Modelling large-scale wind penetration in New Zealand with Plexos Magnus Hindsberger EPOC winter workshop Auckland, 5 September 2008

Outline Background Plexos model Wind output series Reserve requirements Model results Interaction with plug-in hybrid electric vehicles Future work

Wind power integration in New Zealand - a scenario analysis of % wind power in the electricity market in 2025 Iben Moll Rasmussen Mikkel Windolf

Background of analysis Current wind capacity: 321 MW Current projects: ~ 6000 MW Need to understand: –Wind variability issues, such as reserve requirements, grid flows and market price impacts –Interaction with electric vehicles, including charging on a day to day basis

Developed by Drayton Analytics, now Energy Exemplar PLEXOS 4.0 released in Plexos 5.0 appeared 2008 Co-optimization engine based on PhD thesis of Glenn Drayton (University of Canterbury, 1997) PLEXOS licensed in 17+ countries worldwide PLEXOS consists of 4 main modules: –LT-Plan –PASA –MT-Plan –ST-Plan Plexos model overview MS Access

Wind data Starting point: –1 wind farm output series, –1 wind speed series measured at 70 m, –1 wind speed series measured from the top of a building, For the first model, 3 regional series were used based on the data above. Newly obtained: –Multiple 10 m. data series from around NZ –3 data series from Belmont Regional park

Wind power modelling in Plexos Method: Point measurement to wind farm or regional output Generic power curve –Mix of Vestas and Siemens turbines 30 wind speed time series Wind output series Exp. regional utilisation time Scaled wind output series Plexos input files Real wind farm output Verification

Wind farm output Method to go from point estimates to wind farm/region output

Estimating smoothing Belmont Regional Park sites: Tower 2130 m. Tower 66A44 m. Tower 75:42 m. Distances [km] Tower 21 & Tower 66A 6 Tower 21 & Tower 75 3 Tower 66A & Tower 75 4

Wind series Data from NIWA: , typically measured at 10 m.

Achievements 1 hour resolution allowing short-term issues to be analysed. Using historical data where good records are available, limit our number of wind series compared with using synthetic data. But it provides the following benefits: –Regional correlation is kept –Correlation with demand is kept (if same demand year is used) Much better than our previous data

Reserves modelling Most simple model is persistence forecast: –Wind(T+1) = Wind(T) May be too simple as not taking into account point on power output curve

Reserves modelling +400 MW- 600 MW Typically harder to predict timing of a change than the magnitude of the change as shown below (Western Denmark case)

Reserves modelling One has to be created per island and per year of wind data

Reserves modelling Wind risk is in addition to normal reserves as set by risk-setting unit: –Reserves t = LargestRisk t + WindRisk t For this analysis, we fixed largest risk to North Island CCGT and South Island generator at Clyde. Will create a separate reserve market in Plexos in the future and go back to dynamic risk for the generators/HVDC.

Wind scenarios 90 % renewables Wind energy share ~10% ~15% ~15% ~25%

Expected results Increased wind penetration will lead to: –Less efficient thermal generation –Higher reserve costs –Higher costs for peaking capacity –Higher transmission costs Dispersed wind will lead to lower costs than a concentrated wind development Market prices may be lowered significantly Not analysed

Results - reserves Costs ($mill) Accurate forecast Persistence forecast Compact Wind Disperse Wind Costs of reserves for persistence forecast vs. a more accurate forecast Same max capacity, but high difference in costs Clear diversification benefit

Results - Transmission Transmission losses Transmission congestion More (compact) wind appear to lead to higher transmission related costs

Results - Generation Generation share in 2025 (normal inflow year) Little non-zero SRMC capacity

Wind impact on prices Wind revenue vs. average revenue in Western Denmark, ~20% wind (annual energy) and export capability

Results – Market prices 25% wind scenario lower price significantly when generation is high Also impact on wind spill

National costs assessment Cheaper

Interaction with Plug-in Hybrid Electric Vehicles (PHEV)

Why of interest Due to the large potential for renewable electricity generation in NZ, PHEV’s and later on EV’s are likely in larger scale. This will affect the power system as: –Energy demand will be bigger –Load duration curve will change (charging) –They may provide reserve capacity (V2G) –They may be used for peak shifting (V2G) They will also improve the revenue of wind

Modelling in Plexos Daily energy requirement (per region) –Based on vehicle forecast and daily distance travelled –Currently free to choose time of recharge Max capacity (offtake or delivered) based on assumptions on recharge on standard household installations (220 V – 14 Amps) Cut-off price if petrol is cheaper, can be an issue during dry years. A $2/L petrol price was used.

PHEV recharging example

PHEV price paid & cost savings Price ($/MWh) / Cost savings ($ p.a.) Average price paid by the PHEV’s [$/MWh] Annual cost savings per PHEV with a petrol price of 1.5 $/L [$] Annual cost savings per PHEV with a petrol price of 2 $/L [$] Reference Compact Wind Disperse Wind % Wind Potential wholesale price increase and thus extra wind generator revenue (less subsidy) yet to be analysed It was previously shown that more wind power led to lower prices.

PHEV impact on prices PHEV’s may increase price in high wind generation hours Generation Price Demand More wind PHEV demand Supply

Future directions

Future direction Internalise experience –Value of HVDC overload capacity –Wind/hydro interaction Competition modelling –Cournot and RSI Grid Development Strategy –Extend wind/PHEV work to 2050 –Understand peak capacity requirement including Demand Side Response –Wind power variability and investment decisions in LT

GDS overview Objective: To form a long term National Grid development strategy taking into account: –New Zealand's future social, environmental and economic requirements; and –long-term technology trends. Process: The GDS process is likely to take about 18 months, culminating in a final strategy in the first half of 2010.

GDS process Scenario work package started last Friday. RFI published with deadline 19 September.

Questions ?