Electricity Market Modelling of Network Investments: Comparison of Zonal and Nodal Approaches

1. Electricity Market Modelling of Network Investments:Comparison of Zonal and Nodal Approaches 37th IAEE International Conference, New York City, June 2014 Sadhvi Ganga Iain F. MacGill School of Electrical Engineering and Telecommunications & Centre for Energy and Environmental Markets The University of New South Wales Sydney, Australia

2. A Network Investment Challenge Posed by Zonal Electricity Markets I The zonal Australian National Electricity Market (NEM) • Assessment of overalleconomically 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. Economically efficient? The 5 NEM zones: Queensland New South Wales (NSW) Victoria South Australia Tasmania

3. A Network Investment Challenge Posed by Zonal Electricity Markets II • 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? Extent of NSW physical transmission network represented in zonal market dispatch solver Figure Source: T&O Energy Consulting website.

4. Models Developed to Address the Questions Differences in NSW modelling • 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).

5. Linear Program Feasible Solution Space Definition I ZonalModel NodalModel 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 exogenousto 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.

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. feasibleregion 1 feasibleregion 2

7. Empirical Investigations: Intra-Zonal Network Investment • 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. • 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. Nodal Model: Zonal Model: To model augmentation: To model augmentation: + y Increase capacity of links:

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 (%) 2025 2020 2014

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) Zonal Model: Post augmentation, dispatch costs A counter-intuitive result? Nodal Model: Total National Electricity Market Dispatch Costs ($ million) Nodal Model: Post augmentation, dispatch costs An intuitive result?

13. Conclusion • 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. • 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.

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 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.