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Evolving climate change resilient electricity infrastructures
Modeling electricity network evolution
L. Andrew Bollinger
PhD student
Section Energy & Industry
Faculty of Technology, Policy & Management
TU Delft
Supervisors:
M.P.C. Weijnen
G.P.J. Dijkema
I. Nikolic
SPM 4530
25 March 2013
PART 1
The Problem
Power outages
as a result of
Hurricane Sandy
Reliability of the Dutch electricity infrastructure
Average interruption time per customer per year (2007)
Minutes
Source: Renewables International
Reliability of the Dutch electricity infrastructure
Causes of power failures in the Dutch high-voltage grid
70
60
Percent
50
40
2007
30
2006
2005
20
2004
2003
10
0
Source: EnergieNed
The (anticipated) impacts of
climate change
(1)
(2)
(3)
(4)
De Groot, 2006
Wilbanks, et al, 2008
Rothstein and Halbig, 2010
Bresser, et al, 2005
The Climate
(anticipated)
impacts
of
change
and energy
infrastructures
climate change on energy
infrastructures
(1)
(2)
(3)
(4)
De Groot, 2006
Wilbanks, et al, 2008
Rothstein and Halbig, 2010
Bresser, et al, 2005
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
The electricity infrastructure is a network
Research question & approach
Thesis: If we want "climate proof" infrastructures, we have to understand how
changes in weather conditions may affect the performance of the electricity
network as a whole, not just its individual components.
Research question: How can we effectively support the resilience of the
Dutch electricity infrastructure to climate change?
Modeling framework
Simulation model 1
Infrastructure performance
Extreme
events
Agent-based model
Component
impacts
Network
impacts
Simulation model 2
Infrastructure evolution
Power grid
investments
Generation
investments
PART 2
Modeling electricity transmission network evolution
The Dutch electricity
transmission network
Image source: TenneT TSO
Research question and approach
Research (sub)question:
What are the possible impacts of various climate change mitigation policies
on the structure and properties of the Dutch electricity transmission
network?
Approach – 2 stages:
1.
Exploratory model – How can we address this question using ABM?
2.
Case model – More extensive model (calibrated with real data) used to
directly address the research question.
System identification and decomposition
What are the relevant components and how do they relate to one another?
System identification and decomposition
Exploratory model – agents and infrastructure components
AGENTS
Transmission
system
operator (TSO)
INFRASTRUCTURE COMPONENTS
substations
invests in
power lines
transformers
Power
producer
invests in
generators
distribution grids
large loads
manually determined
by the user
Model setup - decision rules
A TSO agent must…
1. ENSURE CONNECTION: accept all applications for connections to the
transmission grid, and construct connections to the respective component(s).
2. ENSURE FUNCTIONALITY: remove or replace grid components that have reached
the end of their lifetime.
3. ENSURE SUFFICIENT CAPACITY: ensure that the capacity of lines is sufficient to
satisfy demand under peak conditions.
4. ENSURE REDUNDANCY: ensure that a given fraction of components are
embedded in loop structures.
5. ENSURE EFFICIENCY:
• implement all investments in the least cost manner.
• link substations exceeding a given supply/demand threshold to the EHV grid
6. LIMIT EXPENDITURES: maintain annual expenditures below a certain (user-set)
level.
Model setup - decision rules
A power producer agent must…
1. ENSURE SUFFICIENT CAPACITY: invest in a new generator if his
projections indicate a deficit of generation capacity within his planning
horizon.
2. MINIMIZE VARIABLE COSTS: choose the technology with the least cost
per MWh when investing in a new generator.
3. FIND SUITABLE LOCATIONS: locate a new generator on a parcel of land
with suitable land-use characteristics.
Model setup - environment
Distribution grids
Random landscape consisting of
100 unconnected distribution grids (green circles)
Keep in mind…
Load centers
1. This is just a random starting point chosen for the sake of simplicity.
2. The quantity and configuration of distribution grids, load centers and
land values can be changed to reflect different scenarios.
3. We can also start with an existing transmission grid and explore how the
system develops further under different scenarios.
Land values
Software implementation
Octaveconnect
extension
(Power flow analysis software)
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
3 examples of an emergent network after 75 years
Summary of metric values over 100 runs at the default parameter settings
Experiments – Parameters and metrics
Parameters varied during experimentation
Metrics tracked during experimentation
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 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
Experiment 4 – Varying the TSO’s annual expenditures cap
Low expenditures cap
High expenditures cap
Experiment 4 – Varying the TSO’s annual expenditures cap
Case model
Case model - Infrastructure data
Power plants
Power grid
Electricity demand
Case model
TSO agent
decision rules
Infrastructure configuration
Power producer agent
decision rules
Infrastructure
evolution model
• Locations and properties
of generators
• Locations and properties
of grid components
• Development of demand
Infrastructure data
Model 2 – Preliminary results
2013
2023
2033
Future work
Test different policy and climate scenarios -> Identify robust policy options for
supporting infrastructure resilience.
Extreme
events
Component
impacts
Network
impacts
Simulation model 2
Infrastructure evolution
Power grid
investments
Generation
investments
Policy scenarios
Climate scenarios
Simulation model 1
Infrastructure performance
Contact:
L. Andrew Bollinger
Delft University of Technology
Faculty of Technology, Policy and Management
Email: L.A.Bollinger@tudelft.nl
Simulation – preliminary results under different scenarios
Default case
• 126 substations
• 146 lines
• 21 loops
• mean degree: 2.87
High demand case
• 177 substations
• 199 lines
• 24 loops
• mean degree: 3.366
Simulation – preliminary results under different scenarios
Low cost of distr. gen.
• 111 substations
• 124 lines
• 15 loops
• mean degree: 2.48
Low expenditures case
• 93 substations
• 92 lines
• 0 loops
• mean degree: 1.98
Simulation – growing a transmission infrastructure
Metrics
Exploratory model – an initial attempt
Problem formulation and actor identification
Research (sub)question:
How can various carbon taxation schemes and RES support mechanisms be
expected to affect the structure and properties of the Dutch electricity
transmission network?
Problem owner: The Dutch transmission system operator
Scope:
• The Netherlands
• The electricity transmission network
Exploratory model – initial attempts
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
Approach – 3 cycles
Cycle 1 – Exploratory model:
• What are the relevant components and relationships?
• Who are my agents? How do they interact?
• Which software platform should I use?
• Get feedback from the problem owner.
• Go back to the system decomposition.
Cycle 2 – Generic model
• Elaborate the decision procedures.
• Implement the model based on these decision procedures.
• Get feedback from the problem owner.
Cycle 3 – Case model
• Calibrate the model with real-world data.
• Improve the decision procedures, as necessary.
• Perform experiments and address the research question.
Results for the default case
Total path length of the Dutch transmission grid (km)
Degree distribution Of the Dutch transmission grid
Results for the default case