California Power Crisis

2017 ◽  
pp. 1-23
Author(s):  
Nabil Al-Najjar ◽  
David Besanko ◽  
Amit Nag
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Wholesale Price ◽  
The State ◽  
Electricity Prices ◽  
Deregulated Electricity Market

Between May 2000 and January 2001, the recently deregulated electricity market in the state of California experienced what many commentators have characterized as a meltdown. Over that period, wholesale electricity prices increased over 500%, power emergencies and the threat of rolling blackouts became daily occurrences, and the state's largest investor-owned utility was thrust into bankruptcy. Details California's attempt to deregulate its wholesale and retail electricity markets.To identify the drivers of increases in the wholesale price of electricity in California and to provide an opportunity to diagnose the causes of California's crisis.

scholarly journals Electricity Markets during the Liberalization: The Case of a European Union Country

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4317
Author(s):  
Štefan Bojnec ◽  
Alan Križaj
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Renewable Energy Sources ◽  
Primary Energy ◽  
Lessons Learned ◽  
Policy Implications ◽  
Daily Maximum ◽  
Energy Prices ◽  
Electricity Prices ◽  
Electricity Power

This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia’s electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers’ and consumers’ market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources.

scholarly journals Do Benefits from Dynamic Tariffing Rise? Welfare Effects of Real-Time Retail Pricing Under Carbon Taxation and Variable Renewable Electricity Supply

2019 ◽  
Vol 75 (1) ◽  
pp. 183-213
Author(s):  
Christian Gambardella ◽  
Michael Pahle ◽  
Wolf-Peter Schill
Keyword(s):  
Real Time ◽  
Electricity Markets ◽  
Electricity Market ◽  
Wholesale Price ◽  
Price Volatility ◽  
Market Model ◽  
High Carbon ◽  
Welfare Gains ◽  
Retail Pricing ◽  
Carbon Taxation

AbstractWe analyze the gross welfare gains from real-time retail pricing in electricity markets where carbon taxation induces investment in variable renewable technologies. Applying a stylized numerical electricity market model, we find a U-shaped association between carbon taxation and gross welfare gains. The benefits of introducing real-time pricing can accordingly be relatively low at relatively high carbon taxes and vice versa. The non-monotonous change in welfare gains can be explained by corresponding changes in the inefficiency arising from “under-consumption” during low-price periods rather than by changes in wholesale price volatility. Our results may cast doubt on the efficiency of ongoing roll-outs of advanced meters in many electricity markets, since net benefits might only materialize at relatively high carbon tax levels and renewable supply shares.

scholarly journals Considerations Regarding the Negative Prices on the Electricity Market

Proceedings ◽  
2020 ◽  
Vol 63 (1) ◽  
pp. 26
Author(s):  
Pavel Atănăsoae ◽  
Radu Dumitru Pentiuc ◽  
Eugen Hopulele
Keyword(s):  
Renewable Energy ◽  
Electricity Markets ◽  
Electricity Market ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Electricity Prices ◽  
Market Areas ◽  
The Impact ◽  
Market Coupling

Increasing of intermittent production from renewable energy sources significantly affects the distribution of electricity prices. In this paper, we analyze the impact of renewable energy sources on the formation of electricity prices on the Day-Ahead Market (DAM). The case of the 4M Market Coupling Project is analyzed: Czech-Slovak-Hungarian-Romanian market areas. As a result of the coupling of electricity markets and the increasing share of renewable energy sources, different situations have been identified in which prices are very volatile.

scholarly journals Short-term dynamics of day-ahead and intraday electricity prices

2017 ◽  
Vol 11 (4) ◽  
pp. 557-573 ◽  
Author(s):  
Georg Wolff ◽  
Stefan Feuerriegel
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Renewable Energy Sources ◽  
Policy Implications ◽  
The European Union ◽  
Electricity Prices ◽  
Short Term ◽  
Content Type ◽  
Major Interest ◽  
Intraday Market

Purpose Since the liberalization of electricity markets in the European Union, prices are subject to market dynamics. Hence, understanding the short-term drivers of electricity prices is of major interest to electricity companies and policymakers. Accordingly, this paper aims to study movements of prices in the combined German and Austrian electricity market. Design/methodology/approach This paper estimates an autoregressive model with exogenous variables (ARX) in a two-step procedure. In the first step, both time series, which inherently feature seasonality, are de-seasonalized, and in the second step, the influence of all model variables on the two dependent variables, i.e. the day-ahead and intraday European Power Energy Exchange prices, is measured. Findings The results reveal that the short-term market is largely driven by seasonality, consumer demand and short-term feed-ins from renewable energy sources. As a contribution to the existing body of literature, this paper specifically compares the price movements in day-ahead and intraday markets. In intraday markets, the influences of renewable energies are much stronger than in day-ahead markets, i.e. by 24.12 per cent for wind and 116.82 per cent for solar infeeds. Originality/value Knowledge on the price setting mechanism in the intraday market is particularly scarce. This paper contributes to existing research on this topic by deriving drivers in the intraday market and then contrasting them to the day-ahead market. A more thorough understanding is especially crucial for all stakeholders, who can use this knowledge to optimize their bidding strategies. Furthermore, the findings suggest policy implications for a more stable and efficient electricity market.

scholarly journals Day-Ahead Electricity Price And Spike Forecasting Using Machine Learning Techniques

2021 ◽  
Author(s):  
Harmanjot Singh Sandhu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Electricity Markets ◽  
Electricity Market ◽  
Numerical Experiments ◽  
Status Report ◽  
Electricity Prices ◽  
Forecasting Accuracy ◽  
Using Data ◽  
The Impact

Various machine learning-based methods and techniques are developed for forecasting day-ahead electricity prices and spikes in deregulated electricity markets. The wholesale electricity market in the Province of Ontario, Canada, which is one of the most volatile electricity markets in the world, is utilized as the case market to test and apply the methods developed. Factors affecting electricity prices and spikes are identified by using literature review, correlation tests, and data mining techniques. Forecasted prices can be utilized by market participants in deregulated electricity markets, including generators, consumers, and market operators. A novel methodology is developed to forecast day-ahead electricity prices and spikes. Prices are predicted by a neural network called the base model first and the forecasted prices are classified into the normal and spike prices using a threshold calculated from the previous year’s prices. The base model is trained using information from similar days and similar price days for a selected number of training days. The spike prices are re-forecasted by another neural network. Three spike forecasting neural networks are created to test the impact of input features. The overall forecasting is obtained by combining the results from the base model and a spike forecaster. Extensive numerical experiments are carried out using data from the Ontario electricity market, showing significant improvements in the forecasting accuracy in terms of various error measures. The performance of the methodology developed is further enhanced by improving the base model and one of the spike forecasters. The base model is improved by using multi-set canonical correlation analysis (MCCA), a popular technique used in data fusion, to select the optimal numbers of training days, similar days, and similar price days and by numerical experiments to determine the optimal number of neurons in the hidden layer. The spike forecaster is enhanced by having additional inputs including the predicted supply cushion, mined from information publicly available from the Ontario electricity market’s day-ahead System Status Report. The enhanced models are employed to conduct numerical experiments using data from the Ontario electricity market, which demonstrate significant improvements for forecasting accuracy.

scholarly journals Day-Ahead Electricity Price And Spike Forecasting Using Machine Learning Techniques

2021 ◽  
Author(s):  
Harmanjot Singh Sandhu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Electricity Markets ◽  
Electricity Market ◽  
Numerical Experiments ◽  
Status Report ◽  
Electricity Prices ◽  
Forecasting Accuracy ◽  
Using Data ◽  
The Impact

Various machine learning-based methods and techniques are developed for forecasting day-ahead electricity prices and spikes in deregulated electricity markets. The wholesale electricity market in the Province of Ontario, Canada, which is one of the most volatile electricity markets in the world, is utilized as the case market to test and apply the methods developed. Factors affecting electricity prices and spikes are identified by using literature review, correlation tests, and data mining techniques. Forecasted prices can be utilized by market participants in deregulated electricity markets, including generators, consumers, and market operators. A novel methodology is developed to forecast day-ahead electricity prices and spikes. Prices are predicted by a neural network called the base model first and the forecasted prices are classified into the normal and spike prices using a threshold calculated from the previous year’s prices. The base model is trained using information from similar days and similar price days for a selected number of training days. The spike prices are re-forecasted by another neural network. Three spike forecasting neural networks are created to test the impact of input features. The overall forecasting is obtained by combining the results from the base model and a spike forecaster. Extensive numerical experiments are carried out using data from the Ontario electricity market, showing significant improvements in the forecasting accuracy in terms of various error measures. The performance of the methodology developed is further enhanced by improving the base model and one of the spike forecasters. The base model is improved by using multi-set canonical correlation analysis (MCCA), a popular technique used in data fusion, to select the optimal numbers of training days, similar days, and similar price days and by numerical experiments to determine the optimal number of neurons in the hidden layer. The spike forecaster is enhanced by having additional inputs including the predicted supply cushion, mined from information publicly available from the Ontario electricity market’s day-ahead System Status Report. The enhanced models are employed to conduct numerical experiments using data from the Ontario electricity market, which demonstrate significant improvements for forecasting accuracy.

scholarly journals Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market

2018 ◽  
Vol 8 (6) ◽  
pp. 4060
Author(s):  
Francesco Arci ◽  
Jane Reilly ◽  
Pengfei Li ◽  
Kevin Curran ◽  
Ammar Belatreche
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Electricity Generation ◽  
Informed Trading ◽  
Electricity Prices ◽  
Customer Demand ◽  
Short Term ◽  
Market Participants ◽  
Energy Demands ◽  
Trading Decisions

Electricity markets are different from other markets as electricity generation cannot be easily stored in substantial amounts and to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a considerable extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks to predict short-term wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. We have identified the features that such a model demands and outline it here.

scholarly journals The Impact of Behavioral and Structural Remedies on Electricity Prices: The Case of the England and Wales Electricity Market

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3420
Author(s):  
Sherzod Tashpulatov
Keyword(s):  
Electricity Market ◽  
Heavy Tails ◽  
Wholesale Price ◽  
Price Volatility ◽  
Arch Model ◽  
Electricity Prices ◽  
Explanatory Variables ◽  
Market Shares ◽  
The Uk ◽  
The Impact

During the liberalization process the UK regulatory authority introduced a behavioral remedy (through price-cap regulation) and structural remedy (through divestment series) in order to mitigate an exercise of market power and lower the influence of incumbent producers on wholesale electricity prices. We study the impact of these remedies on the dynamics of the wholesale electricity price during the peak-demand period over trading days. An extended autoregressive and autoregressive conditional heteroscedasticity (AR–ARCH) model with a novel skew generalized error distribution is used. This distribution allows one to capture the features of asymmetry, excess kurtosis, and heavy tails. The model is extended to include individual incumbent producers’ market shares and other explanatory variables reflecting seasonal patterns and regulatory regimes. We find that the structural remedy was more successful than the behavioral remedy because the effect of market share of the previously larger incumbent producer on the wholesale price is statistically insignificant. Moreover, after the second series of divestments, price volatility reduced.

Nash Equilibrium of a Retail Electricity Market With a High Penetration of Small Renewable Suppliers

2016 ◽  
Author(s):  
Saeed Azad ◽  
Ehsan Ghotbi
Keyword(s):  
Game Theory ◽  
Nash Equilibrium ◽  
Electricity Markets ◽  
Electricity Market ◽  
Financial Incentives ◽  
Active Role ◽  
Global Constraints ◽  
Electricity Prices ◽  
Electricity Grid ◽  
High Penetration

Increasing the level of the competition, a worldwide trend in the evolution of electricity markets, has made game theory a notably popular approach to find the market equilibrium. This paper models a retail electricity market with a high penetration of renewable resources. Using game theory, the clearing electricity prices, as well as the optimum behavior of market participants are obtained. In this model, which is inspired by the “Energy Internet” concept, consumers play an active role in managing their load demands. This highly dynamic model allows us to analyze consumers’ reaction to price fluctuations. Spot pricing, which is employed here to model the electricity market, can make consumers react to the high electricity prices. This is particularly important in the demand side management, where consumers should modify their demand through financial incentives. Two types of active players are considered in this electricity market, small electricity suppliers and consumers. Electricity grid, while present in the market, only takes the responsibility to compensate for the deficiency of power from small and mid-size suppliers. The problem is formulated mathematically, subject to a number of local and global constraints to find the Nash equilibrium.

scholarly journals Determining Factors and Scenarios of Influence on Consumer Migration from the Regulated Market to the Deregulated Electricity Market

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 65
Author(s):  
Heloísa P. Burin ◽  
Julio S. M. Siluk ◽  
Graciele Rediske ◽  
Carmen B. Rosa
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Electricity Markets ◽  
Electricity Market ◽  
Free Market ◽  
Know How ◽  
Deregulated Electricity Market ◽  
Determining Factors ◽  
The Moment ◽  
Free Environment

Due to the constant evolution of the electricity markets around the world, new possibilities for contracting electricity are emerging. In Brazil, there are two models available to the consumer: the regulated contracting environment and the free contracting environment. Because of these possibilities for contracting electricity, it is important that consumers know how to migrate from the regulated to the free environment when it is an advantage. This study was conducted following the premises of three techniques: systematic literature review, gray literature review, and expert panel. The following question was asked: What are the determining factors to be considered by the consumer at the moment decision to migrate from the regulated electricity market to the free market? In total, 7 factors were identified and discussed in the literature review. The experts who participated in the study pointed out 3 influential scenarios in this decision making to migrate. The main contribution of this study is to provide the consumer with subsidies for decision making, given the determining factors to be taken into account when deciding on migration or not. In addition, the study contributed to the sector through a comprehensive discussion about the scenarios faced by consumers and how they can influence decision making.

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