A framework to model and optimize the operation of lithium-ion energy storage in electricity markets, and an assessment of lithium-ion energy storage in Texas

dc.contributor.advisorWebber, Michael E., 1971-en
dc.contributor.committeeMemberBaldick, Rossen
dc.contributor.committeeMemberBickel, James E.en
dc.contributor.committeeMemberChen, Dongmeien
dc.contributor.committeeMemberEdgar, Thomas Fen
dc.creatorFares, Robert Leoen
dc.date.submittedAugust 2015en
dc.description.abstractThe lithium-ion (Li-ion) battery has become an established technology in portable electronics and electric vehicle applications. At the same time, there is rising interest in grid-based battery energy storage to improve the flexibility of the electric grid and integrate intermittent sources of renewable energy. To provide information for energy storage developers, battery system operators, state policymakers, and the general public, this research develops a framework to characterize, operate, and evaluate Li-ion battery energy storage that is connected to the electric grid and participates in a wholesale electricity market. Methods are developed to characterize and model the voltage, temperature, and capacity degradation behavior of a Li-ion battery system. Then, an optimization program is developed to schedule Li-ion storage in an electricity market while modeling and controlling its operating state and rate of capacity loss. The optimization framework is used to simulate operation of Li-ion storage in Texas’s Electric Reliability Council of Texas (ERCOT) electricity from 2002–2014, and the market revenue potential and operating lifetime of Li-ion storage are approximated. It is shown that controlling capacity degradation in operational management can extend the lifetime of Li-ion battery modules by approximately 30–60% without significantly reducing market revenue potential. To test the reliability impact of distributed Li-ion storage, residential electricity data are used to approximate how long a battery system could isolate downstream electricity customers during an outage. Thousands of outage events are simulated to show the expected islanding duration for outages occurring at different times of day. The potential reliability benefit from avoided residential electric outages is calculated and found to be much smaller than the revenue potential from the electricity market, indicating that market applications should be prioritized over residential reliability applications in siting and operating a battery system. It is found that the net-present value (NPV) of a Li-ion battery system providing wholesale energy arbitrage in the ERCOT market is negative across a range of cost and benefit parameters. However, controlling capacity degradation in operational management of the battery system is found to increase its value by approximately $100/kWh of rated energy capacity. The NPV of a battery system providing a combination of energy and Fast Responding Regulation Service (FRRS) is found to be positive across a wide range of cost and benefit parameters, indicating a Li-ion battery system could most likely provide a combination of energy and FRRS service to the ERCOT electricity market at a profit. Controlling capacity degradation in operational management of the battery system for energy and FRRS is found to have little impact on its NPV. However, controlling capacity loss makes the NPV less sensitive to variation in the lifetime of the battery modules, reducing the risks associated with premature battery cell failure.en
dc.description.departmentMechanical Engineeringen
dc.subjectEnergy storageen
dc.subjectLithium-ion batteryen
dc.subjectElectric Reliability Council of Texasen
dc.subjectElectricity marketen
dc.subjectOperational managementen
dc.subjectCapacity degradationen
dc.titleA framework to model and optimize the operation of lithium-ion energy storage in electricity markets, and an assessment of lithium-ion energy storage in Texasen
thesis.degree.departmentMechanical Engineeringen
thesis.degree.disciplineMechanical engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.nameDoctor of Philosophyen

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