely-used energy reservoir model with the physical description of solid electrolyte interphase formation as a degradation mechanism. This model has been tested for long-term studies in both energy and power grid applications. Finally, the third proposed model is a data-driven model that accurately reproduces the degradation processes and nonlinear performance of the lithium-ion cell. The model facilitates long-term assessment of battery energy storage and effectively tracks both capacity and power fade over time. The results obtained from all the models are validated using the digital twin, which is based on the single particle model.
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Keywords
Lithium-ion battery, battery energy storage system, degradation, operation, economic energy arbitrage, neural network, physics-based models
Citation
Vykhodtsev, A. (2023). Development of physics-based models of lithium-ion battery energy storage for power system techno-economic studies (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.