Equilibrium Modeling of Combined Heat and Power Deployment

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Equilibrium Modeling of Combined Heat and Power Deployment

Anand GovindarajanSeth Blumsack

Pennsylvania State University

USAEE Conference, Anchorage, 29 July 2013

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Problem Statement

• Assess the economic potential for Combined Heat and Power (CHP) in electricity-market equilibrium framework, accounting for the impact that CHP adoption will have on electricity prices

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Some Motivation

• U.S. utilization of CHP is low but technical potential is vast

• Utilization pathway for shale-gas suppliesCurrent CHP capacity

Technical potential for additional CHP

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Basic CHP Economics

• Increased efficiency of heat + electricity (adsorptive chiller can add cooling)

• Avoided electricity purchases

• Other benefits : reduced emissions, reliability benefits

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Technical vs Economic potential

• CHP investment reduces demand for grid provided power, lowering market price

• At some point, incremental CHP units become uneconomical

• The economic potential maybe different(less) than the technical potential0 50000 100000 150000 200000

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PJM Demand (GW)

Shor

t run

Mar

gina

l cos

t($/

MW

h)

Oil

GasCoal

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Equilibrium CHP Modeling

Increase in number of CHP units

Decrease in zonal electricty demand

Decrease in wholesale electricity prices

Marginal Savings from avoided electrcity purchase costs decreases

Marginal NPV decreases

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Philadelphia Case Study

• We use Philadelphia, PA as a case study for our equilibrium modeling

• High technical potential, high electricity prices

• Transmission constrained

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Supply curve modeling (Sahraei-Ardakani et al 2012)

We want to identify:

1. Thresholds where the marginal technology changes;

2. The slope of each portion of the locational dispatch curve.

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CHP Load Profiles

• Building-integrated CHP (BCHP) tool used to generate profiles for eight building types

• Electric load-following (FEL) and thermal load-following (FTL)

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Method

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100 200 300 400 500 600 700 800 900 10000

1

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4

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6x 10

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# CHP units

Ma

rgin

al

Savin

gs

($)

FTLFEL

Energy Savings from Incremental CHP Investment in Philadelphia

Assumes $4/mmBTU natural gas price

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100 200 300 400 500 600 700 800 900 10000

1

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3

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9x 10

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# CHP units

Mar

gin

al S

avin

gs (

$)

FTLFEL

Energy Savings from Incremental CHP Investment in Philadelphia

Assumes $8/mmBTU natural gas price

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100 200 300 400 500 600 700 800 900 1000-0.5

0

0.5

1

1.5

2

2.5

3x 10

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# CHP units

Ma

rgin

al

NP

V (

$))

FTLFEL

NPV of Incremental CHP ($4 gas)

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100 200 300 400 500 600 700 800 900 10000

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4

6

8

10

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18x 10

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# CHP units

Marg

ina

l N

PV

($))

FTLFEL

NPV of Incremental CHP ($8 gas)

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Conclusion: Are High Gas Prices Good for CHP?

100 200 300 400 500 600 700 800 900 1000-0.5

0

0.5

1

1.5

2

2.5

3x 10

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# CHP units

Ma

rg

ina

l N

PV

($

))

FTLFEL

100 200 300 400 500 600 700 800 900 10000

2

4

6

8

10

12

14

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18x 10

5

# CHP units

Margin

al

NP

V (

$))

FTLFEL

$4/mmBTU Gas

$8/mmBTU Gas

• Higher gas prices may mean more economic opportunities for CHP, otherwise economic potential is perhaps 1/3 of technical potential.

• Disproportionate impacts on electricity prices relative to operational costs

• FTL maybe a more economical operational strategy when fuel prices are low

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Thank You!

Anand Govindarajanaxg5179@psu.edu

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Locational Marginal Cost Curves

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Life is Heaven When Gas is $7

Price separation between fuels (on $/MBTU basis) means that thresholds are easy to identify.

Note: Other fuel prices – Coal $2/mmBTU; Oil $20/mmBTU

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Life Ain’t a Breeze When Gas is $3

When relative fuel price differences are small, a mix of fuels/technologies can effectively be “on the margin.”

Note: Other fuel prices – Coal $2/mmBTU; Oil $20/mmBTU

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Estimation ProcedureWe want to minimize the SSE of:

CMA-ESOLS

Regression

Regression Parameters / SSEGeneration i-1

Classification parametersGeneration i

1. Choose initial parameters φ 2. Find associated slope

parameters ω using least squares

3. Given estimates for ω and the regression SSE, choose a new set of threshold parameters φ*

4. Repeat until convergence.

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Marginal Fuel Results

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Estimating Threshold Functions

Thresholds are estimated using a fuzzy logic approach to capture multiple marginal fuels:1. Relative fuel price

threshold for having the fuzzy gap

2. Fuzzy gap width coefficient

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Example Result

• Wide band where gas/coal are jointly setting prices.

• More defined threshold between gas and oil.

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Supply Curve Modeling

• Philadelphia is transmission-constrained, so the available capacity of a generator is not relevant – only the amount of electricity that is deliverable to a location in the network.

• Power Transfer Distribution Factor (PTDF):

G1

k

G2

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Piecewise Supply Curve Estimation

Threshold indicator function

Slope of the relevant portion of the supply curve