Energy sustainability through representative large
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Transcript Energy sustainability through representative large
Technische Universität Berlin
Energy sustainability through
representative large-scale simulation :
the logical and physical design of xeona
International Conference on Sustainability Engineering and Science (ICSES)
Auckland, New Zealand • 06–09 July 2004 • www.nzses.org.nz
Robbie Morrison 1, 2 Tobias Wittmann 1 Thomas Bruckner 1
1 Institute for Energy Engineering
Technical University of Berlin
Germany
2 Mathematical and Computing Sciences
Victoria University of Wellington
Issue
New Zealand
D
1
Authors
Thomas Bruckner
Tobias Wittmann
Robbie Morrison
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Resource processing
networked systems
Typical features of resource processing networked systems:
TECHNICAL
ISSUES
high capital cost — and often environmental cost — of infrastructure
limited natural entitlements — rivers, transmission corridors, gas fields, etc
subsystems which operate in (increasingly) volatile circumstances
plant performance which relates to context — ambient conditions, price, etc
decentralized decision-making — whether administered or market pricing
final demand is for services (rather that commodities)
strong implications for biophysical sustainability and societal functioning
The energy sector as a
representative example
Network component
(more later)
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Public interest
performance
Public interest is a normative concept
Resource processing networked systems
should operate, evolve, and innovate
to improve public interest performance:
ETHICAL
ISSUES
whole-system financial cost
depletable resource use
greenhouse gas emissions
local environmental impacts
This presentation looks at the contribution
that representative large-scale simulation
can make to public interest policy
development in the energy sector
Windflow prototype, 500 kW
Christchurch, NZ, 2003
Examples derive mostly from New Zealand
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Motivation for modeling
COMPLEX
SYSTEMS
Complex multi-party systems defy simplistic analysis
Large-scale simulation provides an alternative to econometric modeling
and system dynamics
Versatile model application/interpretation, briefly:
operational mode — scenario investigation
operational plus investment mode — system evolution experimentation
Potential for proactive use:
adaptive resource consents, for instance, for fresh water take (NZ issue)
model-based, not trigger-based, ring-fenced generation (NZ issue)
revenue redistribution among cooperating parties
Can generate important non-observable system metrics, for instance:
weather-normalized, inventory-corrected social energy efficiency
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Simulation environments
deeco
agent-based
extension
COMPUTER
SCIENCE
xeona
Object-oriented: circa 1995
Object-oriented: circa 2004
Status: first use late-1995,
extensive technology library
Status: alpha release
planned for 2005
Category: high-resolution
Category: entity-oriented
Role: technical behavior in
the presence of one internal
decision-maker
Role: in addition, able to
capture multi-participant
domestic and commercial
behavior
License: GPL plus requests
License: GPL plus requests
Web: www.iet.tu-berlin.de/deeco
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hydro-generator
wholesale
retail
household
commercial
relationships
Illustrative example
time interval:
► one hour (say)
authority
time horizon:
► annual (operational)
► decade (plus investment)
public
interest
system
metrics
attribute
exergy
resources
time-series
external
circumstances
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Overlaid
networks
Two foundation networks:
► mathematical graphs
Commercial associations network:
► negotiation pathways
► bilateral contracts
► market-mediated relationships
Physical and instrumental
resources (PIR) network:
Optimal single interval operation:
these arrangements allow use of linear
or mixed integer (LP or MILP) methods
to optimize subsystem operation:
► single operator (merit order)
► bid-informed market (stack order)
► stock and flow model
► also supports instrumental
resources (including carbon
permits and flow of funds)
Optimization
informed
simulation
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Agent-based
modeling
All actors: bounded rationality
► limited processing power
► public information only
Domestic actors:
► investment responses based on
lifestyle classification
Commercial actors:
► commercial motivation
► can call on external software
and even human support
(experimental economics)
Future refinements:
► greater analytical sophistication
► learning and adaptation
► cooperation and coalition stability
Underrecognized
topic
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Technical
components
End-use facilities: have received
limited public policy attention to date
Engineering plant: generalized entity
Component characterization:
Network
programming
Support for heat transport
and storage temperatures:
► engineering controllers
mimicked to determine flo
and return temperatures
► non-ideal storage modeled
such that energy loss
causes temperature decay
Resource
quality
captured
Improved
technical
realism
► input-output relationships
(generalized efficiency)
► plant capacity constraints
(lower, upper)
► cost/impact "creation" equations
Context-dependent performance:
► environmental circumstances
► neighboring plant via "dialog"
► internal state, tracking operating
history and inventory
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Policy issues (1)
mostly large-scale
Licensing: merits of licensing hydrogenerator stack (bidding) models
Carbon tax: efficacy assessment
Market improvement: by simulation
System (n−1) security: based on
minimum cut (bottleneck) analysis
Additionality assessment: for NZ
Projects Mechanism emissions units
(EU) allocation, using in situ analysis
Intermittent renewables: whole of
system evaluation
Extreme event functioning:
including dry cold winters
HVDC link, January 2004
Wind damage
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Policy issues (2)
mostly dispersed
Rewarded end-user responsiveness:
various demand management initiatives
Rebound: take-back effect from
domestic efficiency investments
Solar hot water support: merits of
accelerated domestic solar hot water
uptake
Building performance: merits of
tighter building standards
Whole-system public interest
performance criteria (PIPC):
► financial cost
► depletable resource use
► greenhouse gas emissions
► local environmental impact
Resource consent (RMA) process:
consideration of alternatives
Policy
trade-offs
may be
required
Investment protection: distributed
solutions tend to be vulnerable to
upstream reinforcement
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Huntly 1000 MW power station
Further
subsystems
miscellaneous
components
25ºC max
for river
gas
coal
Some other parts
of the jigsaw
Waikato River
neighborhood fuel cells
(phosphoric acid )
nuclear power
electricity
electricity
low-level
waste
?
high-level
waste
gas
hot water
?
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Trade-off information for
policy makers (single operator case)
Trade-off line
Financial cost increase
200%
Situation:
everything
150%
large solar + seasonal storage
100%
everything
− large solar
50%
small solar
medium solar
0%
oil-fired boilers +
electricity imports
gas heat-pumps
+ heat grid
cogeneration
+ short distance
heat grid
–50%
0%
Business as usual
reference
10%
20%
30%
40%
50%
Complex
municipal
energy
system in
northern
Europe
modeled
using deeco
Source: Bruckner,
Groscurth, and
Kümmel (1997)
Note: LHV is lower
heating value
Depletable fuel savings (LHV)
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Key assumptions
Preamble
extensive state describes prevailing plant duty and/or inventory
intensive state includes quantities like output voltage, flo and return
temperatures, and stratified storage temperatures
1
State orthogonality
extensive state selection has no influence on intensive state
2
Cross-interval operation
extensive state selection covering storage is procedural rather than optimal
applies to single operator managed storage only
3
Efficiency curve convexity
plant efficiency increases stepwise with plant duty
required where linear optimization is employed or where
a global optimum must be guaranteed
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Software design
Object-orientation (taken to include generic programming)
scientific programming — optimization solvers, ordinary differential
equation solvers, implicit variables methods, and graph algorithms
orthodox object-oriented design and analysis (OODA)
multi-agent simulation techniques
Physical design
modularized software architecture
XML
for persistent storage and data exchange
UML
standardized visual language for design and documentation
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Closure
Simulation is cheaper and faster than policy formulation by trial-and-error
Energy-services supply may well be headed toward smarter lighter
networks and greater use of renewable and fuel-passive technologies
Large-scale simulation is indicated and other methods appear less suitable:
a single socially-motivated decision-maker is no longer appropriate
econometric methods struggle to capture technical possibilities
system dynamics struggles to capture network issues
Large-scale simulation may have application in other areas, such as the
management of fresh water take (for hydro-generation, cooling, irrigation)
The method can yield important non-observable system metrics —
essential for the proper auditing of policy efficacy
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Selected references
Bruckner, Thomas, Helmuth-M Groscurth, and Reiner Kümmel. 1997. Competition and synergy
between energy technologies in municipal energy systems. Energy – The International
Journal. 22(10): 1005–1014.
Lindenberger, Dietmar, Thomas Bruckner, Helmuth-M Groscurth, and Reiner Kümmel. 2000.
Optimization of solar district heating systems : seasonal storage, heat pumps, and
cogeneration. Energy – The International Journal. 25(7): 591–608.
Morrison, Robbie, Thomas Bruckner. 2002. High-resolution modeling of distributed energy
resources using deeco : adverse interactions and potential policy conflicts. In – Sergio
Ulgiati et al. (eds.). 2003. Proceedings of the 3rd International Workshop in Advances in
Energy Studies — Reconsidering the Importance of Energy. Held at Porto Venere, Italy,
24–28 September 2002. Padova, Italy: Servizi Grafici Editoriali. 97–107.
Morrison, Robbie, Tobias Wittmann, and Thomas Bruckner. 2003. Energy policy and distributed
solutions : a model-based interpretation. Paper at the Australia New Zealand Society for
Ecological Economics (ANZSEE) Think Tank. Held at University of Auckland, Auckland,
New Zealand, 16 November 2003.
Bruckner, Thomas, Robbie Morrison, Chris Handley, and Murray Patterson. 2003. High-resolution
modeling of energy-services supply systems using deeco : overview and application to
policy development. Annals of Operations Research. 121(1–4): 151–180.
Lindenberger, Dietmar, Thomas Bruckner, Robbie Morrison, Helmuth-M Groscurth, and Reiner
Kümmel. 2004. Modernization of local energy systems. Energy – The International
Journal. 29(2): 245–256.
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