Best Practice in Integrated Resource Planning (2004)

Energy Portfolio Management The Strategy of Choice for Cash Flow at Risk

The state-of-the-art integrated resource planning approach in use today requires understanding cash flow at risk in choices available. This is a fundamental transformation from earlier IRP approaches that were focused on regulatory policy planning and acceptance. Today an acceptable IRP must not only pass regulatory and stakeholder scrutiny, it must also be an integral part of the company’s business plan and action strategy. A key to that change is the focus on cash flow at risk in the portfolio

Integrated Resource Planning

The New Integrated Resource Planning

FEBRUARY 2004 | CONFIDENTIAL AND PROPRIETARY 2

Integrated resource planning was born out of concerns in the late 1970s and early 1980s that regulated utilities were not giving adequate attention to demand side management as an alternative to costly power plant construction. By the early 1990s, 35 states had adopted legislation or regulatory requirements for utilities to prepare integrated resource plans. As competition in the wholesale generation gained strength and industry restructuring caught fire, the perceived need for integrated resource planning waned. But IRP requirements did not go away—they merely fell into disuse. Now they are back as state regulatory commissions and officials worry about resource adequacy and reliability. This Focus on Fundamentals provides an update on how IRP has changed from a cumbersome regulatory mandate to a state-of-the-art portfolio risk management strategy through the use of stochastic analysis to understand the cash flow at risk dynamics in alternative portfolio choices and the implications of uncertainty in portfolio performance.

Table 1 Changing Context for Resource Planning

Conventional Resource Planning

Integrated Resource Planning under Uncertainty

Objective Develop least cost plan Least cost plan balanced with acceptable risk

Market Interaction Treats utility as isolated entity Models portfolio interaction with market across alternate

scenarios Impact of Key

Drivers Model high-mid-low scenarios

to capture impacts Stochastic modeling of key

drivers to capture uncertainty

Resource Adequacy Build or acquire assets to fully meet customer needs

Model competitive wholesale market as source of new

supply as alternative to build

Customer Segment Model all loads within service territory

Model customer retention and switching scenarios

Traditionally the major uncertainties facing electric utility planning were fuel price risk and long-run load growth. Regulated rates enabled pass-through of fuel price changes and new plant capital costs were rate based, so that the profitability impacts of these key sources of uncertainty were reduced. Competition and electricity price deregulation in the wholesale generation segment transformed the electric utility sector from being one of the least risky industries into one of the most volatile. To keep pace with these market transformations, integrated utilities, load-serving entities, and generating companies all focused on business planning and risk management strategies that used sophisticated tools for quantitative risk measurement before making important planning and risk management decisions. The re-emergence of integrated resource planning after the California energy crisis is primarily a focus on resource adequacy and assuring that the portfolio risks are well defined in the broader policy context of balancing supply and demand options in the portfolio. States have also adopted renewable resource portfolio requirements in a further effort to diversify the resource mix. These renewable resource options present a unique set of resource planning needs as some, such as hydro and wind power, are not fully dispatchable and thus bring added uncertainty to the portfolio.



Resource Planning Requirements Today The new state of the art in resource planning today is being put to the test in the Pacific Northwest and California in the aftermath of the energy crisis in the West. What has changed? Integrated resource planning now explicitly includes an assessment of the uncertainties and external market opportunities. Explicit uncertainty in key drivers such as load growth, fuel prices, hydro availability and unit availability are being tested using a robust and objective analytical framework to simulate supply/demand gap over time, by modeling load growth, asset retirement, contract renewals and expirations. The IRP also seeks to integrate new resource or contract alternatives into the utility’s existing portfolio to assess how best to close the supply/demand gap. Lastly, at the fleet level and unit level, the IRP process assesses the physical constraints on the system that affect uncertainty and unit performance potential as well as the optimizing of the portfolio to meet acceptable risk. Electric companies have planning requirements that required analysis for projecting cash flows to produce pro forma profit and loss (P&L) and position reports; preparing fuel procurement budgets; developing integrated resource plans that ensure resource adequacy; making generating plant and transmission capital investment decisions; and structuring and pricing offers to retail customers. In meeting each of these planning requirements the role of uncertainty and risk assessment has loomed larger especially in states that adopted assertive deregulation and restructuring of the electric industry. The emerging competitive markets for wholesale electric energy and ancillary services are making obsolete the traditional pro forma projection of cash flows on the basis of just one or a few discrete scenario forecasts. Increasing uncertainty surrounding the ability of power plants to operate economically has made fuel budgeting and procurement more complex. Mid- to long-term regional electricity market price uncertainty has overtaken load growth uncertainty as the largest source of risk when load serving entities develop integrated resource plans, or when suppliers make capital investment choices. The heady emergence of retail competition also required that utilities become better equipped at offering competitive tariff or contract rates to customers. While much of that retail competition enthusiasm has waned, the risks and uncertainty often remain. Even today, an unregulated generating company needs to assess the value of alternative technology choices and geographic location choices for new plant development—and focus on when such development might again be “in the money.” For generation companies with a portfolio of assets, planning-related risk analysis with a focus on cash flow optimizes the dispatch of flexible plants between the energy and reserve capacity markets to help improve cash flow and mitigate adverse outcomes. For regulated, investor-owned or consumer-owned utilities, resource planning today also requires a focus on cash flow at risk and assuring that resources are adequate to avoid higher risk spot market purchases. For that reason, IRP is re-emerging as the primary risk management strategy for power generators and power purchasing utilities in today uncertain electric utility industry environment. Resource planning needs today are intimately woven into the fabric of business decisions at the level of individual assets and the aggregate level of a portfolio of generation, load, and contract positions.

IRP today explicitly reflects

uncertainty



The resource planning needs that require analysis of risk factors include: • Valuing or pricing assets for price and quantity risks including power plants, capital

improvements, customer loads, and mid- to long-term contracts. • Optimizing operations for dispatchable resources including power plants and physical

contracts with embedded or explicit options. • Optimizing the mix of mid to long-run power supply from generation, physical

contracts, and spot market resources. • Optimizing plant dispatch with spin capacity market and energy market. • Optimizing fuel buying for dual fuel plants from multiple natural gas basins. • Realizing technology or geographic diversity benefits. Scenario planning is still useful for analyzing planning alternatives, but often the qualitative nature of scenario planning is insufficient to provide the “real option value” of alternatives considering uncertainty in the analysis and valuation. For example, the scenario planning method can evaluate the potential investment in a new peaking power plant by examining a handful of projected electricity and fuel price scenarios. The real options approach simulates short-term fluctuations, in addition to diverging long-run price trends or cycles in a full Monte Carlo stochastic simulation. Planning for Risk and Uncertainty is an Essential Part of Every IRP The boundaries between resource planning and risk management are blurring due to the necessity to add a more quantitative approach to risk analysis in the planning function. Risk management is typically narrowly defined to include only direct short- to mid-term hedging (or speculative) trading functions. These hedging activities may be around particular generation plants or customer segments, or pure trading. But post-Enron, the trading function has diminished markedly and the trend will likely be for an even smaller portion of trading activity at physical-heavy companies except where trading provides an essential hedging function.

Table 2 Business Needs for Integrated Resource Planning

• Portfolio position reporting • Optimizing fuel procurement • Valuing and structuring contracts • Managing load size and volatility by attracting or retaining customers • Optimizing the mix of physicals, financial forwards, options, and hedges • Optimizing overall portfolio risk versus reward tradeoff • Factoring renewable resources into the portfolio mix • Factoring emissions credits and resource limitations into resource adequacy

IRP Risk Factor

Analysis

New IRP adds quantitative analysis to

scenario planning



Understanding Cash Flow at Risk in the Resource Portfolio Planning A key element in the state-of-the-art integrated resource planning approach in use today revolves around understanding the implications of cash flow at risk in the choices available. This is a fundamental transformation from earlier IRP approaches that were focused on regulatory policy planning and acceptance. Today an acceptable IRP must not only pass regulatory and stakeholder scrutiny, it must also be an integral part of the company’s business plan and action strategy. A key to that change is the focus on cash flow at risk in the portfolio. Given the potential for commodity markets both for natural gas and electricity to produce rapid price swings or volatility, effective resource planning today must factor this uncertainty into an analysis of alternatives. The underlying function of this analysis is to assess the cash flow at risk in the portfolio as a key factor in both the planning process up front and the risk management process over time. Decisions about how to change the generation mix to meet load, and what purchase or sale contracts to transact, can best be made using a fundamental model to assess the impact of price and quantity risk factors that impact the dispatchable generation and demand management resources of the portfolio. This approach accounts for many constraints related to generation unit operation, electric transmission capability, and fuel limitations in the analysis. It also provides a consistent market price simulation for use in mark-to-model evaluation of contracts. Cash flow at risk (CFaR) is the maximum cash flow shortfall from (expected value or plan) benchmark over a given horizon at given confidence. CFaR is analogous to Value at Risk (VaR), but with a different underlying variable. VaR measures potential loss in asset value, while CFaR measures potential loss in accrued cash flows (or earnings or profits). VaR reports relate to balance sheet risk while CFaR reports relate to operating income, earnings, or P&L report risk. For electric company portfolios that include illiquid physical assets and contract obligations to deliver (take) power, CFaR is a much more relevant or important downside risk measure than VaR. The latter is more relevant to energy trading companies, or the trading department of an electric retailer or genco. In contrast to short-run VaR reporting of liquid positions, CFaR analysis can involve daunting tasks of simulating long-run uncertainties, and physical (volumetric) risks and nonlinear physical system constraints, in order to provide accrual-based cash flow results for CFaR analysis or for other downside risk measures. CFaR allows all of a firm’s assets to be included in a total, integrated portfolio. But VaR still has limited use for non-financials to manage market risks of liquid assets, hedge risks of illiquid assets with liquid instruments and guide overall risk strategy with VaR principles. Some of their key similarities and differences are shown in the following table:

Table 3

Comparison of Value at Risk and Cash Flow at Risk Measures

VaR CFaR Position Value Metric Asset Market Value Sum of Cash Flows

Portfolio Risk Metric Maximum expected loss from

present value (or other benchmark)

Maximum expected loss from projected benchmark value

Lowest expected value Time Horizon Days Months, Years Valuation Methods Mark to Market Mark to Model

Estimation Methods Covariance Monte Carlo

Monte Carlo Weighted Scenarios

Used by Trading departments Financial companies

Non-financial companies Portfolio managers

Quantifying the cash flow at risk makes strategy

options actionable



A key characteristic of cash flow at risk approach to portfolio analysis is its ability to define risk exposures and mitigation options consistent with overall levels of risk tolerance. By defining values for cash flow over time across portfolio options or hedging alternatives, the new IRP zeros in on the cash flow at risk exposure to the enterprise. The figure below illustrates this point by comparing the net present value of a generating unit alone against alternatives including generation plus a contract and generation, contract-hedge combination. The stochastic analysis of these options clearly shows the power of cash flow at risk in mitigating the risk and optimizing the value of the portfolio. This is the new IRP at work.

Figure 1 Generation, Contract and Hedge NPV Distribution

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

-900 -700 -500 -300 -100 100 300 500 700 900 1100 1300Net Present Value (Million Dollars)

Generation

Generation & Contract

Generation & Contract &Hedge

Source: Henwood Energy Services, Inc.

Risk-Reward “Efficient Frontier” Optimization Optimizing the mix of potential and existing contracts around a physical portfolio of generation and/or load resources enables evaluation of alternative potential portfolios on the efficient frontier of tradeoffs between risk and return. Henwood’s approach to this type of analysis uses RISKSYM to provide a downward risk measure of “expected shortfall” or “mean downward deviations,” which allows for skewness (left or right bias) in the overall probability distribution of portfolio net present value by Monte Carlo scenario. The kinks in the efficient frontier curve shown here are due to imposing block size constraints on the purchase and sale of electric and/or fuel contracts. This type of “risk versus reward” optimization analysis allows portfolio managers and risk controllers to quickly screen many possible combinations of physical and financial positions for the best portfolios to choose among as a policy matter, in terms of not exceeding a desired level of risk.



Figure 2 Efficient Frontier Curve and Current Portfolio Risk vs. Reward


The following probability distribution chart illustrates the calculation of “mean downward deviations” or “expected shortfall” from a user-defined target benchmark return. In this example, the target profit is $1 million, dividing the distribution between the left side red area of downside returns and the right side green area of upside returns. Alternatively, Henwood used the expected value of the Monte Carlo scenario results as the benchmark for separating downside from upside. In the example, the mean return is $3.27 million. The mean downward deviations are about $1.01 million (probability weighted average loss, starting at the threshold of $1 million).

Figure 3 Calculation of Mean Downside Risk Measure from

Probability Distribution of Profits

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

-2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

Return ($ M illion)

Prob

abili

ty

M ean Re turn = $3.27 M illion


Efficient Frontier

of Potential Portfolios

Current Portfolio

Downside Risk from Threshold of $1 Million



Case Study: Using Stochastic Integrated Resource Planning at SCE Introduction. This case study is an excerpt from Southern California Edison Company’s (SCE’s) long-term resource plan testimony before the Public Utilities Commission of the State of California, presented in April 2003. It highlights one of RISKSYM’s key business uses in formulating long-term, stochastic-based, integrated resource plans. Customer Profile. Southern California Edison is one of the largest electric utilities in the United States and the largest subsidiary of Edison International. On an average day, SCE provides power for 11 million individuals, 427 communities and cities, 5,000 large businesses, and 280,000 small businesses in Central and Southern California. In 1997, as part of the restructuring of the electric industry in California, SCE sold its 12 fossil fuel generating stations and overhauled nearly every aspect of its business to prepare for the changing environment. While they still own and operate hydro and nuclear power facilities that serve their area, the power needed for their customers is now largely served via contracts with the California Department of Water Resources.

Business Need. The California Public Utilities Commission’s requirement of the three major utilities to develop a 20-year procurement plan created a formidable challenge to Southern California Edison due to staffing, infrastructure and timing issues. SCE’s internal resource planning function was discontinued in the mid-1990s following the start of deregulation in the state. As a result, few personnel remained at the utility with hands-on resource planning experience. SCE no longer had the software tools and the service area specific data and assumptions necessary to develop a long-term resource plan. Developing a 20-year plan is traditionally a six to twelve month process when models and data are readily available and when executed by an experienced team of individuals that are very familiar with this type of work. The relatively short four-month deadline, coupled with the lack of a seasoned in-house staff, provided ample incentive for SCE to seek an accelerated method for completing the mandated plan. To overcome the challenge of preparing a resource plan in four months, SCE brought together some of its most experienced management to lead and manage this process, and after a rigorous vendor selection process, elected to partner with Henwood. The necessary production simulation models, assumptions and datasets were operated and developed jointly between Henwood staff and SCE. A subscription dataset from Henwood served as the information base, with specific data updates by SCE personnel. Developing the integrated resource plan using RISKSYM. The steps involved in development of the integrated resource plan submitted to the California PUC by SCE included the following: • Identifying the load forecast and assembling the portfolio. The first step involved

in this process was to forecast the 20-year, long-term customer demand for electricity. Next, SCE identified and reviewed its existing portfolio’s assets: utility retained generation, qualifying facilities (QF) contracts, commission-approved California Department of Water Resources (DWR) allocated contracts and other transitional contracts. Each resource and contract was disaggregated into its components for accurate modeling.



SCE relied on Henwood’s WECC database of generators and loads within the western United States to represent non-SCE loads and resources, as well as assumptions regarding fuel costs and volatility parameters. Once the database enhancements were completed, a regional analysis was performed using Henwood’s MARKETSYM module, which developed a regional market price and energy availability forecast by geographic region. This price view was then subsequently used in the RISKSYM model for simulation with SCE’s portfolio including future loads, resources and contracts.

• Reserve Margin Analysis. As requested by the Commission, SCE undertook an

analysis to determine what level of planning reserves would equal a loss of load probability (LOLP) of one day in 10 years.

In the past, SCE typically computed the annual LOLP index as the summation of daily probabilities (often termed the “daily risks”), over the entire year. However, for the purpose of this study, SCE employed the stochastics capability within RISKSYM to do this LOLP analysis. This approach was a significant improvement over prior LOLP analysis in that the RISKSYM tool predicted both the probability of not serving a specific amount of load, and in addition provided insights into the dimension and amount of required energy that would not be served—referred to as ENS (“Energy Not Served”). RISKSYM is natively designed to look at risks associated with serving load. The input parameters fed into the model for purposes of this analysis included hourly loads, generating unit availability, forced outage rates of units, and maintenance rates. The model was executed to run over 500 stochastic simulations per year with a random selection of expected load reflecting historical shocks caused by weather and historical mean reversion of those shocks back towards normal load levels. The iterations also included shocks to resource availability based on modification of the input forced outage rates.

• Estimating the resource gap between the load forecast and existing resource

pool. The next step in SCE’s analysis involved measuring the capacity gap between the long-term load forecast and the forecast of expected capacity in the service area, from existing committed resources. To further examine the resource gap, SCE evaluated typical weekly loads and resources in each month for the resource plan study period. This detailed review of the portfolio produced two significant insights for SCE:

1. SCE determined it had no significant need for additional base load resources in

the resource plan study period (assuming no direct access or municipalization related growth).

2. SCE identified a significant need for dispatchable intermediate and peaking resources that could be filled through either dispatchable contracts or utility-owned dispatchable generation.



Summary. SCE used the results from the IRP process to gain insights into the types of contracts and resources needed to meet expected capacity shortfalls. Since the filing of this testimony, SCE and its staff have been actively involved in supporting the work at the CPUC and integrating the IRP recommended actions into Southern California Edison business and operating plans. For more information about the new Integrated Resource Planning contact: Richard Lauckhart Director, Power Market Analysis [email protected] 916-609-7769 or Gary L. Hunt Vice President, Consulting & Advisory Services [email protected] 916-609-7750

Best Practice in Integrated Resource Planning (2004)

Documents

Transcript of Best Practice in Integrated Resource Planning (2004)