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

Three essays on risk management in electric power markets

1 CD-ROMThis dissertation has arisen in the context of the electric power markets, the study of risk management and the relations between physical production and the electricity transactions using financial contracts in particular. Electricity is very difficult to compare with any other commodity, since it has a peculiar characteristic; electricity “must be produced at exactly the same time as it is consumed”. The technological inability to store electricity efficiently and the characteristics of marginal production costs create jumps in the spot price. The electricity power market is heavily incomplete. Load-matching problems occur because electricity prices show volatility because of unexpected variations due to climatic conditions and other associated risk factors. A branch of the literature in risk management has tried to give a definitive answer to the question of how agents in the markets with non-storable underlying asset could hedge their exposure to volatile price and quantity. The first essay tackles the basis of this question, which is the implication of the price of risk when forward risk premia are presented. This essay also shows how the properties and variations of forward risk premia is explained by risk factors variations on expected spot prices, and unexpected changes on the available quantity of water to generate electric power. Forward risk premia are the measure, hour by hour throughout the day, of the price of risk that the agents pay to trade electric power using forward contracts. In this essay forward premia were measured from the unregulated market segment. The results indicate that the average expected forward risk premia could have a positive behavior in seventeen out of twenty-four hours. These results represent the equilibrium compensation for bearing the price risk of the electric power for one year. In the Colombian market, the risk taker is the marketer, specifically in the unregulated market segment, because they are assuming the price risk in the long-term negotiations. The marketer, represented by this demand, tries to ensure their future Profit and Losses P&L and so they sacrifice their premia. It is relevant for further studies to evaluate the efficiency of this market, and the characteristics to determine why the marketer is willing to pay forward risk premia and why the generator has a better position to receive this bonus. Exploring the optimization problem of portfolios my second essay asks whether the agents in the electric power market could hedge their exposure to uncertainties; price and quantity. We propose a close form solution for the optimization problem of portfolios composed by two claims, price and weather, according to factors influencing electric power markets such as price volatility, price spikes, and climatic conditions that influence volume volatility. Results show a positive correlation among price, quantity, and the weather variable. In order to apply the optimal static hedging that includes the second claim on weather indexes for seasonal countries such as United States and tropical countries such as Colombia, the third essay shows an application of the static hedging model, using parameters from US market(PJM), and Colombian market (WPMC2). For the PJM, I used weather indexes from Chicago Mercantile Exchange Group, and the hydrological index from WPMC which is based on the hydrological contributions of rivers on dam levels. We verify that El Niño and La Niña phenomena also influence quantity variations, and the agents in those markets are exposed to both price and quantity volatiles.Introduction – I. Modelling Risk for Electric Power Market -- Bibliography – Appendix – II. Optimal Static Hedging of Energy Price and Volume Risk: Closed-Form Results – III. Applications of Optimal Static Hedging of Energy Price and Volume Risk to markets in the US and Colombia -- IV. Final Discussion -- Bibliograph

Market design for a reliable ~100% renewable electricity system: Deliverable D3.5

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: The goal of this report is to identify in which respects the design and regulation of electricity markets needs to be improved in order facilitate a (nearly) completely decarbonized electricity system. It provides a basis for scoping the modeling analyses that are to be performed in subsequent work packages in the TradeRES project. These simulations will provide the basis for an update of this deliverable in the form of a more precise description of an all-renewable electricity market design. In this first iteration1 of deliverable 3.5, we analyze how the current design of electricity markets may fall short of future needs. Where there is a lack of certainty about the best market design choices, we identify alternative choices. Alternatives may concern a choice between policy intervention and no intervention or different intervention options. Section 2 outlines current European electricity market design and the key pieces of European legislation that underlie it. The European target model is zonal pricing with bidding zones that are defined as geographic areas within the internal market without structural congestion. That implies that within one bidding zone electricity can be traded without considering grid constraints and there are uniform wholesale prices in each zone. The main European markets are Nordpool, EPEX and MIBEL. Trading between zones in the European Price Coupling Region occurs through an implicit auction where price and quantity are computed for every hour of the next day, using EUPHEMIA, a hybrid algorithm for flowbased market coupling that is considered the best practice in Europe at this time.N/

Modeling electricity price and quantity uncertainty: An application for hedging with forward contracts

Energy purchases/sales in liberalized markets are subject to price and quantity uncertainty, which should be jointly modeled by relaxing the unreliable normality assumption for capturing risk. In this paper, we consider the spot price and energy generation to follow a bivariate semi-nonparametric distribution defined in terms of the Gram-Charlier expansion. This distribution allows to jointly model not only mean, variance, and correlation, but also skewness, kurtosis, and higher-order moments. Based on this model, we propose a static hedging strategy for electricity generators that participate in a competitive market where hedging is carried out through forward contracts that include a risk premium in their valuation. For this purpose, we use Monte Carlo simulation and consider information from the Colombian electricity market as the case study. The results show that the volume of energy to be sold under long-term contracts depends on each electricity generator and the risk assessment made by the market in the Forward Risk Premium. The conditions of skewness, kurtosis, and correlation, as well as the type of risk indicator to be employed, affect the hedging strategy that each electricity generator should implement

The variance-minimizing hedge with put options

Certain commodity producers face uncertain output and price, but can trade financial derivatives on price. I consider how best to use a put option on price. I introduce the variance surface, which is a data visualization technique that shows the level of variance across a grid of values for the two choice variables, quantity of options and strike price. The variance-minimizing hedge has strike deep in the money and optimal quantity close to expected output, but the variance surface shows there are near-best choices that are less expensive