Load Granularity Refinements Pricing Analysis Study

Transcription

1 August 8, 2013

2 Table of Contents I. Introduction... 3 II. Comparison of DLAP and SLAP prices... 3 A. Overview of SLAP and DLAP price differences... 4 B. PG&E price differences C. SCE price differences D. Annual average SLAP divergence III. Impact of convergence bidding on load distribution forecast error Load granularity refinements issue paper is a separate attachment available at: CAISO/MD&RP/CCB 2 August 8, 2013

3 I. Introduction This pricing analysis study provides empirical evidence to aid in the determination of whether sufficient benefits exist to support load granularity refinements. Its findings are used in the ISO s Load Granularity Issue Paper. FERC s 9/21/06 Order on MRTU found the default load aggregation point (DLAP) approach reasonable and a simplified method for introducing LMP pricing, while minimizing its impact on load. However, FERC also directed the CAISO to increase the number of LAP zones in order to provide more accurate price signals and assist participants in hedging of congestion charges after three years of experience with the new market. In 2010 the ISO initiated a stakeholder process to evaluate LAP disaggregation benefits and determine potential new LAP zones. At the time, the ISO conducted a historical pricing study to evaluate the different prices in sub-lap regions compared to DLAPs. It was found that with the exception of one small area, prices did not diverge enough to warrant the effort and cost of LAP disaggregation. Stakeholders almost unanimously agreed with this conclusion. FERC accepted the ISO s filing to delay implementation and follow a proposed roadmap, where the ISO would reevaluate prices after 18 months of experience with convergence bidding and proxy demand response implementation, and implement more granular load areas by October, 2014 if necessary. This study updates and expands upon the ISO s May 2010 preliminary LAP pricing analysis as part of new stakeholder process. The pricing study includes additional information gained pricing experience under convergence bidding and participation of proxy demand response resources as well as how pricing differences have evolved since the last study was completed. II. Comparison of DLAP and SLAP prices This section is a historical review of default load aggregation point (DLAP) prices and sub-load aggregation point (SLAP) prices from January 2011 to June 30 th The design of the ISO markets based on locational marginal pricing (LMP) includes the bidding, scheduling, and settlement of most internal load at the three large DLAPs. These follow the service territory boundaries of the three investor-owned utilities in the ISO balancing authority area. Prior to the launch of the LMP market the ISO established SLAPs based on LMP price homogeneity studies. The SLAP geography is used in congestion revenue rights allocation and was designed to have relatively homogenous congestion conditions. There are 23 SLAPs; 1 in the SDG&E LAP 1, 6 in the SCE LAP, and 16 in the PG&E LAP. While the comparison of DLAP and SLAP prices is not as encompassing as a full nodal spatial price dispersion analysis, it is a reasonable beginning point, especially given the already established market rules that use SLAPs (e.g. CRR allocation and PDR maximum aggregation limits). For ease of interpretation, all price differences between SLAPs and DLAPs are expressed as a positive difference unless specifically noted. 1 Because SDG&E s SLAP encompasses its entire DLAP, they are not evaluated in this pricing study. CAISO/MD&RP/CCB 3 August 8, 2013

4 A. Overview of SLAP and DLAP price differences For the purpose of this study, we initially grouped the SLAPs within each DLAP into two groups: Group 1, and Group 2. These groups are listed in Table 1. Because SLAPs were created based on price correlation and not on amount of load scheduled in the area, there is a significant difference in the amount of load in each SLAP. We formed these groups to aggregate the SLAPs into groups more equal in average load. Group 1 is comprised of the largest SLAPs by load, whereas Group 2 is comprised of the smaller SLAPs. On average, group 1 makes up about 65% of the load in the PGE region and 75% of the load in SCE. PG&E Group 1 Group 2 Group 1 Group 2 East Bay (Bay Area) Sacramento Valley Core (LA Basin) SCE North South Bay (Bay Area) Stockton SCE West SCE Northwest Fresno Peninsula (Bay Area) High Desert Los Paderes Central Coast Low Desert San Francisco (Bay Area) North Valley North Bay Geysers Sierra North Coast Humboldt San Joaquin SCE Figure 1 and figure 2 show the average price difference between SLAP prices and DLAP prices by DLAP and group. Group 2 contributes significantly more to the average price differential than the larger SLAPs in group 1. This is particularly true in the SCE region. While the price difference between the PG&E DLAP and SLAPs has remained fairly constant, the SCE area has seen a significant increase in the divergence between the average DLAP and SLAP prices. Given the notable increase in average IFM LMP in 2013 due in part to increased congestion in LA Basin and SCE West when compared to 2011 and 2012, we would expect there to be at least some increase in area price divergence for Group 1. 2 Generally, despite the increase in price divergence, the average difference has remained less than $2.00 in both PG&E and SCE. We show a simple average rather than a load weighted average in order to reflect potential high price differentials in smaller SLAPs that would not impact a load weighted average price even within the two groups. San Joaquin, for example, only has an average of 65 MW scheduled in the IFM compared to Sacramento Valley, which has over ten times this amount. In sub-sections A and B the analysis is broken into SLAPs, which illustrates whether larger or smaller SLAPs within the two groups are contributing to the average price differences. 2 Higher prices in the IFM are primarily due to (1) the cap and trade program, which has increased system energy prices by approximately $6.15/MWh in Q1; and (2) increased congestion, particularly in the SCE area. For more details see the 2013 Department of Market Monitoring Q1 Report. CAISO/MD&RP/CCB 4 August 8, 2013

5 Figure 1: PGE average differences by year, by group 3 Figure 2: SCE average differences by year 3 Figures 1 6 have January 11 th, 2013 removed due to the DLAP price overly influencing the 2013 average. CAISO/MD&RP/CCB 5 August 8, 2013

6 Figure 3 and figure 4 show the average price differences as a percent of the DLAP price. (The absolute value price difference between the DLAP and SLAP divided by the DLAP price.) The difference expressed as a percentage shows the relative price difference between area prices with the market price. Figure 3: PG&E average price difference as a percent of DLAP price by year CAISO/MD&RP/CCB 6 August 8, 2013

7 Figure 4: SCE average price difference as a percent of DLAP price by year Figure 5 and figure 6 break out the average price difference between SLAPs and the two DLAPs into peak and off-peak hours. In the off-peak hours (figure 5) there is only a very small difference between average SLAP and DLAP prices. In both PG&E and SCE there is less than $.80 average price difference. In contrast, figure 5, which plots the average price difference during peak hours, shows a much higher average price difference. In particular there is a significant increase over time in the price difference in the SCE DLAP. The aggregate group average price difference of slightly less than $1.00 in 2012 almost triples to $2.80 in This increase is almost entirely due to congestion in the SLAPs comprising group 1. CAISO/MD&RP/CCB 7 August 8, 2013

8 Figure 5: Average differences by year (Off-Peak) Figure 6: Average differences by year (Peak) CAISO/MD&RP/CCB 8 August 8, 2013

9 In order to examine whether the price differences illustrated in figures 1-6 reflect moderate daily price differences or instances of rare, but extremely high price differences, we removed 20 outlier days from the data set and reran the peak hour analysis. 4 Figure 7 shows the results of this analysis. Most of the outliers occurred in Only two occurred in Removing a single day in January 2013 during which the Gates-Midway 500kV line went on a forced outage from around 10:00 AM to 8:00 PM caused the average price difference during peak hours for the entire four months of 2013 to drop by almost $.75. The only other significant impact appears to be a decrease in the 2012 price difference for group 2 in SCE. 5 Even with the most significant outliers removed, there appears to be a moderate upward trend in price differences, particularly in the SCE region. Figure 7: Average differences by year (Peak hours), outliers removed Figure 8 shows decline in relative the price difference compared to the DLAP price during peakhours. When compared to figure 3 and figure 4, which show the average over all hours, the 2013 peak-hour price difference as percentage of DLAP has a relatively larger increase. This indicates that peak hour prices differences are increasing more than off-peak hour price differences in relationship to the DLAP price. This is expected given that the increase in SCE 4 Note we did not remove days with the worst price differentials. We used LMP outliers as that would be a better measure of days where a significant and unusual event happened that would impact the system as a whole. 5 This is consistent with the congestion and exceptional dispatches that occurred more frequently in Q in the SCE region. CAISO/MD&RP/CCB 9 August 8, 2013

10 congestion is primarily due to peak-hour issues rather than a systematic structural deficiency across all hours. Figure 8: Average price difference as a percent of DLAP price (Peak hours) B. PG&E price differences The following section evaluates peak-hour price differences between the PG&E DLAP and SLAPs. Figure 9 graphs historical SLAP and DLAP price differences for the five largest SLAPs in PG&E. Although there is a small increase in volatility beginning in the third quarter of 2012, price differences have for the most part remained under $2.00. The exception to this is the San Francisco Bay Area SLAP, which saw a large spike in first quarter of 2013 to a $4.69 SLAP price increase over the DLAP. CAISO/MD&RP/CCB 10 August 8, 2013

11 Figure 9: Group 1 SLAP historical average peak hour price differences Figure 10 shows the price difference by price bucket over time for the San Francisco Bay Area SLAP. About 9% of PG&E s load is in this area and the SLAP has consistently had a higher price than the DLAP. In the first quarter of 2013 there were frequent high price differences, with over 20% of the days having the SLAP higher than the DLAP by over $5.00. These had a limited impact on the average; whereas the 2 days over $20.00 account for $1.26 of the increase. One of these days was caused by the aforementioned Gates-Midway 500kV forced outage. There are only two months covered in the second quarter of Thus far, over 60% of the days have seen a price difference greater than $2.00. CAISO/MD&RP/CCB 11 August 8, 2013

12 Figure 10: San Francisco Bay Area SLAP price difference by price bin Figure 11: Group 2 SLAP historical average peak hour price differences CAISO/MD&RP/CCB 12 August 8, 2013

13 Figure 11 shows PG&E group 2 SLAPs that have had at any point in time a quarterly average price difference of more than $2.00 from the DLAP. As a group the SLAPs make up about 14% of the load in PG&E. Humboldt is a clear outlier. The large price difference in the third quarter 2012 is due to the Table Mountain outage. This outage impacted the other SLAPs as well and all experienced a similar increase in price difference with the PG&E DLAP during this time. During this outage the price in the Humboldt SLAP was less than the DLAP price, whereas prices in the other four SLAPs were greater than the DLAP price. Prior to and after the Table Mountain outage the Humboldt SLAP price has fluctuated both above and below the DLAP price. Generally there does not seem to be a clear price trend for the SLAPs to pull away from or move toward the PG&E DLAP price. Another way to examine price differences is to look at whether the standard deviation of price differences is changing across time. Figure 12 charts the standard deviation for PG&E SLAPs with the highest price differences from Q to Q Rather than focus on any individual quarter, this chart is intended as an overview of variability. It shows that there does not seem to be a systematic increase in variability within the SLAP and DLAP prices differences over time, especially after accounting for the unusual conditions during summer Figure 12: Standard deviation for PG&E SLAPs from Q to Q Darkest orange indicates late summer of CAISO/MD&RP/CCB 13 August 8, 2013

14 C. SCE price differences In the SCE DLAP slightly over 75% of load is in two SLAPs, La Basin and SCE West. The price difference in these two SLAPs increased throughout 2012 and into the first quarter of 2013 (figure 13). As there are only two months represented in the second quarter of 2013, it may be premature to conclude average prices differences are now trending downward. Figure 14 shows the price difference in terms of a percentage over the DLAP price. Here, the first 6 months of 2013 clearly indicate a downward trend of the percent difference of SLAP and DLAP prices. Figure 13: Group 1 SLAP historical average peak hour price differences CAISO/MD&RP/CCB 14 August 8, 2013

15 Figure 14: Group 1 SLAP price difference as percent of DLAP price The remaining SCE load, about 25%, is scheduled in group 2 SLAPs (figure 15). These SLAPs have a greater average price difference with the SCE DLAP than group 1 SLAPs and this difference is trending upwards over time. This trend holds when looking at the SLAP prices as a percent difference from the SCE DLAP price as well. CAISO/MD&RP/CCB 15 August 8, 2013

16 Figure 15: Group 2 SLAP historical average peak hour price differences Figure 16 buckets the price differences into price bins to show how daily prices are influencing the quarterly averages. The four SLAPs have highly correlated daily prices with only very small price differences between them. The SLAP prices are consistently below the DLAP price. Figure 14 aggregates group 2 price differences in order to show the monthly price differences between the SLAPs and DLAP. Beginning in September 2012 both the value of the price difference and frequency of high value price differences increased significantly. This is consistent with the Department of Market Monitoring 2012 Annual Report and 2013 Quarterly Report, which show a significant increase in congestion in the SCE region. CAISO/MD&RP/CCB 16 August 8, 2013

17 Figure 16: Group 2 SLAP price buckets CAISO/MD&RP/CCB 17 August 8, 2013

18 D. Annual average SLAP price divergence direction The two charts below show the percentage of days where the average daily price in the SLAP was less than the DLAP price. This indicates whether the SLAP is increasing or decreasing the DLAP price on average and how this has changed over the last three years. Figure 17: Divergence in in PG&E and SCE DLAPs Percent of days where average SLAP price is less than PGE DLAP price Year Central Coast East Bay (Bay Area) Fresno Geysers % 31% 71% 50% % 65% 23% 90% % 56% 40% 60% North Valley Peninsula (Bay Area) Sacramento Valley San Francisco (Bay Area % 7% 85% 1% % 33% 69% 14% % 0% 99% 0% Humboldt Los Paderes North Bay North Coast % 76% 32% 24% % 86% 70% 56% % 97% 54% 39% San Joaquin Sierra South Bay (Bay Area) Stockton % 78% 8% 85% % 71% 30% 64% % 100% 7% 100% Percent of days where average SLAP price is less than SCE DLAP price Year Core (La Basin) High Desert Low Desert SCE North SCE Northwest SCE West % 99% 80% 84% 62% 1% % 99% 89% 80% 95% 2% % 100% 95% 94% 100% 1% CAISO/MD&RP/CCB 18 August 8, 2013

19 III. Impact of convergence bidding on load distribution forecast error The ISO implemented convergence bidding in the day-ahead market on February 1, This provides a mechanism for participants to hedge or speculate against price differences between locations or the day-ahead and real-time market. Physical load is also able to use convergence bidding to hedge against errors in CAISO load distribution forecasting. The load distribution factors are not published until three days after the fact; however, are fairly stable and any systematic discrepancy could be at least partially accounted for through convergence bidding. If a load-serving entity believes they have a better estimate of real-time load distribution than the CAISO forecast, then the load-serving entity can place off-setting virtual demand and virtual supply bids at the relevant nodes in order to financially mimic a change in forecasted load distribution. 6 At this time neither SCE nor PG&E use convergence bidding for this purpose. Overall, financial or marketer trading entities overwhelmingly are the largest participants compared to physical assets or load entities. Figure 18 shows average hourly MWs and revenues by trading entity. Participants with physical load make up only around 1.5% of accepted virtual bids. Figure 18: Virtual bidding participation by trading entity type 7 Given the lack of participation at this time by physical load, there is no available data to assess the effectiveness of using convergence bidding to hedge or account for CAISO load distribution forecast error. Furthermore it does not appear other trading entities are taking advantage of any systematic load distribution errors and instead are focused on other strategies. 8 6 It should be noted that any convergence strategy by the three large LSE s has to be approved by the CPUC and is made public in the each entities LTPP. At this time none of them have the hedging of load distribution forecast error as a strategy Department of Market Monitoring Annual Report, page Ibid. CAISO/MD&RP/CCB 19 August 8, 2013