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 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 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 Clustering electricity market participants via FRM models

2020 ◽  
pp. 1-12
Author(s):  
Ayla Gülcü ◽  
Sedrettin Çalişkan
Keyword(s):  
Electricity Market ◽  
Distance Metrics ◽  
Agglomerative Clustering ◽  
Timely Manner ◽  
Short Term ◽  
Clustering Techniques ◽  
Market Participants ◽  
Cosine Distance ◽  
Collateral Mechanism

Collateral mechanism in the Electricity Market ensures the payments are executed on a timely manner; thus maintains the continuous cash flow. In order to value collaterals, Takasbank, the authorized central settlement bank, creates segments of the market participants by considering their short-term and long-term debt/credit information arising from all market activities. In this study, the data regarding participants’ daily and monthly debt payment and penalty behaviors is analyzed with the aim of discovering high-risk participants that fail to clear their debts on-time frequently. Different clustering techniques along with different distance metrics are considered to obtain the best clustering. Moreover, data preprocessing techniques along with Recency, Frequency, Monetary Value (RFM) scoring have been used to determine the best representation of the data. The results show that Agglomerative Clustering with cosine distance achieves the best separated clustering when the non-normalized dataset is used; this is also acknowledged by a domain expert.

scholarly journals Forecasting Electricity Prices: A Machine Learning Approach

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 119
Author(s):  
Mauro Castelli ◽  
Aleš Groznik ◽  
Aleš Popovič
Keyword(s):  
Machine Learning ◽  
Electricity Market ◽  
Dynamic Environment ◽  
Weather Conditions ◽  
Programming Approach ◽  
Future Price ◽  
Energy Prices ◽  
Electricity Prices ◽  
Market Participants ◽  
Electricity Price Forecasting

The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning technique—namely, a novel genetic programming approach. Drawing on empirical data from the largest EU energy markets, we propose a forecasting model that considers variables related to weather conditions, oil prices, and CO2 coupons and predicts energy prices 24 h ahead. We show that the proposed model provides more accurate predictions of future electricity prices than existing prediction methods. Our important findings will assist the electricity market participants in forecasting future price movements.

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.

Measuring the Benefits of Greater Spatial Granularity in Short-Term Pricing in Wholesale Electricity Markets

2011 ◽  
Vol 101 (3) ◽  
pp. 247-252 ◽  
Author(s):  
Frank A Wolak
Keyword(s):  
Natural Gas ◽  
Electricity Markets ◽  
Electricity Market ◽  
Short Term ◽  
Locational Marginal Pricing ◽  
Marginal Pricing ◽  
Before And After ◽  
Natural Gas Prices ◽  
The Impact ◽  
Generation Unit

Hourly generation unit-level output levels, detailed information on the technological characteristics of generation units, and daily delivered natural gas prices to all generation units for the California wholesale electricity market before and after the implementation of locational marginal pricing are used to measure the impact of introducing greater spatial granularity in short-term energy pricing. The average hourly number of generation unit starts increases, but both the total hourly energy consumed and total hourly operating costs for all natural gas-fired generation units fall by more than 2 percent after the implementation of locational marginal pricing.

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 Review of Information Disclosure in Different Electricity Markets

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3424 ◽  
Author(s):  
Yang Yang ◽  
Minglei Bao ◽  
Yi Ding ◽  
Yonghua Song ◽  
Zhenzhi Lin ◽  
...  
Keyword(s):  
Power Systems ◽  
Electricity Markets ◽  
Electricity Market ◽  
Information Disclosure ◽  
Market Participants ◽  
Operation Process ◽  
Present Information ◽  
The World ◽  
Efficient Information ◽  
Deregulated Power Systems

Electricity markets have been established in many countries of the world. Electricity and services are traded in the competitive environment of electricity markets, which generates a large amount of information during the operation process. To maintain transparency and foster competition of electricity markets, timely and precise information regarding the operation of electricity market should be disclosed to the market participants through a centralized and authorized information disclosure mechanism. However, the information disclosure mechanism varies greatly in electricity markets because of different market models and transaction methods. This paper reviews information disclosure mechanisms of several typical electricity markets with the poolco model, bilateral contract model, and hybrid model. The disclosed information and clearing models in these markets are summarized to provide an overview of the present information disclosure mechanisms in typical deregulated power systems worldwide. Moreover, the various experiences for establishing an efficient information disclosure mechanism is summarized and discussed.

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.

Sign in / Sign up

Export Citation Format

Share Document