2012 Integrated Resource Plan. Chapter. Resource Planning Risk Framework

Transcription

1 Chapter Resource Planning Risk Framework

2 Table of Contents. Introduction Risk Framework Comparing Alternatives Using Multiple Planning Objectives Financial Impacts Environmental Footprint Economic Development Impact IRP Treatment of Multiple Decision Objectives Key Uncertainties and Risks Quantifying Uncertainty Load Forecast Uncertainty DSM Savings Uncertainty Net Load and Net Gap Uncertainty Market Price Forecast Uncertainty Wind Integration Cost and ELCC Uncertainty IPP Attrition Uncertainty Resource Options Applying the Risk Framework to Comparing Alternatives Portfolio Analysis Methodology and Assumptions Portfolio Analysis Models Modelling Constraints Financial Parameters Inflation Rate Discount Rate U.S./Canadian Exchange Rate Load/Resource Assumptions Market Price Assumptions Resource Option Assumptions Other Key Assumptions Transmission Analysis Differential Rate Impact Analysis... - List of Figures Figure - Mid DSM Savings, Adjusted for Uncertainty... - Page -i

3 Figure - Low DSM Savings, Adjusted for Uncertainty... - Figure - High DSM Savings, Adjusted for Uncertainty... - Figure - Range of Potential Savings for DSM Option... - Figure - Size of Deviation from Mid DSM Energy Savings Forecast (F00)... - Figure - Size of Deviation from Mid Gap Capacity (F00)... - Figure - Net Gap for Energy Figure - Net Gap for Capacity... - Figure - Modelling Map and Base Modelling Assumptions... - List of Tables Table - CEA and Other Energy Planning Objectives... - Table - Example Consequence Table... - Table - Approaches to Handling Uncertainty Table - Program Savings and Uncertainty... - Table - Codes and Standards Savings and Uncertainty... - Table - Rate Structure Savings and Uncertainty... - Table - Overall DSM Savings and Uncertainty via Bottom-Up Aggregation... - Table - Five DSM Options Energy Savings After Top-Down Uncertainty Assessment... - Table - Five DSM Options Energy Savings After Top-Down Uncertainty Assessment... - Table -0 Average Annual Energy Savings from DSM Programs as Per cent of Retail Sales (00 to 00)... - Table - Comparing Future DSM Program Savings Energy Targets Table - Energy-Related DSM Capacity Savings and Uncertainty... - Table - Savings from Capacity-Focused DSM and Uncertainty... - Table - IPP Attrition Rates and Uncertainty... - Page -ii

4 0. Introduction The goal of BC Hydro s Integrated Resource Plan (IRP) is to identify, based on good utility practice, B.C. Government policy, and the legislative requirements of the Clean Energy Act (CEA), a cost-effective combination of existing and new demand-side measures (DSM), generation resources, and transmission resources to meet existing and new demand. This chapter sets out the analytical framework that BC Hydro used to compare resource alternatives, addressing multiple objectives, attributes and uncertainties. This framework, and the corresponding results presented in Chapter, led BC Hydro to select the Recommended Actions that are found in Chapter. The key questions that BC Hydro considered in the IRP are as follows: (a) Natural Gas-Fired Generation What is the optimal use of thermal generation within the CEA s per cent clean energy objective? (b) DSM Targets What is the most cost-effective volume of DSM to rely on? (c) Site C Clean Energy Project Should BC Hydro continue to pursue Site C as an option? 0 (d) North Coast What is BC Hydro s strategy to prepare for further significant and uncertain load growth in the North Coast? (e) Fort Nelson/Horn River Basin What is BC Hydro s strategy to prepare for significant and uncertain load growth in the combined Fort Nelson and Horn River Basin regions, while ensuring load growth in Fort Nelson is met? What approach should BC Hydro take to support provincial energy objectives via enabling electrification in this region? (f) General Electrification What role should BC Hydro play to support provincial climate policy? What is BC Hydro s strategy to get ready for potential load driven by general electrification in the absence of load certainty? Page -

5 (g) Acquisition of IPP Energy What is the volume of future IPP clean energy acquisitions and the timing? What are the key parameters/considerations that will inform future acquisition processes? (h) Transmission What transmission needs are foreseen over the long-term planning horizon and what actions need to be taken? 0 (i) Capacity Requirements and Contingency Considerations What additional capacity requirements are foreseen and what strategies and actions are appropriate in response to these future needs? In addition to filling the most likely mid gap, what are some events that might make the gap bigger, what are the size and timing of these events, and what options can BC Hydro prepare as contingency resources? 0 Within these topic areas, different options exist for meeting new electricity demand using demand-side measures, generation resources, and transmission resources. Section. introduces the IRP s Risk Framework as a way in which these options can be compared. This includes how good utility practice and the CEA s objectives were used in the comparisons, the key uncertainties, and the techniques by which these uncertainties were quantified and incorporated into the comparison of alternatives. Section. provides a technical overview of how these options were compared through portfolio modelling, including the methodology and assumptions, the models used, and other technical aspects of the modelling approach.. Risk Framework Integrated resource planning is an inherently uncertain exercise and the choices made today may be judged on their positive and negative impacts for years to come. The IRP s Risk Framework is the approach used to characterize these multiple, uncertain impacts in a way that helps compare resource options and leads to balanced, transparent decisions. Page -

6 There are a number of key elements to the Risk Framework: 0 Multiple objectives for resource planning are addressed in section.. with respect to how they were identified, defined, measured and used. Capturing uncertainty is a significant component of the Risk Framework. The key uncertainties considered are described in section... The methods for capturing these uncertainties in modelling and comparing options are discussed in section... Portfolio analysis is used to combine the above information and test how the various resource options address the key IRP questions. The technical approach, assumptions and models used are outlined in section.. Comparing resource options, based on the above quantitative aspects, and the qualitative aspects not captured through modelling, enables conclusions to be drawn that can be translated into actions (i.e., a Base Resource Plan (BRP )).This is addressed in Chapter. Identifying residual risks and key warning signs and providing strategies, trigger points, and off-ramps to avoid or mitigate these risks is discussed in section.0 and section Comparing Alternatives Using Multiple Planning Objectives Generating and transporting electricity creates a footprint in B.C. a footprint with attributes that include financial, environmental, and social effects. This section lays out the basis for considering these attributes, presents a collection of the measures compiled by BC Hydro, and suggests a way in which this large amount of information can be used to help inform decisions. The BRP is BC Hydro s proposed action plan to fill the load/resource gap over 0 years on a cost-effective basis. The actions required to fill the gap for the first ten years form the Recommended Actions found in Chapter. Page -

7 There are several reasons why BC Hydro is considering a broad set of attributes: 0 The CEA stipulates that BC Hydro must carry out the IRP consistent with good utility practice. Many utilities develop their integrated resource plans by analyzing resource investment trade-offs across multiple objectives or attributes. The CEA also states that the IRP is to report out on its consultation with First Nations and the public regarding long-term electricity planning issues. Based on past experience, BC Hydro anticipates that these groups will be interested in a broad set of attributes, beyond only financial impacts. The B.C. Government explicitly laid out a number of objectives in the CEA that BC Hydro is to pursue through the IRP. These objectives include a mix of financial and non-financial considerations, including a focus on clean/renewable electricity and GHG impacts. 0 In the IRP analysis, a number of the CEA objectives and good utility practice requirements are implemented as constraints that apply to the creation of all portfolios. Examples include not considering nuclear power and meeting reliability criteria. Table - lays out the criteria by which resource options are compared in the IRP and provides the rationale for their consideration. Many of these considerations are embodied in the CEA objectives: GHG emissions, financial impacts, economic development and job creation. These are of interest when comparing options, because different portfolios may perform differently with respect to these objectives. The sections that follow further describe how the financial, environmental, and economic development decision objectives were considered; deliverability risk is addressed in greater detail in section... Page -

8 Table - CEA and Other Energy Planning Objectives Decision Objective Minimize Financial Impacts: Cost Cost Risk Differential Rate Impacts Minimize Environmental Footprint, including: Land Footprint Water Footprint Criteria Air Contaminants GHG Emissions Maximize Economic Development Minimize DSM Deliverability Risk Reason for Inclusion Good utility practice, First Nations, public and stakeholder interests, congruent with CEA objectives. Good utility practice, First Nations, public and stakeholder interests, congruent with CEA objectives. First Nations, public and stakeholder interests, congruent with CEA objectives Good utility practice, First Nations, public and stakeholder interests... Financial Impacts The CEA and good utility practice point towards the importance of tracking costs when comparing resource options. In the IRP, the financial implications of the resource options or strategies to fill the load/resource gap are tracked at a portfolio level, both for the cost of acquiring new resources and also for how these resources interact with the existing system and the external electricity market. Some of the metrics used to understand financial impacts include: 0 Costs are expressed on a Present Value (PV) basis to capture the impact of the timing of costs and trade revenues over the planning horizon. Where uncertainty is relevant, ranges of costs or costs across scenarios are highlighted. The impact of portfolios of resource choices on how rates might evolve over time was also calculated for some IRP questions. These results are shown as rate differentials, i.e., rate changes with respect to a base case. While this information is included for completeness, BC Hydro continues to use the PV of portfolio costs as the primary means of comparing resource options. Page -

9 In its 00 Long Term Acquisition Plan (LTAP) Directive, the British Columbia Utilities Commission (BCUC) requested that BC Hydro develop an approach that allowed comparison between supply-side and demand-side resources based on their risk-weighted unit energy costs. The response to this directive can be found in section... 0 Finally, as outlined in Chapter, BC Hydro makes a distinction between low cost and cost-effective options, where cost-effective includes both cost and considerations of schedule/delivery risk, reliability, timing, location and environmental impacts.this chapter considers how these factors are addressed in the IRP analysis when comparing portfolios of resource options. BC Hydro s professional judgement was also applied in combining these different types of information to draw conclusions (Chapter ) and develop Recommended Actions (section.) Environmental Footprint In addition to financial impacts, the environmental footprint of each portfolio was tracked in the IRP analysis with respect to potential effects on land, freshwater, marine, air (criteria air contaminants) and climate change (GHG emissions). Since portfolios have numerous resources dispersed across a diverse landscape, a number of measures related to these footprints were tracked (refer to section.. for a list of these measures and their definitions and Appendix A- for how these were calculated). These footprints were considered at a portfolio level, as data does not exist at a regional or local level for all projects (in many cases, generation resources are represented as a typical project or bundle of projects). In addition, the resources selected through modelling are not necessarily the ones that would be selected through an actual acquisition process. As such, it is not appropriate to drill down to regional, local or project levels with these metrics. Page -

10 0 0 The full set of environmental information for comparing portfolios with respect to the key IRP questions is presented in Appendix B. Within the body of the IRP (sections. to.), this information is summarized at a level appropriate for comparing portfolios of resource options.... Economic Development Impact In response to the CEA s objectives on economic development, the IRP analysis tracks the possible footprint of each portfolio with respect to effects on employment, Gross Domestic Product (GDP) and government revenue. These measures were generated for a provincial-level view, as the data and modelling did not exist to provide a more regional view of these potential impacts. In addition, given that the modelled resource additions might not be the same as the projects selected through an actual acquisition process, these measures are appropriate for high level comparisons of broad impacts and are not appropriate for regional or more precise analysis. Appendix A- discusses in more detail the methodology behind these measures. Appendix B provides the detailed economic development criteria, including more granular views as to the source of these potential impacts (e.g., construction vs. operation; direct vs. indirect or induced changes). As this additional level of analysis did not provide additional insight into the comparison of portfolios of resource options it is presented at a higher level in the body of the IRP.... IRP Treatment of Multiple Decision Objectives Financial, environmental, and economic development impacts and DSM deliverability risk were tracked for some of the key IRP questions outlined in section.. The resulting views for each question are presented in consequence tables found in sections. to.. A consequence table is a collection of the above information arranged in a matrix format so that the options considered are displayed as column headers (i.e., Page -

11 portfolios representing different strategies for filling the load/resource gap), the relevant decision objectives are displayed as row labels, and for each row, the specific units of measurement are provided. An example from Chapter is presented in Table - below for illustrative purposes. Table - Example Consequence Table Objective Measure Clean Power with Transmission Clean with SCGTs (within % limit) Land Total hectares,00,00 Marine (valued ecological features) Total hectares Affected Stream Length km 0 0 GHG Emissions Local Air Contaminants Local Air Contaminants Carbon dioxide equivalent (000 tonnes) Oxides of nitrogen (000 tonnes) Carbon monoxide (000 tonnes),00,00 GDP $ million PV,00,000 Employment Full-time equivalents,00,000 Government Revenues $ million PV,00,00 Cost $ million PV 0 0 While some judgment is required to reduce the full analysis down to a condensed level, this view allows a reader to easily see the relative impacts of resource options across alternatives and decision objectives. The unabridged versions of these tables can be found in Appendix B. The use of consequence tables also helps clarify the balance that BC Hydro is seeking in developing cost-effective solutions. Given the precision of the measures and the range of their potential impacts across resource options for each question, this cannot be presented as a mechanical weighting and scoring outcome. Rather, the consequence tables attempt to summarize what could be gained and what might be given up across resource options. Qualitative factors not captured in the table Page -

12 also need to be considered and professional judgement is required to balance the quantified and non-quantified factors across these multiple objectives when developing conclusions and recommendations... Key Uncertainties and Risks To provide a clear discussion of the uncertainties and risks that BC Hydro is managing, the following definitions are provided: Uncertainties are variables with unknown outcomes; and Risk is commonly defined as the effect of uncertainty on objectives. Some key uncertainties and related risks include: 0 0 Load growth and the chance that load growth exceeds or falls below expectations; DSM initiatives and the chance that DSM savings exceed or fall below expectations; Features of BC Hydro s existing system and its operations, including inflow water variability; Natural gas and electricity spot market and long-term market price uncertainty; Renewable Energy Credit (REC) prices and GHG emission prices; Current and future regulatory and public policy developments, such as: GHG regulation, Renewable Portfolio Standard (RPS) targets and eligibility requirements; IPP development, including type of resource and location and the risk that these resources require significant capacity and transmission support; IPP attrition rates from power acquisition processes; Delay in Site C s in-service date; Page -

13 Thermal generation resources (including Burrard) and the uncertainty around the ability to permit these resources in time to respond to short term capacity requirements; Transmission supply and the risk that this long lead time resource faces construction delays and permitting problems; and Non-thermal capacity resources and their ability to meet capacity requirements on short notice with high reliability. 0.. Quantifying Uncertainty Section.. laid out some key uncertainties and risks that could potentially influence the comparison of resource options with respect to the IRP s key questions. Where possible, BC Hydro quantified these uncertainties to be transparent about their role in the analysis, results, and conclusions. This section briefly describes the different approaches to handling uncertainty in the IRP analysis. These approaches are addressed in more detail in Appendix A. Table - Approaches to Handling Uncertainty Approach Brief Description Examples Parameterization of Historical Observations Subjective Probability Elicitation Monte Carlo Analysis Scenario Analysis Uses sequences of past data to derive a statistical description of the range of uncertainty Where good historical data does not exist, uses knowledgeable specialists to construct a description of the range of uncertainty Mechanical way to jointly calculate the influence of several uncertain variables through simulation of thousands of combinations An alternative way to jointly calculate the influence of several uncertain variables, but only using a few, select combinations Load forecast inputs, such as economic growth, housing starts, population growth Savings from various DSM tools including Codes and Standards and Programs IPP attrition rates for future calls Load forecasting DSM savings (bottom-up analysis) Market price scenarios Load/resource gap between demand (after DSM) and supply Page -0

14 Approach Brief Description Examples Sensitivity Analysis Conservative Point Estimates / Managed Costs Best Estimates Testing one variable at a time to see whether different values within the range of uncertainty impact policy considerations Incorporates uncertainty by taking a single point estimate, chosen in a conservative fashion Does not take into account uncertainty in any fashion; usually reserved for variables where uncertainty is assumed to have a small or manageable impact Wind integration cost Firm energy expected from hydro projects for IPP projects Site C project and contingency costs ILM in-service date Energy from wind projects 0 The IRP analysis uses a mix of these approaches to explore how uncertainty impacts the comparison of options and the strategies to manage the residual risks of the recommended actions. As always, professional judgment informed by quantitative analysis and qualitative information is required when interpreting data, balancing objectives, and making decisions.... Load Forecast Uncertainty As outlined in section.., BC Hydro produces both a mid Load Forecast, as well as a range of uncertainty around that estimate. This range of uncertainty is derived using a Monte Carlo analysis based on the impact on load of the uncertainty associated with a set of key drivers. These drivers include economic activity, weather, electricity rates and demand elasticities. The results of the Monte Carlo simulation are then split into three discrete forecasts: a high forecast, a mid forecast, and a low forecast. Refer to section... for a description of how this is done and how this is combined with DSM savings. Several key uncertainties are captured through separate sensitivity analyses due to their large size and uncertain timing: North Coast LNG and mining loads; Fort Nelson and Horn River Basin loads; and Page -

15 General electrification loads. 0 These large, discrete additions to load are covered as separate topics of analysis within the IRP. As discussed in section.., and in response to the BCUC 00 LTAP Directive, BC Hydro has been investigating the overlap and interrelationship between load growth and DSM savings (referred to as DSM/Load Forecast Integration). In response to the BCUC s 00 LTAP Directive, BC Hydro undertook work to examine this issue. Details of this can be found in Appendix B of the IRP, however not all issues were resolved. Some gaps still remain to be addressed, including natural conservation and natural load growth assumptions in load forecast and DSM programs baseline assumptions. These still have the potential to impact load forecasting accuracy.... DSM Savings Uncertainty Energy conservation continues to be BC Hydro s first and best option for meeting load growth. Based on current plans, BC Hydro expects to meet the majority of its load growth with DSM. However, precise forecasting of DSM savings for long-term planning purposes is challenging for several reasons, including: 0 Limited BC Hydro experience with respect to targeting and achieving savings at and above current levels; Difficulty in distinguishing between load growth and DSM effects; and Model uncertainty, in particular linking customer response to DSM actions, and forecasting the timing and efficacy of regulatory changes. In view of these challenges, BC Hydro continues to emphasize and build upon approaches described in the 00 LTAP to understand DSM savings uncertainty. These approaches attempt to characterize the range of uncertainty around DSM Page -

16 savings estimates, to better inform decisions regarding energy and capacity planning. Forecasting DSM savings and savings uncertainty is a new field that draws extensively upon unique techniques such as subjective probability judgements. In addition, as discussed further in Chapters and, BC Hydro is filling the majority of its load/resource gap with DSM. As such, a large portion of section.. is devoted to this topic. The discussion on DSM savings and uncertainty is organized as follows: 0 0 The section starts by discussing a bottom-up effort, whereby probability assessments for three DSM tools (i.e, DSM programs, codes and standards, and rate structures) were collected and aggregated to create a picture of the range and distribution of savings within the five DSM options (section...). Since the bottom-up approach aggregates a number of individual uncertainty assessments, this approach may miss some key uncertainty aspects. Therefore, a top-down assessment of the probability distributions for the five DSM options was also conducted to include more macro considerations not captured in the bottom-up approach. The range of DSM savings used for portfolio modelling is then shown and the implications of the results discussed. While an effort was made to quantify all sources of uncertainty, of equal importance is the qualitative aspect of the uncertainty assessment. This topic is addressed in a jurisdictional review of DSM savings targets and achievements (section...). DSM capacity savings, both from energy-related savings (section...) and from capacity-focussed programs (section...) are also addressed. Similar to the analysis for energy, the tools used to capture uncertainty, the results, and the implications for comparing options are discussed. Capturing the range of uncertainty around DSM savings forecasts is the key to understanding DSM deliverability risk and hence, cost-effectiveness when Page -

17 compared to supply-side options. This section finishes with conclusions about the DSM uncertainty assessment carried out for the IRP, including areas of progress since the 00 LTAP and some remaining limitations and information gaps (section...).... Quantifying Uncertainty of Forecast DSM Energy Savings The DSM energy savings uncertainty analysis focuses on quantifying three DSM tools: 0 DSM programs; Codes and standards; and Rate structures. 0 The key sources of uncertainty underlying the savings estimates are highlighted for each of these DSM tools and the range of credible outcomes are reported for each. More details regarding the methods used and the results can be found in Appendix B. This section starts with analyzing DSM energy savings uncertainty using a bottom up approach; first, by developing probability assessments for each discrete DSM tool and then, combining them. A top-down approach is then applied to adjust the results. DSM Energy Savings Uncertainty Bottom Up Approach Energy Savings from DSM Programs The IRP considers more than DSM programs spread across all customer classes. While BC Hydro does have extensive experience working with customer groups to encourage energy conservation and efficiency, the fact that DSM depends on voluntary customer participation makes forecasting DSM savings inherently uncertain. Page -

18 Two key drivers of DSM program savings uncertainty are: Participation rate of customers for that program; and Energy savings per participant. 0 For each of the programs, subjective probability distributions were constructed to capture the range of possible outcomes, both for participation and savings per participant. Total DSM program savings were taken as the sum across the individual programs. Monte Carlo simulation was used to derive a spread of uncertainty around these results for F0. The results are shown in Table -. As described in section., DSM Options, and show programs with progressively higher expected savings, which is reflected in the results. DSM Options and have lower expected savings and higher uncertainty as programs are replaced by rate structures and codes and standards as a way to achieve savings. This effect is apparent by F0 (Table -) and accelerates over the rest of the planning horizon. Table - Program Savings and Uncertainty (GWh/year in F0) Option Option Option Option Option Low (P0 cutoff),,0,,,0 Mid (mean or expected),,0,,, High (P0 cutoff),,,,,0 0 Energy Savings from Codes and Standards For the IRP, BC Hydro identified over 0 changes to codes and standards that could be encouraged or accelerated to achieve energy savings. The estimates of these savings were subject to several sources of uncertainty, including the timing and efficacy of these changes due to support from the business community, the general Page -

19 public, and policy makers, and the level and rate of adoption and compliance with these changes by end users. A probability distribution for savings was developed for each proposed change. These inputs were used in a Monte Carlo simulation to calculate the estimated savings in F0 and the spread of uncertainty around this estimate. The results are presented in Table -. Options, and hold these savings constant, whereas DSM Options and rely on progressively more energy and capacity savings from codes and standards. Table - Codes and Standards Savings and Uncertainty (GWh/year in F0) Option Option Option Option Option Low (P0 cutoff),,0,,,0 Mid (mean or expected),,,,,0 High (P0 cutoff),,,,, 0 0 Energy Savings from Conservation Rate Structures Estimates of energy savings from rate structures are uncertain, particularly in B.C.; customers have been facing low and stable rates for an extended period of time, so data specific to this jurisdiction is only now becoming available to guide forecasts. As a result, it was important to consider the range of uncertainty around rate structure savings estimates. The uncertainty of savings from rate structures was captured through considering a wide range of possible elasticity of demand parameters broad enough to capture the potential range of what might be observed, but narrow enough so that the upper and lower bounds were still considered possible. The only exception to this method was with respect to the industrial transmission level customers. The distribution of savings for these customers was arrived at using a more top-down discussion of the customer class, which yielded a triangular probability distribution of possible energy savings. Page -

20 0 The use of elasticity of demand to capture the range of uncertainty around savings is convenient for modelling purposes. However, because it is a high level summary of a collection of complex consumer responses, it does not give insight into the underlying processes driving savings. This raises difficulties when combining rates savings with other DSM strategic tools. As an example, while potential overlap between rate savings and programs was considered in the design of the five DSM options, the uncertainty around these assumptions and the interaction between rates and programs as they under- or overachieve their estimated targets was not modelled; the current model treats them as two independent variables. How this model uncertainty is addressed is described in a following section entitled Top-Down Assessment of the five DSM options. The estimated energy savings available from rate structures, and the spread of uncertainty around these estimates, is shown in Table -. Table - Rate Structure Savings and Uncertainty (GWh/year in F0) Option Option Option Option Option Low (P0 cutoff),,,,, Mid (mean or expected),0,0,0,, High (P0 cutoff),,,,0,0 Combined DSM Savings Uncertainty Bottom-Up Approach An estimate of overall DSM savings for each option can be found by summing the savings from each of the three DSM tools in a Monte Carlo simulation. The results of this are shown in Table -. 0 Table - Overall DSM Savings and Uncertainty via Bottom-Up Aggregation (GWh/year in F0) Option Option Option Option Option Low (P0 cutoff),0,0,0, 0,00 Mid (mean or expected), 0,0, 0,, High (P0 cutoff),,,0,0,0 Page -

21 0 Combined DSM Savings Uncertainty Top-Down Approach The bottom-up approach to assessing DSM savings discussed above was conducted on a line-by-line basis, focusing on each initiative alone. A second stage of uncertainty assessment was then implemented, one that took a more holistic view of the full portfolio of DSM activities. Two sets of adjustments were applied upon review: one to capture a broader view of the savings uncertainty of the five DSM options and the other to address the specific characterization of DSM Option. These adjustments are discussed in the following two sections. Top-Down Assessment of Five DSM Options Experience from assessing DSM uncertainty for the 00 LTAP revealed several information gaps that were not addressed through a bottom-up analysis. The primary information gaps were as follows: 0 Model uncertainty aggregating the individual activities was done using a specific model, but not necessarily the correct model (other alternatives may be plausible); Dynamic interplay amongst DSM tools some areas of overlap, gaps, and interdependencies were identified, but these were only starting to be understood at the time of IRP development; and Correlation with load growth some areas of overlap and interdependencies had been identified, but this was, and remains, an information gap. With these limitations in mind, BC Hydro reviewed the outcomes of the five DSM options from the bottom-up analysis. The range of outcomes was tested for reasonableness to determine whether it was too narrow or too wide and to see Another information gap addressed in the 00 LTAP was a qualitative comparison to other jurisdictions. A jurisdictional review can be can be found in section... The method of eliciting probabilities from individuals in a structured and defensible manner is addressed in Appendix A Risk Framework Tools and Explanation. Page -

22 whether the spread of outcomes (i.e., the calculated probability distribution) from the bottom-up method matched BC Hydro s level of general confidence/uncertainty. This process resulted in some changes to the distributions of the five DSM options: 0 Some of the extreme high levels of savings for Options, and were reduced, since such high levels of savings were judged implausible given that the tools and approaches in these options were similar to current practice; The downside outcomes to Options and were adjusted to be more similar. This aligns with the observation that if Option represents the same approach as Option, but with a larger emphasis on programs, then if they fail, they should both fail in the same way, with similar results; and The spread of uncertainty across the range of possible outcomes was increased substantially for Options to and. BC Hydro felt confident that the savings would fall within these ranges, but was less confident about where the exact outcome would be within these ranges. These first three changes effectively lowered the expected value (average) of DSM Options, and. Some other adjustments of importance were made to Options and, as follows: 0 The high end of Options and were increased slightly to capture the low probability outcome that these new approaches would be very successful; The spread of uncertainty for the middle portion of the confidence intervals was widened considerably to represent the consensus intuition that a shift to new tactics is highly uncertain; and A significant shift downwards on the downside for Options and was required. Discussions amongst the group highlighted that the success of these DSM portfolios depended on all DSM tools (programs, codes and standards, and rates) succeeding; failure in some component (e.g., adoption of new paradigm in codes and standards, or ambitious new rate designs) is possible Page -

23 0 0 and might lead to underperformance in the other tools. Over-reaching might even set back savings compared to the other DSM options. Top-Down Adjustment of DSM Option After further review, BC Hydro redesigned some elements of Option. By matching Option s level of effort on programs, it was felt that the downside to Option would be significantly reduced. To reflect this, additional program spending was added to Option and the downside uncertainty of that option was shifted to match that of Option. Results of the DSM Uncertainty Analysis The top-down uncertainty assessment was an attempt to capture BC Hydro s broader view of its ability to deliver on DSM savings across a range of options. The discussion was structured so that these judgments took into consideration some of the limitations of the bottom-up approach. As such, it was an attempt to move some of BC Hydro s professional judgment into the realm of quantified results, so that it could inform the data analysis and modelling within the IRP. As discussed in a later section, the emergence of new information and the need to balance competing objectives means that professional judgment will always be needed to interpret modelling results to make decisions. However, this exercise represents a step forward in bringing more transparency to how and where this judgment is used. The results of the top-down uncertainty assessment for F0 were then segmented into high, mid and low level of DSM success for that year. Table - shows these results, which represent the annual rate of savings at the end of F0, at the customer meter using a base year of F00. Discussions also took place regarding reducing the upper tail of Option to reflect the potential impact of a more cautious approach. However, it was not possible to conclude these discussions. Page -0

24 Table - Five DSM Options Energy Savings After Top-Down Uncertainty Assessment (GWh/year savings since F00, at customer meter, F0) Option Option Option Option Option Low (average lower 0%),,,,, Mid (average middle 0%),, 0, 0,00 0, High (average top 0%),,0,,0,0 0 Further adjustments to the DSM values were made to account for differences in the representation of DSM in the IRP analysis. First, the energy and capacity savings were converted from savings at the customer meter to savings at the system level by accounting for reduced losses. Second, savings were converted from an annual rate of savings at the end of the year to a total amount of energy savings during the year. Finally, adjustments were made to reflect F0 as the base year for the Load/Resource Balance (LRB) and portfolio analysis rather than the base year of F00, because DSM savings that occurred in F00 through F0 are now embedded in the 0 Load Forecast before DSM. The results are shown in Table -. Table - Five DSM Options Energy Savings After Top-Down Uncertainty Assessment (GWh/year since F0 at system level, F0) Option Option Option Option Option Low (average lower 0%),,,,, Mid (average middle 0%),,0, 0,0 0, High (average top 0%) 0,,0,,,0 The following figures show these results across the planning horizon. These results were derived from the preceding analysis as follows: For the periods from F0 to F0, a straight line interpolation was made to go from the assessed level of uncertainty in F0 to a zero level of uncertainty in F0. Page -

25 Beyond F0, the level of savings uncertainty relative to the average forecast savings was maintained, which gave rise to the absolute spread of uncertainty rising slowly post F0 with the growth in forecast DSM savings for each option. This method likely understates the range of uncertainty for DSM savings post F0. Figure - shows mid DSM levels with the differences between the DSM options being very small in the early years and growing substantially towards the end of the planning period. Figure - Mid DSM Savings, Adjusted for Uncertainty 0 Figure - shows the savings from the five DSM options when DSM is underperforming. It is clear that the extra spending and effort for Options to have little impact across the planning horizon. If DSM drastically underperforms, each option will produce roughly the same results. Page -

26 Figure - Low DSM Savings, Adjusted for Uncertainty Figure - shows the five DSM options when they are performing much better than forecast. As shown, DSM Options and are clearly distinguished from the first three options by their large potential upside performance in the long-term. Page -

27 Figure - High DSM Savings, Adjusted for Uncertainty Figure - puts the high and low lines from the previous figures into a band of uncertainty around the mid forecast for Option, as a way of illustrating how these quantified uncertainties increase over time. As Figure - shows, uncertainty regarding DSM forecast savings in the near term is low, but this grows over time creating a broad fan of possible levels of DSM savings in the future. Page -

28 Figure - Range of Potential Savings for DSM Option 0... Alternative Perspective of DSM Energy Savings Uncertainty For this IRP, a BRP is built with reference to the mid DSM forecast. Contrasting the preceding figures demonstrates that planning to a mid forecast and then being surprised by DSM underperformance could be of concern. Figure - was constructed to examine the degree to which planning to a mid DSM forecast and being surprised by significant and widespread DSM underperformance could lead to an energy shortfall. For this analysis, it was assumed that it would take several years to determine that DSM was unable to deliver on planned savings and for BC Hydro to then react by bringing additional supply-side resources on-line. The quantities charted in Figure - show the difference in F00 between the mid DSM forecast and the high and low forecasts. Here, negative values show a deficit if DSM underperforms, and positive values show a surplus if DSM over performs. Page -

29 Figure - Size of Deviation from Mid DSM Energy Savings Forecast (F00) 0 Several observations can be made from this analysis. First, there is a substantial amount of uncertainty for all options when planning for the mid forecast. Second, there is no clear demarcation between acceptable and unacceptable DSM options with respect to savings uncertainty; each option shows a considerable range of potential outcomes, with the larger DSM portfolios containing both larger downside and larger upside uncertainty. Finally, the shift in tactics and tools from Options to and Options and are partially captured in the quantified uncertainties, mostly on the upside where DSM over performs expectations. Not Page -

30 0 0 captured in the quantification of uncertainty was that Options and represent an intrinsically different and new approach to pursuing conservation, one in which BC Hydro backs-off of its traditional approach to DSM programs. Some caution may be warranted in interpreting these downside results for Options and. To the extent that BC Hydro can react to this potential magnitude of DSM under-performance and increase DSM electricity savings to target levels over this timeframe, then DSM savings uncertainty is manageable. However, if the size and timing of the under-performance poses concerns, then deliverability of DSM savings is a risk that needs to be considered, both in choosing the appropriate level of DSM and in managing the risk during the implementation of the IRP action plan. This underscores the importance of having robust DSM performance management and a robust contingency plan to backstop BC Hydro s energy and capacity needs. This latter topic is addressed in section DSM Jurisdictional Review The key driver behind the DSM uncertainty assessments was to better understand the degree to which BC Hydro could deliver on its DSM targets. While the bulk of this work was based on internal analysis, BC Hydro also looked externally to determine the extent to which other jurisdictions have been able to deliver on similar DSM targets. The resultant DSM jurisdictional assessment can be found in Appendix B. This section highlights its key findings. The study looked at utilities and DSM implementers based in North America. To a certain extent, results were limited by reporting issues and data availability. This sample comprises a snapshot of the leading and most aggressive applications of DSM in the North American electricity sector and is most useful for comparing changes to program spending and less useful for changes to codes and standards and rate design. Page -

31 0 Table -0 lists these organizations and compares their recent stated energy savings achievements. This shows that over the last five years, BC Hydro s achievements have been close to, but not at the top of, the DSM leaders in North America. Direct comparisons within this table must be made with some caveats. Finding jurisdictions that used a combination of programs, codes and standards, and rates was not possible. No other jurisdictions are implementing energy-focused conservation rate structures. However, given these are relatively high cost jurisdictions, it is not clear how this affects any comparisons. Secondly, from the list in Table -0, only the three California utilities count changes to codes and standards in their reported DSM savings. As a result, Table -0 provides insight into the comparison of DSM program levels, but does not provide insight into how other jurisdictions use a combination of programs, codes and standards, and rates to achieve conservation. Table -0 also only provides a retrospective view and building understanding around future DSM activities also requires a forward looking view. Note that this assumes that claimed savings translate into overall demand reduction. This linkage is a difficult one to make, and is touched upon later in this section. Page -

32 Table -0 Average Annual Energy Savings from DSM Programs as Per cent of Retail Sales (00 to 00) Organization Average (%) San Diego Gas & Electric^.00 Pacific Gas & Electric Co^.00 Southern California Edison Co^.0 Massachusetts Electric Co.0 Vermont.0 Connecticut Light & Power Co.0 Puget Sound Energy Inc.0 Nevada Power Co.00 BC Hydro.00 Interstate Power and Light Co 0.0 Energy Trust of Oregon 0.0 Wisconsin Electric Power Co 0.0 MidAmerican Energy Co 0.0 Idaho Power Co 0.0 Arizona Public Service Co 0.0 Manitoba Hydro 0.0 Wisconsin Power & Light Co 0.0 PacifiCorp 0.0 Hydro Quebec 0.0 New Jersey Clean Energy 0.0 Public Service Co of Colorado 0.0 New York State Research and Development Authority 0.0 Kansas City Power & Light Co 0.0 Consolidated Edison Co-NY Inc 0.0 Florida Power & Light Co 0.0 Ontario Power Authority 0.0 Average Excluding BC Hydro 0.. ^ Includes Codes and Standards. Page -

33 Table - shows how DSM Option (which roughly shares the same targeted savings as the current LTAP Option A) compares against the future DSM plans of other jurisdictions. Table - Comparing Future DSM Program Savings Energy Targets State Energy Savings Target (% of Sales) Average Annual Energy Savings (%) 0 Delaware Maryland.00. New York.00. BCH Option Programs + C&S + Rates.00.0 Arizona BCH Option - Programs + C&S.00. Illinois California* Minnesota Michigan Ohio BCH Option - Programs only.0 0. Indiana New Mexico.00.0 Pennsylvania * Goals include savings from Codes and Standards Comparisons using this table must be made with care. When looking at programs only, BC Hydro is towards the bottom of this group of leading jurisdictions in setting targets over the next few years. However, this picture changes when looking at a combination of programs, codes and standards. California, a jurisdiction that has historically led North America in Page -0

34 0 0 terms of conservation seems to be reducing its future savings targets and is now targeting levels below that of BC Hydro s programs and codes and standards at an Option level. Moving to targets above Option for programs and codes and standards would move BC Hydro ahead of California s future plans, but still within the range of savings targeted by other jurisdictions in the next few years. There are no direct comparables when considering a combined package of programs, codes and standards, and rate structures. However, BC Hydro s near term Option savings (to 0) are below that of some of the jurisdictions listed in Table - that only use programs. Conclusions from Table - must be drawn with care as this is a snapshot of information that does not take into account past DSM success. In particular, California, a past leader in conservation, seems to be ramping down their conservation targets to levels more modest than BC Hydro s. Nevertheless, there are jurisdictions that, based on programs alone, are targeting savings levels above those targeted by BC Hydro using all three DSM tactics. Combining these two tables addresses another key question of this section have other jurisdictions claimed energy savings in line with the DSM IRP options? Using the average annual savings goals for Option (from Table -0) and comparing that to what has been claimed by other jurisdictions (Table -), the following observations can be made: Putting together all three DSM tools planned savings for Option (by 0) puts BC Hydro targets at the top of past claimed energy savings in other jurisdictions (recognizing that no other jurisdiction reported using all three tools as BC Hydro does); but No other jurisdiction in these tables is relying on a combination of programs, codes and standards, and rate design in a coordinated way; Page -

35 This makes an apples to apples comparison very difficult, and particularly problematic for assessing confidence around Options and, that promise to deliver significantly larger savings than Option by relying on a novel, aggressive, and coordinated application of programs, codes and standards, and rates. If Option s (future) program targets are examined alone, then there exists a number of jurisdictions that have claimed past savings (Table -) in excess of Hydro s future savings from DSM programs (Table -0). 0 This jurisdictional assessment was designed to assist in understanding the confidence with which BC Hydro can deliver its planned DSM savings in the coming years. While this gives some reasons for cautious optimism about moving forward with DSM programs at the level of DSM Options to, it also highlights the uniqueness of BC Hydro s combination of all three DSM tools to achieve conservation targets. This further underscores the value of testing out these new tactics in a low risk manner to adequately harness their potential.... Energy-related DSM Capacity Savings Uncertainty Energy-focused DSM measures also bring associated capacity savings. Two sources of uncertainty were built into the IRP analysis regarding DSM energy-related capacity savings: 0 The underlying uncertainty around the energy savings themselves (as discussed earlier); and The capacity factors used to translate energy savings into the associated level of capacity savings. Capacity factors are used to translate general energy savings into peak savings. These parameters were treated as uncertain estimates to capture the lack of precise knowledge about how energy savings from multiple sources would reduce peak Page -

36 demand. The range of estimates used were expressed as triangular probability distributions. Capacity savings for the DSM options were then derived for a sample year using a Monte Carlo simulation, which was run with the DSM energy savings model for F0. These results, segregated into high, mid, and low values, are reported in Table -. These results were then scaled up and down for each year (using the same method as for energy savings) to fill in results across the planning horizon. The outcome of this can be seen in the capacity load/resource balance shown in Figure - in section... 0 Table - Energy-Related DSM Capacity Savings and Uncertainty (MW Savings, F0) Option Option Option Option Option Low (average lower 0%),00,0 Mid (average middle 0%),,0,,0, High (average top 0%),,0,,,0 0 Alternative Perspective on Energy-Related DSM Capacity Savings Uncertainty For the IRP, the BRP is built with reference to the mid Load Forecast. An unexpected departure from the mid Load Forecast is of concern, particularly with respect to capacity planning. For this reason, an additional analysis was carried out to explore the size of the capacity shortfall should DSM underperform. To examine the issue of a capacity shortfall, a parallel approach was taken to underperformance on energy savings. It was assumed that it would take several years to determine that DSM was unable to deliver on planned savings and then for BC Hydro to react by bringing additional supply-side resources on-line. Figure - shows the difference between the mid and low DSM outcomes and the mid and the high DSM outcomes in F00. These values are constructed such that they have about a 0 per cent chance of occurring. More extreme shortfalls are possible, but less likely. Page -

37 Figure - Size of Deviation from Mid Gap Capacity (F00) The observations here parallel those made with regard to DSM savings uncertainty on the energy side: There is significant uncertainty with respect to DSM capacity savings across all options. Moving to higher levels of DSM increases uncertainty around capacity savings. There are qualitative differences between Options to and Options and arising from BC Hydro s reliance on a new and aggressive combination of DSM Page -

38 tools. These show up in the increased upside potential of Options and and represent a significant source of savings, if they can be achieved. However, there is no clear quantified demarcation between acceptable DSM options and unacceptable DSM options with regard to energy-related capacity savings uncertainty. This is despite the qualitative differences noted between Options to and Options and and suggests that some additional caution needs to be layered into the consideration of downside risk of Options and. 0 0 To the extent that surprises of this magnitude over this timeframe are of concern for BC Hydro, uncertainty around the delivery of DSM savings on the capacity side constitute a risk that must be taken into account when comparing DSM options and when implementing the set of recommended actions provided in this IRP. These observations underscore the need for BC Hydro to have a robust monitoring plan, alternatives and contingency resources, in case expected DSM capacity savings are not realized, and a plan to exploit the upside of Options and in a low-risk manner. This topic is revisited in section.0 (Capacity and Contingency Plan) and section. (Recommended Actions).... Capacity-Focused DSM Savings Uncertainty While the energy-focused options discussed in the previous section have associated capacity savings, additional capacity savings are possible through capacity-focused DSM activities. These were described briefly in section.. and at a high level, refer to DSM activities that can reliably reduce peak demand over the long term (also referred to as peak reduction or peak shaving). This section will address the uncertainty around the capacity savings forecasts. Capacity-focused DSM savings were grouped into two broad categories: Industrial load curtailment; and Capacity-focused programs. Page -

39 0 BC Hydro has already entered into load curtailment agreements with the industrial sector; however, it is not clear how easily these can be translated into long-term agreements that can reliably reduce peak demand over the long term when needed. A top-down discussion of the range of possible outcomes and the most likely outcome with respect to industrial load curtailment was undertaken to construct a triangular probability distribution for a selected year. The results are shown in Table -. Capacity-focused programs are a collection of several activities; both demand response and load control, spread across different customer classes. The components of participation rates and savings per participant were highlighted as key parameters through which to express the uncertainty of capacity savings. A structured discussion was used to construct triangular probability distributions for each of these parameters, for each rate class. These distributions were then used in a Monte Carlo simulation to derive the total range of savings across the three customer classes. These results are shown in Table -. Table - Savings from Capacity-Focused DSM and Uncertainty (MW in F0) Industrial Load Curtailment Capacity-Focused Programs Low (P0 cutoff) Mid (mean or expected) High (P0 cutoff) 0 Conclusions Regarding Capacity-Focused DSM Savings Uncertainty Capacity-focused DSM represents an attractive approach to peak reduction. However, there are a number of uncertainties that have been highlighted in this analysis. Since BC Hydro is just starting to develop long term capacity-focused savings options, implementation success is an important issue. In particular, precise program Page -

40 0 0 initiation dates and customer participation rates are unknown. This makes it difficult to rely on these approaches to address near-term capacity and contingency needs. Once these approaches are established, operational experience will still be required to understand how participation and savings per participant translate into peak shaving. In particular, BC Hydro will need to effectively identify and design around free-ridership to generate peak shaving behaviour change. Similarly, experience will be needed to see how savings for each initiative translates into peak reduction for the entire system whether these peaks are coincident with peak load and whether peak shaving leads to other system peaks. In conclusion, there are a number of significant uncertainties underlying capacityfocused DSM, not all of which have been quantified. Exploring capacity-focused DSM has considerable merit, but may be difficult to rely on given current experience.... Overall Conclusions Regarding DSM Savings Uncertainty BC Hydro continues to be a North American leader in pursuing energy and capacity savings in response to increasing electricity demand. BC Hydro is expected to meet the majority of its load growth through energy conservation to achieve the CEA energy conservation objective. As such, a considerable effort to better understand the uncertainty inherent in this demand-side resource and incorporate it into the decision-making framework is warranted. A high level list of information gaps regarding DSM savings uncertainty from the 00 LTAP included: Model uncertainty is total DSM savings just the simple aggregation of the many DSM activities, or are other models that give different results more appropriate? Page -

41 Dynamic interplay amongst DSM tools how do rate structures, programs, and codes and standards fit together in the overall DSM portfolio? Are they independent, overlapping, synergistic or are there dysergies? Relationship with load growth how do conservation and load growth fit together? Is it appropriate to estimate the two separately and then subtract, are? Are there overlaps or gaps? Jurisdictional comparison how do BC Hydro s conservation efforts compare to utilities in other jurisdictions? Have many others demonstrated that our targets are easily achievable, or have many others tried and failed? 0 Progress has been made since the 00 LTAP on many of these questions: A detailed study on load forecast and DSM integration addressed some overlaps and found that other concerns were already adequately addressed by existing processes; A more focused jurisdictional review found evidence pertaining to the experiences of other utilities; and A top-down analysis of overall DSM uncertainty (section...) tried to capture issues of uncertainty not addressed by the more mechanical, bottom-up Monte Carlo studies. 0 Despite the advancement in understanding around some of these issues, uncertainty around the large DSM savings being targeted continues to be a central issue in long-term energy planning. These are difficult issues and none of them can be considered solved. Moreover, data sets and learning continue to evolve over time, even over the course of an energy planning cycle. As such, professional judgment will continue to play an important role in both the interpretation of data and in balancing DSM deliverability risk with other key energy planning objectives. Page -

42 0... Net Load and Net Gap Uncertainty Net load is the level of load after DSM savings. Forecasting net load is subject to the joint uncertainties of forecasting load growth and forecasting DSM savings. Estimates of the range of outcomes around the forecast were developed for load growth (Chapter ) and DSM savings (section...). These were combined to yield a range of possible outcomes for net load, along with the associated relative likelihoods of achieving these outcomes. Details of this process are contained in Appendix A. For most IRP questions, the uncertainty regarding future net load was expressed as a three point, discrete distribution. Combining this with the view of BC Hydro s existing, committed and planned resource stack yields, for a given DSM option, a large gap, mid gap, and small gap. These are shown for each DSM Option in Figure - and Figure - for energy and capacity, respectively. The gap between load (after DSM) and resources represents a deficit that must be filled with supply-side resources. If the comparison between load and resources results in a surplus, the IRP analysis considers the costs of selling that into the market. The mid gap corresponds with the load/resource balance shown in section.. Page -

43 Figure - Net Gap for Energy The y-axis has been magnified to better demonstrate the variation between the gap scenarios. The energy graph y-axis starts at 0,000 GWh/year and the capacity graph y-axis starts at 0,000 MW. Page -0

44 Figure - Net Gap for Capacity 0 The conclusions to the key IRP questions addressed in Chapter are collected into a BRP. The primary focus of the BRP is to address the needs identified by the mid gap. As such, the majority of the analysis in Chapter is based on the mid gap scenario with DSM Option, unless otherwise noted. BC Hydro develops additional actions for contingency plans that ensure that alternative sources of energy and capacity supply are available if the risks materialize or additional loads develop. In section., BC Hydro examines the need for additional energy supply if load differs from the mid gap scenario. In section.0, BC Hydro looks at the need for capacity and the risks arising when BC Hydro pursues a set of actions for the BRP and the resulting net load differs from the mid gap scenario. The large gap scenario is a useful test of how large and how quickly load can differ from the mid gap. It provides guidance on the range of capacity resources that need to be ready, and the required timing of these resources, to respond effectively. Conversely, the small gap scenario provides the Page -

45 0 potential impacts of such a diversion once BRP commitments are made and helps explain the benefits of flexibility in the case that need is decreased. When considering the appropriate balance between DSM and IPP resources, two variations of the mid Load Forecast were considered to better explore the impacts of DSM delivery uncertainty on resource options. Appendix A outlines this in more detail and uses probability trees to demonstrate how these combinations and relative likelihoods were derived.... Market Price Forecast Uncertainty Using costs to compare portfolios of DSM and supply-side options requires estimating not only the cost of acquisitions, but also the costs and trade revenues of each portfolio operating over the planning timeframe. The operating costs and revenues are affected by: Natural gas costs; Electricity prices for import and export; GHG allowance and offset prices; and Renewable Energy Credits. 0 The future price path of each of the above variables is estimated with uncertainty. A further complication is the inter-relationship between these variables. Section. explores each of these price forecasts in more detail. Section.. outlines how these uncertainties were combined into five Market Scenarios, Scenarios A through E, to create combinations of factors that: Represented a very wide, but plausible range of input and output prices; Avoided combinations that were internally inconsistent; and Were large enough in number to cover key combinations, but small enough in number to be tractable within IRP modelling resource constraints. Page -

46 In most cases, the base assumption for the Chapter analysis was Market Scenario C, as BC Hydro considers this the most likely scenario. Where relevant, resource options were compared across Market Scenarios A through E to test whether strategies were robust given possible different market price futures.... Wind Integration Cost and ELCC Uncertainty Two main uncertainties were highlighted with respect to wind resources: Wind integration costs; and Effective Load Carrying Capability (ELCC). 0 0 The wind integration cost is described in section... A value of $0/MWh was used as the base case and additional sensitivity tests were performed using $/MWh and $/MWh as the lower and upper bounds. The determination of the wind ELCC value is described in Appendix C and in section.0. The current analysis suggests an ELCC value of per cent of installed capacity. This value was used as the base assumption for all portfolio modelling. The wind ELCC was modelled as a random variable with a lopsided triangular probability distribution function, using a zero per cent ELCC value as a lower bound (worst case) assumption, per cent as the upper bound (best case) assumption, and per cent as the most likely assumption. Changes to this variable did not make a material impact to the overall analysis.... IPP Attrition Uncertainty IPP clean energy resources are one of the resource options BC Hydro considers to fill the load/resource gap. However, given that recent BC Hydro acquisition processes have resulted in varying rates of attrition, IPP attrition rate was flagged as a key uncertainty that could affect the comparison of resource options. For this IRP, BC Hydro adopted a range of attrition rates, bracketing those evidenced in recent acquisition processes. The lower and upper bounds, as well as a best estimate, are Page -

47 shown in Table -. A triangular distribution was developed for Monte Carlo simulation to help inform the range of uncertainty for net gap estimates. Table - IPP Attrition Rates and Uncertainty Attrition Rates (% of energy) Lowest Credible Bound Mid (Best) Estimate Highest Credible Bound Resource Options Chapter outlined the resource options that could be considered in filling the energy and capacity gaps. However, some of these resource options present operational and developmental challenges, as well as uncertainty around their technological maturity. For this IRP, only resource options that have proven development in B.C. and meet provincial policy objectives were included in portfolio modelling. Section.. provides a list of the resources considered. This list represents a restricted set of resources used for modelling purposes and does not imply that future energy and capacity acquisition processes will be limited in such a way... Applying the Risk Framework to Comparing Alternatives Sections.. to.. outlined how the IRP s Risk Framework provides a process for comparing options, using multiple objectives, given significant planning uncertainty. Figure - is used throughout Chapter in the discussion of modelling results to help clarify which options and uncertainties are being explored and which are fixed with respect to each of the key IRP questions. The legend is intended to clarify the background assumptions against which the resource options are examined. This The upper bound for IPP attrition is based on attrition rates from the F00 Call for Power. The EPAs awarded during this call included two coal-fired generation projects, which were subsequently terminated due to change in government policy. Page -

48 diagram replaces the probability trees used in the 00 LTAP. Appendix A shows how the modelling map and probability tree approaches are connected. As an example, Figure - shows a portfolio run that has fixed the DSM target at Option, the market Scenario at Market Scenario C, etc. When the modelling choice for each row is filled in, it becomes easier to understand the key underlying variables chosen for each set of portfolios. The portfolio shown in Figure - represents the base set of assumptions, and many of the IRP questions are examined in relation to this starting point or analysis. 0 Figure - Modelling Map and Base Modelling Assumptions Page -