Price Formation Options

Transcription

1 Price Formation Options Analysis of Design Element and Options September 21, 2017

2 The Goal Delivering a Single Schedule Market (SSM) that has prices consistent with economic dispatch instructions that consider system and resource operating constraints A single schedule market uses market prices consistent with the economic dispatch accurately reflecting the underlying, as offered, cost of producing energy at a given place and time 2

3 Background Information Data from 2016 was used to provide context in terms of prevailing conditions: discussion in the body of the presentation and figures in the appendix. 3

4 Prevailing Conditions: 2016 Series HOEP 10S 10N Value $14.90/MWh $6.40/MW $5.41/MW 30 $1.86/MW Day-Ahead Dawn Gas Price Winter Peak Demand Summer Peak Demand Total Withdrawals by Ontario Loads Exports Imports $2.56USD/MMbtu 20.7 GWh 23.3 GWh 137 TWh 21.9 TWh 8 TWh 4

5 Analysis 5

6 SSM PRICE FORMATION DESIGN ELEMENTS 1) Energy price congestion component 2) Energy price reference price 3) Energy price loss component 4) Ex post vs. ex ante pricing 5) Intertie congestion pricing 6) Supplier pricing 7) Operating reserve price reference price 8) Operating reserve price congestion component 9) Constraint violations 10) Out of market operator actions 11) Multiple interval optimization 12) Price setting eligibility 6

7 Design Element #1 ENERGY PRICE: CONGESTION COMPONENT 7

8 Energy Price Congestion Component The current IESO market clearing prices do not reflect the cost of incremental congestion in meeting load at each location The current unconstrained settlement price does not include a congestion component The shadow prices calculated in the IESO s constrained schedule include a congestion component 8

9 Options: Energy Price - Congestion Component Including the cost of congestion in prices is a foundational feature of a single schedule market. There is only one viable option for this design element: 1) Include the cost of congestion in energy prices 9

10 Description of Analysis Interval data for 2016 Average energy price congestion component at representative nodes in each of the IESO-defined electrical zones in Ontario Frequency at which the cost of congestion was greater (and less) than $5/MWh at each representative node NOTE: The figure show costs as negative values Data illustrating frequency and magnitude of congestion across Ontario 10

11 Average Energy Nodal Price Average Energy Nodal Price in 2016 Northwest All information provided on this slide is for illustrative purposes only Northeast Essa Ottawa Bruce East Richview (2016 average: $16.74) West Southwest Toronto Niagara

12 Average Energy Price - Congestion Component Average Energy Price Congestion Component in 2016 Northwest All information provided on this slide is for illustrative purposes only Northeast Essa Ottawa Bruce East Richview (2016 average energy price: $16.74) West Southwest Toronto Niagara

13 Energy Congestion Component- Frequency Analysis Congestion Component Range ($/MWh) Northwest (10) Northeast (18) Bruce (2) East (3) Essa (1) Niagara (1) Ottawa (2) Southwest (3) Toronto (3) West (3) < -$5 18.7% 3.6% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.6% -$5 < X < $5 76.6% 94.3% 99.9% 99.9% 99.6% 99.8% 99.9% 99.9% 100.0% 99.0% > $5 4.8% 2.2% 0.0% 0.1% 0.4% 0.1% 0.1% 0.0% 0.0% 0.4% 13

14 Results: Energy Congestion Analysis Findings were: Material and persistent congestion in the Northwest and Northeast zones relative to Toronto Infrequent congestion in the Bruce zone Infrequent congestion delivering power into the Niagara zone Material difference between congestion in the North and congestion in the rest of the province 14

15 FTI Observations The data support the need to include the cost of congestion in market prices There is congestion across the province, with at times materially different costs of meeting incremental load in the real-time dispatch These large cost differences should be reflected in energy market prices to provide efficient incentives for the flexible participation of a diverse mix of resources at a variety of locations across the province 15

16 Design Element #2 ENERGY PRICE: REFERENCE LOCATION 16

17 Energy Price Reference Location The only decision with regard to the energy reference price element is the specification of the reference location The locational marginal price (LMP) at a location does not depend on the choice of the reference location A change in the reference location will only affect the three components of price - not the LMP price The IESO currently uses the Richview Transformer Station location as the reference location in the constrained schedule There is no reference location in the unconstrained schedule because it does not consider actual dispatch of the transmission grid 17

18 Options: Energy Price Reference Location 1) Continue to use Richview as the reference location 2) Use some other reference location 18

19 Description of Analysis It is important that the reference location have a strong connection with the rest of the system Strong connection will maximize availability It is intuitive (but not necessary) for it to be located close to the load centre Proximity to the load centre will align reference price with the cost of service of the largest portion of load 19

20 Description of Analysis The IESO used three metrics to evaluate potential reference locations across the Ontario grid: Proportion of Ontario s peak load that is in the same electrical zone as the potential reference location Fault level an indication of the robustness of the voltage at a station following a disturbance; and Number of connections to the rest of the system an indication of the strength of the connection to the rest of the grid 20

21 Comparison of Candidate Locations for a Reference Location Northwest Northeast Essa Ottawa East Toronto Southwest Niagara Bruce West 230 kv Station Lakehead Hanmer Essa Hawthorne Lennox Richview Trafalgar Beck 2 Bruce A Lambton % of Ontario s peak demand 1.7% 4.8% 6.0% 6.1% 6.1% 40.4% 20.2% 3.6% 0.3% 10.7% Fault level (MVA) 3,118 7,516 10,371 8,879 13,870 23,285 24,507 21,973 16,685 22,018 # of connected circuits and autotransformers

22 Results: Energy Price Reference Location Richview enjoys the following traits: very high fault level (robust voltage) highest number of connections to the rest of the system; and close to the load centre Richview has been used as the reference location for many years without any adverse impacts on dispatch solutions 22

23 FTI Observations Richview continues to be an appropriate choice for the reference location The whereabouts of the reference location does not affect the nodal price at any location Continued use of Richview as the reference location would avoid the cost and potential adverse performance impacts of making a change 23

24 Design Element #3 ENERGY PRICE: LOSS COMPONENT 24

25 Energy Price Loss Component The current IESO market clearing prices (MCP) do not reflect the cost of marginal losses in meeting load at each location. The current unconstrained settlement price does not include a loss component The shadow prices calculated in the IESO s constrained schedule include a loss component 25

26 Options: Energy Price Loss Component 1) Include the cost of marginal losses in the dispatch but exclude this cost from prices (status quo) 2) Exclude the cost of marginal losses from prices and from the dispatch 3) Include the cost of marginal losses in both the dispatch and prices 26

27 Description of Analysis Data regarding the loss factors used for nodes across Ontario for 2016 Interval data for 2016 Average energy loss component at representative nodes in each of the IESO-defined electrical zones in Ontario 27

28 Description of Analysis Frequency at which losses were greater than $5/MWh and less than -$5/MWh at each representative node Loss factors increase with distance from the reference location By definition, the loss factor is zero at the reference location 28

29 Average Energy Price - Loss Component Average Energy Loss Component in 2016 Northwest All information provided on this slide is for illustrative purposes only Northeast Essa 0.00 Ottawa Bruce East Richview (2016 average energy price: $16.74) West Southwest Toronto Niagara

30 Energy Loss Frequency Analysis Loss Component Range ($/MWh) Northwest (10) Northeast (18) Bruce (2) East (3) Essa (1) Niagara (1) Ottawa (2) Southwest (3) Toronto (3) West (3) < -$5 7.8% 0.9% 0.7% 0.2% 0.2% 0.3% 0.0% 0.0% 0.0% 0.0% -$5 < X < $5 92.1% 99.0% 99.3% 99.8% 99.8% 99.7% 100.0% 100.0% 100.0% 99.8% > $5 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.2% 30

31 Results: Energy Price - Loss Component Analysis In 2016, the average cost of marginal losses in the Northwest was more than $2/MWh Nearly 8% of the time, the average cost of marginal losses in the Northwest were material (more than $5/MWh); and Including the cost of losses in market prices is consistent with the goal of the Single Schedule Market (aligning prices with dispatch) 31

32 FTI Observations The data support the need to include the cost of marginal losses in both the dispatch and market prices IESO data show that the cost of marginal losses alone would account for a difference in the average value of generation of at least $5/MWh between the northwest and southern regions in around 8% of all hours In light of these differences, taking account of the cost of marginal losses in the dispatch but not in market prices (Option 1) would create inefficient incentives that would be smaller in magnitude, but similar in nature to those in the current two schedule market 32

33 FTI Observations The marginal losses data also demonstrate that excluding the cost of marginal losses in the economic dispatch model (Option 2) would ignore a factor that makes some generators significantly more expensive than others This could lead to the dispatch of generation with higher losses, therefore raising the cost of meeting Ontario load in many hours We have discussed that other ISOs that did not originally take account of marginal losses in the dispatch have done so over the past 10 years (PJM and California ISO) in order to more efficiently meet load 33

34 FTI Observations Including marginal losses in both the dispatch and prices (Option 3) would support the economic efficiency of the IESO s current real-time dispatch 1) Side payments would not be needed to address inefficient incentives from including marginal losses in the economic dispatch but not in prices 2) Consistency with the best practice towards which other ISOs have moved; and 3) Avoids potential obstacle to implementing new dispatch concepts to better manage higher levels of intermittent resource output 34

35 Design Element #4 PRICE FORMATION: EX-ANTE VS EX-POST 35

36 Ex Post vs Ex Ante pricing The IESO currently uses a form of ex-post pricing to calculate prices in the unconstrained schedule and ex-ante pricing to calculate energy shadow prices in the constrained schedule Ex Ante (before the fact) pricing designs calculate settlement prices based on the dispatch. Prices therefore consider the same information that was available at the time dispatch instructions were sent. Ex Post (after the fact) pricing designs calculate settlement prices using information that was not available at the time dispatch instructions were sent. The IESO s existing ex-post price is calculated immediately following the end of the dispatch interval. 36

37 Options: Ex-Ante vs Ex-Post 1) Ex Post pricing (status quo) 2) Ex Ante pricing 37

38 Considerations Feasibility issues exist with the analysis that the IESO has identified for this design element Tools necessary to simulate ex-ante prices are not available No historical information in Ontario exists with respect to Ex Ante compared to Ex Post pricing 38

39 Considerations Regardless of the option, dispatch instructions will continue to be determined on an ex-ante basis Determining prices on a different basis than that which determines dispatch would not be consistent with the goal of the SSM project Ex-post pricing can introduce discrepancies between the value at which resources are dispatched and the market clearing price. May require additional make whole payments to address differences between dispatch and settlement price 39

40 FTI Observations ISOs previously utilizing ex post pricing designs have undertaken quantitative analysis in deciding to switch to ex ante pricing Identified pricing inconsistencies with ex post pricing that arise due to factors that are not unique to these ISOs (ISO New England and MISO) Issues identified are a consequence of the ex post pricing methodology and would create similar problems in Ontario 40

41 FTI Observations ISO New England shifted to an ex ante pricing design in mid 2015 because of unresolvable problems it identified over time with the ex post pricing design, which are discussed at length in the 2013 external market monitor report Potomac Economics, 2013 Assessment of the ISO New England Electricity Markets June pp Available at: f 41

42 FTI Observations The ISO New England 2013 external market monitor report identified three inconsistencies between ex ante and ex post prices that undermined the incentives of generators to follow dispatch instructions: Small but persistent upward bias in real-time prices during the vast majority (99%) of intervals when reserve constraints were not binding Significantly understated real-time prices when reserve constraints are binding Occasional distortions in the ex post prices leading to inefficient pricing in congested areas 42

43 FTI Observations The MISO independent market monitor began discussing the issues with ex post pricing shortly after the implementation of the market in The 2007 report contained the following observation: Ex post pricing has not been shown either theoretically or empirically to improve the efficiency of real-time prices or the incentives of suppliers. Our analyses in this report indicate that the ex post prices tend to be biased upward (3 percent on average in 2007). Additionally, use of ex post prices sometimes introduces significant inconsistencies between prices at particular locations and generators dispatch signals. Hence, this recommendation should be implemented with the ASM markets or sooner, if feasible. 1. See Potomac Economics, 2007 State of the Market Report for the MISO electricity Markets, June 2008, p. xvi, available at: ket%20report%20-%20final%20text.pdf 43

44 Design Element #5 INTERTIE CONGESTION PRICING 44

45 Intertie Congestion Pricing The IESO currently uses congestion charges in the unconstrained pre-dispatch for intertie congestion pricing (ICP) The intertie congestion charge in the unconstrained predispatch (which includes intertie constraints) is added to the real-time (uniform) price to determine the settlement price for imports and exports at each relevant intertie Intertie congestion costs are not reflected in either the constrained or unconstrained 5 minute dispatch price because intertie schedules are fixed during a given hour 45

46 Options: Intertie Congestion Pricing 1) Settle intertie transactions based on the intertie congestion charge in the constrained pre-dispatch and the price at the intertie in the real-time constrained schedule (similar to status quo) 2) Settle intertie transactions based on: a) If there is no congestion - the real-time price at the intertie b) When export congested- the higher of the intertie nodal price in real-time or pre-dispatch c) When import congested- the lower of the intertie nodal price in real-time or pre-dispatch 46

47 Intertie Congestion Pricing Option 1 Allows the intertie settlement price to increase or decrease relative to the pre-dispatch intertie price The intertie congestion component is static No adjustment (increase or decrease) based on the difference between the prevailing real-time intertie price and the pre-dispatch cost of the marginal intertie transaction Intertie settlement price needs to be capped when real-time price plus ICP exceeds +/- MMCP (Maximum Market Clearing Price) 47

48 Intertie Congestion Pricing Option 2 The intertie congestion component is more dynamic Adjusts based on the difference between the prevailing real-time intertie price and the cost of the marginal intertie transaction May decrease or go to zero (if not-congested in real-time) Intertie settlement price does not change when the real-time price moves against congestion For import congestion, if real-time price increases, intertie settlement price will not increase For export congestion, if real-time price decreases, intertie settlement price will not decrease 48

49 Numerical Examples: Intertie Congestion Pricing Import Congestion Price Increases PD-RT Option 1 Option 2 ON INT ICP ON INT ICP PD PD RT RT Price Decreases PD-RT Option 1 Option 2 ON INT ICP ON INT ICP PD PD RT RT Real-time ON price increases by $10/MWh from PD Option 1 increases the settlement price between PD and RT, keeping the ICP static Option 2 keeps the settlement price fixed to the congested PD price Real-time ON price decreases by $10/MWh from PD Option 1 decreases the settlement price between PD and RT, keeping the ICP static Option 2 reduces the settlement price in-line with the real-time price PD = Pre-dispatch RT = Real-time ON = Ontario Price INT = Intertie Price ICP = Intertie Congestion Price 49

50 Numerical Examples: Intertie Congestion Pricing Export Congestion Price Increases PD-RT Option 1 Option 2 ON INT ICP ON INT ICP PD PD RT RT Price Decreases PD-RT Option 1 Option 2 ON INT ICP ON INT ICP PD PD RT RT Real-time ON price increases by $10/MWh from PD Option 1 increases the settlement price between PD and RT, keeping the ICP static Option 2 increases the settlement price in-line with the real-time price Real-time ON price decreases by $10/MWh from PD Option 1 decreases the settlement price between PD and RT, keeping the ICP static Option 2 keeps the settlement price fixed to the congested pre-dispatch price PD = Pre-dispatch RT = Real-time ON = Ontario Price INT = Intertie Price ICP = Intertie Congestion Price 50

51 Description of Analysis Options compared using three scenarios to determine impact to intertie transaction settlement: a) no congestion b) import congestion c) export congestion Assessment of congestion was based on congestion in the unconstrained schedule Historical assessment may not be reflective of the future Analysis is not predictive of future conditions due to changes in import/export volumes; bid/offer behaviour changes; different unit commitment in RT; changes in congestion patterns 51

52 Analysis: Intertie Congestion Pricing Options are equivalent when there is no congestion 2016 Import Congested Hours (2.6%) 2016 Export Congested Hours (86.4%) Price Increased in RT relative to PD 31.3% Price Increased in RT relative to PD 27.3% Price Decreased in RT relative to PD 68.7% Price Decreased in RT relative to PD 62.6% Price of Congestion stays static under Option 1 Price of Congestion decreases under Option 2 relative to Option 1 Price of Congestion increases under Option 2 relative to Option 1 52

53 Intertie Congestion Frequency (% of Hours) Import Export 0.4% 36.2% Northwest Northeast Import Export 0.7% 45% Import Export 1.9% 0.1% Richview Import Export 0% 84.4% Import Export 0% %

54 FTI Observations Both Options are workable approaches that have somewhat different impacts on the price risk involved in scheduling intertie transactions and on the level of import offer guarantees. 54

55 FTI Observations Option 1 could create more risk of low prices for importers when congestion declines between predispatch and real-time but would provide more upside if real-time prices are higher than in predispatch. 55

56 FTI Observations Option 1 could make it risky to schedule price taking import transactions at times when there might be very high pre-dispatch prices and hence the potential for very low settlement prices for imports if the real-time price was lower, perhaps because of operator actions. 56

57 FTI Observations The IESO is considering other changes related to intertie scheduling More frequent intertie scheduling This consideration favors retaining Option 1 in the near term since it is very similar to the current pricing design and would require the least change 57

58 Design Element #6 SUPPLIER PRICING 58

59 Supplier Pricing The IESO currently pays suppliers based on the uniform market clearing price (MCP). Constrained-on and constrained-off payments compensate suppliers for differences between their theoretical (unconstrained) and actual (constrained) schedules. 59

60 Options: Supplier Pricing 1) Zonal Pricing for supplier for energy 2) Nodal Pricing for supplier for energy 60

61 Description of Analysis Interval data for 2016 Determined measures of distribution of nodal energy prices at representative nodes within each of the electrical zones in Ontario These measures included the average min/max spread ( range ) of nodal prices in a zone The number of representative nodes chosen was: 18 in the NW, 10 in the NE, and 1-3 in all other zones 61

62 Average Energy Nodal Price Average Energy Nodal Price in 2016 Northwest All information provided on this slide is for illustrative purposes only Northeast Essa Ottawa Bruce East Richview (2016 average: $16.74) West Southwest Toronto Niagara

63 Nodal Price Spread Analysis Northwest (18) Northeast (10) Bruce (3) East (3) Essa (1) Niagara (1) Ottawa (2) Southwest (3) Toronto (3) West (3) Average Nodal Price Average Range of Nodal Price Numbers in parentheses indicate the number of units that was used for the zonal comparison. 63

64 Average Energy Nodal Prices (Northwest)

65 Average Energy Nodal Prices (Northeast)

66 Frequency Analysis Nodal Price Range Northwest Northeast Bruce East Essa Niagara Ottawa Southwest Toronto West <$1 42% 56% 91% 96% 100% 100% 100% 100% 100% 95% $1-$5 20% 32% 8% 3% 0% 0% 0% 0% 0% 3% $5-$10 17% 5% 1% 1% 0% 0% 0% 0% 0% 1% $10-$100 11% 5% 1% 0% 0% 0% 0% 0% 0% 1% >$100 10% 2% 0% 0% 0% 0% 0% 0% 0% 0% Shows where range variations are substantial in magnitude/frequency 66

67 Results: Supplier Pricing Analysis Data demonstrates that price variations exist between zones and within zonal locations Material variation in pricing within Northwest and Northeast In general, little variation in southern zones but even minor variations become material as a result of the amount of supply capacity 67

68 FTI Observations The IESO analysis shows that regions such as the Northwest and Northeast would have large and relatively frequent differences between nodal and zonal prices if they were defined as zones Some regions could be priced as zones for suppliers with very little price variation between the nodal and zonal prices It would likely be complicated and impractical to operate an SSM with some suppliers settled nodally and some zonally to accommodate the pattern of price dispersion shown in the IESO data 68

69 FTI Observations For a zonal system to be workably efficient, it requires a low degree of price dispersion within all zones, not just a subset of zones as would be likely for Ontario, per the data A zonal pricing design for suppliers would require some mechanism to incent supply resources to follow dispatch instructions in the Northwest and Northeast Some of the same problems with make whole payments and incentives that are present in the current design would occur, although possibly somewhat reduced in scale depending on how zonal prices were calculated 69

70 FTI Observations Reliance on a zonal pricing design for suppliers would continue to provide a barrier to implementation of a day-ahead market and hinder providing efficient incentives for the supply of flexible capacity 70

71 Design Element #7 OPERATING RESERVE: REFERENCE PRICE 71

72 Operating Reserve Reference Price The IESO currently co-optimizes energy and operating reserve. The unconstrained and constrained schedule simultaneously optimize dispatch and price of energy and operating reserves in real-time. The operating reserve reference price for each reserve type is the incremental cost of increasing Ontario s operating reserve requirement (for that reserve type) by 1 MW. 72

73 Options: Operating Reserve Reference Price There is one option: 1. Co-optimize energy and operating reserve (status quo) The IESO s current design is best practice for SSM markets. There is no reason to spend resources developing less efficient alternatives. 73

74 Considerations The goal is to deliver a Single Schedule Market that has prices consistent with dispatch instructions that consider system and resource operating constraints Co-optimizing energy and operating reserve in the real-time dispatch results in prices that consider system and resource operating constraints All jurisdictions other than ERCOT and CAISO currently cooptimize operating reserves and energy in their real-time dispatch (ERCOT and CAISO may change in the future) Any other method for managing energy and operating reserves would leave us worse off than we are today 74

75 FTI Observations The current IESO design is the best practice market design for reserves and ancillary services to which other ISOs have been moving over the past 15 years It would be inconsistent with the aim of market renewal to move to a less efficient market design that would introduce more challenges in managing higher levels of intermittent resource output 75

76 Design Element #8 OPERATING RESERVE: CONGESTION COMPONENT 76

77 Operating Reserve Congestion Component The IESO currently pays a uniform price for operating reserve (OR). Constrained-on and constrained-off payments compensate for differences between the uniform prices and actual offer/opportunity costs of real-time operating reserve schedules. The IESO co-optimizes the dispatch of energy and scheduling of operating reserves in real-time, while enforcing locational minimum and maximum operating reserve constraints across the province. 77

78 Options: Operating Reserve Congestion Component Similar to the choice to include congestion costs in the energy price, there is only one option for operating reserve congestion pricing: 1) Include the cost of congestion Ignoring locational reserve constraints in the constrained schedule is not a feasible option because most of these constraints represent reliability obligations or have been implemented to better maintain reliable electric system operations in Ontario. 78

79 Description of Analysis Interval data for 2016 Average operating reserve congestion component at representative nodes in each of the IESO-defined operating reserve zones in Ontario Frequency at which operating reserve congestion component was greater than $5/MWh and less than -$5/MWh at each representative node 79

80 Average 10S OR Congestion Component Average Operating Reserve Congestion Component in 2016 Northwest All information provided on this slide is for illustrative purposes only Northeast Essa Ottawa 0.00 Bruce East 0.00 Richview (2016 average 10S OR price: $14.39) West Southwest Toronto Niagara

81 Results: Operating Reserve Congestion Component Analysis There was significant operating reserve congestion in the Northwest and Northeast zones No operating reserve congestion in the South of Ontario Numbers presented are an annual average Any non-zero value can be thought of as applying to each MW in each hour of the year 81

82 OR Congestion Frequency Analysis Congestion Cost Range ($/MWh) Northwest (10) Northeast (18) Bruce (2) East (3) Essa (1) Niagara (1) Ottawa (2) Southwest (3) Toronto (3) West (3) < -$5 27.3% 12.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% -$5 < X < $5 72.7% 87.6% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% > $5 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Congestion cost in this case is the cost of congestion related to provision of operating reserve at the reference location. 82

83 Results: Operating Reserve Congestion Component Analysis Congestion exists and therefore should be included in the OR price Including the cost of congestion in OR prices enhances efficiency and transparency Settlement price is aligned with reserve scheduling Make whole payments apply by exception only Excluding cost of congestion would require settlement using the OR reference price and make whole payments 83

84 FTI Observations The data clearly show that value of reserves frequently differs across Ontario by a material amount relative to the reference price of reserves and that providing an efficient signal for providers of operating reserve will require a price signal that includes the congestion cost of reserves. 84

85 Design Element #9 CONSTRAINT VIOLATIONS 85

86 Constraint Violations The IESO currently uses constraint penalty prices in its constrained and unconstrained schedules, but uses constraint relaxation to determine prices. The magnitudes of the penalty prices determine the priority for respecting the different constraints in the constrained schedule. 86

87 Options: Constraint Violations For all of these options, assume operating reserve and energy prices would continue to be capped at $2000/MWh: 1) Apply current penalty prices in dispatch, but use relaxation and other adjustments for pricing (status quo) 2) Use the same set of penalty prices for both dispatch and pricing 3) Apply current penalty prices in dispatch, but use a different set of penalty prices in pricing 87

88 Options: Constraint Violations Foreseeable Option Outcomes Option 1: Power balance violations and, to a lesser degree, operating reserve violations impact price. Price impact of operating reserve violations is set based on energy and operating reserve bids/offers and continues to be based on relaxation of constraints Option 2: All constraint violations result in material price impact and are determined on the basis of the associated penalty prices Option 3: All constraint violations result in a calibrated price impact and are determined on the basis of the associated penalty prices. Further detailed work would be required to ascertain the penalty prices to best deliver the appropriate market signals 88

89 Description of Analysis The IESO determined in how many instances various operating constraints were violated Hourly and interval data for 2016 Frequency of violations could, in fact, be higher because in some situations it is impossible to capture the absolute number masked by the relaxation process No market data is available that would provide certainty in identifying frequency of security constraint violations 89

90 Frequency of Violations Constraint Energy Under Generation 10S Shortfall 10N Shortfall Total OR Shortfall Area Minimum OR Requirement Area Maximum OR Requirement Pre-Dispatch Intertie Scheduling Limit (Import/Export/NISL) Annual frequency of violations 0.01% (10 intervals) 0.01% (12 intervals) 0.16% (170 intervals) 0.26% (267 intervals) 0.02% (17 intervals) 0% (0 intervals) 0.03% (36 intervals) 90

91 Results: Constraint Violations Frequency Analysis Most frequent violations were for operating reserves Intervals when the 10S requirement was violated were also probably intervals when the 10N and the total operating reserve requirement was violated Overall, constraint violations occurred less than 0.5% of the time in

92 Results: Constraint Violations Analysis Option 1: Least price impact (based on the 2016 constraint violation data). Issue is that relaxation can result in inconsistent pricing outcomes Option 2: Greatest price impact (base on the 2016 constraint violation data). Option 2 likely inflates price impact all constraint violations would result in material price impacts Option 3: Allows calibration of price impact for all constraint violations 92

93 FTI Observations One or more North American ISOs use all three of the approaches presented as options. Constraint relaxation is becoming less workable as a design because it will often not send an appropriate price signal, which is more important as the grid includes more distributed resources that will respond to price signals but not to 5 minute dispatch instructions Constraint relaxation is also the approach most likely to require make whole payments for commitments 93

94 FTI Observations The practical difference between Option 2 and Option 3 depends on the levels of the penalty prices and on their level relative to the overall price cap Implementation of Option 2 or Option 3 with appropriate penalty prices would be most beneficial for the constraints that are likely to be violated in more than a handful of intervals a year 94

95 Design Element #10 OUT-OF-MARKET OPERATOR ACTIONS 95

96 Out-of-market Operator Actions The IESO uses various out-of-market control actions to maintain system reliability. Voltage reductions Emergency imports/exports Export curtailments Curtailing imports during low load conditions Manoeuvring nuclear units during SBG 96

97 Out-of-market Operator Actions Currently, the underlying principle regarding price determination following most control actions is to create a price signal that reflects prevailing market conditions prior to the control action This mitigates against counterintuitive price signals that may arise when implementing out-of-market actions 97

98 Out-of-market Operator Actions Voltage reductions: The MW impact (amount of relief) of the voltage reduction is added back into market demand for the purpose of price determination Emergency imports: The MW amount of emergency imports are added back into market demand for price determination Emergency exports: The MW amount of emergency exports are included in market demand for price determination 98

99 Out-of-market Operator Actions Export curtailments: Exports are removed from the constrained schedule. In general, they are left in market demand for price determination, up to the level of congestion within the province. Curtailing imports during Surplus Baseload Generation (SBG): Imports are removed from the constrained schedule. In general, they are left in market demand for the purpose of price determination. Manoeuvring nuclear units during SBG: Nuclear units are dispatched down in the constrained schedule only. The market schedule is not impacted (i.e, the market schedule is determined economically, as usual). 99

100 Options: Out-of-Market Operator Actions 1) Prevent impact of out-of-market control actions on price determination 2) Use pre-determined prices when implementing out-of-market control actions 3) Allow impact of out-of-market control actions on price determination Options 1 and 2 are intended to ensure that the price reflects prevailing market conditions prior to out-of-market control actions Option 3 aligns locational prices with dispatch of resources (but allows the control action to affect price signals) 100

101 Frequency of Control Actions Control Action 2016 Emergency Imports 0 Emergency Exports 0 Voltage Reduction (test) 0.023% Nuclear Manoeuvering for SBG 12.32% Export Curtailment by IESO 4.18% Import Curtailment by IESO 17.23%

102 Description of Analysis Qualitatively assessed the pricing treatment of control actions under all three options for a given control action (emergency import) 102

103 Example: Emergency Imports into West Zone Prior to the scheduling of emergency imports: $2,000/MWh price across Ontario 1,000 MW of emergency imports scheduled at the Michigan-Ontario Intertie Pre-Market Renewal: Import MWs added back in to Ontario Demand. The Ontario MCP following the emergency import is unchanged at $2,000/MWh Strong price signal for other resources to come to market. 103

104 Example: Emergency Imports into West Zone Under Option 1: 1,000 MW of emergency imports scheduled, this supply is not reflected in the pricing run Nodal prices in the West zone are $2,000/MWh West zone dispatch will not align with West zone prices Strong price signal for other resources 104

105 Example: Emergency Imports into West Zone Under Option 2: 1,000 MW of emergency imports scheduled, this supply is not reflected in the pricing run Prices are set by some pre-determined value; say $1,500/MWh for this example West zone dispatch will not align with West zone prices Strong price signal for other resources, though not as strong as under Option 1 105

106 Example: Emergency Imports into West Zone Under Option 3: 1,000 MWs of emergency imports scheduled, this supply is reflected in the pricing run West zone dispatch aligned with West zone prices Prices in the West zone decrease to $150/MWh Muted price signal for other resources 106

107 FTI Observations There is a balance to be maintained in pricing these actions. It is important on the one hand to provide an appropriate price signal to incent helpful actions by resources in a time frame longer than the real-time dispatch interval Conversely, if the operator action has obviated the need for additional market response, sending a high price signal can simply create uplift costs and conflicting incentives for on dispatch resources 107

108 Design Element #11 MULTIPLE INTERVAL OPTIMIZATION 108

109 Multiple Interval Optimization The IESO currently uses multiple interval optimization (MIO) based on five critical periods to optimize dispatch schedules in the constrained schedule. The prices that are used for settlements are based on an unconstrained single interval dispatch and include other differences 109

110 Options: Multiple Interval Optimization 1. Use multiple interval optimization to determine dispatch schedules but not settlement prices (similar to status quo) Settlement prices determined based on a constrained single interval dispatch 2. Use multiple interval optimization to determine schedules and prices 110

111 Description of Analysis No way to simulate single interval dispatch or MIO for pricing Interval data from 2016 The IESO measured the maximum changes as seen by MIO but not by single interval pricing Changes include demand forecast and intertie schedule data used by MIO (interval-to-interval changes across the next 11 intervals in the future) 111

112 Frequency Analysis 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% < 50 MW MW MW > 350 MW 112

113 Results: Multiple Interval Optimization Analysis The largest demand plus intertie changes seen by MIO were greater than 200 MW in 13% of time More than 73% of the time there was at least a 50 MW change 113

114 Results: Multiple Interval Optimization Analysis Dispatch will continue to utilize MIO Single interval optimization pricing would introduce discrepancies between dispatch and the settlement price Would depend upon make whole payments to address differences between dispatch and settlement price 114

115 FTI Observations While prices and schedules determined by MIO today may differ from the prices and schedules determined by a single interval dispatch, there is a potential for the differences to between the two to become more important Larger morning and evening ramps may cause larger differences Introduction of ramp capability dispatch would likely cause even larger discrepancies A design based on single interval pricing could lead to substantial market distortions in a few years, which would require a change in design 115

116 Design Element #12 PRICE-SETTING ELIGIBILITY 116

117 Price Setting Eligibility Some operating ranges that are not dispatchable are currently able to set price in the unconstrained schedule. Minimum loading points (MLPs) of on-line resources Minimum loading points of off-line quick start resources (5-minute dispatch time frame) Forbidden regions 117

118 Options: Price-Setting Eligibility 1. Do not allow any resources restricted MWs (for example, minimum loading point) to set or impact price (status quo in the constrained schedule) 2. Allow fast-start online resources restricted MWs to set or impact price 118

119 Description of Analysis Interval data from 2016 Measured the frequency in which fast-start gas units were scheduled at their MLP and the shadow price at their location was below their offered cost This identifies when such units may have been either uneconomic, or the marginal unit but not eligible to set price as a result of their MLP When marginal but not setting the price, a lower cost unit is used to set price During the intervals in the measure above, looked at the frequency and magnitude of the MW quantities scheduled at MLP 119

120 Frequency and Magnitude Analysis 5% 4% Frequency 3% 2% 1% 0% MW MW > 200 MW MWs Dispatched 120

121 Results: Price-Setting Eligibility Analysis Frequency analysis identifies: Quantity of MWs from fast-start units Scheduled at MLP With shadow price at the generator below bid price for those MLP MWs Quantities of greater than 200MW were met the criteria approximately 1% of the time 121

122 FTI Observations Option 1 could help send a more efficient price signal to distributed resources that will respond to prices but are not on dispatch, but would require make whole payments to constrained down resources At present, however, there are relatively few fast start resources and the IESO analysis shows that their offers would likely materially impact prices in a very small number of intervals It is possible that Ontario s resource needs and the more efficient incentives provided by SSM pricing will shift the resource mix towards more fast starting resources 122

123 FTI Observations Option 1 pricing would not allow fast start resources to set price except when they are dispatched on the margin and can require more make whole payments to these resources An Option 2 pricing design would perform differently in Ontario with high levels of intermittent resource output and significant variations in net load over the minimum run time of fast start resources than in a market with a traditional resource mix being used to meet summer or winter peak loads Consideration could be given to utilizing Option 1 pricing for the initial SSM implementation, while considering a possible role for Option 2 pricing in the future 123

124 NEXT STEPS 124

125 Next Steps Prioritize design elements requiring further analysis or research What elements require further examination or clarity? Identify any additional analysis/data that may be helpful in evaluating specific options 125