
<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>SECURITY-CONSTRAINED UNIT COMMITMENT RESERVE DETERMINATION IN JOINT ENERGY AND ANCILLARY SERVICES AUCTION</dc:title>
  <dc:creator>Ganji Tanha, Mohammad Mahdi</dc:creator>
  <dc:description>This study presents the method in which the energy and ancillary services auction is simultaneously cleared in electricity market. By the security-constraint unit commitment model proposed in this study Independent System Operators (ISO) can determines the sufficient amount of reserve which is necessary to maintain security and reliability of the system. Before the fixed reserve requirement either equal to a percentage of the system peak load or a thermal unit with highest capacity is considered in energy and ancillary service auction in market clearing. The disadvantage of this method is high cost and insufficiency. When it is insufficient the system operator needs to committee more thermal units or does the load curtailment. At the time the fixed quantity is more than needed customers pay more although it is not necessary. Here the sufficient amount of reserve is determined based on the contingency which has been simulated. Contingencies include thermal unit outage and line outage is considered. The amount of reserves is obtained based on thermal units’ physical constraints and the rate offered in the electricity market. Then the integration of wind generation and its effects on the quantity of the reserve determination is considered. Since the wind power generation brings uncertainties to the power system we need to consider scenarios. In order to generate wind power generation scenarios we use Monte Carlo simulation. Since the number of scenarios are too much and increase the problem complexity we use Fast backward/forward scenario reduction. This problem is solved through direct optimization problem including the minimization of the operational cost as well as satisfying the network security constraints when contingency happens.</dc:description>
  <dc:description>M.S. in Electrical Engineering, July 2012</dc:description>
  <dc:contributor>Li, Zuyi</dc:contributor>
  <dc:date>2012-07-25</dc:date>
  <dc:date>2012-07</dc:date>
  <dc:type>Thesis</dc:type>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>islandora:9040</dc:identifier>
  <dc:identifier>http://hdl.handle.net/10560/2901</dc:identifier>
  <dc:source>ECE / Electrical and Computer Engineering</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>
