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The objective of the paper is to analyse the economic justification of introducing deregulation to Israel's regulated electricity market and infer whether such a policy change makes sense for the country.

The paper employs an analytical model of electricity market equilibrium under regulation and deregulation. It considers two technologies – coal‐fired generators and combined cycle gas turbines, and two time‐of‐day prices – peak and off‐peak. It analyzes pricing, revenues, profits and consumer surplus both for the regulated industry and the deregulated industry where firms compete, during the peak and off‐peak periods, according to the Cournot conjecture. The model is then applied to the Israeli case.

The analysis shows that a workably competitive electricity market with financially viable firms does not improve net benefits to Israeli society. A deregulated market is likely to yield smaller net benefits than a regulated market, and certainly a smaller consumer surplus. This happens since efficiency improvements in generation costs under deregulation will not be sufficient to compensate for the reduction in consumer surplus due to higher electricity prices under deregulation.

The simplifications used in the analytical model for ease of analysis and presentation could have influenced some results.

The findings of the paper would have significant relevance for electricity sector deregulation in Israel and other regions that currently have a regulated electricity sector (e.g. Hong Kong, Africa, and many parts of North America).

The value of the paper lies in its ability to demonstrate the inherent weaknesses of the deregulation policy through an analytical model.

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a comprehensive survey and comparison of these techniques.

The present article provides an overview of the statistical short‐term price forecasting (STPF) models. The basic theory of these models, their further classification and their suitability to STPF has been discussed. Quantitative evaluation of the performance of these models in the framework of accuracy achieved and computation time taken has been performed. Some important observations of the literature survey and key issues regarding STPF methodologies are analyzed.

It has been observed that price forecasting accuracy of the reported models in day‐ahead markets is better as compared to that in real time markets. From a comparative analysis perspective, there is no hard evidence of out‐performance of one model over all other models on a consistent basis for a very long period. In some of the studies, linear models like dynamic regression and transfer function have shown superior performance as compared to non‐linear models like artificial neural networks (ANNs). On the other hand, recent variations in ANNs by employing wavelet transformation, fuzzy logic and genetic algorithm have shown considerable improvement in forecasting accuracy. However more complex models need further comparative analysis.

This paper is intended to supplement the recent survey papers, in which the researchers have restricted the scope to a bibliographical survey. Whereas, in this work, after providing detailed classification and chronological evolution of the STPF techniques, a comparative summary of various price‐forecasting techniques, across different electricity markets, is presented.

This paper aims to present the results of the comparison between different approaches to the prediction of electricity prices. It is well-known that the properties of the data generation process may prefer some modeling methods over the others. The data having an origin in social or market processes are characterized by unexpectedly wide realization space resulting in the existence of the long tails in the probabilistic density function. These data may not be easy in time series prediction using standard approaches based on the normal distribution assumptions. The electricity prices on the deregulated market fall into this category.

The paper presents alternative approaches, i.e. memory-based prediction and fractal approach compared with established nonlinear method of neural networks. The appropriate interpretation of results is supported with the statistical data analysis and data conditioning. These algorithms have been applied to the problem of the energy price prediction on the deregulated electricity market with data from Polish and Austrian energy stock exchanges.

The first outcome of the analysis is that there are several situations in the task of time series prediction, when standard modeling approach based on the assumption that each change is independent of the last following random Gaussian bell pattern may not be a true. In this paper, such a case was considered: price data from energy markets. Electricity prices data are biased by the human nature. It is shown that more relevant for data properties was Cauchy probabilistic distribution. Results have shown that alternative approaches may be used and prediction for both data memory-based approach resulted in the best performance.

“Personalization” of the model is crucial aspect in the whole methodology. All available knowledge should be used on the forecasted phenomenon and incorporate it into the model. In case of the memory-based modeling, it is a specific design of the history searching routine that uses the understanding of the process features. Importance should shift toward methodology structure design and algorithm customization and then to parameter estimation. Such modeling approach may be more descriptive for the user enabling understanding of the process and further iterative improvement in a continuous striving for perfection.

Memory-based modeling can be practically applied. These models have large potential that is worth to be exploited. One disadvantage of this modeling approach is large calculation effort connected with a need of constant evaluation of large data sets. It was shown that a graphics processing unit (GPU) approach through parallel calculation on the graphical cards can improve it dramatically.

The modeling of the electricity prices has big impact of the daily operation of the electricity traders and distributors. From one side, appropriate modeling can improve performance mitigating risks associated with the process. Thus, the end users should receive higher quality of services ultimately with lower prices and minimized risk of the energy loss incidents.

The use of the alternative approaches, such as memory-based reasoning or fractals, is very rare in the field of the electricity price forecasting. Thus, it gives a new impact for further research enabling development of better solutions incorporating all available process knowledge and customized hybrid algorithms.

Market liberalisation has resulted in significant changes not only in the way electricity is traded, but also for the market participants themselves. The bidding behaviour of market participants who are active in a liberalised UK‐like market has been modelled. Both operational and technical parameters associated with the market and its participants are accounted for. Explicit characterization of risk (value at risk) is made with respect to market participants and their attitude to trading. Profit maximization strategies for market participants are then developed based on the minimization of price‐risk under uncertainty. Results are presented for a selected case study and the effect of alternative strategies is compared. The case study concerns several generators who need to determine what proportion of their production they should sell to the market. The results show that based on cost and price forecasts there is scope for generators to profitably take advantage of both contractual and within‐day market trades.

The comparative potential impact of electric restructuring laws and attendant regulations on electric utilities and ultimately on their collective human resources in Ireland, Germany and in the Tennessee Valley, USA, is the focus of this research. It includes personal observations of utilities in both EU countries and in the Tennessee Valley as well as personal interviews with utility officials by Dennis Yocom, who has been associated with the electric utility industry for 27 years.

Following the deregulation of electricity markets in the USA, independent power producers operate as for-profit entities. Their profit depends on the price of electricity and an accurate forecast is critical in making bidding decisions on the electricity and reserve markets or engaging in bilateral contracts. Competing price forecasts have their accuracy expressed in statistical terms but producers need to determine the long-term value of using a given forecast. The purpose of this paper is to address this issue by presenting a method of electricity price forecast valuation which compares forecast models using financial rather than statistical measures.

The objectives of this paper are achieved by mathematical modeling of thermal power plants and price forecast information available to market participants and simulating the operation of a thermal power plant using various price forecasts and perfect information (as a baseline). The operating profit calculated over a long period was used for ranking forecast models.

The framework can be used to estimate the value of a new price forecast as well as to determine if potential gains from developing or acquiring a new forecast will justify the expenses. The results show that an improvement in terms of statistical forecast accuracy measures does not guarantee increased profit.

This paper presents a new method for comparing electricity price forecast models. It can be adapted to various types of thermal power plants that operate on liberalized electricity markets and utilize price-based dynamic economic dispatch models.

This paper presents a simulation-based valuation framework for short-term electricity price. The approach described in this paper can be utilized by independent power producers for different types of generators, operating on deregulated electricity markets.

The paper investigates the production inefficiency of the US electricity industry in the wake of restructuring and emission reduction regulations.

The study estimates a multiple-input, multiple-output directional distance function, using six inputs: fuel, labor, capital and annualized capital costs of sulfur dioxide (SO₂₎, nitrogen oxides (NO_X) and particulate removal devices, two good outputs – residential and industrial-commercial electricity and three bad outputs – SO₂, carbon dioxide (CO₂) and NO_X emissions.

The authors find that restructuring in electricity markets improves deregulated utilities' technical efficiency (TE). Deregulated utilities with below-average NO_X control equipment tend to invest less in these devices, but above-average utilities do the opposite. The reverse applies to particulate removal devices. The whole sample spends more on NO_X, particulate and SO₂ control systems and reduces its electricity sales slightly. Increased investments in SO₂ and NO_X control equipment do not reduce SO₂ and NO_X emissions, but expansions of particulate control systems cut down SO₂ emissions greatly. Stricter environmental regulations have probably shifted the production frontier inwards and the utilities farther from the frontier over time.

Restructuring and environmental regulations do not make all utilities invest more in emission control systems. The US government should devise other schemes to achieve this goal.

The paper unveils heterogeneous reactions of US electric utilities in the wake of restructuring and emission regulations.

This paper aims to develop an instrument for measuring Consumer Protection and its Determinants (CP&Ds). This is because literature on an instrument to measure CP&Ds is scarce.

In Nigeria, 53 questionnaires were distributed to legal practitioners. The study used 24 items to operationalize the CP&Ds. The research data were coded and scored, and the exploratory factor analysis (EFA) was conducted using SPSS version 22. The Bartlett’s test of sphericity, Kaiser–Meyer–Olkin, Cronbach’s alpha and Pearson’s correlation coefficient were used for the EFA, internal consistency reliability and multicollinearity, respectively.

The EFA produced seven factors, and each determinant was found reliable with its measure of internal consistency.

The research result may not be generalized across jurisdiction because of the limited sample size and the fact that the data were collected from Nigerian legal practitioners.

This study can be used by policymakers and even private electricity companies in the deregulated electricity sector in Nigeria for policy design and effective consumer protection.

From the extensive literature review none was identified on the scale development for measuring CP&Ds. This exploratory research is the first attempt to develop an instrument for measuring CP&Ds.

This chapter focuses on the common occurrence of wholesale electricity prices that fall below the cost of production. This “negative pricing” in effect represents payment to high-volume consumers for taking excess power off the grid, thus relieving overload. Occurrences of negative pricing have been observed since the wholesale electricity markets have been operating, and occur during periods of low demand, while generators are being kept in reserve for rapid engagement when demand increases (it is expensive and time-consuming to shut down generators and then restart them, so they are often kept in “spooling mode”). In such situations power production may temporarily exceed demand, potentially overloading the system. When the federal government began subsidizing the construction of wind generation projects, with regulations in place requiring transmission grids to accept all of the electricity produced by the wind generators, negative pricing became more frequent.

Midwest Independent Transmission System Operator, Inc. (MISO) is a nonprofit regional transmission organization (RTO) that oversees electricity production and transmission across 13 states and 1 Canadian province. MISO also operates an electronic exchange for buying and selling electricity for each of its five regional hubs.

MISO oversees two types of markets. The forward market, which is referred to as the day-ahead (DA) market, allows market participants to place demand bids and supply offers on electricity to be delivered at a specified hour the following day. The equilibrium price, known as the locational marginal price (LMP), is determined by MISO after receiving sale offers and purchase bids from market participants. MISO also coordinates a spot market, which is known as the real-time (RT) market. Traders in the RT market must submit bids and offers by 30minutes prior to the hour for which the trade will be executed. After receiving purchase and sale offers for a given hour in the RT market, MISO then determines the LMP for that particular hour.

The existence of the DA and RT markets allows producers and retailers to hedge against the large fluctuations that are common in electricity prices. Hedge ratios on the MISO exchange are estimated using various techniques. No hedge ratio technique examined consistently outperforms the unhedged portfolio in terms of variance reduction. Consequently, none of the hedge ratio methods in this study meet the general interpretation of FASB guidelines for a highly effective hedge.