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Title: Concurrent Optimization of Capital Cost and Expected O&M

Abstract

Concentrating solar power (CSP) technologies can utilize heat from concentrated sunlight from a field of tracking mirrors to generate electricity, reform fuel, provide process heat, or augment fossil plant heat sources. Electricity-generating power tower systems focus light from thousands of independent heliostats onto a thermal receiver, which uses the focused light to warm a heat transfer fluid (HTF), typically, a molten nitrate salt. The HTF is then sent to a power generation cycle or diverted into thermal energy storage (TES) for later use. Thermal storage is – in principle – a straightforward proposition. However, optimal utilization of a TES resource is complex and multi-faceted: thermal energy may be dispatched to produce electricity immediately upon first availability, or thermal energy may be reserved for next-day peak periods at risk of filling storage and dumping energy, or a portion of the thermal energy can be reserved to maintain equipment temperatures, reducing power cycle startup time, etc. Many possible dispatch permutations variously emphasize producing peak power, operating through transients, expediting daily startup, etc. The best operation strategy can change day-to-day throughout the year, depending on the weather and market pricing forecasts. The project we describe in this report develops a software package thatmore » allows users to explore design optimization, operations decisions, and performance characterization of concentrating solar power tower plants. Users interface with the tool through a scripting language, and results are reported in time series tables, plots, runtime logs, and design outputs. Users choose from a list of variables such as tower height, solar multiple, design-point irradiance, thermal storage size, etc., and specify information about the system using a list of parameters. The software can then optimize the specified variables to reduce the cost of energy produced by the system while meeting certain production requirements, accounting for uncertain weather and electricity price forecasts, and correcting for equipment failures or repair time. The software we develop is the first comprehensive design tool of its kind to incorporate all of these aspects while being deployed as open source.« 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:
1770880
Report Number(s):
NREL/TP-5700-79093
MainId:33319;UUID:128d0d81-ee78-4139-ac4d-2c63db5d6a7d;MainAdminID:19721
DOE Contract Number:  
DE-AC36-08GO28308
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
41 EE - Solar Energy Technologies Office (EE-4S); concentrating solar power; operations and maintenance; central receiver plants; design and dispatch optimization; simulation

Citation Formats

Wagner, Michael J., Newman, Alexandra, Morton, David, and Leyffer, Sven. Concurrent Optimization of Capital Cost and Expected O&M. United States: N. p., 2021. Web. doi:10.2172/1770880.
Wagner, Michael J., Newman, Alexandra, Morton, David, & Leyffer, Sven. Concurrent Optimization of Capital Cost and Expected O&M. United States. https://doi.org/10.2172/1770880
Wagner, Michael J., Newman, Alexandra, Morton, David, and Leyffer, Sven. 2021. "Concurrent Optimization of Capital Cost and Expected O&M". United States. https://doi.org/10.2172/1770880. https://www.osti.gov/servlets/purl/1770880.
@article{osti_1770880,
title = {Concurrent Optimization of Capital Cost and Expected O&M},
author = {Wagner, Michael J. and Newman, Alexandra and Morton, David and Leyffer, Sven},
abstractNote = {Concentrating solar power (CSP) technologies can utilize heat from concentrated sunlight from a field of tracking mirrors to generate electricity, reform fuel, provide process heat, or augment fossil plant heat sources. Electricity-generating power tower systems focus light from thousands of independent heliostats onto a thermal receiver, which uses the focused light to warm a heat transfer fluid (HTF), typically, a molten nitrate salt. The HTF is then sent to a power generation cycle or diverted into thermal energy storage (TES) for later use. Thermal storage is – in principle – a straightforward proposition. However, optimal utilization of a TES resource is complex and multi-faceted: thermal energy may be dispatched to produce electricity immediately upon first availability, or thermal energy may be reserved for next-day peak periods at risk of filling storage and dumping energy, or a portion of the thermal energy can be reserved to maintain equipment temperatures, reducing power cycle startup time, etc. Many possible dispatch permutations variously emphasize producing peak power, operating through transients, expediting daily startup, etc. The best operation strategy can change day-to-day throughout the year, depending on the weather and market pricing forecasts. The project we describe in this report develops a software package that allows users to explore design optimization, operations decisions, and performance characterization of concentrating solar power tower plants. Users interface with the tool through a scripting language, and results are reported in time series tables, plots, runtime logs, and design outputs. Users choose from a list of variables such as tower height, solar multiple, design-point irradiance, thermal storage size, etc., and specify information about the system using a list of parameters. The software can then optimize the specified variables to reduce the cost of energy produced by the system while meeting certain production requirements, accounting for uncertain weather and electricity price forecasts, and correcting for equipment failures or repair time. The software we develop is the first comprehensive design tool of its kind to incorporate all of these aspects while being deployed as open source.},
doi = {10.2172/1770880},
url = {https://www.osti.gov/biblio/1770880}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {2}
}