Managing electricity procurement cost and risk by a local distribution company

Electricity derivatives and risk management

“…Suppose an electricity supplier (or, LSE) signs a full-requirement contract with a customer and then utilizes futures contracts to lock in a fixed quantity of electricity supply at a fixed cost for hedging the expected energy consumption of the customer [17,18]. The LSE is then at the risk of either under-or over-hedging, as the consumption quantity of the customer will almost surely deviate from the amount hedged by the futures contracts.…”

“…LSEs providing electricity service at regulated prices in restructured electricity markets are wary of both price and quantity risks [17,18]. As the electricity markets are inherently incomplete, the quantity risk cannot be perfectly hedged.…”

“…As the electricity markets are inherently incomplete, the quantity risk cannot be perfectly hedged. Commonly proposed hedging alternatives include the implementation of a minimal variance hedge through purchasing electricity forwards [18] and the utilization of weather derivatives.…”

Evaluation of static hedging strategies for hydropower producers in the Nordic market

“…Oum et al (2006) use the framework of Brown and Toft (2002) to derive optimal static hedging functions for electricity companies facing both quantity and price uncertainty. Woo et al (2004) and Huisman et al (2007) devise models for static hedging in forward contracts for a retailer or end user of electricity. In this paper we will present an optimization model for deriving static hedging strategies.…”

A research on the purchase quantity allocation between contract market and power auction market

“…Introduction of (the power company's attention to risk) converted a double-objective programming into a single one, therefore, the value of determines the purchase of electricity distribution on a large extent, so the key of this model application is to evaluate correctly. Chi-Keung Woo [11] assessed the risk of purchase cost through cost exposure and VAR which combine the anticipated loss size in future with the possibility of it occurs, it lets the distribution company not only know the loss scale but also know the possibility of it occurs. In [12], a Monte Carlo simulation was used to seek the optimal solution of the contract allocation, convergence rate of Monte Carlo has nothing to do with the problem's dimension, moreover Monte Carlo understands easily, it is easy to code, but there is still weakness, its computation load is extremely big, if you wants to increase a digit precision, you need to increase 100 times of computation loads.…”