INTERIM DECISION ADOPTING THE MULTIATTRIBUTE APPROACH (OR UTILITY EQUIVALENT FEATURES) AND DIRECTING UTILITIES TO TAKE STEPS TOWARD A MORE UNIFORM RISK MANAGEMENT FRAMEWORK Summary Consistent with the directives established by Decision 1412025, this decision, other things: Adopts the Cycla Corporation (Cycla) 10-Step Evaluation Method as a common yardstick for evaluating the maturity of San Diego Gas & Electric Company, Southern California Gas Company, Southern California Edison Company, and Pacific Gas and Electric Company (utility) risk assessment and mitigation models; Approves common elements of existing utility models to the extent that they provide a “bridge” to more sophisticated and administratively efficient multiattribute risk analysis; On an interim basis, adopts the Joint Intervenor “MultiAttribute” Approach (or utility equivalent features) and directs utilities to take steps toward a more uniform approach to risk management in the second phase of this proceeding; Directs utilities to “test drive” the MultiAttribute Approach using real world problems before full scale adoption of any methodology; Directs utilities to share results of pilots that highlight equivalent features of the MultiAttribute Approach; Directs stakeholder working group to promote appropriate data collection, calibration of subject matter expertise, and development of performance metrics and benchmarking procedures; Adopts Working Group Lexicon Proposal;
Utilities believe that they can achieve more standardization in developing a comprehensive, common and stable set of risk categories across all of its operations and risk. Commitment In general, the utilities support the Commission’s objective of standardization. In the shortterm, they support continued use of the 7x7 Matrix. In the longterm, they agree that future opportunities will allow more complex issues of risk tolerance and risk quantification to be addressed through dialogue and shared learning. They have agreed to continue to meet to explore the development of common standards that support movement towards implementing leading practices. SED Staff Comments SED recommends that all common elements identified in the “Combined Utilities SMAP Uniformity Report” introduced in the SMAP workshop on December 4, 2015, be adopted in this SMAP. SED Staff believes that this exercise brought increased clarity to the proceeding and the parties, and can serve as a step in a longterm process of improving risk assessment models and optimizing mitigations. In SED opinion, more can be made common than expressed by the utilities in their Uniformity Report. For example, there is no unique geographical or operational reason why high consequence events are more important to PG&E than they are to SCE or Sempra. Consideration of high consequence events may be desirable, but it is counterbalanced by the distortion to human perception it creates to produce a nonlinearscale risk score formula. Any distortion in a risk
injury might be viewed as either minor or serious depending on the utility, and further clarification of each descriptor is needed. As to impact categories (e.g., safety, environmental, reliability, compliance, financial, etc.), there is no agreement on what the categories should be, which is a precursor to effective multiattribute risk analysis. Utilities agree to use similar likelihood descriptors but further exploration may be warranted before likelihood measures are required. In sum, IS and EPUC believe that “these uniform elements identified by the utilities are best treated simply as a baseline, and in the interests of efficiency, the Commission ought to require additional uniformity across the utilities.” IS and EPUC also contend that even though the utilities do not agree on a common risk algorithm, the Commission should provide further guidance in this area. They also do not think that the Commission should defer consideration of a risk reduction per dollar spent in the upcoming RAMP filing because SCE has already demonstrated that it is possible to calculate. MGRA suggests that the Utilities have made good progress in developing areas of commonality. However, they assert the IOUs can likely accomplish more in the areas of specific risk approaches (e.g., wildfire risk) and use of different scoring algorithms. As to wildfire risk, MGRA points out “the wildfire exposure of different electrical utilities depends on specific landscape and weather type and not upon which particular IOU service territory the risk occurs in.” While “Santa Ana” wind driven wildfire risk and Northern California vegetationdriven wildfire risk are distinct, they need not be specifically tied to a utility. To reinforce this point, why should “Santa Ana” SCE
each attribute, such as “safety,” ”reliability,” etc. are added together to determine the overall CoF score.) Impact Level Description Catastrophic (7) Fatalities: Many fatalities and life threatening injuries Severe (6) Fatalities: Few fatalities and life threatening injuries Extensive (5) Permanent/Serious Injuries and Illnesses: Many Major (4) Permanent/Serious Injuries and Illnesses: Few Moderate (3) Injuries or Illnesses: Minor to many persons Minor (2) Minor Injuries or Illnesses: Minor to few persons Negligible (1) No injury or illnesses, unreported, negligible injury As described in the Uniformity Report, the utilities currently assign LoF values to risky events using a 1 to 7 scale. The scale defines event frequencies or “arrival rates” as demonstrated below. As referred to in Section 5, the utility scales are similar to Richter scales, which are based on “order of magnitude.” According to Joint Intervenors, this 1 to 7 scale is “nonlinear” because the underlying “hazard rates” or frequencies represent changes in a way that are similar to order of magnitude differences. They observe, “Moreover, this scale means that an event occurring every 35 years has the same LoF (2) as an event occurring every 90 years, an event occurring every 3 years has the same LoF (4) as an event occurring every 9 years, and so forth, even though the mathematical probabilities of these events are different.” In essence, “the 1 to 7 LoF values do not correspond to mathematical probabilities.”
should further specify the proper impact categories for consideration in a multiattribute utility function, and if so, whether, based on a record developed in such future SMAP, the Commission also would like to establish the attribute scales, ranges and weights. Together these interim and longterm plans will result in utility reliance on improved risk management methodologies and will achieve the Commission goal of “a more quantitative method for optimized risk mitigation.” Comparison of Utility and Joint Intervenor Approaches Based on an evaluation of the Joint Intervenor model and deeper understanding of utility risk management frameworks, the following table is a comparison of Utility Risk Evaluation Formulas and Risk Frameworks and the Joint Intervenors Framework: (SED Staff Report at 64, slightly modified based on comments.) Comparison of Utility Risk Evaluation Formulas and Risk Frameworks PG&E SCE Sempra Joint Intervenors Risk Management Framework Risk framework based on Enterprise Risk Management yes yes yes yes impact dimensions consider shareholder interests and/or financial performance yes yes yes to be defined by the utility or Commission Input Type predominantly SMEestimated inputs yes yes yes no, only observed data is unavailable Indexing method on frequency score selection(whole integeronly inputs, 1 to 7) partially yes, but allows for override with actual frequency no, uses continuous, linearscale frequency yes no Indexing method on consequence score selections (whole yes yes yes no
integeronly logscale inputs, 1 to 7) Frequency number of frequency levels 7 7 7 model uses actual likelihood or probability representative position within logscale frequency range right hand of range not applicable, uses linear scale frequency fixed point value from each range uses actual likelihood or probability allows for actual frequency data input where available yes yes no uses actual likelihood or probability model specifically considers asset condition on a perasset element basis when determining frequency no model has ability to do so no yes Consequence consequence evaluation standard based on P95 (95th percentile) "probably worst case outcome" based on "worst reasonable direct impact" reasonable worst case scenario use complete probability distribution number of impact (consequence) dimensions 6 5 4 to be defined by the utility or Commission number of levels per impact dimension 7 7 7 to be defined uses weights on impact dimensions yes no yes yes impact (consequence) dimensions and weights safety(30%), reliability(25%), environment(5%), compliance(5%), trust(5%), financial(30%) safety, reliability, environment, compliance, financial, (not weighted, risk scores only summed) safety(40%), reliability(20%), compliance(20%), financial(20%) Joint Intervenors advocate analytically derived weights to be properly defined by the utility or imposed by the Commission Risk Scores linear scale risk score? no yes yes yes relative or absolute risk score relative quasiabsolute quasiabsolute absolute
Group (perhaps as an outgrowth or continuation of the Metrics Working Group) should be established to address questions of data gathering and to identify appropriate milestones and timelines for implementation of a quantitative methodology.” ORA expresses concern that using subject matter experts in place of data may be inadequate for accurate risk estimation. According to MGRA, it “is generally aligned with ORA’s position, though we believe that the Commission should adopt a more aggressive timeline to move towards a common methodology.” MGRA observes that the “multiattribute utility approach” will help the Commission focus on safety priorities and choose from a portfolio of possible risk mitigation strategies. “It is an analytical (rather than simulation) approach that concentrates on the impacts of hazards on utility assets and potential consequences.” MGRA points out a number of advantages of the approach. First, they contend that the Intervenor White Paper “makes a convincing argument that optimization techniques will result in a superior risk mitigation strategy over simple ranking of projects by cost/benefit analysis.” It maintains that the “multiattribute utility” models enables the goals of costbenefit analysis to be realized by ensuring utilities are spending their safety dollars in the most effective way. Using multiple attributes, which represent multiple values (that may not have a dollar value assigned) may facilitate the optimization of solutions by taking into account not only safety concerns, but environmental and other concerns as well. Second, use of subject matter experts provides an interim quantitative risk measure, which “addresses the utilities’ concerns that
The following is the schedule to be followed in opening the OII, filing the RAMP, and incorporating the RAMP into the GRC application filing. RAMP Application Date Activity September 1 of the year prior to the GRC filing date. Utility letter to Executive Director (with a copy to the Chief ALJ) requesting that an OII be initiated for the utility’s upcoming GRC filing, and pursuant to this decision. By November 15 of the year prior to the GRC filing date. OII for the upcoming GRC initiated. By November 30 of the year to the GRC filing date. Utility files RAMP submission in the OII. By December 15 of the year to the GRC filing date. PHC held. By December 15 of the year prior to the GRC filing date. Utility and SED hold public workshop on utility’s RAMP submission. February 28, prior to the GRC filing date. SED files and serve staff report on utility’s RAMP submission. By March 15, prior to the GRC filing date. hosts public workshop on SED staff report. 10, prior to the GRC filing date. Other parties serve comments on utility’s RAMP submission, and on SED’s staff report. April/May If needed, additional workshops on RAMPrelated May to August, prior to the GRC filing date. Utility incorporates RAMP results into its GRC filing. GRC Application Filing