Using PRISM As A Planning Tool Using PRISM As A Planning Tool Remy Garderet Remy Garderet EPOC Winter Workshop EPOC Winter Workshop Sept 2005 Sept 2005 E NERGY C ENTRE Outline 1. The Energy Centre & PRISM 2. Building Scenarios for PRISM input 3. Analysing & interpreting the output 4. Example: Simulating a “Grid Investment Test” E NERGY 2 C ENTRE 1
The Energy Centre and PRISM About the Energy Centre at University of Auckland – Goal is to provide national research leadership on energy systems and issues. – An interdisciplinary and inter-sectoral approach to NZ energy priorities – Housed in economics department, the team comprises faculty, PhDs, post docs and visiting professors and fellows. – Executive Director is Robert Kirkpatrick Launching Research Topic: The Electricity Sector – Understand the risks of alternative future electricity scenarios – Grid critical to understanding => needed spatial model Collaboration with EPOC – Spatial and stochastic attributes of PRISM well suited – Joint development of an excel-based interface for PRISM – Energy Centre responsible for the analysis and interpretation of output E NERGY 3 C ENTRE The Energy Centre & Prism (cont) A tool to understand system. – Dynamics of the system – variability and constraints – How future investment affects which constraints become binding – How these changes might impact price pressure and profitability – Inform thinking on implications, or possible reactions A tool to analyse future developments – Grid upgrades, using the 18 nodes of the model – Wind Penetration -> relation to transmission and hydro storage. – Energy price escalation -> stochastic treatment of fuel price, CO2 charge, NZ$ Important to recognise limitations – Spot market analysis in general. • Spot offers and prices are driven heavily by the contract position and operational factors, rather than simple costs. – PRISM-specific limitations • Historically based offers. Hydro offers & thermal offers we use reflect market conditions of the period during which they were sampled. Offer strategies will change with evolving market structure and power. • Simple for period analysis • No feedback loops. Neither investment decisions nor offer strategies are related to evolving market structure or the electricity price. E NERGY 4 C ENTRE 2
PRISM Interface: Inputs and Scenarios PRISM Case x: “ Gas Thermal” with “Grid upgrade”, under MED Fuel projections Generation Transmission Demand Gas World Fuel Scenario Scenario Scenario Scenario Scenario Renewables Scenario, No Grid upgrade 2% Growth, Local gas amount, Price oil, coal, Gas Thermal Scenario, Whakamaru only, DC Comalco goes, price, LNG avail- NZ$, CO2, etc.. etc only, etc AKL electric rail able… All stations Network Fuel Data Base New Name, location, Existing, new, Carbon Demand Loads size, owner , upgrades content, etc. Demand by Where, how node much Station Offers Types Random, user, wind Costs, Emissions, curve… etc E NERGY 5 C ENTRE PRISM Inputs and Scenarios (cont) Hydro Stacks – Historic samples Thermal Stacks – Small stations generally dispatched at SRMC – For large stations, have experimented with variety of stacks. – Observing real price and dispatch patterns, have settled on • Large thermals offer for a minimum run rate, then approx SRMC for the remainder (ie not seeking to gouge) • Huntly balances the thermals with a multi-level stack. E NERGY 6 C ENTRE 3
Run PRISM Run it for… 20 years, 4 periods/yr, 50+ samples/period E NERGY 7 C ENTRE Outputs from each snapshots Output from each run – Demand, by node – Price, by node – Dispatch, by station Calculations done on this for a given period – Average prices, standard deviations of prices and probability distributions of prices and dispatch – Emissions – Costs to consumers (price at offtake node x nodal demand), – Revenue to generator (price at dispatch node x dispatch quantity) E NERGY 8 C ENTRE 4
Annual figures derived from the snapshots Extrapolating annual figures from 4 periods… – Take energy use over a year, and in weeks of a season, and compared to peak and off-peak demand during those times, using July ’04 and Jan ’05 data. – Annual factors come out as: • Winter Peak represents 37.5% of annual energy use • Winter Off-peak: 29.5% • Summer Peak: 19.1 • Summer Off Peak: 13.9% – combined with probability of dry yr, normal yr, wet yr Allows calculation of: – Average prices & dispatch over the year – Standard deviation for the year – Annual consumer cost & generator net revenue (ie include fixed & variable costs) – Annual emissions Warning: – Treat with caution in absolute terms, but interesting for comparisons – Further calibration still required to test across years E NERGY 9 C ENTRE PRISM output compares with real prices Real NZ spot market Actual Price for Otahuhu - April-Jul 2003 – Shows several price bands, rather than normal distribution… 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345 360 375 390 405 420 435 450 465 480 495 510 Generally, PRISM appears to match bands – $40-100…Spare capacity PRISM Price for Otahuhu - Winter 2006 Thermals setting price (most off peaks) – $150-200…Scarce capacity. Thermals v. high. Hydro stacks setting. Usually in Winter Peak – $400+ Unsustainable. Thermals at maximum, Hydro stacks probably on unsustainable lake drawdown... 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345 360 375 390 405 420 435 450 465 480 495 510 Dry year winter peak… PRISM P rice for Otahuhu, W inter 2011 Caveats: – “Annualising” factors have been used! – Calibration only done for winter prices – For summer, calibration would require maintenance scheduling 0 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400 425 450 475 500 525 PRISM graphs above are 50/50 normal and dry for demonstration E NERGY 10 C ENTRE 5
Understanding flow patterns Wet Winter - Peak Dry Winter - Off Peak Winter Off Peak Winter Off Peak Winter Peak Winter Peak 45.89 43.98 MDN MDN HEN 44.81 HEN 42.95 LEGEND LEGEND $43 $45 OTA 44.63 44.63 OTA 42.89 42.89 MDN Marsden MDN Marsden HND Henderson HND Henderson OTA Otahuhu OTA Otahuhu 43.02 HLY 42.28 HLY HLY Huntly HLY Huntly WKM Whakamaru WKM Whakamaru NPL New Plymouth NPL New Plymouth WKM 41.84 41.84 WKM 44.45 44.45 TOK Tokaanu TOK Tokaanu 39.89 43.29 WHI Whirinaki WHI Whirinaki BPE Bunnythorpe BPE Bunnythorpe 40.39 NPL TOK 42.68 NPL TOK HAY Hayw ards HAY Hayw ards 42.29 44.92 WHI WHI BPE BPE 40.55 44.31 40.08 40.08 45.89 45.89 HAY HAY 39.1 48.52 STK STK $47 $37 39.16 ARN 48.56 ARN ISL 39.07 ISL 48.49 LEGEND LEGEND 37.17 37.17 BEN 47.06 47.06 BEN STK Stoke STK Stoke ARN Ashburton ARN Ashburton ISL Islington ISL Islington BEN Benmore BEN Benmore ROX 36.27 ROX Roxburgh ROX 47.45 ROX Roxburgh HWB Halfw ay Bush HWB Halfw ay Bush 33.58 46.31 TIW Tiw ai TIW Tiw ai HWB 36.82 MAN Manaupauri HWB 47.69 MAN Manaupauri MAN MAN TIW 35.45 TIW 48.89 E NERGY 11 C ENTRE Understanding grid constraints A grid constraint W inter Peak W inter Peak Otahuhu: $189 194.89 MDN Whakamaru: $0.36 190.31 HE N LEGEND Haywards: $40 189.02 MDN Marsden OTA 189.02 HND Henderson Benmore $37 OTA Otahuhu 131.33 HLY HLY Huntly WKM Whakamaru NPL New P lymouth 0.36 WKM 0.36 TOK Tokaanu WHI Whirinaki 11.34 BPE Bunnythorpe 66.68 TOK NP L HAY Hayw ards WHI 0.8 BPE 39.21 39.68 HAY 39.68 42.13 STK 0 ARN ISL 37.91 LEGEND 36.79 36.79 BE N STK Stoke ARN Ashburton ISL Islington BEN Benmore ROX Roxburgh ROX 36.49 HWB Halfw ay Bush 34.5 TIW Tiw ai HWB 37.2 MAN Manaupauri MAN TIW 36.42 E NERGY 12 C ENTRE 6
Observing long term average price dislocation Averages show price R elative N o dal Price E v olution 100% divergence… 98% 96% – With grid upgrade 94% 92% 90% 88% O TA P rice , a s % of m a x 86% H AY P rice , a s % of m a x BE N P rice , a s % of m a x 84% 82% 80% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 R elativ e N od al Price E v o lu tion 100% – Without grid 98% upgrade, 96% 94% dislocation occurs 92% 90% 88% O TA P rice , a s % of m a x H AY P rice , a s % of m a x 86% BE N P rice , a s % of m a x 84% 82% 80% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 E NERGY 13 C ENTRE Examining lumpy new investment Observing generator dispatch shows: – Lumpiness of new investment (see E3P below). Size of plant is very important. – SRMC-based dispatch is unrealistic, from dispatching profile, pricing, and excessive sensitivity to relative fuel prices E NERGY 14 C ENTRE 7
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