Decision Support for Life Cycle Management of Energy Supply Networks - - PowerPoint PPT Presentation

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Decision Support for Life Cycle Management of Energy Supply Networks - - PowerPoint PPT Presentation

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Decision Support for Life Cycle Management of Energy Supply Networks Jim Petrie 12 Ruud Kempener 1 Jessica Beck 1 Brett Cohen 2 Lauren Basson 3 1. School of Chemical & Bio-molecular Engineering, Univ. of Sydney, Australia 2. Department of

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

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Decision Support for Life Cycle Management

  • f Energy Supply Networks

Jim Petrie12 Ruud Kempener1 Jessica Beck1 Brett Cohen2 Lauren Basson3 1. School of Chemical & Bio-molecular Engineering, Univ. of Sydney, Australia 2. Department of Chemical Engineering, University of Cape Town, South Africa 3. Centre for Environmental Strategy, University of Surrey, United Kingdon

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

System Influences on Decision Making

Decision making process

  • f organisation

Social embeddedness Resource scarcity Learning & adaptation Background systems Multiple autonomous decision makers

Evolution

  • f industrial

system

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

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
  • n CAUSE and SYSTEM

INTERACTION.

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

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

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

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, accepted Kempener, Beck & Petrie (2007), “An Integrated Analysis of Bio-energy Technologies – A complex Adaptive Systems Approach, Eur Fed Chem E (B), PSEP, in review

5

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

PAPER & PULP INDUSTRY FOOD INDUSTRY ELECTRICITY INDUSTRY TRANSPORT INDUSTRY SUGAR MILLS ENGINEERING/ R&D BIO-ENERGY NETWORK GOVERNMENT

SUBSIDIES TARGETS BAGASSE ELECTRICITY TECHNOLOGY DEVELOPMENT BAGASSE DEMAND SUGAR PRICES OIL PRICES ETHANOL ELECTRICITY DEMAND ELECTRICITY PRICES INFRASTRUCTURE

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

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 PRODUCER

Ethanol

hydrolysis

FUEL GEL

Gel

gel production

  • n-site use
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Petrie, Kempener, Beck, Cohen, and Basson, LCM 2007

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

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

50 100 150 200 250 300 350 400 450 3 6 9 12 15 18 21 24 27 30 years electricity (GWh/month 20 40 60 80 100 120 140 160 ethanol (1000 m3/month electricity production ethanol production

1. 2. 3. 4.

Infrastructure Shifts

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

S6 S5 S4 S3 S1 S2 S12 S10 S9 S7 S8 grid EP PS IPP local GP rural development S11

pel pel

electricity bagasse ethanol gel S6 S5 S4 S3 S1 S2 S12 S10 S9 S7 S8 grid EP PS IPP local GP rural development S11

pel pel pel pel

electricity bagasse ethanol gel S6 S5 S4 S3 S1 S2 S12 S10 S9 S7 S8 grid EP PS IPP local GP rural development S11

pel pel pel pel

electricity bagasse ethanol gel S6 S5 S4 S3 S1 S2 S12 S10 S9 S7 S8 grid EP PS IPP local GP rural development S11

pel pel pel pel pel pel pel pel pel pel pel pel

electricity bagasse ethanol gel 1. 2. 4. 3.

Network Structures yr.9 yr.15 yr.18 yr.21

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

100 200 300 400 500 600 700 3 6 9 12 15 18 21 24 27 30

years MW S12 S10 S5 S8 S3 S2 S11 S1 S7 S6 S4 S9 PS 100 200 300 400 500 600 700 3 6 9 12 15 18 21 24 27 30 years MW S12 S11 S10 S9 S8 S7 S6 S5 S4 S3 S2 S1 PS

market price dry bagasse

500 1000 1500 2000 2500 3000 3500 3 6 9 12 15 18 21 24 27 30 years R/MWh (eq.) green electricity subsidy low IRR

ethanol production

50 100 150 200 250 300 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

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

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 0.2 308.3 637 Social behaviour

  • 59.3

102.2 415.8 1219 Economic rational 12.7 226.6 913

GDOM ABM

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

Comparison – no biofuels

0.2 0.4 0.6 0.8 1 50 100 150 200 250 300 350 TWh

env ironmental social economic

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

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

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

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 H2O CO2 SO2 Ash SO2 CO2

Emissions/inputs

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Petrie, Kempener, Beck, Cohen, and Basson, LCM 2007 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 Cumulative capacity (MW) Existing system Recommisioned coal stations Open cycle gas turbine Coal fired Fluidized bed combustion (FBC) Coal-Fired Pulverized fuel combustion (PF) Pebble bed modular reactor Combined cycle gas turbine (pipeline) Advanced light water nuclear reactor Peak demand

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

0.0E+00 5.0E+06 1.0E+07 1.5E+07 2.0E+07 2.5E+07 3.0E+07 3.5E+07 A r n

  • t

O C / O C D u v h a O C / U G H e n d r i n a O C / U G K r i e l U G / U G M a t l a U G / U G L e t h a b

  • O

C / U G M a j u b a W e t U G / U G T u t u k a U G / U G C a m d e n O C / O C G r

  • t

v l e i O C / U G K

  • m

a t i O C / U G K e n d a l U G / O C M a j u b a D r y U G / O C M a t i m b a O C / U G N e w 1

  • P

F O C / O C N e w 2

  • P

F O C / U G N e w 3

  • F

B C O C / U G MWhSO

contracted additional (contracted) additional (alt conv) additional (alt road) Wet/Wet Wet/Dry Dry/Dry

Least Cost: Year 8

Lethabo and Komati Only run at contracted load Some source additional coal from contracted mine

  • thers from alternative mine
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Petrie, Kempener, Beck, Cohen, and Basson, LCM 2007

0.0E+00 5.0E+06 1.0E+07 1.5E+07 2.0E+07 2.5E+07 3.0E+07 3.5E+07 A r n

  • t

O C / O C D u v h a O C / U G H e n d r i n a O C / U G K r i e l U G / U G M a t l a U G / U G L e t h a b

  • O

C / U G M a j u b a W e t U G / U G T u t u k a U G / U G C a m d e n O C / O C G r

  • t

v l e i O C / U G K

  • m

a t i O C / U G K e n d a l U G / O C M a j u b a D r y U G / O C M a t i m b a O C / U G N e w 1

  • P

F O C / O C N e w 2

  • P

F O C / U G N e w 3

  • F

B C O C / U G MWhSO contracted additional (contracted) additional (alt conv) additional (alt road) Wet/Wet Wet/Dry Dry/Dry

Least CO2: Year 8

Lethabo and Komati still only run at contracted load, as well as Camden Conveyor from alternative mine preferred to road

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

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/MWhSO

Base Case - Least Cost Base Case - Least SO2 Alternative Build Plan least Cost Alternative Build Plan Least SO2

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

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/MWhSO Base Case - Least Cost Base Case - Least CO2 Alternative Build Plan - Least Cost Alternative Build Plan - Least CO2

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

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 desulphurisation technologies Geographical Specificity

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

Overall Conclusions

  • A combination of Agent-based models and Global
  • ptimisation models has real potential for analysis
  • f complex industry networks
  • Insights helpful in guiding policy formulation,

rolling out investment and development strategies, and positioning industries for enhanced competitive advantage