Parameter Tuning for Uneconomic Adjustments. Lorenzo Kristov, Principal Market Architect

Transcription

1 Parameter Tuning for Uneconomic Adjustments Lorenzo Kristov, Principal Market Architect Stakeholder Meeting May 13, 2008

2 Topics for Discussion Objectives of Current Parameter Tuning Effort Parameter Tuning Concepts Uneconomic Adjustment Scheduling Run and Pricing Run Potential for Extreme Results LAP Demand Clearing in the IFM Need for Changes to MRTU Tariff Illustrative Examples (Edward Lo) Parameter Tuning Analysis Overview Proposal for Ongoing Updates to Parameters Initial Results for IFM Parameters (Jim Price) Practices of Other ISOs 2 Parameter Tuning Stakeholder Meeting May 13, 2008

3 Objectives of Current Parameter Tuning Effort Explain role of these parameters in the MRTU market optimizations, particularly for Uneconomic Adjustments Particular focus on a specific uneconomic adjustment case LAP Demand Clearing Describe CAISO s process for tuning the parameters Parameter settings comprise an essential element of MRTU market design, needed for start-up After initial values are determined, CAISO proposes updating as needed with publication of current parameter parameters Provide initial parameter settings for ongoing market simulation activities Develop any needed tariff changes for July filing Compliance regarding LAP Demand Clearing Flexibility to avoid unreasonable, extreme outcomes. 3 Parameter Tuning Stakeholder Meeting May 13, 2008

4 Concepts 1 Self Schedules and Scheduling Priorities A fundamental design feature of MRTU since 2002, broadly desired by market participants, is to allow SCs to Self Schedule supply and demand at their desired operating levels and thus opt out of CAISO market optimizations. Therefore, SC Bid submissions may include: Economic Bids for Energy and AS which include both MW quantities and corresponding bid prices Self Schedules for Energy, and AS (Self Provision) which include only MW quantities, no prices. Related to Self Schedules is a set of Scheduling Priorities a hierarchical sequence in which different Self Schedule types may be adjusted by the CAISO if necessary to satisfy required constraints (e.g., flow limits, energy balance). 4 Parameter Tuning Stakeholder Meeting May 13, 2008

5 Concepts 2 Uneconomic Adjustment MRTU market optimizations first try to solve using only Economic Bids, without adjusting Self Schedules or relaxing any constraints (e.g., transmission limits, AS requirements) Solution must balance Energy supply and demand, procure required AS, and manage congestion to keep scheduled flows within limits If Economic Bids are insufficient to reach a solution, the optimization resorts to Uneconomic Adjustments to reduce Self Schedules or relax some constraints The frequency of such cases is expected to be low, but will depend on the proportion of Self Schedules in the market optimization. Incorporating different types of Self Schedules (e.g., TOR, ETC, generic) and other constraints, and assigning each type a different priority for adjustment, requires a system of numerical parameters to enforce the pre-specified priorities. 5 Parameter Tuning Stakeholder Meeting May 13, 2008

6 Concepts 3 Penalty Prices Uneconomic Adjustment process is structured by setting Penalty Prices parameters in the optimization that work analogously to bid prices of Economic Bids, but are Outside the range of acceptable Economic Bids outside the Tariff Bid Cap and Bid Floor values, and Set by the CAISO in the software, to enforce the correct adjustment priorities. Example 1: Penalty price is -$550 in IFM for price-taker supply bids (aka generic Energy supply self-schedules) This means that the generic self-schedule will have an associated DEC Bid of -$550 per MW in the optimization, which is outside the allowable range of Economic Bids, and thus forces the optimization to use Economic Bids until it becomes cheaper to use the -$550 DEC Bid. Example 2: By comparison, penalty price is -$3200 in IFM for Energy supply self-schedules under ETC/CVR ETC/CVR supply has higher priority protection against curtailment than generic supply self schedules. 6 Parameter Tuning Stakeholder Meeting May 13, 2008

7 Concepts 4 Penalty Price Values Penalty prices must have extreme values in order to enforce required hierarchies, for example, Adjust generic self-schedules before adjusting ETC/CVR schedules, and adjust ETC/CVR before TOR schedules Reduce generic export self-schedules (not linked to non-ra supply) before reducing internal CAISO demand or export self-schedules linked to non-ra supply => Penalty prices must be different from Economic Bid range, and sufficiently separated from each other to enforce different priorities When uneconomic adjustment occurs, the optimization sets prices that are reflective of the penalty price settings but are not appropriate for settlement. For example, if the optimization reduces an ETC self-schedule at the penalty price of -$3200 to relieve a transmission constraint, the price differential across the constraint will be at least $ Parameter Tuning Stakeholder Meeting May 13, 2008

8 Concepts 5 Scheduling and Pricing Runs Problem: Extreme penalty prices needed for enforcing required priorities in Uneconomic Adjustment will result in calculated prices (LMPs, ASMPs) that are too extreme to be used in settlement. Solution: Each MRTU market process includes two runs: First, a scheduling run is performed using penalty prices to ensure that Uneconomic Adjustment, if needed, results in the correct resource schedules, Second, a pricing run where the penalty prices are replaced by appropriate pricing parameters so that LMPs and ASMPs are economically reasonable and consistent with Tariff. ** Market processes include Pre-IFM-CC, Pre-IFM-AC, IFM, RUC, RTUC (aka RTPD), RTD (aka RTID) 8 Parameter Tuning Stakeholder Meeting May 13, 2008

9 Concepts 6 Simple Example Suppose there are insufficient Energy Bids offered in the IFM to clear all Self-Scheduled CAISO Demand and Self-Scheduled Exports linked to non-ra supply capacity. Penalty price on reducing such demand is $1600 Assume no congestion, no losses only relevant constraint is the supply-demand balance Scheduling run will: Reduce such demand until it can be met by the available supply, say to 25,000 MW Determine a scheduling run energy price = $1600 Policy indicates, however, that the price for serving such price-taker demand should be set by the Energy Bid Cap, i.e., $500 Pricing run will therefore: Utilize the $500 pricing parameter for a small epsilon range around the 25,000 MW demand level Calculate $500 as the Energy price for settlement purposes. 9 Parameter Tuning Stakeholder Meeting May 13, 2008

10 Concepts 7 Potential for Extreme Prices Under Economic Clearing MRTU Tariff says that the optimization will utilize all Economic Bids before adjusting Self Schedules The word all precludes any flexibility, leading to extreme scheduling and pricing results in certain cases Example. A large volume of supply self-schedules result in a transmission constraint being overloaded by 2 MW Only available DEC bid is a 300 MW unit that bid $20 and is 1% effective on the overloaded constraint Unit must be curtailed by 200 MW to obtain 2 MW reduction on the constrained line Shadow price of constraint will = 200 x $20 = $4000, which means $4000 congestion cost differential across the line Result does not depend on Uneconomic Adjustment, because Economic Bids were available and were used BUT Outcome is not operationally or economically reasonable. 10 Parameter Tuning Stakeholder Meeting May 13, 2008

11 Concepts 8 LAP Demand Clearing in IFM Demand in IFM can bid at Default LAP aggregate level, and will be cleared so that resulting price and quantity is a point on the Default LAP demand curve To ensure this economically consistent outcome, the LDFs for the Default LAP are held fixed in the IFM optimization Unintended consequence a binding transmission constraint can cause large reduction in cleared demand for the entire Default LAP. Tariff Section states that in such cases IFM will First release self-provided AS to provide energy to serve the load behind the constraint, Second, if needed, relax the transmission constraint Third, if needed, vary some LDFs (not implemented at this time) Tariff solution is implemented through appropriate penalty price settings. 11 Parameter Tuning Stakeholder Meeting May 13, 2008

12 Some Observations Generally, extreme scheduling and price outcomes can result from a large proportion of Self Schedules (i.e., small proportion of Economic Bids) in the market optimization. SCs can mitigate their exposure to such extreme outcomes by, for example, Prioritizing their use of Self Scheduling Submitting Economic Bids for a portion of the operating range of a self-scheduled resource. A system of scheduling priorities as included in MRTU requires a system of penalty prices for scheduling purposes and pricing parameters for determining settlement prices. Parameter values must be tuned to balance objectives of enforcing priority hierarchy while avoiding outcomes that are operationally or economically unreasonable. 12 Parameter Tuning Stakeholder Meeting May 13, 2008

13 Criteria for Parameter Values Fulfill MRTU design principle of protecting self-schedules according to the priority order specified in the tariff Provide appropriate economic signals in the form of large magnitude (positive or negative) prices when Uneconomic Adjustments must be utilized Prevent unreasonable price outcomes, which can occur if there is no flexibility in the hierarchy structure of priorities among the various types of Self Schedule and constraints Achieve scheduling and pricing outcomes that are consistent with good operational practice and support reliable grid operation. 13 Parameter Tuning Stakeholder Meeting May 13, 2008

14 Need for Tariff Changes First, the LAP Demand Clearing problem is one case where strict adherence to priorities and enforcement of constraints can lead to extreme outcomes FERC approved solution of Section but directed CAISO to modify tariff language to clarify how the penalty price interacts with Economic Bids in this case Second, the ineffective economic bid of the previous example represents a circumstance where flexibility is appropriate rather than strict adherence to the principle of using all Economic Bids before adjusting Self Schedules CAISO intends to revise tariff to allow flexibility to avoid solutions that are operationally and economically unreasonable. (see Tariff Section 31.4) 14 Parameter Tuning Stakeholder Meeting May 13, 2008

15 Detailed Illustrative Examples Presentation by Edward Lo 15 Parameter Tuning Stakeholder Meeting May 13, 2008

16 Scheduling and Pricing Run Illustrative Examples Two examples are presented to illustrate: Formulation and results of scheduling and pricing runs Scheduling run establishes energy schedules Pricing run establishes settlement prices Comparison of using Uneconomic Adjustments to Energy Self Schedules versus Economic Bids to resolve congestion in the scheduling run Calculation of settlement LMPs in the pricing run under Uneconomic Adjustment Roles of the penalty prices in the scheduling run and pricing run. 16 Parameter Tuning Stakeholder Meeting May 13, 2008

17 2 Bus Example Scheduling Run Setup G1: 120MW Self Schedule G1 1 2 Flow Limit: 80MW G2: G2 [0,150]MW@$50/MWh Fixed Load: 200MW $10 120MW 200MW -$250 Price Curve of G1 in Scheduling Run Scheduling Run penalty price of -$250 protects the G1 self schedule from curtailment. NOTE: The value -$250 is convenient for purposes of this example but is not the actual value proposed for use in the MRTU markets. Transmission line is assumed loseless. 17 Parameter Tuning Stakeholder Meeting May 13, 2008

18 2 Bus Example Scheduling Run Solution G1=80 MW LMP1 = -$250 G1 1 Flow = 80MW Shadow Price = $300 2 G2 = 120 MW G2 LMP2 = $50 Fixed Load: 200MW G1 could have supplied all energy to serve load if no limitation on power flow through line 1-2. Curtailment of G1 self schedule is necessary to resolve the flow constraint violation. Economic signal at bus 1 prior to self schedule curtailment of G1 is $ Parameter Tuning Stakeholder Meeting May 13, 2008

19 2 Bus Example Pricing Run Setup G1: G1 1 Flow Limit: 80MW 2 G2: Original Economic G2 Bid Fixed Load: 200MW $10 120MW 200MW -$30 Hard Lower Limit at 80-ε MW -$30 Bid Floor price is used for the curtailed G1 self-schedule, with MW lower bound expanded by ε. Epsilon ε is small positive number to: Ensure feasible solution in pricing run Ensure pricing run MW solution is very close to the scheduling run Provide room for the -$30 price segment of G1 to interact with economic bids of other resources 19 Parameter Tuning Stakeholder Meeting May 13, 2008

20 2 Bus Example Pricing Run Solution G1=80 MW LMP1 = -$30 G1 1 Flow = 80MW Shadow Price = $80 G2 = 120MW G2 LMP2 = $50 Fixed Load: 200MW MW solution of pricing run is identical to scheduling run. G1 is marginal at 80MW; ε above the lower bound. LMP of bus 1 is set by the -$30 Bid Floor pricing parameter, where this parameter exceeds on the negative side the economic signal ($10) prior to self-schedule curtailment. 20 Parameter Tuning Stakeholder Meeting May 13, 2008

21 3 Bus Example Scheduling Run Initial Setup G1: 360MW Self Schedule G1 1 2 G2 G2: [0,100] Flow Limit: 260MW 3 G3 G3: [0,110] $70/MWh [110,200] $500/MWh Fixed Load: 500MW The -$250 penalty price protects the G1 self schedule from curtailment. NOTE: The value -$250 is convenient for purposes of this example but is not the actual value proposed for use in the MRTU markets. The three transmission lines are equal in reactance and are assumed loseless. 21 Parameter Tuning Stakeholder Meeting May 13, 2008

22 3 Bus Example Scheduling Run Solution with no Self Schedule Curtailment G1= 360MW LMP1 = -$50 G1 1 2 G2 G2 = 60MW LMP2 = $10 Flow = 260MW Shadow Price = $180 3 G3 Fixed Load: 500MW G3 = 80MW LMP3 = $70 Flow of line 1-3 caused by G1 self schedule is 240MW. Flow constraint on 1-3 limits inexpensive G2 to 60MW schedule. Remaining 80 MW supply is from G3. All MW schedules are economical. G2 and G3 are marginal and set LMPs. Shadow price of line 1-3 constraint is $180. For 1 MW increase in line limit, 3 MW of energy supply for fixed load can be shifted from G3 to G2 for reduction of 3*($70-$10) in total bid cost. LMP of -$50 for bus 1 means that 1 MW withdrawal at bus 1 reduces the total bid cost by $50. For 1 MW of such withdrawal supplied from G2 at $10, 1/3 MW of line capacity on 1-3 is freed up. Therefore, 1 MW of energy supply for the fixed load can be shifted from G3 to G2 for $60 bid cost reduction. Hence the net reduction in bid cost is $ Parameter Tuning Stakeholder Meeting May 13, 2008

23 Modified 3 Bus Example Partial Scheduling Run Solution Consider Flow Limit on 1-3 Reduced to 240MW G1= 360MW Congestion is partially reduced without curtailing G1 by shifting energy supply from G2 to G3, to obtain 1/3 MW reduction of flow violation per MW of supply shift. Such re-dispatch is applied up to the MW level shown, which hits the break point in G3 s bid curve. Two options to resolve the last 10 MW of flow violation: 1. Continue the same shift at a high rate of increase in bid cost of 3*($500-$10) = $1470 for 1 MW of reduction in flow violation. LMP at bus 1 would be ($500-2*$10) = -$480. Alternatively, 2. Curtail G1 self-schedule and shift energy supply from G1 to G2 to obtain 1/3 MW reduction of flow violation per MW of supply shift. Rate of increase in bid cost is 3*($250+$10) = $780 for 1 MW reduction in flow violation = lower cost solution. 23 Parameter Tuning Stakeholder Meeting May 13, 2008 G1 Flow = 250MW G3 Fixed Load: 500MW G2 G3 = 110MW G2 = 30MW

24 Modified 3 Bus Example Scheduling Run Solution with Self Schedule Curtailment G1= 330MW LMP1 = -$250 G1 1 2 G2 G2 = 60MW LMP2 = $10 Flow = 240MW Shadow Price = $780 3 G3 Fixed Load: 500MW G3 =110MW LMP3 = $270 Second re-dispatch strategy is adopted for the final scheduling solution. G1 and G2 are marginal. G3 is not marginal. LMP3 = $270. For 1 MW additional withdrawal at bus 3, the lowest cost solution is 2 MW increase of G2 and 1 MW reduction of G1. Without self schedule curtailment, more expensive economic adjustment is necessary to resolve congestion, and LMP1 would be -$480, more negative than the -$250 penalty price of scheduling run. 24 Parameter Tuning Stakeholder Meeting May 13, 2008

25 Modified 3 Bus Example Pricing Run Solution G1= 330-ε MW LMP1 = -$50 G1 1 2 G2 G2 = 60+2ε MW LMP2 = $10 Flow = 240 MW Shadow Price = $180 3 G3 G3 = 80-ε MW LMP3 = $70 Fixed Load: 500MW Pricing run MW solution is very close to scheduling run. G2 is marginal. G3 moves away from the breakpoint of the bid curve and becomes marginal. G1 is at the lower bound and becomes non-marginal. LMP1 = -$50, the economic signal prior to self-schedule curtailment in the scheduling run set by economic bids of other resources. Should economic signal set by other resources be within the -$30 pricing parameter as in the 2-bus example, pricing run will result in LMP set by this parameter. 25 Parameter Tuning Stakeholder Meeting May 13, 2008

26 Conclusions from the Examples Penalty Prices for the scheduling run set limits on how extreme (large positive or negative) economic signals can be before adjusting self schedules or relaxing constraints. Thus well-chosen penalty prices can limit the occurrence of extreme scheduling or pricing outcomes due to using ineffective economic bids to resolve constraint violations. Pricing Parameters for the pricing run serve as administrative floor (minimum positive or negative magnitude) for the settlement price if the economic signals based on economic bids, i.e., prior to uneconomic adjustment, do not accurately reflect the cost impact of adjusting self schedules or relaxing constraints. In both examples, a high level of self scheduling leads to either The need to reduce the self schedule to achieve feasibility (2-bus example), or Extreme results based on Economic Bids if all such bids must be used before adjusting Self Schedules (3-bus example) 26 Parameter Tuning Stakeholder Meeting May 13, 2008

27 End of Illustrative Examples Presentation 27 Parameter Tuning Stakeholder Meeting May 13, 2008

28 Parameter Tuning Analysis Overview 1 Criteria for choosing parameter values 1. Scheduling run should observe required priorities for protecting Self Schedules and enforcing constraints, consistent with MRTU design and tariff 2. Pricing run should provide appropriate price signals (e.g., shortage of economic bids) 3. Prevent unreasonable extreme price outcomes due to inflexible adherence to curtailment priorities 4. Achieve scheduling and pricing outcomes that are consistent with good operational practice and support reliable grid operation. 28 Parameter Tuning Stakeholder Meeting May 13, 2008

29 Parameter Tuning Analysis Overview 2 Utilize market simulation cases selected to contain the following conditions 1. LAP Demand Reduction in IFM/pre-IFM Identified by release of self-provided AS or relaxation of a transmission constraint 2. Use of Economic Bids with low effectiveness Cases having different levels of constraint shadow prices 3. Shortage of supply to achieve system energy balance Focus on RUC and RTM where demand is set by CAISO forecast 4. Shortage of AS supply to meet requirements 5. Export priority enforcement in IFM and HASP. 29 Parameter Tuning Stakeholder Meeting May 13, 2008

30 Ongoing Updating of Parameter Values Current effort will determine parameter values to be used in market simulation (initial results presented today) CAISO will continue evaluating market simulation cases to verify performance of parameter settings under various market conditions Will timely inform participants of any updated values Prior to Go Live CAISO will create an Operating Procedure that contains start-up values of parameters During first 12 months of operation, CAISO will issue updated versions of Operating Procedure as needed After 12 months CAISO will incorporate parameter values into a BPM and will utilize BPM Change Management to update values 30 Parameter Tuning Stakeholder Meeting May 13, 2008

31 Analysis Results for IFM Parameters Presentation by Jim Price 31 Parameter Tuning Stakeholder Meeting May 13, 2008