Managing Risk of a Power Generation Portfolio

Transcription

1 Managing Risk of a Power Generation Portfolio 1

2 Portfolio Management Project Background Market Characteristics Financial Risks System requirements System design Benefits 2

3 Overview Background! TransAlta Corporation is Canada's largest non-regulated electric generation company, more than $7-billion in assets 9,500 megawatts of capacity.! Energy Marketing plays a key strategic role for the Corporation delivering generation output to the best markets; optimizing individual generation asset returns managing a portfolio of generation assets to meet shareholder expectations.! SAS Risk Management is working with TEM developing a solution to manage price risk of operating in a volatile deregulated power market! Dynamics of the North American power market require timely and informed decisions be made management of a mix of contracts and products that are sold for the total generation portfolio requires that risks such as price, credit, operational and foreign exchange are both measured and managed within shareholder expectations.! First phase of this project is focused on managing revenue risk and earnings,! Subsequent phases of SAS Risk Management project could include everything from market information to better risk mitigation against the fluctuation in foreign exchange; and credit risk 3

4 Volatility Comparison Market Volatility Comparison Energy & Equities 1000% 900% Dow Jones Nasdaq Crude Oil Natural Gas Mid C P eak P ower 800% 700% 600% 30 D a y A ve rage V olatility 500% 400% 300% 200% 100% 0% 'J un 2000 'S ep 2000 'J an 2001 'A pr

5 Volatilities and Correlations Market Power Price (US$/MW) $ $ $ $ $ $ $ $ $ $80.00 $60.00 $40.00 $20.00 $ Jan Feb Mar May-99 New York Power & Gas Prices 22-Jun-99 4-Aug Sep Oct Dec Jan-00 3-Mar Apr May Jul Aug-00 4-Oct Nov Dec Feb Mar May Jun Aug Sep-01 $42.50 $40.00 $37.50 $35.00 $32.50 $30.00 $27.50 $25.00 $22.50 $20.00 $17.50 $15.00 $12.50 Gas Price (US$/MMBtu) $10.00 $7.50 $5.00 $2.50 $0.00 NYW On Peak Power PriceTransco Z6 Gas Price C o rre latio n P owe r - G as Annualized daily volatilities for power ranged from a high of 413% in 1999 to a low of 241% ytd 2001 Natural gas annualized daily volatilities ranged from a high of 188% in 2000 to a low of 91% in % 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Q199 Q299 Q399 Q499 Q100 Q200 Q300 Q400 Q101 Q201 Q301 Quarterly correlations of power to gas ranged from a low of 7% to a high of 45% 5

6 Characteristics of Energy Commodities Market 7500 $350 Alberta SMP / Demand Dec 2000 Feb 2001 $300 Demand MW Demand Price $250 $200 $150 $ MWh Power Market Characteristics create both risk and opportunities Characteristics include: 5500 $100 $50 Convenience yield - power has no storage Dec 01-Jan 01-Feb $0 Seasonality $400 $350 $300 Alberta SMP Jan Feb 2001 O ff P e ak On Peak Volatility Mean Reversion $250 Jumps $200 $150 Autocorrelation $100 Cross commodity correlation $50 $0 01 -J a n 04-Ja n 07 -Ja n 10-Jan 13-Jan 16-Jan 19-Jan 22-Ja n 25-Ja n 28-Ja n 31 -J a n 03-F eb 06-F eb 09-F eb 12-Feb 15-Feb 18-Feb 21 -F eb 24-F eb 27 -F eb 6

7 Forward Market Prices Market $600 $500 $400 $300 $200 $100 $0 US$/MW $350 $300 $250 $200 $150 Mid - C 2001 Forward Price Curve 01-J a n 10-Jan 20-J an 31-J an 10-Feb 20-F eb 28-F eb 10-Mar 20-Mar J an F eb Mar A p r May J un J ul A ug S ep O ct N ov D ec Mid - C 2001 / 09 Forward Price Curve 30-O ct 06-Nov 13-Nov 20-Nov 27-Nov 04-D ec 18-D ec 22-D ec 05-J an 08-J an 16-J an 16-J an 22-J an 29-J an 05-F eb 09-F eb 11-Dec Forward market prices are volatile greatest volatility in current year prices are shaped to reflect seasonality risk premiums are built for uncertainty and liquidity $100 $50 $

8 Supply / Demand Market C$/MWh 2001 Peak: 25,269 MW Peak: 23,188 MW Supply Supply (coal units derated to 75%) Gas Oil & Other Coal Gas Hydro Nuclear ,000 10,000 15,000 20,000 25,000 30,000 MW s Key Factors Impacting Supply - current supply stack for hydro; nuclear; coal; gas - plant derates - new additions - export capability $/MWh $100 $80 $60 $40 $20 System Capacity Demand Off Peak Peak Hours Demand - seasonality - weather - time spreads - market segmentation $ Average Capacity MW price 8

9 Impact on Revenues Risks Price volatility requires good operations and continual margin management $350.0 $ $ $ Capital O & M Fuel Cost Margin $250.0 $ $ $8 $150.0 $6 150 $ $4 100 $50.0 $ Ja n 04-Ja n 07 -Ja n 10-Jan Power Prices 13-Jan 16-Jan 19-Jan 22-Ja n 25-Ja n 28-Ja n 31 -Ja n 03-F eb Gas Prices 06-F eb 09-F eb 12-Feb $2 $ /01/01 01/01/07 01/01/13 01/01/19 01/01/25 01/01/31 01/02/06 01/02/12 9

10 Finding Optimal Level of Risk Risks Price / MWh Managing Sales - Term to Delivery Term Price Volatility Daily Price Volatility Manage power sales from term to physical delivery - Jan Feb Mar Apr May Jun Risk ($ millions/year) Risk Trade-Off Price Risk Dominates Operational Risk Dominates Optimal Firm Hedge Price Risk Operational Risk Combined Risk Optimal balance of -price - volume - credit risk - is just one risk number Percent of Output Contracted (Firm) 10

11 Outage Risk Generation Plant Position Report /01/00 12/03/00 12/05/00 12/07/00 12/09/00 12/11/00 12/13/00 12/15/00 12/17/00 12/19/00 12/21/00 12/23/00 12/25/00 12/27/00 12/29/00 12/31/00 02/01/01 04/01/01 06/01/01 08/01/01 10/01/01 12/01/01 01/14/01 01/16/01 01/18/01 01/20/01 01/22/01 01/24/01 01/26/01 01/28/01 MWH's 01/30/01 $ / MWh Sch. Gen Actual Gen Firm Contracts Contract Price Daily Avg. Price Risks

12 Risk Mitigation Tactics - example Risks Generation 125 Plant Sequence of events Plant generation full out and fulfilling contracts One plant generation unit goes down and must source supply from market to fulfill contract obligation Real time desk mitigates risk by switching from buying to selling in matter of minutes

13 Business Needs Requirements Financial risk associated with a deregulated power market requires the Generation Companies develop sophisticated and real time decision making tools to manage their business Decisions are continuously being made and updated on Short and long term business plans Optimal hedges Adding value and optimizing revenue of generation assets Contract negotiations Fuel diversity and contracts Geographic diversity Fuel contracts New asset build or acquisition Tools must be capable of modeling Generation asset diversity Multiple markets with changing and different characteristics Integration of vast amounts of data - market prices - supply / demand - transmission flow - weather - credit, FX, interest rates - emissions Timely Interaction with other systems Sophisticated and easily modified reporting capability 13

14 Incorporating Market and Risks - Models Design Factors to consider in models Non Goemetric Brownian Motion Returns do not follow a normal distribution Speed of mean reversion varies but gravitates price to marginal cost of production Model Parameters Generation assets modeled as a series of European call options Asset owner will run unit as long as marginal price of power higher than marginal cost of generating Spikes are usually asymmetric process is considered to be a floor reverting process Incorporating jump - diffusion models is standard practice What you need to remember Price volatility constantly changes Fat tails in price distribution Payoff for each spark spread option = Max[P e h* P g K,0] P e = market price of electricity ( probably day ahead market ) P g = market price of fuel H = generation asset heat rate K = other variable cost of operating including maintenance, ramp rates etc. 14

15 Data Flow Map Design Tier 1 External Data ISO Hourly Prices Exchange Data Web Scrapers Plant data Message Application Tier Tier 2 Staging Area Message information Transformation Infrastructure Message Business Tier Tier 3 Data Warehouse Risk Data Storage SAS Data Storage Real time data Storage Tier 4 Data Mart Risk Dimensions 15

16 Logical System Design Design SAS Risk Dimensions Markov chain model Physical plant revenue - dispatch costs - economic dispatch - startuo costs Contract revenue Derate costs Replacement power costs Foregone spot sales Foregone contract revenue Asset Model - decide if generation plant should be economically dispatched Plant availability net of maintenance Two factor model Financial & Physical contracts -price - volume - duration - interruptible Noninterruptible flat volume Interruptible contracts Market upticks Maintenance impact Outage cost model 16

17 Portfolio Management Benefits Cumulative Probability 120% 100% 80% 60% 40% 20% 0% Opportunity Expected Income / Cash flow Business plan Capital program Increased debt Income loss Portfolio optimization project will initially provide Earnings at Risk and distribution of expected cash flows Nonlinear optimization of risk adjusted returns Return / risk tradeoffs Risk propensity Short term optimization Basic Portfolio Theory Expected returns Indifference curve for corporation Variance of returns on portfolio Future potential includes integration of all corporate risks and developing management tools for Credit, FX and Interest rate exposure Real time market price analysis Structured products Continuous hedge optimization Income / Cash flow Variance 17