Efficiency Impact of Convergence Bidding in the California - - PowerPoint PPT Presentation

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Efficiency Impact of Convergence Bidding in the California - - PowerPoint PPT Presentation

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Efficiency Impact of Convergence Bidding in the California Electricity Market Ruoyang Li*, Alva Svoboda**, Shmuel Oren* *University of California at Berkeley **Pacific Gas and Electric Co. Presented at Conference on the Economics of Energy and

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Efficiency Impact of Convergence Bidding in the California Electricity Market

Ruoyang Li*, Alva Svoboda**, Shmuel Oren* *University of California at Berkeley **Pacific Gas and Electric Co. Presented at Conference on the Economics of Energy and Climate Change Toulouse, France September 8‐9, 2015

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Scope

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Pricing Mechanisms

The prevailing mechanism for pricing electric energy in US electricity markets operated by Independent System Operators (ISO) is Locational Marginal Prices (LMP), defined as the incremental (least) cost of supplying a marginal MW of power to the specific location while meeting all security constraints

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4

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Over‐generation, congestion and no storage capability can lead to negative prices

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Two‐settlements Electricity Markets

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California ISO Market Timeline

Day Ahead Market (DAM)

10:00 13:00 DAM Process Begins Clear the Market Publish Results CMRI Bids Submitted SIBR T - 7 days T-75min Beginning at midpoint of each 5min period RTM Process Begins Clear the Market Bids Submitted SIBR T-1 after 13:00

Real Time Market (RTM)

Receive Dispatches ADS Triggers the Real Time Market

Applications:

  • SIBR - Scheduling and Infrastructure Business Rules
  • CMRI – California ISO Market Results Interface
  • ADS – Automated Dispatch System
  • SLIC – Scheduling and Logging for ISO of California – Outages
  • MRI-S – Market Results Interface-Settlements

Settlements MRI-S

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Typical Daily Schedule

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Example of DA‐RT Price Spread

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Convergence (Virtual) Bidding (CB)

CB CB also also enables enables ma market et partici participan ants ts ex executing ph physic ical al tr trade ades to to

  • p
  • pt fo

for RT RT pri prices es in instead of

  • f DA

DA pri prices.

  • es. It

It Al Also so in incr creases ma market et liqui quidi dity ty by by enabl enabling ng participan participants ts wi with no no asse assets ts to to ta take posi positions tions arbitr arbitraging aging the the DA DA‐RT RT spr spread ad

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Theoretical Benefits of CB

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Typical Submitted and Cleared CB Volumes

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Con Conver ergence nce bidding bidding vo volume mes and and we weight hted price price di differ erences ences Q4 Q4 2014 2014

‐$15 ‐$10 ‐$5 $0 $5 $10 $15 $20 $25 ‐3 000 ‐2 000 ‐1 000 1 000 2 000 3 000 4 000 5 000 Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 Weighted price difference between day‐ahead and real‐time ($/MWh) Average hourly megawatts

Virtual supply Virtual demand Weighted price difference for virtual supply Weighted price difference for virtual demand

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The Efficient Market Hypothesis

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Our Approach

  • Use the perspective of a trader attempting to construct

an optimal portfolio of virtual hourly positions.

  • Construct and estimate a statistical time series model of the

returns from historical data

  • Obtain an optimal portfolio (based on appropriate constraints
  • n risk)
  • Evaluate the arbitrage profits of the optimal portfolio using in

sample and out of sample data

  • Asses market efficiency based on profitable arbitrage
  • pportunity.
  • Alternative approach proposed by Jha and Wolak

[2013] was based on evaluating the “implied trading cost” for which one cannot reject the null hyphotesis of “no arbitrage” (based on average return = uniform portfolio assumption )

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Portfolio Optimization

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Portfolio Optimization (cont’d)

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Portfolio Optimization with VaR Constraints

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Portfolio Optimization with VaR Constraints (cont’d)

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Portfolio Optimization with CVaR Constraints

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Portfolio Optimization with CVaR Constraints (cont’d)

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Risk Measures: VaR vs. CVaR

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Time Varying Forward Premium

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Time Varying Forward Premium (cont’d)

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Time Varying Forward Premium (cont’d)

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Model Description

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Model Description (cont’d)

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Data

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Optimal Numbers of States and Clusters

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Transition probabilities of the Post‐CB GMHMM

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Post‐CB Posterior State Probabilities

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Marginal Distribution of Post‐CB DA‐RT Spread

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Regression Analysis

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In Sample and Out of Sample Tests

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Performance Under a VaR constraint

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Performance Under a CVaR Constraint

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Conclusions