Abstract
The integration of thermal energy storage into a concentrating solar power system allows for mitigating some of the risk associated with uncertain solar irradiance and uncertain energy prices. We solve a 48 h dispatch optimization model with continually updated conditional point forecasts of both direct normal irradiance (DNI) and electricity prices with a rolling-horizon scheme at hourly resolution over the course of a year. Joint, conditional forecasts for DNI and prices are formed using an autoregressive moving-average time series model with exogenous weather predictors. We guide dispatch using a mixed-integer programming model, but in order to evaluate performance we use the System Advisor Model (SAM) of the National Renewable Energy Laboratory. SAM is a techno-economic simulation model that accounts for plant thermodynamics with higher fidelity. Our conditional DNI forecasts improve annual revenue by 4%–12% over using historical forecasts based on data from previous years. Conditional price forecasts improve annual revenue by 6%–19% in the real-time market over analogous historical forecasts. Updating these forecasts every six hours, rather than every 24 h, further improves annual revenue by 5%–6%. We also investigate a method that values terminal inventory in our dispatch optimization model, again when used in a rolling-horizon scheme.
| Original language | English (US) |
|---|---|
| Article number | 119978 |
| Journal | Applied Energy |
| Volume | 326 |
| DOIs | |
| State | Published - Nov 15 2022 |
Funding
This work was authored in part by NREL, operated by Alliance for Sustainable Energy, LLC, for the U.S. DOE under Contract No. DE-AC36-08GO28308. Funding was provided by the U.S. DOE's Office of Energy Efficiency and Renewable Energy, Solar Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. Gökçe Kahvecioğlu's work was also partially supported by a Fulbright Doctoral Student Scholarship. The authors thank William Hamilton and Alexandra Newman of the Colorado School of Mines for valuable discussions regarding the dispatch optimization model and its integration in NREL's SAM. This work was authored in part by NREL , operated by Alliance for Sustainable Energy, LLC, for the U.S. DOE under Contract No. DE-AC36-08GO28308 . Funding was provided by the U.S. DOE’s Office of Energy Efficiency and Renewable Energy , Solar Energy Technologies Office . The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. Gökçe Kahvecioğlu’s work was also partially supported by a Fulbright Doctoral Student Scholarship . The authors thank William Hamilton and Alexandra Newman of the Colorado School of Mines for valuable discussions regarding the dispatch optimization model and its integration in NREL’s SAM. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors’ work was funded the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308 from the U.S. DOE’s Office of Energy Efficiency and Renewable Energy, Solar Energy Technologies Office. Gökçe Kahvecioğlu’s work was further supported by a Fulbright Doctoral Student Scholarship.
Keywords
- Optimization
- Simulation
- Thermal storage
ASJC Scopus subject areas
- Building and Construction
- Renewable Energy, Sustainability and the Environment
- Mechanical Engineering
- General Energy
- Management, Monitoring, Policy and Law
