Hedging Exposure to Electricity Price Risk in a Value at Risk Framework

This paper deals with the question how an electricity end-consumer or distribution company should structure its portfolio with energy forward contracts. This paper introduces a one period framework to determine optimal positions in peak and off-peak contracts in order to purchase future consumption volume. In this framework, the end-consumer or distribution company is assumed to minimize expected costs of purchasing respecting an ex-ante risk limit defined in terms of Value at Risk. Based on prices from the German EEX market, it is shown that a risk-loving agent is able to obtain lower expected costs than for a risk-averse agent.Electricity prices;Forward risk premium;Hedge ratios;Mean variance

Electricity Portfolio Management: Optimal Peak / Off-Peak Allocations

Electricity purchasers manage a portfolio of contracts in order to purchase the expected future electricity consumption profile of a company or a pool of clients. This paper proposes a mean-variance framework to address the concept of structuring the portfolio and focuses on how to allocate optimal positions in peak and off-peak forward contracts. It is shown that the optimal allocations are based on the difference in risk premiums per unit of day-ahead risk as a measure of relative costs of hedging risk in the day-ahead markets. The outcomes of the model are then applied to show 1) whether it is optimal to purchase a baseload consumption profile with a baseload forward contract and 2) that, under reasonable assumptions, risk taking by the purchaser is rewarded by lower expected costs.G11;electricity portfolio management;forward risk premiums;hedge ratio;optimal electricity sourcing

A Real Options Evaluation Model for the Diffusion Prospects of New Renewable Power Generation Technologies

This study presents an investment planning model that integrates learning curve information on renewable power generation technologies into a dynamic programming formulation featuring real options analysis. The model recursively evaluates a set of investment alternatives on a year-by-year basis, thereby taking into account that the flexibility to delay an irreversible investment expenditure can profoundly affect the diffusion prospects of renewable power generation technologies. Price volatility is introduced through stochastic processes for the average electricity price and for input fuel prices. Demand for peak-load capacity is assumed to be increasingly price-elastic, as the electricity market deregulation proceeds, and linearly dependent on the extent of market opening. The empirical analysis is based on data for the Turkish electricity supply industry. Apart from general implications for policymaking, it provides some interesting insights about the impact of uncertainty on the diffusion of various emerging renewable energy technologies.Dynamic programming, Investment planning, Renewable energy technology diffusion, Real options, Learning curve, Turkey

Hedging Exposure to Electricity Price Risk in a Value at Risk Framework

This paper deals with the question how an electricity end-consumer or distribution company should structure its portfolio with energy forward contracts. This paper introduces a one period framework to determine optimal positions in peak and off-peak contracts in order to purchase future consumption volume. In this framework, the end-consumer or distribution company is assumed to minimize expected costs of purchasing respecting an ex-ante risk limit defined in terms of Value at Risk. Based on prices from the German EEX market, it is shown that a risk-loving agent is able to obtain lower expected costs than for a risk-averse agent

Electricity Portfolio Management: Optimal Peak / Off-Peak Allocations

Electricity purchasers manage a portfolio of contracts in order to purchase the expected future electricity consumption profile of a company or a pool of clients. This paper proposes a mean-variance framework to address the concept of structuring the portfolio and focuses on how to allocate optimal positions in peak and off-peak forward contracts. It is shown that the optimal allocations are based on the difference in risk premiums per unit of day-ahead risk as a measure of relative costs of hedging risk in the day-ahead markets. The outcomes of the model are then applied to show 1) whether it is optimal to purchase a baseload consumption profile with a baseload forward contract and 2) that, under reasonable assumptions, risk taking by the purchaser is rewarded by lower expected costs

Hedging strategies in energy markets: the case of electricity retailers

As market intermediaries, electricity retailers buy electricity from the wholesale market or self-generate for re(sale) on the retail market. Electricity retailers are uncertain about how much electricity their residential customers will use at any time of the day until they actually turn switches on. While demand uncertainty is a common feature of all commodity markets, retailers generally rely on storage to manage demand uncertainty. On electricity markets, retailers are exposed to joint quantity and price risk on an hourly basis given the physical singularity of electricity as a commodity. In the literature on electricity markets, few articles deal on intra-day hedging portfolios to manage joint price and quantity risk whereas electricity markets are hourly markets. The contributions of the article are twofold. First, we define through a VaR and CVaR model optimal portfolios for specific hours (3 am, 6 am,. . . ,12 pm) based on electricity market data from 2001 to 2011 for the French market. We prove that the optimal hedging strategy differs depending on the cluster hour. Secondly, we demonstrate the significantly superior efficiency of intra-day hedging portfolios over daily (therefore weekly and yearly) portfolios. Over a decade (2001–2011), our results clearly show that the losses of an optimal daily portfolio are at least nine times higher than the losses of optimal intra-day portfolios

Optimal Static Hedging of Energy Price and Volume Risk: Closed-Form Results

As an extension of the VaR-constrained hedging, we propose a closed-form solution to the problem of optimizing portfolios, based on price and weather. For electric power companies, price and quantity are volatile, and in hydro-electricity generation quantity can be related to weather conditions. An optimum portfolio is derived from expected utility maximization problem, including weather indices to minimize losses. Due to electric power features, agents in this market are facing price and volume risks, the difficulty to storage efficiently electric power cannot permit to mitigate volumetric risk and alternatively weather instruments can be used in order to hedge unexpected changes in weather; the purpose of weather derivatives is to smooth out the temporal fluctuations in the company’s revenues. For electric power companies price and quantity are volatile, and quantity is correlated to the weather conditions. Moreover, exposures to price and volume risks make necessary the inclusion of the weather pay-off. Thus, we derive the optimal portfolio from the expected utility maximization problem including electric power and weather derivatives whose payoffs will minimize losses