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Electricity Market Modelling of Network Investments: Comparison of Zonal and Nodal Approaches Sadhvi Ganga Iain F. MacGill Centre for Energy and Environmental.

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Presentation on theme: "Electricity Market Modelling of Network Investments: Comparison of Zonal and Nodal Approaches Sadhvi Ganga Iain F. MacGill Centre for Energy and Environmental."— Presentation transcript:

1 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 37 th IAEE International Conference, New York City, June 2014 Sydney, Australia

2 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) Cost Market 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 Australia Tasmania

3 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

4 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

5 Linear Program Feasible Solution Space Definition I Zonal ModelNodal Model

6 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. feasible region 1 feasible region 2

7 High-level feasibility study. Network investment modelled: NSW 300 MW intra-zonal augmentation in southern area. Commissioned in Aimed to increase thermal capacity of transmission corridor. Empirical Investigations: Intra-Zonal Network Investment 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.

8 Generation Dispatch Outcomes - Results I : Change in Total New South Wales Plant Annual Energy Generation: Augmented Case – Base Case.

9 Generation Dispatch Outcomes - Results II : Change in Total Queensland Plant Annual Energy Generation: Augmented Case – Base Case.

10 Power Transfer over QNI - Results III : Modelled Power Flow Cumulative Frequency Distribution Post Augmentation: Queensland New South Wales Interconnector (%)

11 Generation Dispatch Outcomes - Results IV : Change in Total South Australia Plant Annual Energy Generation: Augmented Case – Base Case.

12 Total NEM Generation Dispatch Costs - Results V : Zonal Model: Total National Electricity Market Dispatch Costs ($ million) 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?

13 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

14 Acknowledgements The authors gratefully acknowledge the support of TransGrid, and thank Enrico Garcia and Can Van.

15 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 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 , Intelligent Energy Systems, PROPHET User Guide, vol. 1, p. 388, April Powerlink, Annual Planning Report TransGrid, Annual Planning Report Transend, Annual Planning Report 2011.


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