Long term challenges in reflecting network costs: Pricing and other solutions to manage network challenges. (feat. Network Opportunity Maps)

Transcription

1 Long term challenges in reflecting network costs: Pricing and other solutions to manage network challenges. (feat. Network Opportunity Maps) Chris Dunstan (Research Director, ISF) AER Tariff Structure Statement Forum 14 December, 2015

2 OVERVIEW 1. Challenges 2. Network Opportunity Maps Information for the decentralised energy era 3. Conclusions

3 CHALLENGES

4 CHALLENGES FOR COST REFLECTIVE NETWORK PRICING Locational specificity Time responsiveness: by hour, day, month, year It s not just about peak demand Low voltage network; voltage management; power factor; ramp rates; fault current, reliability, asset replacement, reliability and forecast unserved energy (USE) New technology: Solar PV, Electric Vehicles, Batteries, energy management, hydrogen & fuel cells isf.uts.edu.au

5 CENTRALISED ELECTRICITY SUPPLY: Have we already peaked? Energy efficiency Rooftop solar PV Annual Energy Forecast for the NEM (NEFR, June 2014) isf.uts.edu.au

6 PEAK DEMAND Still rising? Low load factor = higher prices Source: 2015 AEMO National Electricity Forecasting Report (NEFR) (June 2014)

7 ELECTRICITY NETWORKS: LOCATION, LOCATION, TIME Cents per kilowatt-hour (AUD 2010) Electricity prices by state (2011/12) ACT Qld NSW VIC* SA GST Carbon Price ESS/REES GGAS /Qld Gas SRES LRET FiT Metering Retail Distribution Transmission Generation Source: AEMC, Future Possible Retail Electricity Price Movements, 2011; Treasury modelling (*Vic = 2012) Networks comprise >50% power bills (nationally) Network costs are highly location & season specific Mapping can help identify priority areas for nonnetwork alternatives (Decentralised Energy) isf.uts.edu.au

8 New Technology: This is just the beginning isf.uts.edu.au

9 Decentralised Energy includes: Small Gas Generation Solar Photovoltaics Standby Generation Distributed Generation Cogeneration Peak Load Management Time of Use tariffs Ice Storage Interruptible loads Battery Storage Electric Vehicles Electric to Gas Hot Water Power factor correction Gas Chillers Fuel Cells Biomass Generation Behaviour change Efficient motors & chillers Efficient Lighting Efficient showerheads Efficiency Retrofits Energy Efficiency isf.uts.edu.au

10 THE CHANGING ELECTRICITY SECTOR How to deliver a win-win for networks (NSPs) and customers? The past Centralised supply Forecast demand Flat prices Build least cost infrastructure Little engagement with customers (end users) The future Decentralised supply Manage demand Cost reflective prices Invest in least cost supply and demand side mix Extensive engagement with customers (and retailers and service providers) Information and collaboration are key isf.uts.edu.au

11 NETWORK OPPORTUNITY MAPS

12 Wherewithin the electricity network do the most cost-effective DE opportunities exist? How much could DE be worth at these locations? Whenare the key years and times of constraint? To answer these questions, ISF created Network Opportunity Maps (AKA.the Dynamic Avoidable Network Cost Evaluation or DANCE model) isf.uts.edu.au

13 NETWORK OPPORTUNITY MAPS PROJECT A resource to show where/when to target Renewable Energy and DE technologies & services: Annually updated through streamlined process Consistently applied in every (NEM) jurisdiction Freely available on online platform Woven into Networks Demand Side Engagement Strategies isf.uts.edu.au

14 NETWORK OPPORTUNITY MAPS PROJECT Three year project, Sept 2014 Sept 2017 Funded by ARENA, UTS, NSW Govt, and Ergon Energy Formal network business partners: Ergon Energy, Electranet, TransGrid Data provision and collaboration with all NSPs in the NEM Produced on NICTA s new Australian Renewable Energy Mapping Infrastructure (AREMI) portal This is first public release of Sample Maps isf.uts.edu.au

15 PROJECT TIMELINE Sample Maps (Oct 2015) 1 st Full Iteration (May 2016) 2 nd Full Iteration (May 2017) isf.uts.edu.au

16 WHAT DATA GOES IN? All data comes from Network Service Providers For Sample Maps: generally as published in 2014 Distribution Annual Planning Reports, and 2015 Transmission Annual Planning Reports) Proposed network investments (augmentation, replacement, other) Cost of capital (WACC), depreciation NSP demand forecasts for each network asset Current capacity of lines and substations Zone substation region boundaries (+ how different assets connect) Hourly load data (load profiles)

17 WHAT MAPS COME OUT? 1. Available Capacity (MVA) 2. Planned network investment ($m) 3. Value of potentially avoidable investment ( annual deferral value; $/kvayr) 4. Peak Day Available Capacity (% exceedance in each hour) isf.uts.edu.au

18 WHAT NETWORK LEVELS ARE COVERED? Network Opportunity Maps isf.uts.edu.au

19 THE MAPS: live online See:

20 Network Opportunity Maps - Available Capacity

21 Network Opportunity Maps - Available Capacity

22 Network Opportunity Maps - Available Capacity

23 MAP 2: PROPOSED INVESTMENT [$MILLION BY YEAR] See AREMI platform:

24 MAP 2: PROPOSED INVESTMENT [$m]

25 Map 2: Proposed Investment [$m]

26 Map 2: Proposed Investment [$m] (c.f. NSW)

27 Note: Each zone has detailed clickable data

28 ANNUAL DEFERRAL VALUE [$/KVA/YEAR] x ( + )

29 Annual Deferral Value [$/kva/year]

30 Annual Deferral Value [$/kva/year]

31 Annual Deferral Value [$/kva/year]

32 Annual Deferral Value [$/kva/year]

33 Annual Deferral Value [$/kva/year]

34 Annual Deferral Value [$/kva/year]

35 MAP 4: HOURLY AVAILABLE CAPACITY (PEAK DAY- % OF FIRM CAPACITY) Compares forecast hourly demand to firm local network capacity BUT generally only provided where load is forecast to exceed capacity. Shown for peak day only (in relevant peak season). Shows Transmission, Sub-transm n & Distrib n Zone capacity. Note that this map has changed from hourly deferral value in previous network opportunity map versions.

36 Hourly available capacity- Summer Peak Day, 1pm

37 PEAK DAY (HOURLY) AVAILABLE CAPACITY MAP isf.uts.edu.au

38 PROJECT TIMELINE Sample Maps (Oct 2015) 1 st Full Iteration (May 2016) 2 nd Full Iteration (May 2017) Targeted User Feedback Short user feedback survey (see project website) isf.uts.edu.au

39 EXPECTED PROJECT OUTCOMES Meeting the information of needs the new decentralised energy era Develop a more diverse market of non-network service providers Enhance service offers and choice for customers Develop new business for NSPs in decentralised energy Demonstrate effective collaboration to deliver win-win outcomes for NSPs and customers isf.uts.edu.au

40 Victoria is well placed to lead on CRNP and DE Lower network investment in recent years Less surplus capacity Higher load at risk Tradition of probabilistic network planning Smart meter rollout means: Better understanding of network conditions More data on voltage excursions and other code noncompliance More capacity for smarter control Network businesses looking for opportunities Government desire to lead on new and clean technology isf.uts.edu.au

41 Committed 1: Decoupling (via Revenue Cap) 2: Capital Savings Incentive (CESS) 3: Contestable Metering 4: Cost reflective network pricing 5: Customer Information Proposed (DM Incentive Scheme) 6: Least cost objective 7: DM Incentive Payments Potential 8: Voluntary DM targets 9. Performance Reporting POLICY TOOLS FOR NETWORK DM isf.uts.edu.au

42 Conclusions Cost reflective pricing is crucial but not sufficient Clear accessible information is key to this more decentralised energy market The future needs more competition and more collaboration The future is in flux - flexibility is crucial Customers need better incentives - and so do utilities

43 QUESTIONS? View the maps: [click Electricity Infrastructure, Network Opportunities ISF ] Chris Dunstan, chris.dunstan@uts.edu.au (02) Ed Langham, edward.langham@uts.edu.au (02)