CCHP Plant Optimization via Reduced-Order Modeling
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Solution Overview
Problem
Current thermo-economic modeling and optimization of Combined Cooling, Heating, and Power (CCHP) plants face challenges in determining optimal set-points for efficient operation due to large computation times and practical concerns related to the integration of detailed models, necessitating the development of reduced order models that accurately capture important dependencies for efficient and robust optimization.
Innovation Solution
The method involves converting complex models of CCHP plant components into simplified models, using these simplified models as constraints for optimization to output decision variables, and adjusting plant controls based on the output variables, thereby optimizing operating costs and plant performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If detailed models are integrated for plant-wide optimization, then manufacturing precision is improved, but computation time increases
Solution Approach 1:
The detailed plant model is segmented into multiple sub-models representing different components (boiler, turbine, condenser, etc.). Each sub-model can be independently developed, validated, and optimized. This segmentation allows parallel computation of individual components while maintaining the ability to integrate them for overall plant optimization, thereby reducing total computation time while preserving accuracy.
Solution Approach 2:
Interface models or boundary condition models are introduced as intermediaries between detailed component models and the overall plant optimization system. These intermediary models simplify the coupling between components by using reduced-order representations or pre-computed lookup tables, maintaining accuracy where needed while reducing computational burden in the overall optimization loop.
2Measurement precision
If detailed models are used for component representation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
Different levels of model detail are applied to different plant components based on their specific requirements. Critical components where high accuracy is essential (e.g., turbine performance, boiler efficiency) use detailed models, while less critical components use simplified representations. This local differentiation maintains overall model accuracy while reducing total system complexity and improving tractability.
Solution Approach 2:
The model complexity is made dynamic and adaptive. The system can automatically adjust the level of detail in different sub-models based on operating conditions, optimization needs, and computational resource availability. This allows the model to be as detailed as necessary for accurate optimization while avoiding unnecessary complexity in regions where simplified models suffice.
Data Source
AI summary
A method to manage operating costs of a combined cooling heating and power (CCHP) plant that includes converting complex models of underlying components of the plant into simplified models (S101), performing an optimization that uses the simplified models as constraints of the optimization to output at least one decision variable (S102), and adjusting controls of the plant based on one or more of the output decision variables (S103).