2.
Provide your organization’s feedback on the Overview of the CAISO’s Production Simulation Model topic as described in section 4:
While SCE is not necessarily opposed to the approach of evaluating RA based on a production simulation model, the approach should be thoroughly examined to ensure its consistency with the RA program. If there are any changes deemed necessary based on simulation results and related findings, those changes should first be submitted to LRA and CPUC for adoption. Without including those changes in the LRA/CPUC RA program, and instead setting RA requirements based on production simulation results could cause a significant deviation from the LRA/CPUC RA program. Fundamentally, the RA programs of the LRAs have been established with rules that are set a priori and as such can be fully complied without after-the-fact review of the resources chosen. The only element that this differs within is the local RA requirements in which effectiveness of the resources is determined after showings are made. The paper on the CAISO portfolio assessment appears to apply a similar after-the-fact analysis for system and potentially flex RA resources. As the CAISO works through this issue with stakeholders and the LRAs, particular attention should be provided to how such analysis can help to inform the RA requirements such that parties are able to comply with the RA needs with certainty rather than make best efforts only to be faced with the uncertainty of cure periods and backstop procurement. In addition, if such learnings are integrated into the RA procurement requirements, the process will be better able to assign costs to those that cause them rather than perform backstop in a “cumulative deficiency” manner.
SCE is concerned that the CAISO’s study approach is not consistent with the RA program design. Specifically, due to various modeling assumptions, as discussed below, the study results capacity requirements and the reliability level that the RA program may not be designed to achieve. For instance, the preliminary simulation results show that the July 2020 RA showing would provide for approximately a 3% loss of load expectation (LOLE). The study seems to conclude that if July is representative of all months, then the 3% LOLE would be equivalent to 10.95 days LOLE for the year.[1] In other words, the CAISO study seems to conclude that the July RA showing is 100 times worse than the 1-in-10 LOLE standard[2]. The study also suggests that the CAISO would need to backstop 5,800MW of capacity to bring July to 1-in-10 LOLE[3]. SCE is concerned that the study includes several input assumptions that may not be intended by the RA program, as discussed below. SCE notes that within the CPUC Track 3B process, the topic of planning reserve margin and more generally the level of reliability expected from the RA program is under discussion. SCE believes that the study data put forth by the CAISO is helpful in examining the range of potential outcomes.
The definition of “deficiency”
The CAISO approach defines deficiency as any hour in which the production simulation shows the CAISO would have to call a Stage Two Emergency[4]. The CAISO should clarify if this definition is consistent with a LOLE definition that is commonly applied in production simulations. Since the CAISO models only the RA fleet, would this definition result in more deficiencies than the approach of applying a common LOLE definition to a system including both RA and non-RA resources?
Load forecast
The CAISO approach models load forecast levels that are significantly higher than the 1-in-2 load forecast. Specifically, the CAISO study models 1-in-10 load forecast, 1-in-20 load forecast (or 10% higher than the 1-in-2 load forecast), and a load forecast that is 9,000MW (or 20%) higher than the 1-in-2 load forecast[5].
The RA requirements today are based on 1-in-2 load forecast and a 15% planning reserving margin (PRM). It appears that the CAISO study models forced outages and reserves explicitly; therefore, to accommodate the 15% PRM, the CAISO study would only need to model load forecast error, which can be less than 2.5% (or equivalent to 1,100MW)[6]. This seems to imply that the CAISO study models scenarios where the peak load was up to 8,000MW higher than the peak load plus the 15% PRM that the RA program requires.
If the understanding of the load forecasts used in the CAISO study is correct, then the CAISO study essentially assumes that the RA program addresses the need to meet peak load forecast that is well beyond the 1-in-2 load forecast, which can lead to “deficiencies” tied to extreme level of load forecasts that the current RA program is not intended to address. If the CAISO believes that the current RA 1-in-2 load forecast should elevate to a different level, then the CAISO should seek a change under the CPUC RA proceeding for adoption.
The load forecast assumption, as well as other assumptions, as discussed below, may have led to significant “deficiencies” and the modeled need for backstop procurement. Under the CAISO approach, the CAISO is essentially requiring all LSEs to procure RA capacity to meet load forecasts that are well beyond the current RA requirements established by LRAs and CPUC. As mentioned above, if the CAISO believes that the current 1-in-2 load forecast standard or the 15% PRM should be elevated, then this should be addressed in the CPUC proceeding, since the RA program is in the LRA/CPUC’s jurisdiction.
Resource input and energy requirement for RA
The CAISO approach models resources’ production in meeting hourly load under various scenarios. For wind and solar, effective load carrying capability (ELCC) values as currently calculated by the CPUC are replaced with energy production of the resource. For other resources, individual resource constraints are modeled to the greatest extent possible[7]. External areas are also modeled, including 35 WECC zones and path limits between zones, while net imports into the CAISO are limited to the amount of RA imports.
SCE agrees with the approach of using production data for wind and solar in the simulation. The approach of using production data for wind and solar resources instead of ELCC values likely will more accurately represent the contribution from those resources. SCE understands the CAISO’s motivation of including individual resources’ constraints in meeting hourly load. SCE believes that this information can be very helpful in evaluating the various models that have been proposed in the CPUC’s track 3B RA OIR to ensure that not only is the load/net load peak met but so are the energy needs of the system.
Reliability metric and framework
Regarding discussions of reliability framework and appropriate reliability service level for RA, SCE believes that the discussion should also take place under the CPUC RA proceeding. There the discussion should address key issues including: the appropriate reliability service level that the RA program should be designed to achieve and key considerations for determining this reliability level and how to ensure this reliability level can be achieved through upfront RA procurement. While the preliminary assessment provides useful information, further discussion on these topics is essential.
Other observations and issues
- The approach that CAISO adopted in this study might not be consistent with industry’s common practice. The probabilistic LOLE analysis is typically performed to determine the amount of capacity that needs to be acquired to meet a desired reliability target (industry practice of 0.1 days/year) as compared to the study’s inclusion of high/extreme load forecasts and Stage 2 Emergency in deriving a capacity shortfall curve in meeting the load forecasts.
- As an after-the-fact analysis, the study results indicate capacity shortage for July 2020 in order to avoid unserved load or unmet ancillary service requirements. However, there was no stage-2 emergency issued by CAISO in July 2020. The study results may not be consistent with actual market conditions. The CAISO should provide information on which load scenarios the simulated deficiencies occurred.
- The CAISO should provide information on whether, and to what extent, deficiencies may be addressed by energy storage resources, since it appears most of the deficiencies are small and over 90% of the days with deficiencies, those deficiencies were less than four hours in duration. To the extent possible, the CAISO should consider simulating the effects of potential energy storage resources in addressing those deficiencies.[8] In addition, instead of capping net imports at RA imports, the simulation may consider modeling those RA imports as a self-schedule or bid with negative bid prices per the latest rules for RA imports. The simulation should examine whether there are any exports from the CAISO to WECC areas under a deficiency.
[1] “However, based on the results from the CAISO’s study, the July 2020 RA showing would provide for approximately a three percent LOLE…. This would result in an equivalent 10.95 days LOLE for the year.” Report, at 20, available at http://www.caiso.com/InitiativeDocuments/PreliminaryPortfolioAnalysis-ResourceAdequacyEnhancements.pdf.
[2] A 1-in-10 LOLE standard would be equivalent to a 0.1 day for the year. 10.95 days would be 100 times worse than 0.1 day for the day.
[3] Report, at 21.
[4] This means the model shows the CAISO would have inadequate capacity to meet the aggregate of non-spin, spin, regulation, and load.
[5] Figure 1 of the Report.
[6] CAISO Presentation shows a less than 2.5% of peak load forecast for July 2020, available at http://www.caiso.com/Documents/Presentation-MarketPerformance-PlanningForum-Sep9-2020.pdf, at page 55. Although this percentage may or may not be aligned with the 15% PRM when it was established, the information provides a useful reference.
[7] Resource confidential Masterfile parameters for each resource are modeled in the simulation, including minimum run time, minimum down and ramp rates. Presentation, at 23, available at http://www.caiso.com/InitiativeDocuments/Presentation-ResourceAdequacyEnhancements-Nov122020.pdf.
[8] It was also discussed during the November 13, 2020 MSC meeting that energy storage resources may reduce the frequency of deficiencies but increase their magnitude when deficiency does occur. Such effects should be quantified through simulation if possible.
9.
Additional comments on the preliminary portfolio analysis: