
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>PRICING AND APPLICATION OF ELECTRIC STORAGE</dc:title>
  <dc:creator>Zhao, Jialin</dc:creator>
  <dc:subject>Dynamic Programming</dc:subject>
  <dc:subject>Electric Storage</dc:subject>
  <dc:subject>Monte Carlo Simulation</dc:subject>
  <dc:subject>Pricing</dc:subject>
  <dc:description>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.</dc:description>
  <dc:description>Ph.D. in Management Science, May 2017</dc:description>
  <dc:contributor>Kang, Sang Baum</dc:contributor>
  <dc:date>2017</dc:date>
  <dc:date>2017-05</dc:date>
  <dc:type>Dissertation</dc:type>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>islandora:9250</dc:identifier>
  <dc:identifier>http://hdl.handle.net/10560/4172</dc:identifier>
  <dc:source>SSB / Stuart School of Business</dc:source>
  <dc:source>Illinois Institute of Technology</dc:source>
  <dc:language>en</dc:language>
  <dc:rights>In Copyright</dc:rights>
  <dc:rights>http://rightsstatements.org/page/InC/1.0/</dc:rights>
  <dc:rights>Restricted Access</dc:rights>
</oai_dc:dc>
