Transcript Slide 1

Electricity Market Modelling of Network Investments:
Comparison of Zonal and Nodal Approaches
Sadhvi Ganga
Iain F. MacGill
37th IAEE International Conference, New York City, June 2014
School of Electrical Engineering and Telecommunications &
Centre for Energy and Environmental Markets
The University of New South Wales
Sydney, Australia
A Network Investment Challenge Posed by
Zonal Electricity Markets I
The zonal Australian
National Electricity Market (NEM)
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Assessment of overall economically efficient
network investment, both within and
between zones.
Generally, in cost-benefit analysis terms,
economically efficient if:
Cost
<
Market
Benefits
Figure Source: T&O Energy Consulting website.
The 5 NEM zones:
Queensland
New South Wales (NSW)
Victoria
South Australia
Tasmania
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•
Generally, if market benefits > cost,
signal: invest!
Therefore, the methods of quantifying
market benefits may play critical role in
network investment decision making.
Methods need to capture fidelity of network.
A Network Investment Challenge Posed by
Zonal Electricity Markets II
•
•
•
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Physical network representation for each
market zone is limited to 1 node and only
inter-zonal interconnectors.
Intra-zonal network limitations represented
via constraint equations which define
bounds of Linear Program (LP) underlying
market dispatch solver.
Accordingly, electricity market simulation
models of NEM developed by Australian
Energy Market Operator (AEMO) as part of
its National Transmission Network
Development Plant (NTNDP) have adopted
a zonal modelling approach.
Questions
How can intra-zonal network investments be
modelled? Use an explicit nodal approach?
•
Are the electricity market outcomes the
same for zonal and nodal modelling
approaches?
•
What are the implications for investment
decision-making?
Figure Source: T&O Energy Consulting website.
Extent of NSW
physical
transmission
network
represented in
zonal market
dispatch solver
Models Developed to Address the Questions
Differences in NSW modelling
NSW Single Node Model NSW Multi-Node Model
Nodes
1
68
HV Transmission Lines
4 inter-zonal
4 inter-zonal
82 intra-zonal
Load traces
1 for entire zone
35 Bulk Supply Points
Load treatment
As Generated
At Node
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PROPHET* electricity market
simulation software tool used for model
development and simulation.
•
AEMO 2010 NTNDP dataset and
assumptions largely formed basis for
NSW Single Node Model (zonal
model) and NSW Multi-Node Model
(nodal model) development.
•
Each of the other 4 NEM zones
represented by 1 node and inter-zonal
interconnectors only.
•
15-year forecast load traces developed
for each of the 5 NEM zones (2011 –
2025).
•
Other key difference in modelling for
NSW between zonal and nodal
models: LP feasible solution space
definition.
NSW Multi-Node Model – NSW network representation
*PROPHET is a product owned and supported by Intelligent Energy Systems (IES).
Linear Program Feasible Solution Space Definition I
Zonal Model
Intra-zonal N-1 thermal contingency constraint
definition as per AEMO 2010 NTNDP.
Nodal Model
For NSW, N-1 thermal contingency constraints
were dynamically, endogenously formulated
by the PROPHET ‘N Minus One’ module.
These constraints are generally of the form:
𝒂𝟏 𝒙𝟏 + 𝒂𝟐 𝒙𝟐 + ⋯ + 𝒂𝒏 𝒙𝒏 ≤ 𝒃
where 𝑥𝑛 is a market variable which is optimised
for dispatch, 𝑎𝑛 is the coefficient of the market
variable 𝑥𝑛 , and 𝑏 is a pre-calculated constant
value.
For some constraint equations, formulation of
static coefficients 𝒂𝒏 by AEMO, involved
calculation exogenous to the 2010 NTNDP
PROPHET market model.
These constraints are of the form (Intelligent
Energy Systems, April 2013):
𝒍𝒊𝒏𝒆𝒊 𝒇𝒍𝒐𝒘 + 𝒔𝒉𝒊𝒇𝒕 𝒇𝒂𝒄𝒕𝒐𝒓𝒊𝒋 ∗ 𝒍𝒊𝒏𝒆𝒋 𝒇𝒍𝒐𝒘 ≤ 𝒍𝒊𝒏𝒆𝒊 𝒍𝒊𝒎𝒊𝒕
where 𝑠ℎ𝑖𝑓𝑡 𝑓𝑎𝑐𝑡𝑜𝑟𝑖𝑗 is the proportion of the
power flow on 𝑙𝑖𝑛𝑒𝑗 which is transferred to 𝑙𝑖𝑛𝑒𝑖
when 𝑙𝑖𝑛𝑒𝑗 fails.
Linear Program Feasible Solution Space Definition II
Thus, the formulation of N-1 thermal contingency constraints for the zonal and nodal models are
inherently different.
Consequently, the LP feasible solution space definition between the two modelling approaches are
not identical.
Conceptually:
𝒙𝟐
𝒙𝟐
feasible region 1
feasible region 2
𝒙𝟏
𝒙𝟏
Therefore, both approaches aim to solve the same problem, but define the problem differently.
Empirical Investigations:
Intra-Zonal Network Investment
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High-level feasibility study.
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Network investment modelled:
NSW 300 MW intra-zonal augmentation in
southern area. Commissioned in 2014.
Aimed to increase thermal capacity of
transmission corridor.
Zonal Model:
To model augmentation:
𝒂𝟏 𝒙𝟏 + 𝒂𝟐 𝒙𝟐 + ⋯ + 𝒂𝒏 𝒙𝒏 ≤ 𝒃 + y
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Simulated time-sequential Security
Constrained Economic Dispatch (SCED) for
Base Case and Augmentation.
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Market benefit: Incremental benefit of a
credible option (augmented case) over the
base case.
Nodal Model:
To model augmentation:
Increase capacity
of links:
Generation Dispatch Outcomes - Results I :
Change in Total New South Wales Plant Annual Energy Generation:
Augmented Case – Base Case.
New South Wales
New South Wales Multi-Node Model (Nodal Model)
500
500
400
400
300
300
200
200
New Plant - Solar
100
New Plant - CCGT
New Plant - CCGT
100
New Plant - Biomass
Existing - Natural Gas
0
Existing - Liquid Fuel
-100
Existing - Hydroelectric
-200
Existing - Black Coal
Ener gy (GWh)
Ener gy (GWh)
New South Wales
New South Wales Single Node Model (Zonal Model)
New Plant - Biomass
0
Existing - Natural Gas
Existing - Liquid Fuel
-100
Existing - Hydroelectric
-200
-300
-300
-400
-400
Existing - Black Coal
-500
-500
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
Generation Dispatch Outcomes - Results II :
Change in Total Queensland Plant Annual Energy Generation:
Augmented Case – Base Case.
Queensland
New South Wales Multi-Node Model (Nodal Model)
Queensland
New South Wales Single Node Model (Zonal Model)
100
100
50
50
0
New Plant - OCGT
New Plant - CCGT
-50
New Plant - Biomass
Existing - Natural Gas
-100
Existing - Liquid Fuel
Ener gy (GWh)
Ener gy (GWh)
0
New Plant - OCGT
New Plant - CCGT
-50
New Plant - Biomass
Existing - Natural Gas
-100
Existing - Liquid Fuel
Existing - Hydroelectric
Existing - Hydroelectric
-150
Existing - Black Coal
-150
Existing - Black Coal
-200
-200
-250
-250
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
Power Transfer over QNI - Results III :
1200
1000
1000
1000
600
400
Zonal
Nodal
200
0
10
20
30
40
50
60
70
80
90
100
NSW to QL D
NSW to QL D
400
Zonal
Nodal
200
0
0
10
20
30
40
50
60
70
80
90
100
-400
-400
-600
Proportion of Time Flow Exceeded (%)
2014
800
600
400
Zonal
Nodal
200
0
0
10
20
30
40
50
60
70
-200
-200
-200
-600
600
NSW to QL D
0
800
QNI Power Flow (M W)
800
QL D to NSW
1200
QL D to NSW
1200
QNI Power Flow (M W)
QNI Power Flow (M W)
QL D to NSW
Modelled Power Flow Cumulative Frequency Distribution Post Augmentation:
Queensland New South Wales Interconnector
-400
-600
Proportion of Time Flow Exceeded (%)
2020
Proportion of Time Flow Exceeded (%)
2025
80
90
100
Generation Dispatch Outcomes - Results IV :
Change in Total South Australia Plant Annual Energy Generation:
Augmented Case – Base Case.
South Australia
New South Wales Multi-Node Model (Nodal Model)
South Australia
New South Wales Single Node Model (Zonal Model)
80
80
70
70
60
60
New Plant - OCGT
New Plant - Geothermal
40
New Plant - Biomass
Existing - Wind
30
Existing - Natural Gas
Existing - Liquid Fuel
20
New Plant - OCGT
50
Ener gy (GWh)
Ener gy (GWh)
50
New Plant - Geothermal
40
New Plant - Biomass
Existing - Wind
30
Existing - Natural Gas
Existing - Liquid Fuel
20
Existing - Brown Coal
Existing - Brown Coal
10
10
0
0
-10
-10
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
Total NEM Generation Dispatch Costs - Results V :
Zonal Model: Total National Electricity Market Dispatch Costs ($ million)
Base Case
Augmentation Case
Change in Dispatch Cost (Base Case - Augmentation Case)
2014 2015 2016 2017 2018
6628 3258 3361 3486 3603
6636 3264 3370 3497 3616
-8
-6
-9 -11 -13
2019
3783
3801
-18
2020
4045
4062
-18
2021 2022 2023 2024 2025
4264 4687 5001 5264 5646
4282 4694 5002 5267 5648
-19 -7
-1
-3
-3
Zonal Model: Post augmentation, dispatch costs
A counter-intuitive result?
Nodal Model: Total National Electricity Market Dispatch Costs ($ million)
Base Case
Augmentation Case
Change in Dispatch Cost (Base Case - Augmentation Case)
2014
6964
6947
17
2015
3542
3529
13
2016
3663
3649
13
2017 2018 2019 2020
3816 3943 4183 4473
3806 3935 4172 4464
11
8
11
9
Nodal Model: Post augmentation, dispatch costs
An intuitive result?
2021
4796
4784
13
2022
5222
5205
17
2023
5574
5555
19
2024 2025
5892 6273
5877 6265
15
8
Conclusion
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Highlighted, is the impact LP feasible
solution space definition, and approach to
modelling augmentations, may have on
electricity market optimal dispatch
outcomes.
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Ideally, tools used in such optimisation
processes should simultaneously capture
the intra- and inter-zonal market dynamics,
and the trade-offs for network investment
within and between zones.
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The results of this high-level feasibility study
demonstrate that different paths for
progression of network investment
assessment can potentially be taken due to
zonal/nodal modelling approach: the nodal
model indicated potential benefits to
the market, while the zonal model indicated
the opposite.
•
The adopted modelling approach may be
pivotal in the network investment decisionmaking process, and therefore warrants due
consideration.
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These results apply to modelling undertaken
with PROPHET, but may well apply to other
electricity market simulation software tools
as well.
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Locational decisions for potential
economically efficient network investment
may also be impacted by adopted modelling
approach. An important finding, since
economic-based assessments such as the
RIT-T aim to assess overall economic
outcomes.
Acknowledgements
The authors gratefully acknowledge the support of TransGrid, and thank Enrico Garcia and Can Van.
References
Australian Energy Market Operator, 2010 National Transmission Network Development Plan.
Australian Energy Market Operator, 2011 Victorian Annual Planning Report.
Australian Energy Market Operator, 2011 South Australian Supply and Demand Outlook.
Australian Energy Regulator, Regulatory investment test for transmission (RIT-T) and application
guidelines 2010.
A.M. Foley, B.P. O Gallachoir, J. Hur, R. Baldick and E.J. McKeogh, “A strategic review of electricity
systems models,” ELSEVIER Energy, vol. 35, pp. 4522-4530, 2010.
Intelligent Energy Systems, PROPHET User Guide, vol. 1, p. 388, April 2013.
Powerlink, Annual Planning Report 2011.
TransGrid, Annual Planning Report 2011.
Transend, Annual Planning Report 2011.