Decision Support for Life Cycle Management of Energy … · Decision Support for Life Cycle...

Petrie, Kempener, Beck, Cohen, and Basson, LCM 2007

1

Decision Support for Life Cycle Managementof Energy Supply Networks

Jim Petrie12

Ruud Kempener1

Jessica Beck1

Brett Cohen2

Lauren Basson3

1. School of Chemical & Bio-molecular Engineering, Univ. of Sydney, Australia2. Department of Chemical Engineering, University of Cape Town, South Africa3. Centre for Environmental Strategy, University of Surrey, United Kingdon


System Influences on Decision Making

Decision making processof organisation

Social embeddedness Resource scarcity

Learning & adaptationBackground systemsMultiple autonomousdecision makers

Evolution of industrial system


Global Vs Distributed Control

EFFECT CAUSE

Perspective for distributed control approach

EFFECT CAUSE

Has to make assumptions about the CAUSE and SYSTEM INTERACTION delivering a specific EFFECT.

Perspective for global control approach

CAUSE CAUSE

CAUSE

SYSTEM INTERACTION

SYSTEM INTERACTION

Can make no clear prediction about EFFECT. The EFFECT evolves based on CAUSE and SYSTEM INTERACTION.


Global targets –Distributed performance

Present energy network performance

Network performance

Time

Desired energy network development pathway

Attainable energy network development pathway

GLOBAL CONTROL APPROACH: IRP setting

DISTRIBUTED CONTROL APPROACH: Analysis of possible policy intervention and feasibility of desired IRP


Application of Framework

Simulation and optimization of industrial networks » Consideration of multiple objectives, dynamics, and uncertainty

Modeling of agent behavior in industrial networks – bioenergy in developing countries; coal-based power generation

Possible combined approach:» Using optimization to identify preferred trajectory» Using ABM to determine feasibility and means to narrow the gap» Applied in Beck et al., 2007; Kempener et al., 2007 (In Review)Beck, Kempener, Cohen & Petrie (2007), A Complex Systems Approach to Planning, Optimisation and

Decision Making for Energy Networks: A South African Bio-energy Case Study, Energy Policy, acceptedKempener, Beck & Petrie (2007), “An Integrated Analysis of Bio-energy Technologies – A complex

Adaptive Systems Approach, Eur Fed Chem E (B), PSEP, in review5


PAPER & PULP INDUSTRY

FOODINDUSTRY

ELECTRICITYINDUSTRY

TRANSPORTINDUSTRY

SUGAR MILLS

ENGINEERING/R&D

BIO-ENERGYNETWORK

GOVERNMENT

SUBSIDIESTARGETS

BAGASSEELECTRICITY

TECHNOLOGYDEVELOPMENT

BAGASSE DEMAND

SUGAR PRICES

OIL PRICESETHANOL

ELECTRICITY DEMANDELECTRICITY PRICESINFRASTRUCTURE


Technologies considered

Onsite (distributed)generation

GENERATOR

IPP

Drying and pelletisation

gasification

steam cycle

gasification

steam cycle

Wet bagasse Dry bagasse Ethanol

Sugar mill i

Power

Power

Pulp and Paper

Power

Paper

cofiring

FUEL PRODUCEREthanolhydrolysis

FUEL GELGelgel production

on-site use


Units State variables ABM GDOM

Autonomous

agents (SI, PS,

IPP, EP)

• Bagasse/ethanol availability or purchased (both wet & dry)

• Bagasse /ethanol for own use

• Production capacity

• Costs and efficiency of technologies available

• Profit

• Capital

• Location

• Farmers benefits and/or other obligations

• Minimum IRR threshold

• Preferred contract length

• Market share ambitions

• Time span for future prediction

• Relationships

• Economic, social & environmental weightings in decision making

• Importance of risk, benevolence, conflict, status, past

experience, length relationship, trust, loyalty

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•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

Regional

government

• Number of municipalities

• Rural electricity demand (not-electrified)

• Price of grid connections

• Municipal Infrastructure Grants (MIG)

•

•

•

•

•

•

•

•

National

government

• Subsidies for bio-ethanol

• Fuel exemption

• Investment subsidies new technologies

• Subsidies for green electricity

• Subsidies for gel-fuel

• Subsidies for grid connections

• Subsidies for non-grid connections

• Policy target for green electricity

• Policy target for biofuels

• MIG allocation

• Electricity Basis Services Support Tariff Policy (EBSST)

•

•

•

•

•

•

•

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•


0

50

100

150

200

250

300

350

400

450

0 3 6 9 12 15 18 21 24 27 30

years

elec

trici

ty (G

Wh/

mon

th

0

20

40

60

80

100

120

140

160

etha

nol (

1000

m3/

mon

th

electricity production ethanol production

1.

2.

3. 4.

Infrastructure Shifts


S6S5S4S3S1 S2 S12S10S9S7 S8

grid

EP PS IPP

local

GP

rural development

S11pel pel

electricity

bagasseethanol

gel


grid

EP PS IPP

local

GP

rural development

S11pel pel pel pel

electricity

bagasseethanol

gel


grid

EP PS IPP

local

GP

rural development

S11pel pel pel pel

electricity

bagasseethanol

gel


grid

EP PS IPP

local

GP

rural development

S11pel pel pel pel pel pel pel pel pel pel pel pel

electricity

bagasseethanol

gel

1. 2.

4.3.

Network Structures

yr.9 yr.15

yr.18 yr.21


0

100

200

300

400

500

600

700

0 3 6 9 12 15 18 21 24 27 30years

MW

S12S10S5S8

S3S2S11S1S7S6

S4S9PS

0

100

200

300

400

500

600

700

0 3 6 9 12 15 18 21 24 27 30

years

MW

S12S11S10S9S8S7S6S5S4S3S2S1PS

market price dry bagasse

0

500

1000

1500

2000

2500

3000

3500

0 3 6 9 12 15 18 21 24 27 30

years

R/M

Wh

(eq.

)

green electricity subsidy low IRR

ethanol production

0

50

100

150

200

250

300

0 3 6 9 12 15 18 21 24 27 30

years

1000

m3/

day

green electricity subsidy low IRR

Network evolution under green electricity subsidies Network evolution under low IRRs

Emergent Behaviour


Comparison GDOM and ABM

economic environmental social energy

provision

(billion ZAR) (Mt CO2 averted)

(rural energy supply) (PJ)

Economically rational agents 6.6 36.9 1.1 121.7 Agents who allow MCDM 3.4 186.9 16.0 355.8

economic environmental social energy

provision

(billion rand)

(Mtonnes co2 averted)

(rural energy supply) (PJ)

Environmental behaviour 0.2 308.3 0 637 Social behaviour -59.3 102.2 415.8 1219 Economic rational 12.7 226.6 0 913

GDOM

ABM


Comparison – no biofuels

0

0.2

0.4

0.6

0.8

1

0 50 100 150 200 250 300 350TWh

environmental social economic


Bio Energy Network Conclusions

The bio-energy network develops only when the price of electricity rises to a factor of 3-4 above the current price in South Africa.

Under a set of reasonable assumptions, this will likely happen only about 15 years from now (though sometimes as early as 5 years)

There is genuine potential to address rural electrification needs by decentralised power production at the various sugar mills.

Under a wide range of scenarios, Power Producers take up the bagasse resource

The production of green electricity on the basis of wet bagasse is detrimental to the environment, as CO2 emissions from transport would outweigh the CO2 averted through the production of green electricity.

It seems that investment subsidies are more beneficial than price subsidies.

Investment subsidies would allow sugar mills to invest in pelletisers, which would allow Power Producers to produce green electricity more quickly and with higher profit margins


Coal Network Considered

PS 2

Adjacent Mine

Alternative Mine

Coal up to“contracted” LF

Additional Coal sourced In one of three ways

Road

Conveyor

PS 1

Adjacent Mine

Alternative Mine

Road

Conveyor

PS n

National electricity demand

Other non-coal based electricity sources (nuclear,

hydro etc)H2O CO2 SO2

H2OCO2 SO2

Ash

SO2CO2

Emissions/inputs


31000

36000

41000

46000

51000

56000

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Cum

ulat

ive

capa

city

(MW

)

Existing system Recommisioned coal stations Open cycle gas turbineCoal fired Fluidized bed combustion (FBC) Coal-Fired Pulverized fuel combustion (PF) Pebble bed modular reactorCombined cycle gas turbine (pipeline) Advanced light water nuclear reactor Peak demand


0.0E+00

5.0E+06

1.0E+07

1.5E+07

2.0E+07

2.5E+07

3.0E+07

3.5E+07

Arnot O

C/OC

Duvha

OC/U

GHen

drina

OC/U

GKrie

l UG/U

GMatl

a UG/U

GLe

thabo

OC/U

G

Majuba

Wet

UG/UG

Tutuka

UG/U

GCam

den O

C/OC

Grootvl

ei OC/U

GKom

ati O

C/UG

Kenda

l UG/O

C

Majuba

Dry

UG/OC

Matimba

OC/U

G

New 1

- PF O

C/OC

New 2

- PF O

C/UG

New 3

- FBC O

C/UG

MW

hSO

contracted additional (contracted) additional (alt conv) additional (alt road)

Wet/Wet Wet/Dry Dry/Dry

Least Cost: Year 8

Lethabo and KomatiOnly run at contracted load

Some source additionalcoal from contracted mineothers from alternative mine


0.0E+00

5.0E+06

1.0E+07

1.5E+07

2.0E+07

2.5E+07

3.0E+07

3.5E+07

Arnot O

C/OC

Duvha

OC/U

GHen

drina

OC/U

GKrie

l UG/U

GMatl

a UG/U

GLe

thabo

OC/U

G

Majuba

Wet

UG/UG

Tutuka

UG/U

GCam

den O

C/OC

Grootvl

ei OC/U

GKom

ati O

C/UG

Kenda

l UG/O

C

Majuba

Dry

UG/OC

Matimba

OC/U

G

New 1

- PF O

C/OC

New 2

- PF O

C/UG

New 3

- FBC O

C/UG

MW

hSO

contracted additional (contracted) additional (alt conv) additional (alt road)

Wet/Wet Wet/Dry Dry/Dry

Least CO2: Year 8

Lethabo and Komatistill only run at contracted load, as well as Camden

Conveyor from alternative mine preferred to road


SO2 Emissions/MWhSO

0.009

0.0095

0.01

0.0105

0.011

0.0115

0.012

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Year

t/MW

hSO

Base Case - Least Cost Base Case - Least SO2

Alternative Build Plan least Cost Alternative Build Plan Least SO2


CO2 emissions/MWhSO

0.99

1

1.01

1.02

1.03

1.04

1.05

1.06

1.07

1.08

1.09

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Year

t/MW

hSO

Base Case - Least Cost Base Case - Least CO2Alternative Build Plan - Least Cost Alternative Build Plan - Least CO2


Model Opportunities

Nature of coal mine-power station supply contracts:» Length of contracts» Quality and price considerations» Opportunity for renegotiation» Decision related to coal sourcing» Decision making around transport modes

Improved resolution on coal quality

Trade offs between beneficiation and desulphurisationtechnologies

Geographical Specificity


Overall Conclusions

• A combination of Agent-based models and Global optimisation models has real potential for analysis of complex industry networks

• Insights helpful in guiding policy formulation, rolling out investment and development strategies, and positioning industries for enhanced competitive advantage

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