Kojenerasyon santrallerinin üretim planlaması için stokastik bir yaklaşım.

In this thesis, load scheduling problem for cogeneration plants is interpreted in the context of stochastic programming. Cogeneration (CHP) is an important technology in energy supply of many countries. Cogeneration plants are designed and operated to cover the requested time varying demands in heat and power. Load scheduling of cogeneration plants represents a multidimensional optimization problem, where heat and electricity demands, operational parameters and associated costs exhibit uncertain behavior. Cogeneration plants are characterized by their ‘heat to power ratio’. This ratio determines the operating conditions of the plant. However, this ratio may vary in order to adapt to the physical and economical changes in power and to the meteorological conditions. Employing reliable optimization models to enhance short term scheduling capabilities for cogeneration systems is an important research area. The optimal load plan is targeted by achieving maximum revenue for cogeneration plants. Revenue is defined for the purpose of the study as the sales revenues minus total cost associated with the plant operation. The optimization problem, which aims to maximize the revenue, is modeled by thermodynamic analyses. In this context, the study introduces two objective functions: energy based optimization, exergy-costing based optimization. A new method of stochastic programming is developed. This method combines dynamic programming and genetic algorithm techniques in order to improve computational efficiency. Probability density function estimation method is introduced to determine probability density functions of heat demand and electricity price for each time interval in the planning horizon. A neural network model is developed for this purpose to obtain the probabilistic data for effective representation of the random variables. In this study, thermal design optimization for cogeneration plants is also investigated with particular focus on the heat storage volume.Ph.D. - Doctoral Progra