Showing papers in "The Journal of Energy Markets in 2009"

The information premium for non-storable commodities

Abstract: For non-storable commodities forward looking information about market conditions is not necessarily incorporated in today’s prices, and the standard assumption that the information filtration is generated by the asset is fundamentally wrong. Electricity and weather are the typical markets we have in mind. We discuss pricing of forward contracts on non-storable commodities based on an enlargement of the information filtration. The method is able to incorporate future information of the spot, which is not accounted for in the present spot price behaviour. The notions of information drift and premium are introduced, and we argue that significant parts of the supposedly irregular market price of risk observed in electricity markets is in reality due to information miss-specification in the model. Some examples based on Brownian motion and Lévy processes and the theory of initial enlargement of filtrations are considered, where we are able to shed some insight into the nature of the information drift and premium being relevant for the electricity markets. The examples include cases where we take temperature forecasts and CO2 emission costs into account when pricing electricity forwards.

Development of Open Source Software for Power Market Research: The AMES Test Bed

Abstract: Open Source Software (OSS) expresses the idea that developers should be able to license the publication of their software in a manner permitting anyone to freely use, modify, and distribute the software. Today OSS is widely used in the software industry, such as for language development tools (e.g., NetBeans for Java), office document processors (e.g., OpenOffice), and operating systems (e.g., Linux, OpenSolaris). Yet OSS has been slow to penetrate the power industry; heavy reliance is still placed on closed-source commercial software packages. The OSS in use tends to be for specialized purposes (e.g., circuit design) rather than for the general-purpose analysis of power systems. This study discusses potential benefits and drawbacks of developing OSS for power market research, using the AMES Wholesale Power Market Test Bed for concrete illustration.

Stochastic behaviour of the electricity bid stack: from fundamental drivers to power price

Abstract: We develop a fundamental model for spot electricity prices, based on stochastic processes for underlying factors (fuel prices, power demand and generation capacity availability), as well as a parametric form for the bid stack function which maps these price drivers to the power price. Using observed bid data, we find high correlations between the movements of bids and the corresponding fuel prices. We fit the model to the PJM and New England markets in the US, anddiscuss its performance, in terms of capturing key properties of simulated price trajectories, as well as comparing implied forward prices with observed data.

Market power in the German wholesale electricity market

Abstract: Since 2001, wholesale electricity prices have increased dramatically in Europe and especially in Germany. It has been argued that utilities have been exercising market power by withholding available power plant capacity. In this paper we investigate the exercise of market power in the German wholesale electricity market with an agent-based simulation model that uses detailed German wholesale power market data. The analysis was carried out for the years 2001, 2004, 2005 and 2006. We start with 2001 as it is seen as a year with well-functioning competition that validates this model. The year 2004 was chosen because it was the last year without emissions trading. In 2005 the EU emissions trading scheme started; this was accompanied by rising prices and “windfall profits” for electricity generating companies; 2006 was chosen because it supposedly suffered from “bad competition”. We test our results with the Lerner Index, but find that they do not necessarily confirm the exertion of market power.

Cointegration between gas and power spot prices

Abstract: In this paper we show how cointegration can be applied to capture the joint dynamics of multiple energy spot prices. For an example system we study the Title Transfer Facility, the Zeebrugge gas spot market and the National Balancing Point gas spot market, and, additionally, the Amsterdam Power Exchange power spot market, since these markets are strongly connected in terms of physical transportation and generation of power from gas. We develop a cointegrating multi-market model framework that is able to plausibly connect different singlemarket spot-price models. This is achieved by considering the mean-reverting spot-forward price spreads instead of spot prices only. Our analysis shows that the gas prices are strongly cointegrated, with a specific connection pattern for the markets, whereas cointegration of gas and power prices is at long-term forward price levels only.

Derivation of Locational Marginal Prices for Restructured Wholesale Power Markets

Abstract: Although Locational Marginal Pricing (LMP) plays an important role in many restructured wholesale power markets, the detailed derivation of LMPs as actually used in industry practice is not readily available. This lack of transparency greatly hinders the efforts of researchers to evaluate the performance of these markets. In this paper, different AC and DC optimal power flow (OPF) models are presented to help understand the derivation of LMPs. As a byproduct of this analysis, we are able to provide a rigorous explanation of the basic LMP and LMP-decomposition formulas (neglecting real power losses) presented without derivation in the business practice manuals of the U.S. Midwest Independent System Operator (MISO).

Integrating Multiple Commodities in a Model of Stochastic Price Dynamics

Abstract: In this paper we develop a multi-factor model for the joint dynamics of related commodity spot prices in continuous time. We contribute to the existing literature by simultaneously considering various commodity markets in a single, consistent model. In an application we show the economic significance of our approach. We assume that the spot price processes can be characterized by the weighted sum of latent factors. Employing an essentially-affine model structure allows for rich dependencies among the latent factors and thus, the commodity prices. The co-integrated behavior between the different spot price dynamics is explicitly taken into account. Within this framework we derive closed-form solutions of futures prices. The Kalman Filter methodology is applied to estimate the model for crude oil, heating oil and gasoline futures contracts traded on the NYMEX. Empirically, we are able to identify a common non-stationary equilibrium factor driving the long-term price behavior and stationary factors affecting all three markets in a common way. Additionally, we identify factors which only impact subsets of the commodities considered. To demonstrate the economic consequences of our integrated approach, we evaluate the investment into a refinery from a financial management perspective and compare the results with an approach neglecting the co-movement of prices. This negligence leads to radical changes in the project's assessment.

Modeling conditional correlations for risk diversification in crude oil markets

Abstract: This paper estimates univariate and multivariate conditional volatility and conditional correlation models of spot, forward and futures returns from three major benchmarks of international crude oil markets, namely Brent, WTI and Dubai, to aid in risk diversification. Conditional correlations are estimated using the CCC model of Bollerslev (1990), VARMA-GARCH model of Ling and McAleer (2003), VARMA-AGARCH model of McAleer et al. (2009), and DCC model of Engle (2002). The paper also presents the ARCH and GARCH effects for returns and shows the presence of significant interdependences in the conditional volatilities across returns for each market. The estimates of volatility spillovers and asymmetric effects for negative and positive shocks on conditional variance suggest that VARMA-GARCH is superior to the VARMA-AGARCH model. In addition, the DCC model gives statistically significant estimates for the returns in each market, which shows that constant conditional correlations do not hold in practice.

Intra-day risk premia in European electricity forward markets

Abstract: Using a detailed data set of electricity forward prices in Central Europe, we compute the intra-day risk premium and market price of risk for the two electricity exchanges European Energy Exchange (EEX) and Energy Exchange Austria (EXAA). Given the significant volatility and jump risk of electricity prices, these closely linked markets offer an opportunity to study whether market participants are willing to pay a premium to secure day-ahead delivery prices earlier in a trading day. Generally, we find such a positive risk premium, leading to a statistically significant negative market price of risk and the implication that forward prices are upward-biased predictors of expected electricity spot prices.

New renewable electricity capacity under uncertainty: the potential in Norway

Abstract: Uncertainty affecting project values makes investors hesitate to build new capacity unless profitability is significant. When analysing the potential for new renewable power system capacity in a region, it is therefore necessary to properly capture both uncertainty effects and decision-making behaviour of investors. Important stochastic factors typically include wholesale electricity prices and certificate prices. We calculate trigger levels for the sum of these factors, and compare these with the current long-term contract prices to estimate the potential for new renewable electricity capacity. We take into account the cost and technical potential of small hydro and wind in Norway, the number of prenotifications, concession applications and grants, and the capacity targets of subsidising governmental bodies. With an electricity certificate policy target of 41 TWh per year of new renewables for Sweden and Norway combined until 2016, we estimate that 12 TWh wind power and 6.2 TWh hydropower will be built in Norway. Due to the option value of waiting, most of this capacity will come after 2010.

Russian gas to western Europe: a game-theoretic analysis

Abstract: Since the fall of the Soviet Union it has been necessary for Russia to form a coalition with at least one of the transit countries Belarus and Ukraine in order to be able to ship gas to western Europe. This paper models the gas transit game using a cooperative module to determine the bargaining power of the three countries. The bargaining power is dependent on the coalition that is achieved. In the non-cooperative module, the three countries involved decide whether or not to cooperate, with Russia using side payments to induce cooperation. On the basis of published demand and cost estimates, the predicted Nash equilibrium is the cooperative one resulting in the grand coalition. Predicted gas quantities correspond quite closely to actual 2004 and forecast 2010 and 2030 figures. The completion of the North Transgas pipeline will benefit Russia, to the detriment of the others, particularly Ukraine.

China's impact on price shocks in the world oil markets

Abstract: This paper examines China's influence on price shock transmission in the world oil markets. In this paper, its impact is studied by testing for cointegration, mapping causality using a technique called directed acyclic graphs (DAG), and integrating the results of DAG into an error correction model to conduct variance decomposition. Using data from the period 1997-2007, evidence is presented that China has had little impact on the volatility in international oil markets, and that innovations in the Chinese market (in the long run) are largely driven by innovations from external markets. This study also indicates that the Chinese market is the largest source of its own volatility over short horizons, and that the Organization of the Petroleum Exporting Countries and the US oil markets are responsible for much of the price volatility observed in the world markets.