Efficient prediction of concentrating solar power plant productivity using data clustering
Abstract
Concentrating solar power (CSP) plants convert solar energy to electricity and can be deployed with a thermal storage capability to shift electricity generation from time periods with available solar resource to those with high electricity demand or electricity price. Rigorous optimization of plant design and operational strategies can improve the market-competitiveness and commercial viability; however, such optimization may require hundreds of annual performance simulations, each of which can be computationally expensive when including considerations such as optimization of dispatch scheduling, sub-hourly time resolution, and stochastic effects due to uncertain weather or electricity price forecasts. This paper proposes a methodology to reduce the computational burden associated with simulation of electricity yield and revenue for CSP plants over a single- or multi-year period. Data-clustering techniques are employed to select a small number of limited-duration time blocks for simulation that, when appropriately weighted, can reproduce generation and revenue over a single year or within each year of a multi-year period. After selection of appropriate data features and weighting factors defining similarity between time-series profiles, the methodology captured annual revenue within 2.3%, 1.7%, or 1.2% using simulation of 10, 30, or 50 three-day exemplar time blocks, respectively, for each of three single-year location/weather/market scenariosmore »
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- OSTI Identifier:
- 1808851
- Report Number(s):
- NREL/JA-5700-74928
Journal ID: ISSN 0038-092X; MainId:6147;UUID:3a1696cd-48da-e911-9c26-ac162d87dfe5;MainAdminID:25787
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Solar Energy
- Additional Journal Information:
- Journal Volume: 224; Journal ID: ISSN 0038-092X
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 14 SOLAR ENERGY; concentrating solar power; CSP; data clustering; dispatch optimization; thermal energy storage
Citation Formats
Martinek, Janna, and Wagner, Michael J. Efficient prediction of concentrating solar power plant productivity using data clustering. United States: N. p., 2021.
Web. doi:10.1016/j.solener.2021.06.002.
Martinek, Janna, & Wagner, Michael J. Efficient prediction of concentrating solar power plant productivity using data clustering. United States. https://doi.org/10.1016/j.solener.2021.06.002
Martinek, Janna, and Wagner, Michael J. Thu .
"Efficient prediction of concentrating solar power plant productivity using data clustering". United States. https://doi.org/10.1016/j.solener.2021.06.002. https://www.osti.gov/servlets/purl/1808851.
@article{osti_1808851,
title = {Efficient prediction of concentrating solar power plant productivity using data clustering},
author = {Martinek, Janna and Wagner, Michael J.},
abstractNote = {Concentrating solar power (CSP) plants convert solar energy to electricity and can be deployed with a thermal storage capability to shift electricity generation from time periods with available solar resource to those with high electricity demand or electricity price. Rigorous optimization of plant design and operational strategies can improve the market-competitiveness and commercial viability; however, such optimization may require hundreds of annual performance simulations, each of which can be computationally expensive when including considerations such as optimization of dispatch scheduling, sub-hourly time resolution, and stochastic effects due to uncertain weather or electricity price forecasts. This paper proposes a methodology to reduce the computational burden associated with simulation of electricity yield and revenue for CSP plants over a single- or multi-year period. Data-clustering techniques are employed to select a small number of limited-duration time blocks for simulation that, when appropriately weighted, can reproduce generation and revenue over a single year or within each year of a multi-year period. After selection of appropriate data features and weighting factors defining similarity between time-series profiles, the methodology captured annual revenue within 2.3%, 1.7%, or 1.2% using simulation of 10, 30, or 50 three-day exemplar time blocks, respectively, for each of three single-year location/weather/market scenarios and five plant configurations ranging from low to high solar multiple and storage capacity. When applied to multi-year datasets, the proposed methodology can capture inter-year variability that is unavailable from typical meteorological year (TMY) datasets while simultaneously requiring simulation of less than a single year of data.},
doi = {10.1016/j.solener.2021.06.002},
journal = {Solar Energy},
number = ,
volume = 224,
place = {United States},
year = {2021},
month = {6}
}
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