skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

This content will become publicly available on June 24, 2022

Title: 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 » 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.« less

Authors:
; ORCiD logo
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
MainId:6147;UUID:3a1696cd-48da-e911-9c26-ac162d87dfe5;MainAdminID:25787
Grant/Contract Number:  
DE-AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Solar Energy
Additional Journal Information:
Journal Volume: 224
Country of Publication:
United States
Language:
English
Subject:
41 EE - Solar Energy Technologies Office (EE-4S); 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. https://doi.org/https://dx.doi.org/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/https://dx.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/https://dx.doi.org/10.1016/j.solener.2021.06.002.
@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 = {https://dx.doi.org/10.1016/j.solener.2021.06.002},
journal = {Solar Energy},
number = ,
volume = 224,
place = {United States},
year = {2021},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on June 24, 2022
Publisher's Version of Record

Save / Share:

Works referenced in this record:

Concentrating solar power plant investment and operation decisions under different price and support mechanisms
journal, October 2013


SolarPILOT: A power tower solar field layout and characterization tool
journal, September 2018


Comparison of three different approaches for the optimization of the CSP plant scheduling
journal, July 2017


Least squares quantization in PCM
journal, March 1982


Optimal operation of a solar-thermal power plant with energy storage and electricity buy-back from grid
journal, March 2013


Optimized dispatch in a first-principles concentrating solar power production model
journal, October 2017


Carpe diem: A novel approach to select representative days for long-term power system modeling
journal, October 2016


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
  • DOI: 10.1109/TEM.2013.2284386

Self-scheduling for energy and spinning reserve of wind/CSP plants by a MILP approach
journal, December 2014


A systems approach to quantifying the value of power generation and energy storage technologies in future electricity networks
journal, December 2017


The Value of Concentrating Solar Power and Thermal Energy Storage
journal, October 2010


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
  • DOI: 10.1109/TSTE.2016.2533637

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
  • DOI: 10.1109/JPROC.2011.2144950

Clustering by Passing Messages Between Data Points
journal, February 2007


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
  • DOI: 10.1109/TPWRS.2016.2596803

FCM: The fuzzy c-means clustering algorithm
journal, January 1984


Optimal offering strategy for a concentrating solar power plant
journal, October 2012