Aleksandr Rudkevich Margarita Sapozhnikov Bruce Tsuchida. 28 th USAEE/IAEE North American Conference. New Orleans, December 4, 2008

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1 Empirical and Simulation Analysis of the Efficiency of Congestion Management across Electricity Markets. The Case of PJM Interconnection and Midwest ISO Aleksandr Rudkevich Margarita Sapozhnikov Bruce Tsuchida 28 th USAEE/IAEE North American Conference. New Orleans, December 4, 2008

2 Study objectives Analyze rules and procedures used by PJM and MISO to manage congestion across market seams Identify and analyze market-based indicators which could be used to measure efficiency of these rules Develop an unambiguous analytical methodology to assess the impact of imperfect congestion management on generation production costs and prices in MISO and PJM 2

3 A 5-bus network example Brighton MW $20 Brighton MW $20 Alta 110 MW $19 E A Park City 100 MW $24 Flowgate 240MW Load 300 MW B C Load 300 MW D Solitude 500 MW $30 Sundance 200 MW $34 Load 400 MW This example is often used by PJM to illustrate the properties of the LMP market. Electrical properties of this network could be found in PJM Interconnection LLC, LMP 101 available online at 3

4 Supply, demand and network properties of the 5-bus model Bus Generator Capacity Gen Offer ($/MWh) Load GLDFs for E D *) A Alta A Park City B C Solitude C D Sundance D E Brighton E Brighton *) GLDF= Generator to Load Distribution Factor = Shift factor w/r/t load center. GLDFs obtained from Eugene Litvinov et al, Marginal Loss Modeling in LMP Calculations, IEEE Transactions on Power Systems, Vol. 19, No. 2, May 2004

5 A 5-bus network example. Optimal Dispatch Brighton $20 G= 450 LMP=$20.8 Brighton $20 G=150 LMP=$20.8 E A Alta 110 $19 G=110 LMP=$24 Park City 100 $24 G=47.6 LMP=$24 Flowgate 240MW E D flow = 240MW SP = $25.08 Load 300 MW LMP=$28.3 B C Load 300 MW LMP=$30 D Sundance 200 $34 G=400 LMP=$34.6 Load 400 MW LMP=$34.6 Solitude 500 G=42.4 LMP=$30 Objects located at the same bus have identical LMPs 5

6 Two Control Areas on a 5-bus Network Brighton MW $20 Brighton MW $20 Alta 110 MW $19 E A Park City 100 MW $24 Flowgate 240MW Red System 60 MW Blue System 180 MW Load 300 MW B C Load 300 MW D Solitude 500 MW $30 Sundance 200 MW $34 Load 400 MW 6 Red generators (Alta, Park City, Brighton 2) serve Red load (bus B), Blue generators (Brighton 1, Solitude, Sundance) serve Blue load (buses C, D). Flowgate E-D is a reciprocal flowgate for the Red and Blue systems with allocated capacity The impact of each system on flowgate E-D is measured with the use of Generator to Load Distribution Factors (GLDFs) computed for each system Red and Blue systems do not trade with each other

7 Supply, demand and network properties of the 5-bus model with two control areas Bus Coordinating Area Generator Capacity Gen Offer Price Load Systemwide GLDF GLDF Red A Red Alta A Red Park City GLDF Blue B Red C Blue Solitude C Blue D Blue Sundance D Blue E Red Brighton E Blue Brighton *) System-specific GLDFs are computed for generators as shift factors w/r/t load center of generator s control area 7

8 Two system dispatch and prices differ from optimal Brighton MW $20 G=450 LMP=21.95 Brighton MW $20 G=131.5 LMP=20.0 Alta 110 MW $19 G=110 LMP=$24 E Limit 60 MW, flow 60 MW, SP = $35.71 Limit 180 MW, flow 180 MW, SP=$25.08 Park City 100 MW $24 G=58.5 LMP=$24 A Load 300 MW LMP= $25.7 B C Load 300 MW LMP=$30 D Solitude 500 MW $30 G=72.9 LMP=$30 Sundance 200 MW $34 G=177.1 LMP=$34 Load 400 MW LMP=$34 8 Objects within the same system located at the same bus have identical LMPs. But that no longer holds for objects controlled by different systems, e.g. Brighton 1 and 2 generators. Shadow prices on the same constraint are different in the two systems

9 Optimal and Two-System Dispatch are Different Dispatch: Optimal vs. Two-System Two-System Optimal Alta Park City Solitude Sundance Brighton 2 Brighton 1 9 Optimal dispatch cost = $23,305 Two-System Dispatch Cost = $23,332 Efficiency Loss = $27

10 Two-System and Optimal dispatch result in different prices LMPs: Optimal vs. Two System Dispatch Two System Optimal Red and Blue generators connected to the same bus receive different prices Alta R Park City R Solitude B Sundance B Brighton 2 R Brighton 1 B Bus B load R Bus C load B Bus D load B 10

11 Observations A Two-system dispatch with static allocation of transmission capacity is suboptimal It results in distorted prices which are most visible for shared facilities: shadow prices on constraints associated with the same flowgate differ between systems LMPs for generators in electrically equivalent locations but controlled by different systems may be different Observing similar price distortions in real systems will be indicative of potential inefficiency of coordination in these two systems 11

12 PJM/MISO transmission coordination is based on reciprocal flowgates 12

13 PJM/MISO Joint Congestion Management MISO/PJM congestion management is based on the concept of reciprocal flowgates. A reciprocal flowgate is a transmission constraint that is managed by one Coordinating Area (CA) but recognized and monitored for by another Coordinating Area. An example of a reciprocal constraint is Prunty-Mt. Mt.Storm (flo) Black Oak Beddington which is managed by PJM but is included in the market engine for both PJM and MISO Reciprocal flowgates are determined based on the analysis of the impact of generators on the flowgate. A flowgate becomes reciprocal to multiple CAs if each CA has at least one generator with the absolute value of the Generator to Load Distribution Factors (GLDFs) in excess of 5%. A GLDF for a given generator is computed as a shift factor for that generator with the reference bus located at the CA s load center Transfer limits of reciprocal flowgates are allocated between CAs based on historical flows and using Firm Market Flow Calculation Rules specified in Section 4.4 of the PJM/MISO Joint Operating Agreement (JOA). The impact of each CA on the flowgate is computed as the impact of generators in that CA on the flowgate using GLDFs. Initially, PJM and MISO conducted proportional allocation of flowgate capacity between CAs based on the April 1, 2004 Freeze Date. JOA describes an on-going procedure for changing this allocation over time. According to that description, forward looking allocations are adjusted by-annually in April and October of each year thus yielding 12 forward looking monthly allocations for each reciprocal flowgate. These allocations, however, could further be adjusted monthly, weekly and daily and achieving hourly temporal resolution 13

14 Management of Reciprocal Flowgates PJM and MISO manage flows on reciprocal flowgates both in Day Ahead (DA) and Real-Time (RT) markets. As summarized in the JOA Capacity is allocated between CAs dynamically. PJM and MISO exchange information on shadow prices seen for reciprocal flowgates in their market engines and request a relief when the neighboring system could provide it at a lower cost (e.g. has lower shadow price for the same constraint) Iterative redispatch is conducted to achieve convergence in shadow prices In RT this process is facilitated by the automatic data exchange between PJM and MISO How efficient is this process in reality? The answer could be obtained from comparing congestion patterns and shadow prices for reciprocal flowgates in both, DA and RT markets 14

15 Forming top constraint lists for MISO and PJM using market information PJM DA: Top by bind hrs PJM DA: Top by shadow price PJM RT: Top by bind hrs PJM RT: Top by shadow price MISO DA: Top by bind hrs MISO DA: Top by shadow price MISO RT: Top by bind hrs MISO RT: Top by shadow price Top 100 PJM DA Top 100 PJM RT Top 100 MISO DA Top 100 MISO RT Top PJM Constraints Top MISO Constraints 15

16 Forming a list of top reciprocal flowgates PJM/MISO Reciprocal Flowgates Top PJM Constraints Top MISO Constraints Top PJM/MISO Reciprocal Flowgates Identified 30+ reciprocal flowgates between PJM and MISO which are among top binding constraints in one or both markets 16

17 Comparison of Shadow Prices for Selected Reciprocal Flowgates ( ). Example 1. State Line - Wolf Lake (flo) Burnham - Sheffield hourly PJM to MISO Hurdle DAH If miso.sp >pjm.sp miso.sp - pjm.sp are shown PJM to MISO Hurdle RTH If miso.sp >pjm.sp miso.sp - pjm.sp are shown day_hour day_hour MISO to PJM Hurdle DAH If pjm.sp >miso.sp pjm.sp - miso.sp are shown MISO to PJM Hurdle RTH -1 1 If pjm.sp >miso.sp pjm.sp - miso.sp are shown day_hour day_hour 17

18 Comparison of Shadow Prices for Selected Reciprocal Flowgates ( ). Example 2. Pruntytown Mt Storm (flo) Blackoak - Bedington hourly PJM to MISO Hurdle DAH -1 1 If miso.sp >pjm.sp miso.sp - pjm.sp are shown PJM to MISO Hurdle RTH If miso.sp >pjm.sp miso.sp - pjm.sp are shown day_hour day_hour MISO to PJM Hurdle DAH If pjm.sp >miso.sp pjm.sp - miso.sp are shown MISO to PJM Hurdle RTH -1 1 If pjm.sp >miso.sp pjm.sp - miso.sp are shown day_hour day_hour 18

19 Analyzing discrepancies in LMPs for electrically equivalent gen units controlled by MISO and PJM MISO PJM Conesville East Bend Killen Station 19 Delta DA Delta DA LMP (MISO > LMP (PJM > PJM MISO) % time MISO LMP > PJM LMP % of time PJM LMP > MISO LMP Plant Name Conesville % 44.08% East Bend % 47.13% Killen Station % 57.61% Average

20 Observations from the analysis of market data There are over 30 reciprocal PJM/MISO flowgates which are among top binding transmission constraints in either one or both markets As a rule, a reciprocal flowgate does not bind at the same time in both markets. Moreover, shadow prices generated for that flowgate by two market engines are significantly different Generating units connected to the grid in nearly identical location but controlled by different systems receive significantly different prices These factors indicate that the coordination of congestion between PJM and MISO is not perfect 20

21 What these observations do not tell us What is the impact of this imperfection in congestion coordination on electricity prices in PJM and MISO? on the efficiency of generation production within the PJM/MISO footprint This information could not be directly obtained from the market because we do not know what the optimal dispatch would be and what LMPs it would produce To answer this question we can do an analysis of market simulations 21

22 Modeling challenges Production costing (SCUC/SCD) models and necessary data are available to simulate an outcome of an ideal system with no inefficiency How to use these models to simulate imperfectly coordinated markets? A typical approach applied in a variety of market formation/market expansion cost-benefit studies addressing similar problems is to use hurdle rates Hurdle rates tax power exchanges between imperfectly coordinated systems: e.g., system A would not sell power to system B unless price in B exceeds price in A by more than the hurdle rate There are 2 problems with using this approach: We don t know how to empirically determine these hurdle rates; and We don t have a theoretical foundation to conclude that at least some set of hurdle rates used will in fact replicate the operation of the imperfect market 22

23 Back to the 5-bus example Assume that instead of separately dispatching the Blue and Red systems we use a single dispatch model But we postulate that power transactions between these systems are subject to trade hurdles (directionally different). In a single dispatch model, hurdles are added to total dispatch costs In addition, we postulate that Red and Blue Systems can exceed their allocated capacity on the flowgate at a cost to the system (congestion hurdle) Brighton MW $20 Brighton MW $20 Alta 110 MW $19 E Park City 100 MW $24 Flowgate 240MW Red System 60 MW Blue System 180 MW A B Load 300 MW D C Load 300 MW Solitude 500 MW $30 Sundance 200 MW $34 Load 400 MW We set up a trade hurdle equal an LMP difference between Brighton 1 and Brighton 2 observed in Two-System dispatch We set up a congestion hurdle equal the difference in Red and Blue shadow prices for reciprocal flowgate in Two-System dispatch Then a single dispatch model exactly replicates the dispatch and LMPs of the Two-System dispatch solution! This result cannot be achieved with one type of hurdles only. Both, trade hurdles and congestion hurdles are necessary 23

24 Applying the same approach to modeling PJM and MISO Develop trade hurdles based on LMP differences for selected units, as described above Develop congestion hurdles based on observed shadow price differentials observed for top reciprocal constraints Use the GE MAPS modeling tool and a CRA dataset for Eastern Interconnection Run Ideal Case with no hurdles between PJM and MISO Incorporate trade and congestion hurdles into GE MAPS model and simulate the Base Case Compare LMPs and generation costs between the Ideal and Base Cases to assess the impact of coordination inefficiencies on market participants and system efficiency 24

25 The following results are highly preliminary, not benchmarked to reality or vetted for errors and omissions. Provided for illustrative purposes only

26 Imperfect coordination results in an increase in prices all over PJM These results are highly preliminary, not benchmarked to market data or vetted for errors and omissions. Provided for illustrative purposes only 4.00 $/MWh Allegheny Power PP&L MetroEdison PJM 500 kv System BG&E Delmarva P&L PECO Energy Atlantic Electric Jersey Central P&L PEPCO PSEG Dominion PennElectric Dayton P&L AEP Rockland Electric ComEdison 26

27 27 Imperfect coordination results in non-uniform impact on prices in MISO These results are highly preliminary, not benchmarked to market data or vetted for errors and omissions. Provided for illustrative purposes only (2.00) (4.00) (6.00) (8.00) Indianapolis Power Alliant-East Cinergy Illinois Power Hoosier Energy Madison Gas & Electric Northern Indiana Public Wisconsin Public Pwr (E) Consumers Power Wisconsin Electric Power Muni Coop Coord Upper Penninsula Power Southern Indiana Gas WPS Resources Co. Detroit Edison Wisconsin Public Pwr (W) Minnesota Municipal Powe South Illinois Power Minnesota Power Northern States Power Ameren Great River Energy Otter Tail Power Willmar Municipal Utilit Central Illinois Light Mont-Dakota Util First Energy Columbia MO W & L Alliant-West Springfield - Illinois $/MWh

28 Impact on production costs GE MAPS simulations indicate that the loss of generation efficiency within the footprint of PJM+MISO+MAPP could be as high as $400 million a year These results are highly preliminary, not benchmarked to market data or vetted for errors and omissions. Provided for illustrative purposes only 28

29 Conclusions PJM and MISO employ the most advanced, sophisticated and theoretically sound system of congestion coordination across market seams The efficiency of this coordination could be evaluated by comparing prices on shared facilities (reciprocal flowgates, certain generating units) Analysis of these market data points to the remaining imperfection in congestion coordination between PJM and MISO Modeling of market imperfection requires the use of two types of hurdles: trade hurdles and congestion hurdles Hurdles could be empirically evaluated from market data Preliminary simulation results suggest that imperfect coordination may have a significant impact on prices and production efficiency in PJM and MISO 29

30 Alex Rudkevich CRA International 50 Church Street Cambridge, MA (617)

NYISO/PJM Market-to-Market Coordination

NYISO/PJM Market-to-Market Coordination Joint Stakeholders Meeting July 21, 2011 / Rensselaer, NY Agenda Joint Operating Agreement (JOA) Overview Project Timeline & Progress Report Key Concepts Real-Time