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Negative Wholesale Power Prices: Why They Occur and What to Do about Them. A Study of the German Power Market Maria Woodman, Student Economics Department, New York University. Research Motivation: The German Power Market and Negative Wholesale Power Prices . Market Anomaly
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Negative Wholesale Power Prices: Why They Occur and What to Do about Them A Study of the German Power Market Maria Woodman, Student Economics Department, New York University
Research Motivation:The German Power Market and Negative Wholesale Power Prices • Market Anomaly • Electricity doesn’t obey traditional commodity price behaviors • Negative wholesale prices can result • Three Causes • Increases in wind infeed when demand is low • Government incentives to favor wind producers • Flat rate prices cause customers to consume electricity regardless of the value of each MW
Power Prices and Wind Generation [ALZ1] [ALZ1]FIX THESE!!!!
Power Generation Supply (without Wind) Supply Demand Merit Order Curve Marginal Cost Gas Turbine Wholesale clearing price Combined Cycle Gas Turbine Bituminous Coal Lignite Coal Nuclear Hydro MW of Capacity
Power Generation Supply (with Wind) Supply – No Wind Demand Marginal Cost Supply – with Wind Shifted Merit Order Curve Combined Cycle Gas Turbine WIND 0 Wholesale clearing price Bituminous Coal Lignite Coal Nuclear MW of Capacity
Increased Wind Infeed An influx of wind power shifts that merit order curve rightward, which drives prices down
Flat Rate Retail Prices D Retail Market • Retail prices don’t represent a consumers true willingness to pay • In the case of negative wholesale prices, they grossly overpay in the retail market. P S Wholesale Market D S D 0 L P L
Is Dynamic Pricing the Answer? • What is dynamic (“time-of-day”) pricing? • Allows retail prices to match wholesale prices in real time • Stimulates a demand side price response • How can it impact negative prices? • The solution isn’t simple • An estimated retail demand curve isn’t defined when prices are negative
Existing Studies of Dynamic Pricing • Studies evaluating the results of implemented programs have returned varied ranges of end-user price response • Long Run Elasticity – 0.3-0.5 (Borenstein, 2005) • Short Run Industrial End-User Elasticity – 0.01-0.27 (Boisvert, 2007; Neenan, 2004; Braithwait and Sheasy, 2002; Patrick and Wolak, 1997) • Using these ranges, the studies focused on the use of dynamic pricing to reduce peak price and load
Method for Analyzing RTP • Construct wholesale supply and demand curves for a set of hours representing different combinations of demand and wind infeed • Construct demand curves representing different levels of price response using differing price elasticities • Induce an increase in wind power by shifting the supply curve • Solve for the new equilibrium points given the new supply and demand curves
Allocation of Hours • It was found that a necessary condition for negative prices appeared to be either high wind in-feed ( >12 GW) coupled with moderate system demand (40-50 GW) or low system demand ( <40 GW) coupled with moderate wind in-feed (5-10 GW) (Genoese, 2010). • Using these metrics, I was able to disaggregate the hours into their respective buckets for analysis Hours of focus
Preliminary Results • For the hour type of low demand and high wind infeed, on average, a price elasticity of at least -0.14 was needed to have a market clearing price of €0.00 • For the case of increased wind generation • Following the same logic, a price elasticity of approximately -0.44 was necessary to raise the price to equal the existing retail flat rate price. • The elasticity value is unrealistic given previous estimates of consumer price response
Negative Prices: Hours of Occurrence The vast majority fell during early morning and weekend hours Of the 1% of hours that were affected in 2009
Conclusions • RTP may not have a significant effect and in some cases might even be a hindrance to the market. • Other demand side management techniques may be more effective in mitigating the market inefficiency of negative prices • Additional R&D in electric vehicles, smart grid technology and implementation, and smart appliances could aid in making demand side management viable
Thank You!Questions? Maria Woodman, New York University Email: mjw399@nyu.edu