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We analyze the daily returns of Nordic electricity swaps and identify significant risk premia in the short end of the market. On average, long positions in this part of the swap market yield negative returns. The daily returns are distinctively non-normal in terms of tail-fatness, but we find little evidence of asymmetry. We investigate if the flexible four-parameter class of normal inverse Gaussian (NIG) distributions can capture the observed stylized facts and find that this class of distributions offers a remarkably improved fit relative to the normal distribution. We also compare the fit with that of the four-parameter class of stable distributions; the NIG law outperforms the stable law in the vast majority of cases. Thus, the NIG family of distributions, which allows for stochastic dynamics in terms of Levy processes that are suitable for pricing derivatives and Value-at-Risk measurements, is a serious candidate for modeling term structure dynamics in the Nordic electricity market., 1. INTRODUCTION This paper analyzes the distributions of daily log returns of financial contracts traded in the Nordic electricity exchange, Nord Pool. These contracts are settled against the average spot [...]

A cointegrated vector autoregressive (CVAR) model is estimated to determine the dynamic relationship between Nordic wholesale electricity prices and EU emissions trading scheme (EU-ETS) C[O.sub.2] allowance prices. An impulse response analysis reveals that electricity prices have large short-term responses to C[O.sub.2] price shocks, but that this response dampens over time. Using hourly Nordic electricity spot market prices, I find that the value of short-term response of electricity prices to a shock in C[O.sub.2] prices in off-peak hours is consistent with expected values for near complete pass-through of C[O.sub.2] emission costs when coal-generated power is at the margin. Likewise, the estimates reveal that peak hour electricity price responses to C[O.sub.2] price shocks are as expected for a market that has near complete pass-through of C[O.sub.2] emission costs when natural gas-generated power is at the margin. These results further suggest the Nordic electricity market is pricing as a competitive market., 1. INTRODUCTION In order to reach emission reduction goals set forth in the Kyoto Protocol, the EU implemented a member-wide C[O.sub.2] emissions trading scheme (EU-ETS). Under the EU-ETS, some 11,500 [...]

Organized trading for electricity includes both the pool and the futures market. Pool prices are volatile while the prices of the futures-market products are comparatively more stable. Thus, futures-market products constitute hedging instruments to reduce the risk suffered by any market agent. Electricity market agents engage in both pool and futures market transactions seeking to maximize their respective profits/utilities for a given risk level on profit variability. To make informed decisions, the market agent must gather as much accurate information as possible on the pool prices covering the whole time horizon spanned by the futures-market product. This paper provides a novel technique to represent conveniently the uncertainty associated with pool prices during long- or medium-term horizons through a set of scenarios, that is, pool price realizations. The proposed technique uses the prices of the futures-market products as long-term explanatory variables and exploits the short-term structure of the pool prices. Journal of the Operational Research Society (2010) 61, 235-245. doi:10.1057/jors.2008.140 Published online 7 January 2009 Keywords: electricity pool prices; year-ahead forecasting; forward trading; futures prices; scenarios

Most models that are used to analyze support policies for renewable electricity neglect important market features like oligopolistic behavior, emission trading, and restricted cross-border transmission capacities. We use a quantitative electricity market model that accounts for these aspects and decompose the impact of the German Feed-in tariff (FIT) into two frequently counteracting effects: a substitution effect and a permit price effect. We find that the total effect of the policy increases the German consumer price slightly by three percent, while the producer price decreases by eight percent. In addition, emissions from electricity generation in Germany are reduced by eleven percent but are hardly altered on the European scale. Finally, it turns out that price-cost margins of almost all firms are increased by the FIT, while nonetheless, the profits of firms are significantly lowered unless the firms combine relatively carbon-intensive production with a weak connection to the German grid., 1. INTRODUCTION Today, in most industrialized countries, renewable energy is supported by policy schemes to bring this favorable option to the market. When compared with fossil sources, major advantages attributed [...]

In this article, we propose a method of computing a smooth curve from observed forward prices with settlement over a period. We consider the electricity market, where such contracts are [...]

Short-run forecasting of electricity prices has become necessary for power generation unit schedule, since it is the basis of every profit maximization strategy. In this article a new and very easy method to compute accurate forecasts for electricity prices using mixed models is proposed. The main idea is to develop an efficient tool for one-step-ahead forecasting in the future, combining several prediction methods for which forecasting performance has been checked and compared for a span of several years. Also as a novelty, the 24 hourly time series has been modelled separately, instead of the complete time series of the prices. This allows one to take advantage of the homogeneity of these 24 time series. The purpose of this paper is to select the model that leads to smaller prediction errors and to obtain the appropriate length of time to use for forecasting. These results have been obtained by means of a computational experiment. A mixed model which combines the advantages of the two new models discussed is proposed. Some numerical results for the Spanish market are shown, but this new methodology can be applied to other electricity markets as well. Index Terms--Design of experiments, electricity markets, forecasting, marginal price, time series analysis.

Electric utility tariff revisions in Brazil and its effect on industry earnings is evaluated.

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining-based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are first described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms--support vector machine and probability classifier--are chosen to be the spike occurrence predictors and are discussed in detail. Realistic market data are used to test the proposed model with promising results. Index Terms--Classification, data mining, electricity market, electricity price forecast, feature selection, price spike reduction.

In this paper we develop a regime switching model which can generate long memory (fractional integration) in each of the regime states. This property is relevant, e.g., for the deregulated market for electricity power in the Nordic countries, which is characterized by electricity spot prices with a high degree of long memory. It occurs that in some time periods bilateral prices are identical whereas in other periods the prices differ. If the price series are fractionally integrated, then in the former regimes, an extreme form of fractional cointegration amongst prices will exist. The latter regimes occur when a capacity congestion exists across regions and multiple price areas will result. We define a Markov switching fractional integration model from which the fractional orders of integration in separate states can be estimated using maximum likelihood techniques. The model is adapted to data for the Nordic electricity spot market, and we find that regime switching and long memory are empirically relevant to co-exist. In particular, we find that the price behaviour for single markets can be very different depending upon the presence or absence of bottlenecks in electricity transmission. Using Monte Carlo forecasting we find that the regime switching model appears to be especially attractive in forecasting relative prices. JEL classification: C2; C22; C32 Keywords: Cointegration; Electricity prices; Forecasting; Fractional integration and cointegration; Long memory; Markov switching

A dynamic tool for calculating near-equilibrium in electricity markets under minimum profit conditions and marginal pricing is presented.

Details of the study conducted to analyze the impact of electricity market restructuring on the electricity prices in Texas are presented.

Deregulated electricity markets use an auction mechanism to select offers and their power levels for energy and ancillary services. A settlement mechanism is then used to determine the payments resulting from the selected offers. Currently, most independent system operators (ISOs) in the United States use an auction mechanism that minimizes the total offer costs but determine payment costs using a settlement mechanism that pays uniform market clearing prices (MCPs) to all selected offers. Under this setup, the auction and settlement mechanisms are inconsistent since minimized costs are different from payment costs. Illustrative examples in the literature have shown that for a given set of offers, if an auction mechanism that directly minimizes the payment costs is used, then payment costs can be significantly reduced as compared to minimizing offer costs. This observation has led to discussions among stakeholders and policymakers in the electricity markets as to which of the two auction mechanisms is more appropriate for ISOs to use. While methods for minimizing offer costs abound, limited approaches for minimization of payment costs have been reported. This paper presents an effective method for directly minimizing payment costs. In view of the specific features of the problem including the nonseparability of its objective function, the discontinuity of offer curves, and the maximum term in defining MCPs, our key idea is to use augmented Lagrangian relaxation and to form and solve offer and MCP subproblems by using the surrogate optimization framework. Numerical testing results demonstrate that the method is effective, and the resulting payment costs are significantly lower than what are obtained by minimizing the offer costs for a given set of offers. Index Terms--Augmented Lagrangian relaxation, deregulated electricity markets, market clearing price (MCP), offer cost minimization, payment cost minimization, surrogate optimization.

The need for integrated technologies across European Union, to generate cost-effective electricity from renewable energy sources, is examined.

A study examining the employment of nonlinear tariffs by utility services, the response of customers to these volatile tariffs and the impact of deregulated retail rates on energy consumption in the city of San Diego, is presented.

The market performance of the Australian National Electricity Market (NEM) is analyzed. The strategies adopted by the NEM are examined and identified using the public data for the period from May 1, 2002 to May 31, 2003. The results also analyze the role played by the generation companies in the wide range of variations in the frequency and causes for the market irregularities such as excessively high prices and capacity storages.

In this paper, we propose a new formulation for the 'price duration curve' (PDC) using probability considerations and provide a procedure for constructing it. This curve is expected to be useful in the long-term prediction of market prices and is similar in spirit to the load duration curve. It shows the proportion of time over a given time horizon during which the real-time market price of electricity is expected to exceed specified dollar amounts. The price over a long term is a stochastic quantity that depends on physical factors such as production cost, load, generation availability, unit commitment, and transmission constraints. It also depends on economic factors such as strategic bidding and load elasticity. We illustrate a procedure for constructing a stochastic system-based model for the PDC, taking into account the randomness associated with load and generator outages. The effects of unit commitment, transmission congestion, and transmission outages are not considered. We use two economic models. One is due to Bertrand and represents perfect competition. The other model is due to Rudkevich et al. who have given a closed-form expression for the market-clearing price in an oligopoly consisting of several identical firms. Index Terms--Deregulation, electricity price, method of cumulants, oligopoly, price duration curve (PDC), reliability, risk analysis.

The impact of economic growth in Hong Kong on electricity supply industry is examined.

The impact of information technology on power rate competition of electric utilities in Japan is examined.

A survey of industrial customers who have used Duke Power optional real-time rates for the past eight years reveals that customers have understood the nuances of hourly pricing and have shown marked load reduction during higher priced hours. The own and cross price elasticity of the industrial customers have been calculated.

This paper proposes a novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models. The historical and usually ill-behaved price series is decomposed using the wavelet transform in a set of better-behaved constitutive series. Then, the future values of these constitutive series are forecast using properly fitted ARIMA models. In turn, the ARIMA forecasts allow, through the inverse wavelet transform, reconstructing the future behavior of the price series and therefore to forecast prices. Results from the electricity market of mainland Spain in year 2002 are reported. Index Terms--ARIMA models, electricity market, price forecasting, wavelet transform.

Competitive markets for electricity determine either a uniform marginal price (UMP), a set of nodal marginal prices (NMPs), or a smaller set of zonal marginal prices (ZMPs). In theory, the NMP system is best, as it correctly accounts for transmission constraints (and losses in some versions), but critics allege that the large number of prices is confusing. Since the UMP or ZMP solution must be adjusted to account for transmission constraints and losses, it is reasonable to consider a market design in which actual dispatch corresponds to an NMP model that accounts for losses, and if a UMP or ZMP model is used at all, it is only to compute prices for settlements and compensation for constrained-on or -off generation or load. We prove that, if used in this way, the UMP or ZMP models a) do not affect the total economic surplus, b) redistribute the surplus among generators and loads at the different nodes, and c) give perverse incentives for generation expansion. We illustrate on a realistic system. Index Terms--Economic surplus, locational marginal price, nodal marginal price (NMP), uniform marginal price (UMP), uplift, zonal marginal price (ZMP).

Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize profits. This paper provides an approach to predict next-day electricity prices based on the Generalized Autoregressive Conditional Heteroskedastic (GARCH) methodology that is already being used to analyze time series data in general. A detailed explanation of GARCH models is presented and empirical results from the mainland Spain and California deregulated electricity-markets are discussed. Index Terms--Electricity markets, forecasting, GARCH models, time series analysis, volatility.

We propose a stochastic linear programming model for constructing piecewise-linear bidding curves to be submitted to Nord Pool, which is the Nordic power exchange. We consider the case of a price-taking power marketer who supplies electricity to price-sensitive end users. The objective is to minimize the expected cost of purchasing power from the day-ahead energy market and the short-term balancing market. The model is illustrated using a case study with data from Norway. Index Terms--Demand-side bidding, electricity markets, stochastic optimization.

We examine price by season and by year in 14 deregulated markets, by looking at diurnal price patterns and price volatility. Power price volatility is measured by price velocity, the daily average of the absolute value of price change per hour, which is broken into a component expected from the average diurnal pattern, and an unexpected component. Deregulated markets can be categorized into three groups: stable markets, markets that experienced one bad period or season of high price excursion, and chaotic markets. Britain, Spain and Scandinavia show consistent price patterns and low unexpected price velocity; a thoughtful consumer could reasonably implement demand side management (DSM) by shaping consumption patterns to reflect price. California, New Zealand and Alberta are examples of markets that had a period of very high price excursion; we discuss factors affecting public reaction to this. Australian power markets have inconsistent price patterns from season to season and year to year, and very high unexpected price velocity. Planned DSM in these markets would be very difficult. We offer four policy considerations for markets considering deregulation in the future. Index Terms--Load management, power demand, power industry, power system economics, risk analysis.

The energy procurement decisions made by California in response to different energy scenarios over the years are discussed. The likely reasons behind the state's continued reliance on polluting and diminishing fossil fuel and its failure to develop cleaner and more abundant alternative energy sources are analyzed.

An analysis is made to check the levels of tariff hikes in private households in Ukraine to put an end to price distortions. An evaluation is made to examine the impacts of these hikes on different social groups.

Local distribution companies (LDCs) have the mandate to supply electricity to the customers, upon demand, with whom they have contracted. An LDC can acquire electricity through its own plant facility, spot market purchases, and fixed-price forward contracts.

The authors look at electricity prices under two sets of auction conditions.

A study is done to analyze whether electricity and natural gas prices depend on the market structure. The regulators are likely to respond to interest group activity.

The usage of financial risk management methods for managing de-regulated electricity market prices is discussed.

Wholesale electricity payments are comprised of three parts, i.e., production costs, competitive rents and market power. A study of the electricity payments data from June 1998 to October 2000 in California reveals that competitive pricing is indirectly proportionate to demand.

Byline: Carl Blumstein (1), Richard Green (2) Keywords: electricity markets; electricity restructuring; California's electricity crisis Abstract: This short piece introduces the papers in the special issue on the California Energy Crisis of 2000/2001. The papers illustrate aspects of the crisis, such as the role of the gas market, future trends for investment, and the crisis' impact on consumers. Three papers analyze policy-makers' responses to the crisis, with case studies of over-lapping regulation, the trade-off between prices, reliability and the environment, and policies that helped achieve demand reductions in the summer of 2001. Two papers consider lessons for other countries, in Latin America and Europe. Author Affiliation: (1) University of California Energy Institute, 2539 Channing Way, Berkeley, CA, 94720-5180, USA (2) Economics Group, University of Hull Business School, Cottingham Road, Hull HU6 7RX, UK Article History: Registration Date: 12/10/2004

In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models. These techniques are explained and checked against each other. Results and discussions from real-world case studies based on the electricity markets of mainland Spain and California are presented. Index Terms--Electricity markets, forecasting, market clearing price, time series analysis.

Research into the effect on costs of the deregulation of electric power generation in the northeast of the US is presented, with focus on the need for more investment in the transmission grid to reduce prices further.

The restructuring of the electricity industry in the US following deregulation is examined. Consumers may have to bear increased costs in the short term in order to receive the benefit of restructuring in the long term.

The abolishment of the dominant electricity 'pool' in England and Wales and its replacement with bilateral markets that allow better pricing, more competition, and less bias against coal-fired generators is analyzed.

This paper presents the System Marginal Price (SMP) short-term forecasting implementation using the Artificial Neural Networks (ANN) computing technique. The described approach uses the three-layered ANN paradigm with back-propagation. The retrospective SMP real-world data, acquired from the deregulated Victorian power system, was used for training and testing the ANN. The results presented in this paper confirm considerable value of the ANN based approach in forecasting the SMP. Keywords: Artificial Neural Networks, System Marginal Price, Electricity Market.

A modeling approach was employed to study the impact of price increases for gasoline and electricity on the struggle of Mexico to recover from its economic problems. The model employs the Shoven and Whalley tax analysis research method and considers the differences in preferences of consumers as a function of their incomes and specifies a distinct demand system for each group of households. Results showed that the price increases are beneficial because of the reduction in consumption of gasoline, resulting in less carbon emissions. While some sectors will be affected negatively, it appears that the price increases are justified.