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.

This paper presents the latest developments of a methodology for the initial design of the water/steam cycle in combined-cycle power plants, which aims at delivering optimal designs from an operator’s perspective. To this end, an evolutionary algorithm optimization toolbox is coupled to a process model of the water/steam cycle. The process model requires the definition of a number of boundary conditions (like GT type and ambient conditions) and the selection of the cycle configuration (number of pressure levels, single or double reheat, supplementary firing, heat integration with GT coolers, fuel gas preheating, steam extraction from the steam turbine and type of cold end, among others). Based on a number of thermodynamic parameters assigned by the optimizer, the process model derives an initial dimensioning and/or selection of the key components and systems from the OEM’s portfolio: HRSG (full, geometry-based technical dimensioning), piping, steam turbines, condenser and generator, among others. For each of those, realistic designs are ensured by checking and enforcing the component design rules. Finally, performance and cost are derived. In the latest development, the process model computes the plant performance in a number of off-design conditions, specified in a plant operating profile. These may include different ambient conditions, GT loads, power augmentation (e.g. supplementary firing, inlet fogging and evaporative cooling) and steam exports (e.g. to district heating, desalination plant, carbon capture system) or imports (e.g. from a solar field). The cost of electricity (CoE), net present value (NPV) or average efficiency of the plant design in the given operating profile is the feedback to the optimization algorithm. This guides the process towards the definition of a plant design that gives the best thermo-economic performance under the specified economic boundary conditions and operating scenario. In a typical example, an air-cooled peaking plant needs to be optimized to maximize NPV in an operating scenario characterized by large spikes of the electricity price in hot summer days, during which the plant operator wants to use supplementary firing to boost power production. The described methodology is applied to find the most advantageous dimensions of the supplementary firing to be installed and the right HRSG design pressure at design conditions, ensuring that all design rules and technical limits are respected in all operating conditions. In this way, an optimal point is found in the trade-off between amount of supplementary firing and dimensions of HRSG and air-cooled condenser, delivering the highest possible benefit to the plant operator.

This article presents an initial design methodology of the water/steam cycle of combined-cycle power plants. From prescribed boundary conditions such as the GT type or ambient conditions, the water/steam cycle process model performs a computation and initial design of all key components, leading to cycle performance and cost. Particular focus is given here to the Heat Recovery Steam Generator (HRSG), a key component for heat integration having a large impact on both plant cost and performance. With the assistance of an optimization toolbox, optimal designs are found with respect to cost and performance. The process model allows a number of water/steam cycle configurations. Features include the number of pressure levels, the choice of single or double reheat, options for supplementary firing in the HRSG, heat integration with GT coolers, fuel gas preheating and steam extraction from the steam turbine. From prescribed thermodynamic inputs, the model computes and/or selects key components and systems from the Original Equipment Manufacturer (OEM) portfolio: HRSG, piping, steam turbine, condenser and generator. For each key component and system, the performance and cost are derived. The initial design of the HRSG fully integrates all interfaces and is supported by a sub-optimization step, which provides proper surfacing and sequencing of heat exchanger components with the target of minimizing cost. To achieve the required accuracy, the HRSG is first designed technically in detail, namely dimensioning and material selection of finned tubes, structural steel, casing and insulation. The resulting partial bill of quantities is then converted into cost, applying appropriate material rates. This approach guarantees full sensitivity of the model to mass flow, pressure or temperature changes at any location in the HRSG. Coupled to this process model, the multi-objective optimization toolbox allows identifying the pareto front for plant net performance and plant cost, clearly two conflicting objectives. In the example application of a KA26–1 combined-cycle power plant, steps are identified on the pareto front, which can be associated with the number of HRSG modules. For selected project economic conditions and plant operation profile, the pareto front can be post-processed to identify the design with minimum COE or maximum project NPV. Simultaneous optimization of the complete cycle ensures the best possible integration of all key components. Flexibility, speed and effectiveness of the methodology allow exploring many cycle variants, maximizing the chances of finding the global plant optimum in less time. Having been thoroughly validated, the initial design methodology is applicable for development of standard plants as well as integration of specific customer requirements.