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Assessing the market benefits of large-scale interconnectors – a case study from the NEM

Assessing the market benefits of large-scale interconnectors – a case study from the NEM. Magnus Hindsberger Senior Manager Market Modelling Australian Energy Market Operator. Overview. Background Modelling approach Results Conclusions. AEMO in brief. Merged in 2009.

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Assessing the market benefits of large-scale interconnectors – a case study from the NEM

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  1. Assessing the market benefits of large-scale interconnectors – a case study from the NEM Magnus Hindsberger Senior Manager Market Modelling Australian Energy Market Operator

  2. Overview Background Modelling approach Results Conclusions

  3. AEMO in brief Merged in 2009 • Core functions of the Australian Energy Market Operator: • National Electricity Market (NEM) - System and Market Operator • Transmission planning/procurement for Victoria • National Transmission Planner • Gas Markets Operator • Energy Market Development • Most roles taken over from predecessor organisations: • NEMMCO • VENCorp • ESIPC • Retail Energy Market Company • Gas Market Company • Gas Retail Market Operator

  4. Background – the real issue Renewable Energy Target (LRET) – 20% renewable by 2020 Good wind resource in South Australia. Capacity factors up to 42%, which is higher than in Queensland, New South Wales and Victoria. Wind penetration in South Australia has reached 20% (annual energy) Rather weak interconnection with the rest of the NEM. This results in transmission constraints and suppressed prices making new wind uneconomic. Feasibility study to see if economic to expand interconnector capacity to improve access to the good wind resource from the rest of the NEM

  5. Background – the academic issue • Why present this? • Not many practical examples of CBA of transmission • Huge interest in investments in interconnectors – especially in those enabling renewable energy • Literature review: • Turvey (2006) • LBNL (2009) • Budhraja et al (2009) • de Nooij (2010) • One of the key recommendations: Include dynamic benefits • New Zealand HVDC study: dynamic benefits 160% of the static • Potentially underbuilding (for this and other reasons)

  6. Types of benefits Productive efficiency is realised by using the least amount of resources to produce a given amount of goods or services. In general, this is ensured by cost reflective bidding (i.e. competitive markets) and least cost economic dispatch to meet the demand based on received bids. Allocative efficiency gains arise from firms producing those goods and services most valued by society. This is achieved when the marginal cost equal the marginal benefit. Again, cost reflective bidding (i.e. competitive markets) is a requirement to ensure this. Dynamic efficiency gains come from more efficient capital investments in generation, transmission and demand side stock. Examples of dynamic efficiency gains are deferral of investments in generation from connecting to an area with surplus generation or from allowing sharing of reserve capacity between regions.

  7. Case study – South Australia • Cost/benefit analysis of increased capacity between South Australia and the rest of NEM • Joint study between AEMO and ElectraNet. • Options considered: • Transformer upgrade in VIC • Northern AC option (SA-NSW) • Northern DC option (SA-NSW) • Central AC option (SA-VIC-NSW) • Southern AC option (SA-VIC) QLD NSW SA VIC TAS

  8. Modelling approach I 1 Identify interconnector options, scenarios and assumptions 2 Horizon: 20 years (2015-35) Time: Monthly, 5 load blocks Grid representation: Nodal Transmission: DC OPF w. SCUC Losses: 2 iterations Generation built Transmission built PLEXOS LT 3 PSS/E Generation built Assess losses 4 Horizon: 1 year at a time Time: Monthly, 15 load blocks Grid representation: Nodal Transmission: DC OPF w. SCUC Losses: Approx. quadratic losses Assign year for transmission built PLEXOS MT 6 • Results per run • NPV of: • Interconnector built • Other transmission built • Generation CAPEX • Generation OPEX* • Unserved energy Hydro usage 5 Horizon: 1 day at a time for 1 year Time: Chronological, hour by hour Grid representation: Nodal Transmission: DC OPF w. SCUC Losses: Approx. quadratic losses PLEXOS ST * Includes fuel and carbon costs and additional generation to cover losses

  9. Modelling approach II • Results for option run • NPV of: • Interconnector built • Other transmission built • Generation CAPEX • Generation OPEX • Unserved energy • Results for base run • NPV of: • Interconnector built • Other transmission built • Generation CAPEX • Generation OPEX • Unserved energy – = BENEFITS • Assumptions • 20 year horizon (2015-2035) • Least cost generation/transmission expansion plan • Perfect competition • Approach • Compare total discounted system costs for each option, each timing and each scenario with a base case without any upgrades

  10. Network Model 5 Regions, 16 Zones 270 Nodes 378 Lines 69 Transformers

  11. Results – Capacity expansion Scenario 1 – Fast rate of Change, Generation build in base case (MW) LRET Market driven RES

  12. Results – Least cost model (step 2) Scenario 1 – Fast rate of Change, Discounted total market cost ($mill)

  13. Results – Hourly model (step 5) Scenario 1 – Fast rate of Change, Discounted total market cost ($mill) 1

  14. Results – Cost breakdown Scenario 1 – Fast rate of Change, Discounted total market cost ($mill) * includes capital costs and fixed operating and maintenance costs ** includes fuel and variable operating and maintenance costs, but not emission costs

  15. Results – dynamic efficiency Transmission minor component. May not be worth the effort Generation capital costs increases when adding interconnectors. Not a failure of the modelling but a reflection of the model building more higher capital cost generation (wind) with lower variable cost (than gas/coal) Dynamic efficiency can’t be separated with the simulations done

  16. Conclusions • Based on the assumptions used, additional capacity seems warranted: • Incremental option during the 2020s (Green Grid: 2017/18) • Southern Option from 2025-30 • Modelling captured dynamic benefits of both transmission and generation, but at a cost of significantly increased complexity. Computation power could maybe have been used more wisely to assess things like: • Impact of changes in losses • Competition benefits • Additional scenarios/sensitivities (given the uncertain world)

  17. Some feedback sought • Bridging the gap – where do traditional CBAs underestimate value of transmission? • Several cases of transmission investments, which has paid off much quicker than anticipated • Acknowledging some companies have incentives to overbuild, but as independent planner, that is not the case for AEMO • Competition benefits – considered a major issue? • High-Impact Low-Probability (HILP) events? • Discount rates – transmission as social service (e.g. facilitator of low carbon energy supply) or market actor (competing on equal level with generation)?

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