Conceptual Design of Integrated Energy Systems with Market Interaction Surrogate Models
- University of Notre Dame
- NETL Site Support Contractor, National Energy Technology Laboratory
- National Renewable Energy Laboratory (NREL)
- Sandia National Laboratories (SNL)
Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with the market and can result in overestimated economic values. In this work, we propose a machine learning surrogate-assisted optimization framework to quantify IES/market interactions and thus go beyond price-taker. We use time series clustering to generate representative IES operation profiles for the optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with a polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.
- Research Organization:
- National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
- Sponsoring Organization:
- USDOE Office of Fossil Energy and Carbon Management (FECM); USDOE Office of Electricity (OE)
- OSTI ID:
- 2439594
- Country of Publication:
- United States
- Language:
- English
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