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.

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 Impact of market-coupling on electricity price modeling in fundamental unit-commitment approaches

2020 ◽  
Author(s):  
Moritz Nobis ◽  
Lothar Wyrwoll ◽  
Albert Moser ◽  
Stephan Raths
Keyword(s):  
Power Plant ◽  
Unit Commitment ◽  
System Modeling ◽  
Energy System ◽  
Fundamental Unit ◽  
Energy Prices ◽  
Electricity Prices ◽  
Multi Stage ◽  
Energy System Modeling ◽  
Market Coupling

Fundamental unit commitment approaches are of central importance in energy system modeling for the generation of detailed power plant schedules. However, existing approaches, which reduce complexity in a multi-stage process, often fail to generate realistic electricity prices. A new type of single-stage approach considers market-coupling implicitly so that, in addition to detailed power plant schedules, electricity prices reflecting real prices very well can be generated. In this paper, we show in a back-test for 2014 that an endogenously modeled market-coupling is the driving factor for the quality of resulting electricity prices. Conversely, it can be concluded that conventional multi-stage approaches show a significant distortion of modeled electricity prices due to missing price signals from neighboring market zones. Against the background of expanding trading capacities between market zones within the European power system, this issue becomes increasingly relevant when fundamentally modeling energy prices.

scholarly journals Impact of market-coupling on electricity price modeling in fundamental unit-commitment approaches

2020 ◽  
Author(s):  
Moritz Nobis ◽  
Lothar Wyrwoll ◽  
Albert Moser ◽  
Stephan Raths
Keyword(s):  
Power Plant ◽  
Unit Commitment ◽  
System Modeling ◽  
Energy System ◽  
Fundamental Unit ◽  
Energy Prices ◽  
Electricity Prices ◽  
Multi Stage ◽  
Energy System Modeling ◽  
Market Coupling

Fundamental unit commitment approaches are of central importance in energy system modeling for the generation of detailed power plant schedules. However, existing approaches, which reduce complexity in a multi-stage process, often fail to generate realistic electricity prices. A new type of single-stage approach considers market-coupling implicitly so that, in addition to detailed power plant schedules, electricity prices reflecting real prices very well can be generated. In this paper, we show in a back-test for 2014 that an endogenously modeled market-coupling is the driving factor for the quality of resulting electricity prices. Conversely, it can be concluded that conventional multi-stage approaches show a significant distortion of modeled electricity prices due to missing price signals from neighboring market zones. Against the background of expanding trading capacities between market zones within the European power system, this issue becomes increasingly relevant when fundamentally modeling energy prices.

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 Prosumer Community Portfolio Optimization via Aggregator: The Case of the Iberian Electricity Market and Portuguese Retail Market

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3747
Author(s):  
Ricardo Faia ◽  
Tiago Pinto ◽  
Zita Vale ◽  
Juan Manuel Corchado
Keyword(s):  
Portfolio Optimization ◽  
Electricity Markets ◽  
Electricity Market ◽  
Retail Market ◽  
Wholesale Market ◽  
Generation Capacity ◽  
Wholesale Electricity Market ◽  
Portfolio Optimization Model ◽  
Photovoltaic Generators

The participation of household prosumers in wholesale electricity markets is very limited, considering the minimum participation limit imposed by most market participation rules. The generation capacity of households has been increasing since the installation of distributed generation from renewable sources in their facilities brings advantages for themselves and the system. Due to the growth of self-consumption, network operators have been putting aside the purchase of electricity from households, and there has been a reduction in the price of these transactions. This paper proposes an innovative model that uses the aggregation of households to reach the minimum limits of electricity volume needed to participate in the wholesale market. In this way, the Aggregator represents the community of households in market sales and purchases. An electricity transactions portfolio optimization model is proposed to enable the Aggregator reaching the decisions on which markets to participate to maximize the market negotiation outcomes, considering the day-ahead market, intra-day market, and retail market. A case study is presented, considering the Iberian wholesale electricity market and the Portuguese retail market. A community of 50 prosumers equipped with photovoltaic generators and individual storage systems is used to carry out the experiments. A cost reduction of 6–11% is achieved when the community of households buys and sells electricity in the wholesale market through the Aggregator.

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 A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 26-46 ◽  
Author(s):  
Radhakrishnan Angamuthu Chinnathambi ◽  
Anupam Mukherjee ◽  
Mitch Campion ◽  
Hossein Salehfar ◽  
Timothy Hansen ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Moving Average ◽  
Percentage Error ◽  
Spot Prices ◽  
Multi Stage ◽  
Proposed Model ◽  
Forecasting Method ◽  
Auto Regressive ◽  
Stage 1

Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods.

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.

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