1 California ISO Market Overview

California ISO Market
Pricing Forum
Guillermo Bautista Alderete, Ph.D.
Manager, Market Validation & Quality Analysis
April 2014
CAISO Markets
Transmission-Right
Markets
CRR
allocation
CRR
auction
Monthly, seasonal
and TOU intervals
Congestion Revenue
Rights
(Obligations )
Day Ahead
Market
Market
Power
Mitigation
Integrated
Forward
Market
Real-Time
Market
Residual Unit
Commitment
Hourly intervals
Energy
Capacity
a) Residual
b) Ancillary services
-Spinning
-Non Spinning
-Regulation
Mileage
Real-Time
Unit
commitment
Real-Time
dispatch
15- or 5-minute intervals
Energy
Capacity
Ancillary services
-Spinning
-Non Spinning
-Regulation
Mileage
Slide 2
CAISO Markets -Core Engine
• Co-optimization of energy and ancillary services based
on least-cost solution.
• Iterative and linearized security constrained unit
commitment (SCUC) uses SCUC-Network Application
loop.
• Two back-to-back runs:
– Scheduling run enforces priorities for uneconomical
adjustments and constraint relaxations.
– Pricing run generates clean prices.
– Prices and awards from the pricing run are used
downstream in both day-ahead and real-time
markets
Page 3
CAISO Markets -Core Engine
• Mixed Integer Programming: Linearized Formulation
• Solution within MIP gap, absolute or relative
MIP gap for day-ahead market
- MIP Gap: At any given iteration, difference
between current best MIP solution and best LP
solution.
-Best MIP solution enforces {0,1} constraints.
-Best LP solution relaxes {0,1} constraints.
• Constraint violations modeled with penalty prices.
- One-segment penalty for all transmission constraints.
- Constraint relaxation to attain a feasible solution.
Page 4
CAISO Pricing
• CAISO approach follows the standard marginal pricing
theory.
• Solution within MIP gap results in infrequent material
suboptimal outcomes given the tight tolerance in place.
• (Now you see me) now you don’t: knife edge solution.
• Units being constrained may not set the price.
•
Pricing AS scarcity.
Page 5
Commitments and its Implications
• Commitment decisions are discrete and their costs are
lumpy.
• Least-cost UC based on bids to better reflect physical
reality
– Minimum load , start-up and transition costs,
– Minimum times and ramps,
– Incremental bids.
• Optimal schedules means both unit commitment
decisions (status) and economic dispatch (MW).
Page 6
Commitments and its Implications
• Incremental bids only means continuous and
convex problem.
• UC decisions are discrete and lead to nonconvexities.
• The marginal cost of meeting
demand is not necessary
monotonically increasing because
of UC.
• Non-convexity of UC-based markets inherently may lead to some
units dispatched uneconomically.
• Bid cost-recovery (make-whole) mechanisms are in place to recover
missed rents.
Page 7
Least Cost Solution
• For illustration, a demand of 110 MW can be met with G1=110MW
at a cost of $0+$0+$50/MWh*100MW+$100/MWh*10MW=$6,000
and marginal price of $100/MWh.
• As demand increases, there may be a inflexion point where G2
becomes cheap enough to be committed.
Page 8
Least Cost Solution
•
Assume now a demand of 130 MW. Three outcomes can be evaluated
1. All demand is met with G1
Cost=$0+100MW*$50/MWh+30MW*$100/MWh=$8,000 at $100/MWh on margin
2. All demand is met with G2
Cost=$2500+$500+100MW*$30/MWh+30MW*$60/MWh=$7,800 at $60/MWh on margin
3. Both G1 and G2 are dispatched
G1=30MW at a cost of 0+30MW*$50/MWh=$1,500
G2=100MW at a cost of $2500+$500+100MW*$30/MWh=$6,000
Total cost is $1500+$6000=$7,500 at $50/MWh on margin
Page 9
Least Cost Solution
Generators’ profit
G1: $50/MWh*30MW-$1500=$0 as it’s marginal
G2: $50/MWh*100MW-$6000= -$1,000
G2 has a shortfall; a BCR payment =$1000 will be needed.
The marginal cost to meet 130 MW of demand is $50/MWh, but the optimal
way to meet 110 MW demand is all from G1 at a marginal price of $100/MWh
The marginal cost is not monotonically increasing, and the cost is nonconvex.
Page 10
Constrained Output Generator (COG) Model
• Units with operational range (Pmax-Pmin) not greater
than five percent of Pmax or 3MW can be eligible for
COG model.
• COGs are allowed to be
scheduled continuously
between 0 and its minimum
operating limit.
• Bid equals Average Pmin
cost.
• COG can set the price.
Page 11
Market Pricing
• Alternative mechanisms include²:
– Restricted Model: Takes as known the UC decisions.
– Dispatchable model: Allows units to be dispatched
below Pmin.
– Convex Hull: Convex-ify the problem.
• MISO is going though an effort for Extended LMP
• What are the trade-offs?
• Do they eliminate or reduce uplifts?
2 Market-Clearing Electricity Prices and Energy Uplift. P. Gribik, W. Hogan and S. Pope. Dec. 2007
Page 12
Voltage Support and Post-Contingency Limits
• Exceptional Dispatches (ED) or Minimum Online
Constraints (MOC) may be used.
• MOC is commitment constraint not an energy- or flowbased constraint and thus do not have a price
component in the LMP.
• MOC is internalized within the day-ahead market
solution, unlike post-market EDs thus reducing impact on
RT and DA price differences.
• Once a unit is committed due to MOC it may be
dispatched economically and able to set price.
Page 13
Minimum Online Constraint
• In many instances the MOC requirement met naturally
with the commitment from the system economics.
The higher the bar, the
more the MOC
requirement was
naturally met by
Economics
• In some instances units may be dispatched
uneconomically; standard BCR will cover these units.
• Contingency modeling enhancement feature will allow
less reliance on MOCs and price the ramp capability.
Page 14
What pricing areas have the most interest?
• Price effects and alternatives that address lumpiness.
• Pricing during emergency or demand response conditions.
• Pricing during constraint relaxation.
• Pricing of non-energy or flow based constraints.
• Pricing effects of other manual or other external effects.
• Transparency of conditions effecting prices.
Page 15

Download Report