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L. Andrew BollingerPhD studentSection Energy & IndustryFaculty of Technology, Policy & ManagementTU Delft
Supervisors:M.P.C. WeijnenG.P.J. DijkemaI. Nikolic
SPM 453025 March 2013
Evolving climate change resilient electricity infrastructuresModeling electricity network evolution
PART 1 The Problem
Power outages as a result of Hurricane Sandy
Source: Renewables International
Minutes
Reliability of the Dutch electricity infrastructure
Average interruption time per customer per year (2007)
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ture
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Causes of power failures in the Dutch high-voltage grid
Source: EnergieNed
Reliability of the Dutch electricity infrastructurePe
rcen
t
(1) De Groot, 2006(2) Wilbanks, et al, 2008(3) Rothstein and Halbig, 2010(4) Bresser, et al, 2005
The (anticipated) impacts of climate change
Climate change and energy infrastructures
(1) De Groot, 2006(2) Wilbanks, et al, 2008(3) Rothstein and Halbig, 2010(4) Bresser, et al, 2005
The (anticipated) impacts of climate change on energy infrastructures
• Thermal power plants: Reduced output due to cooling water shortages or restrictions
• Thermal power plants: Reduced generation efficiency
• Hydroelectric plants: Reduced resource availability
• Increased A/C and refrigeration demand• Increased market penetration of A/C• Power lines and cables: Increased resistance• Overhead power lines: Increased line sag and
increased risk of flashover• Underground cables: Increased risk of failure
due to soil movement
Reduced generation capacity
Immediate increased load demand
Long-term increase in peak summertime load demand
Reduced network capacity
Increased network losses
Increased potential for network disruption
COMPONENT IMPACTS NETWORK IMPACTS
Potential impacts of a heat wave on electricity systems
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.
Modeling frameworkSimulation model 1
Infrastructure performance
Simulation model 2Infrastructure evolution
Extreme events
Component impacts
Network impacts
Power grid investments
Generation investments
Research question: How can we effectively support the resilience of the Dutch electricity infrastructure to climate change?
Agent-based model
PART 2 Modeling electricity transmission network evolution
Image source: TenneT TSO
The Dutch electricity transmission network
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?
Research question and approach
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
Transmission system operator (TSO)
Power producer
invests in
substations
power lines
transformers
invests in generators
distribution grids
large loads
manually determined by the user
AGENTS INFRASTRUCTURE COMPONENTS
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.
Random landscape consisting of 100 unconnected distribution grids (green circles)
Load centers
Land values
Model setup - environmentDistribution grids
Keep in mind…
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.
Software implementation
Octaveconnectextension
(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”?
1 2
3 4 75 years
0 years
blue lines 150kV (HV) lines
red lines380kV (EHV) lines
gray lines under construction
line widthline capacity
line intersectionssubstations/transformers
green circles distribution grids
blue circleslarge generators
brown circleslarge loads
Results for the default case
Summary of metric values over 100 runs at the default parameter settings
3 examples of an emergent network after 75 years
Experiments – Parameters and metrics
Metrics tracked during experimentation
Parameters varied 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
Power plants Power grid Electricity demand
Case model - Infrastructure data
Infrastructure evolution model
Infrastructure configuration• Locations and properties
of generators• Locations and properties
of grid components• Development of demand
Infrastructure data
TSO agent decision rules
Power producer agent decision rules
Case model
2013 2023 2033
Model 2 – Preliminary results
Simulation model 1Infrastructure performance
Simulation model 2Infrastructure evolution
Policy scenariosCl
imat
e sc
enar
ios
Extreme events
Component impacts
Network impacts
Power grid investments
Generation investments
Future work
Test different policy and climate scenarios -> Identify robust policy options for supporting infrastructure resilience.
Contact:
L. Andrew BollingerDelft University of TechnologyFaculty of Technology, Policy and ManagementEmail: [email protected]
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 infrastructureMetrics
Exploratory model – an initial attempt
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
Problem formulation and actor identification
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.
EACH TIME STEP:1. Calculate power flows through each line2. 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
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
