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 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 Forecast Error Sensitivity Analysis for Bidding in Electricity Markets with a Hybrid Renewable Plant Using a Battery Energy Storage System

2020 ◽  
Vol 12 (9) ◽  
pp. 3577 ◽  
Author(s):  
Jon Martinez-Rico ◽  
Ekaitz Zulueta ◽  
Unai Fernandez-Gamiz ◽  
Ismael Ruiz de Argandoña ◽  
Mikel Armendia
Keyword(s):  
Sensitivity Analysis ◽  
Energy Storage ◽  
Electricity Markets ◽  
Electricity Market ◽  
Forecast Error ◽  
Storage System ◽  
Energy Storage System ◽  
Prediction Errors ◽  
Electricity Grid ◽  
Battery Energy

Deep integration of renewable energies into the electricity grid is restricted by the problems related to their intermittent and uncertain nature. These problems affect both system operators and renewable power plant owners since, due to the electricity market rules, plants need to report their production some hours in advance and are, hence, exposed to possible penalties associated with unfulfillment of energy production. In this context, energy storage systems appear as a promising solution to reduce the stochastic nature of renewable sources. Furthermore, batteries can also be used for performing energy arbitrage, which consists in shifting energy and selling it at higher price hours. In this paper, a bidding optimization algorithm is used for enhancing profitability and minimizing the battery loss of value. The algorithm considers the participation in both day-ahead and intraday markets, and a sensitivity analysis is conducted to check the profitability variation related to prediction uncertainty. The obtained results highlight the importance of bidding in intraday markets to compensate the prediction errors and show that, for the Iberian Electricity Market, the uncertainty does not significantly affect the final benefits.

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.

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 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 Evolution of Fundamental Price Determination within Electricity Market Simulations

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5454
Author(s):  
Lothar Wyrwoll ◽  
Moritz Nobis ◽  
Stephan Raths ◽  
Albert Moser
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Unit Commitment ◽  
Simulation Models ◽  
Price Determination ◽  
Wholesale Market ◽  
Electricity Prices ◽  
Price Distribution ◽  
Multi Stage ◽  
Market Coupling

Electricity prices are the key instrument for coordinating electricity markets. For long-term market analyses, price determination based on fundamental unit commitment simulations is required. Within the European wholesale market, electricity prices result from a market clearing, which finds a welfare-optimal price–quantity tuple considering a coupling of multiple market areas with limited transmission capacity. With increasing exchange capacities in Europe, the precise modeling of the market coupling is required. Many market simulation models use multi-stage approaches with a separation of market coupling and price determination. In this paper, we analyze a new single-stage approach that combines both steps and theoretically and empirically demonstrate its precision by a backtest. For this purpose, we compare a simulated versus a historical electricity price distribution. Moreover, we explain the necessary adjustments for future regulatory developments of the European electricity market regarding flow-based market coupling and propose a concept for the application of future regulatory developments. We demonstrate further developments using a future scenario.

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