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
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