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Managing Volume Risk in a Retail Energy Business. Jon Stamp Head of Portfolio Management, npower Commercial 29 th January 2008

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2 npower, RWE Group 29/01/08 Introduction Aims of this presentation: To focus on Gas Swing as an example of a factor that can create substantial volume risk for a retail energy business. Outline what gas swing is and illustrate it graphically. Highlight the risks that arise as a result of gas swing and that influence the process of modelling it. Discuss some ways to model gas swing risk – so that it can be forecast, priced and hedged. Suggest possible ways to mitigate the volume risk related to gas swing. Put forward some areas that provide further mathematical challenges.

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3 npower, RWE Group 29/01/08 Gas Swing - What is it? More problematic on residential side of business where most customers are NDM (Non Daily Metered) and particularly weather sensitive. Seasonally normal demand (SND) is forecast by summing customer meters with End User Category (EUC), a profile of customer type created by National Grid. Long term position is established by hedging to SND. SND changes monthly as customer numbers fluctuate. Nearer real time, weather (the main driver of gas consumption) forecasts become more reliable and have considerable influence on demand levels. Short term trading (STT) takes place within the final 10 days to balance this. Swing is the difference in volume between the long term position and STT position and results in a change to revenue and a change to cost. The mathematical challenge is to model, estimate and mitigate the costs of swing.

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4 npower, RWE Group 29/01/08 Gas Swing – change in volume

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5 npower, RWE Group 29/01/08 Gas Swing - The risks If demand > hedged position, need to buy extra gas, greater demand pushes prices up, may have to buy at a price greater than tariff. If demand < hedged position, need to sell gas, lower demand pushes prices down, may have to sell at a price lower than tariff. Swing cost = DfSND * ( TF - DA ) Where: TF= tariff price, DfSND= deviation from seasonal normal demand (which is assumed to be the hedged position), DA= day ahead price

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6 npower, RWE Group 29/01/08 Gas Swing - The risks DfSND and DA must be simulated to calculate the gas swing cost. However they can not be simulated separately as they are correlated with each other. Temperature and demand are approximately -80% correlated. Implying temperature drives demand, this must be modelled with caution because it is not always true. For example the increased use of air conditioning in the summer can increase demand when temperatures are high. Demand and price are approximately 48% correlated. This relationship is most strongly recognisable 12 days from delivery when accurate weather forecasts become available. Modelling these sometimes unpredictable relationships can be a challenge.

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7 npower, RWE Group 29/01/08 Gas Swing - How can it be modelled? Gas Swing models aim to simulate forecasted spot price paths. They can then be used to give an associated deviation from SND based on the relationship between demand and price. A pricing equation can be developed using Geometric Brownian motion, with Poisson jump processes used to simulate price spikes. Temperature, and in turn demand, has a tendency to experience persistence in the data i.e. any days temperature has a correlation to the previous days temperature. Hence, the demand process is modelled using an ARMA (Auto-Regressive Moving Average) to establish the specification and consistency of the deviation trends. Monte Carlo modelling can be used to simulate thousands of price and volume deviations. The simulations can be used to construct a graph showing the distribution of the gas swing costs. Probability can be added to this.

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8 npower, RWE Group 29/01/08 Gas Swing - The risks (illustrative) Hedge to minimise, the risk of incurring and size of, gas swing costs. The hedging costs may be greater overall but the probability of being exposed to large swing costs will have been reduced. Difficult to estimate the exact effect of the hedge. It will also alter as the composition of the portfolio changes. Modelling these changes can be troublesome.

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9 npower, RWE Group 29/01/08 Gas Swing - Mitigating the risk. Gas Storage Contracts – Physical storage facilities, can inject during low price periods and withdraw in high price periods. Protects against short term price spikes but not against a collapse in prices. Need to value the purchasing of storage and create a model that forecasts optimal timings for injection and withdrawal. LNG Gas Storage - Liquefied Natural Gas that is available for delivery at very short notice and in greater volumes than gas storage. Capacity is only available to purchase in an annual auction and only provides protection against rising prices. Need to create a model for bidding in the auction, and for injection and withdrawal as above.

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10 npower, RWE Group 29/01/08 Gas Swing - Mitigating the risks Weather Swaps - Financial instruments that pay out when weather is unseasonably high or cold. Can protect against selling back gas into a low price market or buying in high price market. Relies on the correlation between weather, demand and price remaining as expected. A model is required to value product. Swing Contracts - Financial options that payout when portfolio of customers demand deviates from seasonal normal. Focuses on demand rather than weather thus provides some protection when weather and demand correlation moves in the opposite way to expected. Need to create a model to value the product. Demand Side Management - Interact with customers to incentivise selling back of gas on high price days. Need to model the portfolio effect of customers altering their consumption behaviour. Only applicable to larger customers who are usually daily metered.

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11 npower, RWE Group 29/01/08 Further mathematical challenges Modelling the portfolio effect and impact of layering on the hedge, from using a variety of products to mitigate gas swing risk. Price is often assumed to be normally distributed, when it is actually fat tailed (Leptokinic). Using Poisson analysis helps to capture this, but it is just an overlay to the model and it would be more accurate to incorporate it. Price Volatility is not constant as would be expected. One method which could be explored to overcome this would be using ARCH (Autoregressive Conditional Heteroscedastic) modelling. Consumer behaviour is ever-changing, hence models need to be developed to factor in these changes e.g. increases in price sensitivity and demand destruction. Historic data reflects the market conditions at that time, e.g. doubts over the security of supply pushing up prices. These may no longer be present e.g. completion of the interconnector improving the supply network. To reflect this, models need to be created that recognise historic regime switches.

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12 npower, RWE Group 29/01/08 Backup slide

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13 npower, RWE Group 29/01/08 Appendices

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