optimization of ccs-enabled coal- gas-solar power generation
TRANSCRIPT
Optimization of CCS-Enabled Coal-Gas-Solar Power Generation
Philip G. Brodrick, Charles A. Kang,Adam R. Brandt, Louis J. Durlofsky
May 21st, 2014
Stanford Center for Carbon Storage – Annual Meeting
Optimization of CCS-Enabled Coal-Gas-Solar Power Generation
Outline
Problem Design
System Model
Optimization Methodology
Input Data
Optimization Results
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Auxiliary Heat Source
Flue Gas
Compressed CO2
Scrubbed Flue Gas
Gas Fired
Subsystem
Carbon Capture
Subsystem
Electricity Produced Electricity Consumed Design Decision Variable
Coal Plant
Steam
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Auxiliary Heat Source
Flue Gas
Compressed CO2
Scrubbed Flue Gas
Gas Fired
Subsystem
Solar Thermal
Subsystem
Carbon Capture
Subsystem
Electricity Produced Electricity Consumed Design Decision Variable
Coal Plant
Steam
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Problem Setup Coal plant in New Mexico, exporting power to
Southern California
System and component emissions cap at 500 kg / MWh, based on California Law SB 1368
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Problem Setup Coal plant in New Mexico, exporting power to
Southern California
System and component emissions cap at 500 kg / MWh, based on California Law SB 1368
May 21st, 2014
Source: Visualizing the US Electricity Grid, by NPR
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Based on the GlassPointsystem
Designed to produce process heat for enhanced oil recovery
Relatively low capital cost
Mid-level temperature
www.utilities-me.com 11 Dec. 2012.GlassPoint Installation, Oman – 7 MW
Solar Thermal Subsystem
GlassPoint Troughswww.glasspoint.com. 2013.
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Design Decision Variables
Size of concentrating parabolic troughs
Size of steam turbine
Operational Decision Variables
Percent of steam output to utilize
Percent of steam to run through steam turbine
Solar Thermal Subsystem
(uSol)
(uSol-ST)
(xSize,Sol)
(xSize,Sol-ST)
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(xSize,Sol)(xSize,Sol)
(uSol)
(uSol-ST)
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Capture Subsystem
Amine-based temperature swing absorption system
Thermodynamic and cost data comes from Carnegie Mellon’s Integrated Environmental Control Model (IECM) for a default amine CO2 capture design
Includes storage of CO2 rich solvent to allow for regeneration when steam is available
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Design Decision Variables
Size of absorber
Size of regenerator
Size of amine storage system
Operational Decision Variables
Percent of absorbed CO2 to be stored
Capture Subsystem
(uCap-Stor)
(xSize,Abs)
(xSize,Reg)
(xSize,Stor)
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Mixed integer nonlinear optimization problem
Two-level optimization – design and operations are optimized iteratively with joint optimization upon convergence
Constrained by CO2 emissions, and by mass and energy balances
Optimization Methodology
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Data
Solar irradiation and electricity prices on an hourly basis are needed
Hourly optimization is expensive, so there is a need to use a limited number of representative days
k-means clustering is used to cluster data based on centroid values
Clusters are based on a 24 element day vector –(12 irradiation, 12 electricity price)
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Paired Electricity and Irradiation Data
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Capture System Operations in Solar Design 54.5 USD/MWh, 30% Investment Tax Credit
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Late Spring Cluster
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Capture System Operations
Solar Thermal System
Operations
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Late Spring Cluster
Late Spring Cluster
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54.5 USD/MWh
6.5 USD/GJ
30%
8%
Average Electricity Price:
Natural Gas Price:
Investment Tax Credit:
Discount Rate:
Base Case
Solar
Thermal
Design
Natural
Gas
Design
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Parametric Study
Investment Tax Credit
Power Purchase Agreement
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Parametric Study
Investment
Tax Credit
Solar Power
Purchase Agreement
Investment Tax Credit
Power Purchase Agreement
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Solar / Natural Gas NPV Comparison
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PPA : 100 USD/MWh
Discount Rate : 8%
PPA : None
Discount Rate : 6%
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Solar / Natural Gas NPV Comparison
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PPA : 100 USD/MWh
Discount Rate : 8%
PPA : None
Discount Rate : 6%
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Concluding Remarks
Developed a modeling and optimization procedure for CCS-enabled fossil fuel and solar thermal systems
Solar thermal designs profitable with average electricity prices ≥ 54.5 USD/MWh and a 30% investment tax credit, though natural gas systems are generally preferable
Future work may include more detailed system models, improved optimization procedures, the treatment of other locations, and additional carbon policies
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Acknowledgements
John O’Donnell and GlassPoint Inc.
Obi Isebor
Stanford Center for Computational Earth and Environmental Sciences
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Problem Setup Coal Plant in New Mexico, exporting power to
Southern California.
System and component emissions cap at 499 kg / MWh, based on California Law SB 1368.
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Design Optimization
max NPV 𝐱, 𝐮 = −𝐶 𝐱 + 𝜏=1𝑁𝑦𝑒𝑎𝑟𝑠 𝑃𝜏(𝐱,𝐮)
(1−𝑟)𝜏s.t. 𝐡des + 𝐡op ≤ 𝟎
Optimization Problem
Operations Optimization
max P 𝐱, 𝐮 = 𝑡=1
𝑁𝑡𝑖𝑚𝑒−𝑠𝑡𝑒𝑝𝑠 𝑅𝑡(𝐱, 𝐮𝑡) − 𝐸𝑡(𝐱, 𝐮𝑡) s.t. 𝐡op ≤ 𝟎
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x ∊ X
u ∊ U
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Design Algorithm – PSO-MADS
Gradient free search
Handles discreet and continuous variables
Dispatch Algorithm – SNOPT
Gradient based search algorithm
Handles continuous variables
Optimization Algorithms
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CO2 intensity – system and subsystems
Total power production – limited to twice the coal plant capacity
Mass balances
System Constraints
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Paired Electricity and Irradiation Data
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Accuracy vs Number of Clusters
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Accuracy vs Number of Clusters
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Early Summer Cluster Winter Cluster
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Solar / Natural Gas NPV Comparison (8% Real DR)
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Solar / Natural Gas NPV Comparison (8% Real DR)
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Solar / Natural Gas NPV Comparison (8% Real DR)
May 21st, 2014