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 Laboratory (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 = {Thu Jun 24 00:00:00 EDT 2021},
month = {Thu Jun 24 00:00:00 EDT 2021}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referenced in this record:
Concentrating solar power plant investment and operation decisions under different price and support mechanisms
journal, October 2013
- Kost, Christoph; Flath, Christoph M.; Möst, Dominik
- Energy Policy, Vol. 61
SolarPILOT: A power tower solar field layout and characterization tool
journal, September 2018
- Wagner, Michael J.; Wendelin, Tim
- Solar Energy, Vol. 171
Comparison of three different approaches for the optimization of the CSP plant scheduling
journal, July 2017
- Petrollese, Mario; Cocco, Daniele; Cau, Giorgio
- Solar Energy, Vol. 150
Least squares quantization in PCM
journal, March 1982
- Lloyd, S.
- IEEE Transactions on Information Theory, Vol. 28, Issue 2
Optimal operation of a solar-thermal power plant with energy storage and electricity buy-back from grid
journal, March 2013
- Lizarraga-Garcia, Enrique; Ghobeity, Amin; Totten, Mark
- Energy, Vol. 51
Optimized dispatch in a first-principles concentrating solar power production model
journal, October 2017
- Wagner, Michael J.; Newman, Alexandra M.; Hamilton, William T.
- Applied Energy, Vol. 203
System Advisor Model, SAM 2014.1.14: General Description
report, February 2014
- Blair, Nate; Dobos, Aron P.; Freeman, Janine
Carpe diem: A novel approach to select representative days for long-term power system modeling
journal, October 2016
- Nahmmacher, Paul; Schmid, Eva; Hirth, Lion
- Energy, Vol. 112
Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability
journal, July 2017
- Pfenninger, Stefan
- Applied Energy, Vol. 197
Divide and Conquer? ${k}$-Means Clustering of Demand Data Allows Rapid and Accurate Simulations of the British Electricity System
journal, May 2014
- Green, Richard; Staffell, Iain; Vasilakos, Nicholas
- IEEE Transactions on Engineering Management, Vol. 61, Issue 2
Self-scheduling for energy and spinning reserve of wind/CSP plants by a MILP approach
journal, December 2014
- Pousinho, H. M. I.; Silva, H.; Mendes, V. M. F.
- Energy, Vol. 78
A systems approach to quantifying the value of power generation and energy storage technologies in future electricity networks
journal, December 2017
- Heuberger, Clara F.; Staffell, Iain; Shah, Nilay
- Computers & Chemical Engineering, Vol. 107
The Value of Concentrating Solar Power and Thermal Energy Storage
journal, October 2010
- Sioshansi, Ramteen; Denholm, Paul
- IEEE Transactions on Sustainable Energy, Vol. 1, Issue 3
Optimal Offering Strategy for Concentrating Solar Power Plants in Joint Energy, Reserve and Regulation Markets
journal, July 2016
- He, Guannan; Chen, Qixin; Kang, Chongqing
- IEEE Transactions on Sustainable Energy, Vol. 7, Issue 3
How Thermal Energy Storage Enhances the Economic Viability of Concentrating Solar Power
journal, February 2012
- Madaeni, Seyed Hossein; Sioshansi, Ramteen; Denholm, Paul
- Proceedings of the IEEE, Vol. 100, Issue 2
A comparison of price-taker and production cost models for determining system value, revenue, and scheduling of concentrating solar power plants
journal, December 2018
- Martinek, Janna; Jorgenson, Jennie; Mehos, Mark
- Applied Energy, Vol. 231
Clustering by Passing Messages Between Data Points
journal, February 2007
- Frey, B. J.; Dueck, D.
- Science, Vol. 315, Issue 5814
Selecting Representative Days for Capturing the Implications of Integrating Intermittent Renewables in Generation Expansion Planning Problems
journal, May 2017
- Poncelet, Kris; Hoschle, Hanspeter; Delarue, Erik
- IEEE Transactions on Power Systems, Vol. 32, Issue 3
FCM: The fuzzy c-means clustering algorithm
journal, January 1984
- Bezdek, James C.; Ehrlich, Robert; Full, William
- Computers & Geosciences, Vol. 10, Issue 2-3
Optimal offering strategy for a concentrating solar power plant
journal, October 2012
- Dominguez, R.; Baringo, L.; Conejo, A. J.
- Applied Energy, Vol. 98