creator Zhao, Jialin advisor Kang, Sang Baum Electric storage provides a vehicle to store power for future use. It contributes to the grids in multiple aspects. For instance, electric storage is a more effective approach to provide electricity ancillary services than conventional methods. Additionally, electric storage, especially fast-responding units, allows owners to implement high-frequency power transactions in settings such as the 5-min real-time trading market. Such high-frequency power trades were limited in the past. However, as technology advances, the power markets have evolved. For instance, the California Independent System Operator now supports the 5-min real-time trading and the hourly day-ahead ancillary services bidding. Existing valuation models of electric storage were not designed to accommodate these recent market developments. To fill this gap, I focus on the fast-responding grid-level electric storage that provides both the real-time trading and the day-ahead ancillary services bidding. To evaluate such an asset, I propose a Monte Carlo Simulation-based valuation model. The foundation of my model is simulations of power prices. This study develops a new simulation model of electric prices. It is worth noting that, unlike existing models, my proposed simulation model captures the dependency of the real-time markets on the day-ahead markets. Upon such simulations, this study investigates the pricing and the application of electric storage at a 5-min granularity. Essentially, my model is a Dynamic Programming system with both endogenous variables (i.e., the State-of-Charge of electric storage) and exogenous variables (i.e., power prices). My first numerical example is the valuation of a fictitious 4MWh battery. Similarly, my second example evaluates the application of two units of 2MWh batteries. By comparing these two experiments, I investigate the issues related to battery configurations, such as the impacts of splitting storage capability on the valuation of electric storage. Submitted by Erma Thomas (thomase@iit.edu) on 2017-11-06T21:36:20Z No. of bitstreams: 1 etdadmin_upload_499682.zip: 1792292 bytes, checksum: 8345dd77d028538ede62bf98c14c8de4 (MD5) Made available in DSpace on 2017-11-06T21:36:20Z (GMT). No. of bitstreams: 1 etdadmin_upload_499682.zip: 1792292 bytes, checksum: 8345dd77d028538ede62bf98c14c8de4 (MD5) Previous issue date: 2017-05 Ph.D. in Management Science, May 2017 2017 2017-05 http://hdl.handle.net/10560/4172 en Dynamic Programming Electric Storage Monte Carlo Simulation Pricing Dissertation born digital application/pdf In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access SSB / Stuart School of Business Illinois Institute of Technology Affiliated department