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Introduction. NZ dominated by hydro. Dry year problem

The impact of large amounts of intermittant wind generation ON THE NZ ELECTRICTY market Oliver Brown, Stephen Poletti and David Young University of Auckland. Introduction. NZ dominated by hydro. Dry year problem. NZEM dominated by hydro (60%) Most of rest thermal Wind 5%

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Introduction. NZ dominated by hydro. Dry year problem

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  1. The impact of large amounts of intermittant wind generation ON THE NZ ELECTRICTY marketOliver Brown, Stephen Polettiand David YoungUniversity of Auckland

  2. Introduction. NZ dominated by hydro. Dry year problem • NZEM dominated by hydro (60%) • Most of rest thermal • Wind 5% • No subsidies for wind but large number of wind farms planned. 20-30% penetration likely by 2020. NZ very Windy! • 5 big gentailers • Nodal market.

  3. Major new investment in wind farms expected • Wind energy resource very promising as average wind speed high with long coastline. • But New Zealand is isolated so can’t rely on backup from offshore (cf Denmark )

  4. Increase in wind poses significant challenges to grid • By 2020 wind in NZ may generate between 5%-80% of power depending on wind speed and time of day. • Increased reserve requirements impose additional costs on system. How much? • Market power issues? • More volatile prices? • Is it best to build wind generation where there is lots of wind? • More wind means lower prices when wind blowing reducing viability of extra wind generation. • What is optimal amount of wind?

  5. Literature • Many papers on impact of wind with competitive markets. Eg UK study 20% wind means more reserve requirements adds £5-8/MWh of wind. ge • Less studies with market power • Green and Vasilakos (2008) look at the UK market using SFE. Find that market power increases price volatility. • Twomeyand Neuhoff (2010) Cournotfindthermalsgain from more wind. • Sensfußet al (2008) agent based model finds in short run merit order effect pushes down prices in Germany.

  6. Methodology • Model NZ market using Agent Based Computer Model (simulates market power) • Look at different scenarios with increased wind penetration. • Very few studies with market power and nodal prices. • Wind speed over different years quite different so look at a number of years.

  7. Agents try different actions. Actions which yield “high profit” • are more likely next round

  8. Agent Based Model • If profit from an action is “good” then action more likely next round. • Action here is to specify price for the generation capacity of plant to be offered into the wholesale market. • Typically 1500 rounds for agents to learn. • Computers not very smart!

  9. E-R model of NZ market • Firms have portfolio of generators. Usually choose different price to offer capacity of each generators to the wholesale market. So firm step function supply curve. • Simplified 19 node network. Market solver similar to one used by ISO. • Reserves market accounted for. • Line losses and line capacity. • Must runs bid in at costsuch as somehydro, wind and geothermal.

  10. Young et. al. (2011) Calibration and validation. Much better than comp model. • Calibrates E-R parameters • Hydro key feature. Calibrates water value curve. Less water in lakes more valuable water is.

  11. Scenarios for 2020. • Most likely scenario has 20% wind as fraction • of peak demand. • High wind has 45%. • At night 80%+ of generation possibly wind for High. • High Wind scenario maximum that Electricity Authority • considers feasible

  12. 2020 High Wind generation.Most extra gen in lower North Island and South Island 200MW 800MW 135MW 760MW 250MW 830MW 300MW

  13. Results • For each scenario 4/48 trading periods simulated every day for a year. (1460 over year) • Even with high wind scenario for NZ no extra peak load capacity needed. Particular nature of NZ market. Energy limited during dry year not capacity constrained. • Wind capacity usage comparatively high. Eg 58% for 2020 (2008 wind &hydro data) Expected scenario, 56% High Wind. 2008

  14. Result 1 Volatility increases with market power (ABM). ($1NZ= €0.50).

  15. Result 2. More wind equals more volatility (and at times more market power). Prices for Auckland 2006 wind and hydrology. 2020 scenarios More wind mostly pushes prices down

  16. Result 2. Similar results Prices for Auckland node 2008 wind and hydrology

  17. Result 3: Spread of nodal prices more variable. Max-Min (over 19 nodes) for 2020 (2008 hrdrology and wind) More separation of prices at Different locations

  18. Result 3. Little gross correlation between price and wind dispatch even for High Wind scenario. Water value and demand factors (presumably) dominate.

  19. Tentative Conclusions • Surprising result is that even for a “dry year” and high wind penetration no need for extra peaking plants. So no “security of supply” costs of intermittency in NZ. Wind complements hydro. • As expected with more wind price duration curve shows mostly lower prices but with a high priced tail. • More wind (bid in at zero) decreases market power in general except for small fraction of the year when wind not blowing and market power exacerbated. • Long run dynamics? Are there enough high price periods to induce investment? If not what is eventual mix? • Expected to see correlation between prices and wind dispatch. No clear initial correlation. Why?

  20. Work in progress. Further research questions • Recall NZ has no subsidies. • What is the optimal amount of wind? • Does a competitive market reach the optimal mix of wind? • With market power is there too much or too little wind penetration?

  21. THE END!

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