Electricity Market Modelling of Network Investments:

Comparison of Zonal and Nodal Approaches

Sadhvi Ganga

Iain F. MacGill

Centre for Energy and Environmental Markets

School of Electrical Engineering and Telecommunications &

The University of New South Wales

37th IAEE International Conference, New York City, June 2014

Sydney, Australia

A Network Investment Challenge Posed by Zonal Electricity Markets I

• Assessment of overall economically efficient network investment, both within and between zones.

• Generally, in cost-benefit analysis terms, economically efficient if:

• 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.

Figure Source: T&O Energy Consulting website.

The zonal Australian National Electricity Market (NEM)

CostMarket

Benefits

Other market benefit categories

Reduction in unserved energy

Dispatch (fuel) cost savings

Direct costs of the augmentation

option

Economically efficient?

<

The 5 NEM zones:Queensland

New South Wales (NSW)Victoria

South AustraliaTasmania

• 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?

A Network Investment Challenge Posed by Zonal Electricity Markets II

Figure Source: T&O Energy Consulting website.

Extent of NSW physical

transmission network

represented in zonal market

dispatch solver

• 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.

Models Developed to Address the Questions

*PROPHET is a product owned and supported by Intelligent Energy Systems (IES).

NSW Multi-Node Model – NSW network representation

Differences in NSW modelling

NSW Single Node Model NSW Multi-Node ModelNodes 1 68

HV Transmission Lines 4 inter-zonal 4 inter-zonal82 intra-zonal

Load traces 1 for entire zone 35 Bulk Supply PointsLoad treatment As Generated At Node

Linear Program Feasible Solution Space Definition I

Intra-zonal N-1 thermal contingency constraint definition as per AEMO 2010 NTNDP.

These constraints are generally of the form:

where is a market variable which is optimised for dispatch, is the coefficient of the market variable , and is a pre-calculated constant value.

For some constraint equations, formulation of static coefficients by AEMO, involved calculation exogenous to the 2010 NTNDP PROPHET market model.

For NSW, N-1 thermal contingency constraints were dynamically, endogenously formulated by the PROPHET ‘N Minus One’ module.

These constraints are of the form (Intelligent Energy Systems, April 2013):

where is the proportion of the power flow on which is transferred to when fails.

Zonal Model Nodal Model

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:

Therefore, both approaches aim to solve the same problem, but define the problem differently.

𝒙𝟏

𝒙𝟐

𝒙𝟏

𝒙𝟐

feasible region 1

feasible region 2

• High-level feasibility study.

• Network investment modelled:

NSW 300 MW intra-zonal augmentation in

southern area. Commissioned in 2014.

Aimed to increase thermal capacity of

transmission corridor.

Empirical Investigations:

Intra-Zonal Network Investment

+ y

To model augmentation: To model augmentation:

Increase capacity of links:

Zonal Model: Nodal Model:

• Simulated time-sequential Security Constrained Economic Dispatch (SCED) for Base Case and Augmentation.

• Market benefit: Incremental benefit of a credible option (augmented case) over the base case.

Generation Dispatch Outcomes - Results I :

-500

-400

-300

-200

-100

0

100

200

300

400

500

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Ene

rgy

(GW

h)

N ew South W alesN ew South W ales Single N ode M odel (Z onal M odel)

N ew P lant - C C G T

N ew P lant - B iomass

E xisting - N atur al G as

E xisting - L iquid F uel

E xisting - H ydr oelectr ic

E xisting - B lack C oal

-500

-400

-300

-200

-100

0

100

200

300

400

500

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Ene

rgy

(GW

h)

N ew South W alesN ew South W ales M ulti -N ode M odel (N odal M odel)

N ew P lant - Solar

N ew P lant - C C G T

N ew P lant - B iomass

E xisting - N atur al G as

E xisting - L iquid F uel

E xisting - H ydr oelectr ic

E xisting - B lack C oal

Change in Total New South Wales Plant Annual Energy Generation: Augmented Case – Base Case.

Generation Dispatch Outcomes - Results II :

Change in Total Queensland Plant Annual Energy Generation: Augmented Case – Base Case.

-250

-200

-150

-100

-50

0

50

100

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Ene

rgy

(GW

h)

Q ueenslandN ew South W ales S ingle N ode M odel (Z onal M odel)

N ew P lant - O C G T

N ew P lant - C C G T

N ew P lant - B iomass

E xistin g - N atur al G as

E xisting - L iquid F uel

E xisting - H ydroelectric

E xistin g - B lack C oal

-250

-200

-150

-100

-50

0

50

100

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Ene

rgy

(GW

h)

Q ueenslandN ew South W ales M ulti -N ode M odel (N odal M odel)

N ew P lant - O C G T

N ew P lant - C C G T

N ew P lant - B iomass

E xisting - N atural G as

E xisting - L iquid F uel

E xisting - H ydroelectric

E xisting - B lack C oal

Power Transfer over QNI - Results III :

Modelled Power Flow Cumulative Frequency Distribution Post Augmentation: Queensland New South Wales Interconnector

-600

-400

-200

0

200

400

600

800

1000

1200

0 10 20 30 40 50 60 70 80 90 100QN

I P

ower

Flo

w (

MW

)

P r opor tion of T im e F low E xceeded (% )

Z onal

N odal

QL

Dto

NSW

NSW

to Q

LD

-600

-400

-200

0

200

400

600

800

1000

1200

0 10 20 30 40 50 60 70 80 90 100QN

I P

ower

Flo

w (

MW

)

P r opor tion of T im e F low E xceeded (% )

Zonal

N odal

QL

Dto

NSW

NSW

to Q

LD

-600

-400

-200

0

200

400

600

800

1000

1200

0 10 20 30 40 50 60 70 80 90 100QN

I P

ower

Flo

w (M

W)

P r op or tion of T im e F low E xceeded

Zonal

N odal

QL

Dto

NSW

NSW

to Q

LD

(%)

2014 2020 2025

Generation Dispatch Outcomes - Results IV :

Change in Total South Australia Plant Annual Energy Generation: Augmented Case – Base Case.

-10

0

10

20

30

40

50

60

70

80

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Ene

rgy

(GW

h)

South A ustr al iaN ew South W ales Single N ode M odel (Z onal M odel)

N ew P lant - O C G T

N ew P lant - G eothermal

N ew P lant - B iomass

E xisting - W ind

E xisting - N atur al G as

E xisting - L iquid F uel

E xisting - B r ow n C oal

-10

0

10

20

30

40

50

60

70

80

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Ene

rgy

(GW

h)

South A ustr al iaN ew South W ales M ulti -N ode M odel (N odal M odel)

N ew P lant - O C G T

N ew P lant - G eother m al

N ew P lant - B iomass

E xisting - W ind

E xisting - N atur al G as

E xisting - L iquid F uel

E xisting - B r ow n C oal

Total NEM Generation Dispatch Costs - Results V :

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025Base Case 6628 3258 3361 3486 3603 3783 4045 4264 4687 5001 5264 5646Augmentation Case 6636 3264 3370 3497 3616 3801 4062 4282 4694 5002 5267 5648Change in Dispatch Cost (Base Case - Augmentation Case) -8 -6 -9 -11 -13 -18 -18 -19 -7 -1 -3 -3

Zonal Model: Total National Electricity Market Dispatch Costs ($ million)

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025Base Case 6964 3542 3663 3816 3943 4183 4473 4796 5222 5574 5892 6273Augmentation Case 6947 3529 3649 3806 3935 4172 4464 4784 5205 5555 5877 6265Change in Dispatch Cost (Base Case - Augmentation Case) 17 13 13 11 8 11 9 13 17 19 15 8

Nodal Model: Total National Electricity Market Dispatch Costs ($ million)

Zonal Model: Post augmentation, dispatch costs

A counter-intuitive result?

Nodal Model: Post augmentation, dispatch costs

An intuitive result?

• Highlighted, is the impact LP feasible solution space definition, and approach to modelling augmentations, may have on electricity market optimal dispatch outcomes.

• 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.

• 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.

• 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.

• The adopted modelling approach may be pivotal in the network investment decision-making process, and therefore warrants due consideration.

• These results apply to modelling undertaken with PROPHET, but may well apply to other electricity market simulation software tools as well.

Conclusion

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.