Transcript Document

Evolving climate resilient electricity infrastructures
Modeling the evolution of electricity networks
L. Andrew Bollinger
PhD candidate
Section Energy & Industry
Faculty of Technology, Policy & Management
TU Delft
Supervisors:
Dr. G.P.J. Dijkema
Dr. I. Nikolic
Prof. M.P.C Weijnen
SPM 4530
20 March 2014
Problem
Climate change and electricity infrastructures
2012 Hurricane Sandy (USA)
8.5 million customers without power
2012 blackouts in India
620 million people without power
2013 Christmas floods (UK)
50,000 homes without power
Partial loss of electricity at Gatwick Airport
2003 European heat wave
France shuts down the equivalent of 4 nuclear power stations
“Code red” situation in the Netherlands
The Dutch electricity infrastructure
• One of the most reliable
electricity systems in Europe.
• Average interruption time
(2010) = 33.7 minutes1,2
• In 2012, weather caused only
0.6 % of total interruptions3 .
1 Compared
to a European average of 112 minutes
CEER, 2011
3 Source: Netbeheer Nederland and Movares Energy, 2013
2 Source:
Image source: TenneT TSO
The (anticipated) impacts of
climate change on energy
infrastructures
The Climate
(anticipated)
impacts
of
change
and energy
infrastructures
climate change on energy
infrastructures
The electricity infrastructure is a network
The electricity infrastructure is an evolving network
Evolution of the Dutch fuel mix in production
(1955 – 1998)
Source: Verbong and Geels, 2007
Offshore wind
Smart grid
Thesis: If we want "climate proof" infrastructures, we have to understand:
1.
How changes in weather conditions may affect the performance of the
electricity
network
as a whole,
not just
individual components.
Growth of solar
photovoltaic
generation
in its
Germany
2.
How the electricity infrastructure may change over the coming decades.
(2009 – 2011)
Powertown.no
Electric vehicles
Research question & approach
Research question:
How may different development trajectories of the Dutch electricity infrastructure
affect its vulnerability to climate change?
Modeling framework
Simulation model 1
Infrastructure performance
Extreme
events
Component
impacts
Network
impacts
Simulation model 2
Infrastructure evolution
Power grid
investments
Generation
investments
Electricity transmission network
• The portion of the electricity system that
transports power from large power plants to
population centers (distribution grids) and large
consumers
Technical infrastructure components:
• Power plants
• Power lines
• Substations
• Large loads
Social components:
• Power producers
• Transmission system operator (TSO)
• Large consumers
Image source: Tennet TSO, 2012
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How can we model the evolution of
electricity transmission networks?
Modeling the long-term development of electricity networks
Transmission system expansion
planning models
Given:
• Base year topology
• Possible future topologies
• Future generation and load data
• Investment restrictions
Solve for:
• Optimal set of infrastructure investments
(incl. location, timing, quantity)
Image source: Choi et al, 2005
• Good at identifying optimal near-term investment strategies
• Bad for exploring long-term infrastructure evolution under conditions of
path dependency and bounded rationality of actors
How can infrastructure development be modeled in a bottom-up way?
Tero et al, 2010
•
Develop a mathematical model emulating the process by which slime molds develop
nutrient transport networks.
Is this a feasible approach for modeling the evolution of electricity
transmission networks?
The challenge:
• A set of electricity consumers and
producers are distributed
randomly in a landscape.
• Each piece of the landscape is
characterized by a value
representing the
feasibility/efficiency of putting a
transmission line across it.
The goal:
• Link consumers to producers in an
efficient way.
Exploratory model – initial attempts
EACH TIME STEP:
1. Calculate power flows through each line
2. Remove the link with the least power flowing through it
REPEAT UNTIL removing the next link will disrupt supply to the consumer
Exploratory model – initial attempts
Limitations:
1. Doesn’t capture
growth & evolution
2. Only bottom-up
3. Computationally
expensive
Exploratory model – initial attempts
Model setup – agents and infrastructure components
AGENTS
INFRASTRUCTURE COMPONENTS
Transmission
invests in
system
operator (TSO)
Power
producer
invests in
substations
power lines
generators
distribution grids
large loads
manually determined
Model setup – decision rules
A TSO agent must…
1. accept all applications for connections to the transmission grid.
2. ensure that the capacity of transmission lines is adequate to supply
power under all demand scenarios, including peak conditions.
3. ensure that the failure of any one component of the transmission grid will
not cause a failure elsewhere (n-1).
4. implement all investments in the least cost manner.
5. maintain annual expenditures below a certain (user-set) level.
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Model setup – decision rules
A power producer agent must…
1. invest in a new generator if his projections indicate a deficit of generation
capacity within his planning horizon.
2. choose the least-cost technology when investing in a new generator.
3. locate a new generator in the vicinity of an existing substation.
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Model setup - environment
Random landscape consisting of
100 unconnected distribution grids (green circles)
Simulation – what happens when we press “go”?
During the course of a simulation…
1. The demand of distribution grids grows/shrinks at user-defined rates.
2. Large loads are constructed/decommissioned at a user-defined rate.
3. Power producers and the grid operator act according to their defined
decision rules.
Simulation – what happens when we press “go”?
blue lines
150kV (HV) lines
red lines
380kV (EHV) lines
gray lines
under construction
line width
line capacity
1
0 years
2
line intersections
substations/transformers
green circles
distribution grids
blue circles
large generators
3
4
75 years
brown circles
large loads
Results for the default case
How much variation do we see in the network structures that emerge?
3 examples of an emergent network after 75 years – no two networks are identical
Results for the default case
BUT, there is quite some stability in the properties of the networks.
Summary of metric values over 100 runs at the default parameter settings
Experiments – Parameters and metrics
Parameters varied during experimentation
Metrics tracked during experimentation
A sampling of results
High redundancy requirement
Low cost of distr. generators
Low redundancy requirement
Low TSO expenditures cap
Default
High rate of demand growth
Experiment 2 – Varying the demand growth rate
Low demand growth
High demand growth
Experiment 2 – Varying the demand growth rate
Experiment 3 – Varying the cost of distributed generation
High cost of distributed generation
Low cost of distributed generation
Experiment 3 – Varying the cost of distributed generation
Next step: seeding the model with the Dutch transmission grid
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•
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•
•
•
86 generators > 10 MW
402 transmission lines
4 different voltage levels
320 substations
9 interconnectors
238 distribution grids
Next step: seeding the model with the Dutch transmission grid
Future network scenarios (2050)
Current network
Centralized
scenario
Distributed
scenario
Offshore wind
scenario
Import
scenario
Potential impacts of a heat wave on electricity systems
COMPONENT IMPACTS
• Thermal power plants: Reduced output due to
cooling water shortages or restrictions
NETWORK IMPACTS
Reduced generation
capacity
• Thermal power plants: Reduced generation
efficiency
Immediate increased load
demand
• Hydroelectric plants: Reduced resource
availability
• Increased A/C and refrigeration demand
• Increased market penetration of A/C
• Power lines and cables: Increased resistance
Long-term increase in peak
summertime load demand
Reduced network capacity
• Overhead power lines: Increased line sag and
increased risk of flashover
Increased network losses
• Underground cables: Increased risk of failure
due to soil movement
Increased potential for
network disruption
Results for the default case
Total path length of the Dutch transmission grid (km)
Degree distribution Of the Dutch transmission grid
Modeling framework
Software implementation
MatpowerConnect
extension
(Power flow analysis software)
Experiment 1 – Varying the TSO’s redundancy requirement
(looped percentage)
Low redundancy requirement
(looped percentage)
High redundancy requirement
(looped percentage)
Experiment 1 – Varying the TSO’s redundancy requirement
(looped percentage)
Experiment 4 – Varying the TSO’s annual expenditures cap
Low expenditures cap
High expenditures cap
Experiment 4 – Varying the TSO’s annual expenditures cap