Optimization of CCS-Enabled Coal- Gas-Solar Power Generation

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


Outline

Problem Design

System Model

Optimization Methodology

Input Data

Optimization Results

2May 21st, 2014


3May 21st, 2014

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


5

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


6

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


7

Process Flow Model

May 21st, 2014


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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.

May 21st, 2014


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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)

May 21st, 2014

(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

May 21st, 2014


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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)

May 21st, 2014


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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

May 21st, 2014


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Optimization Overview


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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)

May 21st, 2014


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Paired Electricity and Irradiation Data

May 21st, 2014


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Capture System Operations in Solar Design 54.5 USD/MWh, 30% Investment Tax Credit

May 21st, 2014

Late Spring Cluster


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Capture System Operations

Solar Thermal System

Operations

May 21st, 2014

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


Parametric Study

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20May 21st, 2014

Parametric Study


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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

May 21st, 2014

PPA : 100 USD/MWh

Discount Rate : 8%

PPA : None

Discount Rate : 6%


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May 21st, 2014

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

May 21st, 2014


26

Acknowledgements

John O’Donnell and GlassPoint Inc.

Obi Isebor

Stanford Center for Computational Earth and Environmental Sciences

May 21st, 2014


27

Questions?

May 21st, 2014


28

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.

May 21st, 2014


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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

May 21st, 2014

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

May 21st, 2014


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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

May 21st, 2014


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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)

May 21st, 2014


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May 21st, 2014

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Optimization of CCS-Enabled Coal- Gas-Solar Power Generation

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