Electricity Hedging with Load Forecasts and Forward Price Distributions
Summary
The document considers how an industrial electricity buyer might manage purchase-price variability using spot and futures markets. The problem includes electricity’s limited storability and resulting spot-price spikes, futures with monthly, quarterly, or annual delivery periods, a minimum share of spot purchases, and a long-only constraint. It raises the difficulty of applying conventional minimum-variance portfolio methods when the buyer cannot take short positions and is hedging commodity prices rather than freely traded returns.
The response emphasizes that hedges can cover expected consumption but cannot eliminate uncertainty in actual load. It recommends estimating load, for example by simulating temperatures with an Ornstein–Uhlenbeck process and relating them to historical consumption through regression. Hourly modeling can help capture the shape of demand. A forward-market price distribution is proposed as a guide to hedge timing. These are general directions rather than a specified optimization procedure: the answer supplies no hedge weights, empirical results, or operational details for enforcing the purchase constraints, and notes that load-following fixed-price contracts can be costly.
Key ideas
- Electricity’s limited storability contributes to spot-price volatility and jumps.
- Hedging against expected consumption leaves exposure when actual load differs from the forecast.
- Temperature simulations and historical load regressions can support long-term demand estimates.
- Hourly modeling can represent the shape of electricity consumption.
- Forward price distributions can inform hedge timing, though the source gives no tested allocation method.
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Full text
# Answer by PatternMatching (score 2) # Electricity market : how to design an optimal hedging strategy using spot and futures markets for an industrial consumer? Here is the problem : we should adopt the point of view of an industrial company which purchases electricity as an input in its production line and which wants to achieve the following two goals : -minimize the variance of its total purchase price on a weekly/monthly/yearly basis depending on the delivery period of the futures used -minimize its purchase price and beats the yearly average spot price The specific features of the electricity market to take into account are the fact that electricity is a non-storable good which leads to some jumps in the spot prices which are then more volatile than futures prices. Moreover, futures contracts are designed with delivery periods on specific lengthes of time : month, quarter or year depending on the contract. For instance, you fix a certain price today for receiving a fixed volume of electricity everyday for the next month/quarter/year. Finally, we have two constraints to take into account : -the industrial company wants to buy at list 25% of its electricity on the spot market to manage its volume risk -the company can only be long : we cannot sell electricity on the spot or futures market. My first idea was to compute the minimum variance portfolio weights to allocate to spot, futures with montly delivery, quarterly delivery and yearly delivery. Though the problem comes from the fact that modern portfolio theory applies to assets' returns assuming that we can either be Long/short an asset whereas here we are only interested in prices since the company can only be long. Thanks for your help should you have any insight or references. Vincent ## Answer by PatternMatching (score 2) https://quant.stackexchange.com/a/12680 Only certain aspects of the risks that you bear in power markets given exposure to variable quantity swaps can be hedged. To your point, you have to have some expectation of what the load will look like. Even if you immediately go out and buy power against this expected qty you are subject to the risk that the load will deviate from said qty. There is no product except for 'load-following' fixed price contracts sold by power retailers (who charge extremely high margins) that offer protection against this risk. There is plenty of literature out there on load forecasting models -- no need to overengineer this. Typically simulation of temperatures (via OU process) can be married to a linear/nonlinear historical regression of the load in question to produce some long-term estimate of the load. You should try to capture behavior at an hourly level to best estimate the shape of the load. As for when to hedge, a model of the forward market price distribution will be your best guide to timing the market. Without going into specific details, this presentation by the guys at Hess's power marketing arm is very good: http://www.slideshare.net/EricMeerdink/euci-may2011-ericmeerdink
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