Show simple item record

dc.contributor.advisorEdgar, Thomas F.en
dc.creatorKim, Jong Suken
dc.date.accessioned2014-06-25T19:29:45Zen
dc.date.issued2014-05en
dc.date.submittedMay 2014en
dc.identifier.urihttp://hdl.handle.net/2152/24830en
dc.descriptiontexten
dc.description.abstractCombined heat and power (CHP) is a technology that decreases total fuel consumption and related greenhouse gas emissions by producing both electricity and useful thermal energy from a single energy source. In the industrial and commercial sectors, a typical CHP site relies upon the electricity distribution network for significant periods, i.e., for purchasing power from the grid during periods of high demand or when off-peak electricity tariffs are available. On the other hand, in some cases, a CHP plant is allowed to sell surplus power to the grid during on-peak hours when electricity prices are highest while all operating constraints and local demands are satisfied. Therefore, if the plant is connected with the external grid and allowed to participate in open energy markets in the future, it could yield significant economic benefits by selling/buying power depending on market conditions. This is achieved by solving the power system generation scheduling problem using mathematical programming. In this work, we present the application of mixed-integer nonlinear programming (MINLP) approach for scheduling of a CHP plant in the day-ahead wholesale energy markets. This work employs first principles models to describe the nonlinear dynamics of a CHP plant and its individual components (gas and steam turbines, heat recovery steam generators, and auxiliary boilers). The MINLP framework includes practical constraints such as minimum/maximum power output and steam flow restrictions, minimum up/down times, start-up and shut-down procedures, and fuel limits. We provide case studies involving the Hal C. Weaver power plant complex at the University of Texas at Austin to demonstrate this methodology. The results show that the optimized operating strategies can yield substantial net incomes from electricity sales and purchases. This work also highlights the application of a nonlinear model predictive control scheme to a heavy-duty gas turbine power plant for frequency and temperature control. This scheme is compared to a classical PID/logic based control scheme and is found to provide superior output responses with smaller settling times and less oscillatory behavior in response to disturbances in electric loads.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.subjectSchedulingen
dc.subjectUnit commitmenten
dc.subjectEconomic dispatchen
dc.subjectCombined heat and poweren
dc.subjectDay-ahead wholesale energy marketen
dc.subjectMixed-integer nonlinear programmingen
dc.subjectEmergency response serviceen
dc.subjectModel predictive controlen
dc.titleModeling, control, and optimization of combined heat and power plantsen
dc.typeThesisen
dc.date.updated2014-06-25T19:29:45Zen
dc.description.departmentChemical Engineeringen
thesis.degree.departmentChemical Engineeringen
thesis.degree.disciplineChemical Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record