This paper presents the fundamentals of an evolutionary, thermo-economic plant design methodology, which enables an improved and customer-focused optimization of the bottoming cycle of a large Combined Cycle Power Plant. The new methodology focuses on the conceptual design of the CCPP applicable to the product development and the pre-acquisition phase. After the definition of the overall plant configuration such as the number of gas turbines used, the type of main cooling system and the related fix investment cost, the CCPP is optimized towards any criteria available in the process model (e.g. lowest COE, maximum NPV/IRR, highest net efficiency). In view of the fact that the optimization is performed on a global plant level with a simultaneous hot- and cold- end optimization, the results clearly show the dependency of the HRSG steam parameters and the related steam turbine configuration on the definition of the cold end (Air Cooled Condenser instead of Direct Cooling). Furthermore, competing methods for feedwater preheating (HRSG recirculation, condensate preheating or pegging steam), different HRSG heat exchanger arrangements as well as applicable portfolio components are automatically evaluated and finally selected. The developed process model is based on a fixed superstructure and copes with the full complexity of today’s bottoming cycle configurations as well with any constraints and design rules existing in practice. It includes a variety of component modules that are prescribed with their performance characteristics, design limitations and individual cost. More than 100 parameters are used to directly calculate the overall plant performance and related investment cost. Further definitions on payment schedule, construction time, operation regime and consumable cost results in a full economic life cycle calculation of the CCPP. For the overall optimization the process model is coupled to an evolutionary optimizer, whereas around 60 design parameters are used within predefined bounds. Within a single optimization run more than 100’000 bottoming cycle configurations are calculated in order to find the targeted optimum and thanks to today’s massive parallel computing resources, the solution can be found over night. Due to the direct formulation of the process model, the best cycle configuration is a result provided by the optimizer and can be based on a single-, dual or triple pressure system using non-reheat, reheat or double reheat configuration. This methodology enables to analyze also existing limitations and characteristics of the key components in the process model and assists to initiate new developments in order to constantly increase the value for power plant customers.
Distributionally robust optimization with multiple time scales: valuation of a thermal power plant
