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This project is developing and validating an open source modeling and simulation tool that optimizes the design and operation for concentrating solar power (CSP) plants by characterizing and forecasting operations and maintenance costs, component failure behavior, and the impact of design and maintenance policies. In addition, researchers will develop detailed performance and cost models leveraging the National Renewable Energy Laboratory’s System Advisor Model (SAM), which is a performance and financial model designed to facilitate decision making for people involved in the renewable energy industry. These models will maximize profit through thermal storage dispatch optimization and will account for forecast uncertainty, heliostat and receiver stochastic degradation and failure, and O&M costs including steam turbine service.
Mathematical models will be constructed to describe the behavior of the heliostat field for optical performance, reliability, degradation, and failure. The modeling framework will interface with SAM and be validated against operating plants models and gathered operational data. This will ensure the optimization model provides accurate results that reflect operation over time. Finally, the team will evaluate the optimization model and perform a sensitivity analysis to determine the primary drivers to maximize plant profitability and minimize the cost of energy.
This project will identify the key variables in heliostat field design and plant operation and dispatch the results in a substantial reduction of plant cost. The final optimization tool will be implemented with SAM and use Argonne National Laboratory’s MINOTAUR open-source optimization toolkit. Combining the new optimization model with previous research will allow this project to help CSP reach market parity with traditional forms of energy.