IGCC Power Plant Design of Dispatch...

transcript

IGCC Power Plant Design of Dispatch Capabilities

Ming-Wei Yang, Benjamin P. Omell and Donald J. Chmielewski

Illinois Institute of Technology

AIChE Annual meeting 2012

100 101 102 103 104 105 106 107 108 109

Time (days)

r) Dispatch

"o Dispatch

Department of Chemical and Biological Engineering

Outline

• Motivation and Objective

• Control of IGCC Dispatch Operation

• Equipment Design

• Case Study

Integrated Gasification Combined Cycle

Power System Operation

Gas Turbine

Coal Fired

DemandNuclear

IGCC Role: Conventional Wisdom

Gas Turbine

Coal Fired

DemandNuclearIGCC

IGCC with Dispatch Capabilities

Gas Turbine

Coal Fired

DemandNuclear

IGCC With Dispatch

- Respond to Market Prices - Increase Average Revenue

Electricity Spot Price

Opportunity:

0 5 10 15 200

time (days)

Energy Value

($/MWhr)

Process Modification for Dispatch Capabilities

IGCC with Throughput Manipulation

Manipulation

of Fuel Flow

Hydrogen Gas Storage

Hydrogen

Storage

Assume Carbon

Capture Operations

IGCC with Compressed Air Storage

Compressed

Storage

Dispatch Opportunities

Gasification Block(Includes ASU Distillation,

Gasifier and Acid Gas Removal)

Power Block(Includes Expansion Turbine,

Combustion Turbine, HRSG,

and Steam Turbine)

ASUs,A

s,H2H2

H2 Storage(MH2)

Compressed

Air Storage(MA)

MACASU Main

Air Compressor

Dispatch Manipulations

and Steam Turbine)

ASUs,A

s,H2H2

H2 Storage(MH2)

Compressed

Air Storage(MA)

MACASU Main

Air Compressor

Process Model

and Steam Turbine)

ASUs,A

s,H2H2

H2 Storage(MH2)

Compressed

Air Storage(MA)

MACASU Main

Air Compressor

νββ

Constrained Process Model

and Steam Turbine)

ASUs,A

s,H2H2

H2 Storage(MH2)

Compressed

Air Storage(MA)

MACASU Main

Air Compressor

≤≤

νν 322

νββ

Outline

• Case Study

Controller Design Using Instantaneous Revenue

Economic ModelPredictiveControl

Economic LinearOptimal Control

Economic Model Predictive Control

Maximize Average Revenue with Point-wise-in-time Constraints

min max

C P dtT

s t x Ax Bu Gw

z D x D u

z z zτ

≤ ≤

• Braun, 1992 • Morris et al., 1994• Kintner-Meyer and

Emery, 1995• Henze et al., 2003

• Oldewurtel et al, 2010• Ma et al, 2012 • Mendoza-Serrano and

Chmielewski, 2012

Literature on EMPC

• Conceptual Development and Stability Issues: Rawlings and Amrit (2009); Diehl, et al. (2011); Huang and Biegler (2011); Heidarinejad, et al. (2012)

• Process Scheduling: Karwana and Keblisb (2007); Baumrucker and Biegler (2010); Lima et al. (2011); Kostina et al. (2011)

• Power Systems: Zavala et al. (2009); Xie and Ilić (2009), Hovgaard, et al. (2011), Omell and Chmielewski (2011)

• HVAC Systems: Braun (1992); Morris et al. (1994); Kintner-Meyer and Emery (1995); Henze et al. (2003); Braun (2007); Oldewurtel et al. (2010), Ma et al. (2012); Mendoza and Chmielewski (2012)

Economic Linear Optimal Control

{ }( )

2 min max

1max lim

E[ ] min ,

C P dtT

s t x Ax Bu Gw

z D x D u

→∞

≤ −

Maximize Average Revenue with Statistical Constraints

• Yang, 2009• Yang, Omell, Chmielewski,

• Yang, Omell, Chmielewski,2012

• Mendoza-Serrano andChmielewski, 2012

Electricity Price Model

Electric Price Spectral Density

Frequence (rad/hr)

Electricity Electric Price Realization

100 101 102 103 104 105 106 107 108 109

Time (days)

Scaled Electricity Prices

x Ax Bu Gwα= + +&

Overall Process Model:

min max

x Ax Bu Gw

z D x D u

α= + +

≤ ≤

β β ν

≤ ≤

Constrained State Space Model

Stochastic Constraint

GwBuAxx

uxxuxjj

LDDLDD

GGSBLABLA

...1,2 and 2

such that Find

minmax

=−<<

++Σ+Σ+=

ρρζ

Stochastic Constraints

Constraints:2 ii z<ζ

uxxuxjj

LDDLDD

GGSBLABLA

...1,2 and 2

such that Find

minmax

=−<<

++Σ+Σ+=

ρρζ

[ ][ ]GGGGG

ELOC Objective Function

PCEdtPCT

∫∞→

ELOC Objective Function

PCPCEdtPCT

∫∞→

Then, enforce the conditioneG CP~~

CCEPCE

ELOC Synthesis

uxxuxjj

LDDLDD

GGSBLABLA

...1,2 and 2

minmax

=−<<

++Σ+Σ+=

ρρζ

{ }GeCe

+Σ≥Σ

σζ ,,,0,min

xLu ELOC=⇒

ELOC Synthesis

uxxuxjj

LDDLDD

GGSBLABLA

...1,2 and 2

minmax

=−<<

++Σ+Σ+=

ρρζ

{ }GeCe

+Σ≥Σ

σζ ,,,0,max

xLu ELOC=⇒

Can be Solved with a Convex Optimization Problem

Case Study Process Parameters

Adapted from NETL Baseline Report (Case 2)

- PNnom= 544MW PG

nom= 611MW PCnom = 67MW

- ννννH2nom = ννννGnom = 90 tonneH2/hrννννCnom = ννννASUnom = 790 tonne compressed air/hrννννcoal nom = 220 tonne coal/hr

- ββββ1 = 0.085 MW hr /tonne airββββ2 = 8.78 tonne air / tonneH2ββββ3 = 6.79 MW hr /tonneH2ββββ4 = 2.44 tonne coal /tonneH2

ELOC Example

Synthesis

Storage

MW 1222

H tonne705

Ce /MWhr)20($=Σ

ELOC Example

100 101 102 103 104 105 106 107 108 109

Time (days)

Instantaneous

Average

Maximum

ELOC Example

100 101 102 103 104 105 106 107 108 109

Time (days)

Revenue (

$1000/h

Dispatch

"o Dispatch

Average Revenue without Dispatch= 48,000 $/hrAverage Revenue with Dispatch = 55,000 $/hrOverall increase of 14.6%

ELOC Example

Synthesis

Storage

MW 1222

H tonne705

Ce /MWhr)20($=Σ

Outline

• Case Study

Equipment Size Variables

−−−−

−+Σmax

maxmax

222 ))((max

GHcAHHAA

HHHCef

PcPcMcMc

cPV ννα

uxxuxjj

LDDLDD

GGSBLABLA

...1,2 and 2

minmax

=−<<

++Σ+Σ+=

ρρζ

−−−−

−+Σmax

maxmax

222 ))((max

GHcAHHAA

HHHCef

PcPcMcMc

cPV ννα

uxxuxjj

LDDLDD

GGSBLABLA

...1,2 and 2

minmax

=−<<

++Σ+Σ+=

ρρζ

−−−−

−+Σmax

maxmax

222 ))((max

GHcAHHAA

HHHCef

PcPcMcMc

cPV ννα

uxxuxjj

LDDLDD

GGSBLABLA

...1,2 and 2

minmax

=−<<

++Σ+Σ+=

ρρζ

−−−−

−+Σmax

maxmax

222 ))((max

GHcAHHAA

HHHCef

PcPcMcMc

cPV ννα

uxxuxjj

LDDLDD

GGSBLABLA

...1,2 and 2

minmax

=−<<

++Σ+Σ+=

ρρζ

Outline

• Case Study

Case Study Economic Parameters

Adapted from NETL Baseline Report (Case 2)- cf = $33/tonne coal

- cG = $2.55x105/MW

cC = $1.04x106/MW

- cA = $35.2/tonne compressed air cH2 = $540/tonneH2.

- Interest Rate = 7%

Project Horizon = 30yrs

Average Price of Electricity = $90/MW hr

Case 1 ΣΣΣΣCe = ($20/MWhr)2

Standard Deviation Electricity Price is $20/MWhr

AMmax*

= 90 tonneH2/hr,

= 0.0 tonne air

= 705 tonneH2

= 67 MW

= 1222 MW

Present value of $5.08x108 and

Revenue increase of 14.6%

Solution:

Case 1: ΣΣΣΣCe = ($20/MWhr)2

100 101 102 103 104 105 106 107 108 109

Time (days)

Instantaneous

Average

Maximum

Changes in Electric Price Variation

($/MWhr)2 (tonne/hr) (tonne) (MW) (tonne) (MW)

Present

($x106)

Revenue

Increase

202 90 0.0 67 705 1222 508 14.6

2Hνmax*AM

max*CP

max*2HM

max*GP

Present

($x106)

Revenue

Increase

52 90 0.0 67 0.0 611 0.0 0.0

202 90 0.0 67 705 1222 508 14.6

2Hνmax*AM

max*CP

max*2HM

max*GP

Present

($x106)

Revenue

Increase

52 90 0.0 67 0.0 611 0.0 0.0

102 90 0.0 67 705 1222 175 7.3

202 90 0.0 67 705 1222 508 14.6

2Hνmax*AM

max*CP

max*2HM

max*GP

Present

($x106)

Revenue

Increase

52 90 0.0 67 0.0 611 0.0 0.0

102 90 0.0 67 705 1222 175 7.3

202 90 0.0 67 705 1222 508 14.6

402 90 0.0 67 705 1222 1172 29.3

452 90 6200 134 705 1222 1426 36.6

2Hνmax*AM

max*CP

max*2HM

max*GP

100 101 102 103 104 105 106 107 108 109-200

100 101 102 103 104 105 106 107 108 109

Time (days)

Instantaneous

Average

MaximumC

100 101 102 103 104 105 106 107 108 109

Time (days)

Revenue ($1000/hr)

Dispatch

"o Dispatch

100 101 102 103 104 105 106 107 108 109

Time (days)

Revenue ($1000/hr) Dispatch

"o Dispatch

Why is Throughput Manipulation Not Selected?

Conclusions

1. Three opportunities to make IGCC dispatchableinvestigated.

- H2 storage found to be most viable.

- Gasifier throughput found to be non-viable.

- Compressed air storage only viable under extreme price variability.

Acknowledgements

• Former Students and Collaborators:

Amit Manthanwar

Dr. Jui-Kun Peng (ANL)

Professor Javad Abbasian (ChBE, IIT)

• Funding:

National Science Foundation (CBET – 0967906)

Chemical & Biological Engineering Department, IIT

IGCC Power Plant Design of Dispatch...

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