Idaho Power Wind Integra0on Study September 15, 2011
Wind Integra0on Study -­‐ Update •  Spring 2011 – PLEXOS Solu5ons selected •  PLEXOS model of IPC system –  Undesigna5on of network resources •  Reserve modeling –  Dynamic (i.e. func5on of wind produc5on) •  Fall 2011 – study release & public workshop Timeline – Wind & Idaho Power Wind Nameplate by Online Date Wind Genera0on Projects Source: Renewable Northwest Project website Service Area and Resources Upper Salmon B Hydro Output % of Nameplate – JAN thru APR Wind Project Output % of Nameplate – JAN thru APR Wind Project Output % of Nameplate – JAN thru APR Nameplate Genera0on Nameplate Genera0on of the Future Transmission System Map Balance Load & Resources Load Resources (including wind genera1on) Load Variability Respond to System Variability Load net wind moves up ↔ another resource(s) moves down Load net wind moves down ↔ another resource(s) moves up IPC Resources for Responding Hells Canyon Complex • Brownlee • 5 genera1ng units totaling 585 MW • Nearly one million acre-­‐feet storage • Oxbow • 4 genera1ng units totaling 190 MW • Hells Canyon • 3 genera1ng units totaling 392 MW • Total Complex • Normal water year about 68% of hydroelectric genera1on & 35% of total energy generated Jim Bridger & North Valmy Coal • Bridger • Rock Springs, WY • IPC owns ⅓ share • 4 coal-­‐fired genera1ng units totaling 771 MW (IPC share) • Valmy • Valmy, NV • IPC owns ½ share • 2 coal-­‐fired genera1ng units totaling 284 MW (IPC share) Langley Gulch Power Plant • Langley Gulch • Paye^e County, ID • Combined-­‐cycle combus1on turbine (300 MW) • Scheduled online July 2012 Gas Peakers • Danskin Power Plant • Mountain Home, ID • One 171 MW & two 46 MW natural gas-­‐fired simple cycle combus1on turbines • Benne^ Mountain Power Plant • Mountain Home, ID • 173 MW natural gas-­‐fired simple cycle combus1on turbine Wind Integra0on – Balancing Reserve • IPC resources having balancing reserve capability (e.g. Hells Canyon Complex) are operated differently to integrate wind • This opera1on is less op1mal & comes at a cost • System reliability is not compromised Wind Build-­‐outs Wind Build-­‐out Scenarios Wind Integra0on – Cri0cal Study Features •  Undesigna5on of network resources –  Select generators undesignated for serving network load –  CommiQed in support of firm off-­‐
system sales (surplus energy) –  Level of commitment ↔ minimum generator output –  Constrains capability to respond to wind upramps Wind Integra0on – Cri0cal Study Features •  Stochas5c modeling of day-­‐ahead scheduling –  Cri5cal inputs to day-­‐ahead scheduling – forecasts for load & wind –  Forecast errors require real-­‐5me (intra-­‐day) response –  Short (i.e. under forecast load / over forecast wind) •  Real-­‐5me purchase (off-­‐system) •  Ramp up dispatchable generators –  Long (i.e. over forecast load / under forecast wind) •  Real-­‐5me sale (off-­‐system) •  Ramp down dispatchable generators Hells Canyon OuSlow – Sep. 8-­‐15 Wind Integra0on – Cri0cal Study Features •  Stochas5cs –
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100 different day-­‐ahead forecasts of wind & load 100 different day-­‐ahead system schedules Each schedule is forced to respond to actual wind & load condi5ons Distribu5on of produc5on costs Wind Integra0on – Genera0on Forecast •  Ongoing effort to develop wind genera5on forecast –  Weather Research and Forecas5ng (WRF) Model –  Day-­‐ahead genera5on forecast (day-­‐
ahead system scheduling) –  Intra-­‐day genera5on forecast (real-­‐
5me system scheduling) •  Incorporate forecast into opera5ons scheduling Wind Integra0on Public Workshop – March 16, 2011