Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices

Maciejowska, Katarzyna; Nitka, Weronika; Weron, Tomasz

doi:10.1016/j.eneco.2021.105273

Cited by 48 publications

(25 citation statements)

References 27 publications

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“…A multiple linear regression model was applied for Germany's electricity markets [32,77], which showed that 15 min scale helped significantly to reduce imbalances in intraday trading, and a considerable share of spot price variance was explained by fundamental modelling. The ARX models, which are linear models, were applied for Germany [30,78], Poland [78], European countries, and the US [79], and the findings supported more accurate EPPs in the mentioned electricity markets. The ARMAX model was applied for Germany, where it showed that wind energy generation decreased market spot prices [80].…”

Section: Electricity Market Price and Load Forecasting Through Wind Energy Productionmentioning

confidence: 76%

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Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Acaroğlu

Márquez

2021

Energies

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Forecasting the electricity price and load has been a critical area of concern for researchers over the last two decades. There has been a significant economic impact on producers and consumers. Various techniques and methods of forecasting have been developed. The motivation of this paper is to present a comprehensive review on electricity market price and load forecasting, while observing the scientific approaches and techniques based on wind energy. As a methodology, this review follows the historical and structural development of electricity markets, price, and load forecasting methods, and recent trends in wind energy generation, transmission, and consumption. As wind power prediction depends on wind speed, precipitation, temperature, etc., this may have some inauspicious effects on the market operations. The improvements of the forecasting methods in this market are necessary and attract market participants as well as decision makers. To this end, this research shows the main variables of developing electricity markets through wind energy. Findings are discussed and compared with each other via quantitative and qualitative analysis. The results reveal that the complexity of forecasting electricity markets’ price and load depends on the increasing number of employed variables as input for better accuracy, and the trend in methodologies varies between the economic and engineering approach. Findings are specifically gathered and summarized based on researches in the conclusions.

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Section: Electricity Market Price and Load Forecasting Through Wind Energy Productionmentioning

confidence: 76%

“…In addition to these markets, the eventual balancing of the supply and demand is accomplished by the BPMs, which are regulated by the transmission system operator (TSO). The system stability is provided in the context of security in these markets [30] (see [31,32] for detailed information).…”

Section: Electricity Market: Structure and Componentsmentioning

confidence: 99%

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Acaroğlu

Márquez

2021

Energies

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“…Firstly, the predictions can be made on the morning of day d − 1, when market participants need to decide how much electricity to bid in the DA market and how much to buy/sell in the ID market or leave for the balancing market. Forecasts of the price spread between DA and ID/balancing markets can provide valuable insights for decision-making (Maciejowska et al, 2019(Maciejowska et al, , 2021.…”

Section: The Marketplacementioning

confidence: 99%

“…Covariance stationarity may not be satisfied by forecasts in day-ahead electricity markets, since the H predictions for the next day are made at the same time, using the same information set. Hence, either H independent tests (one for each load period of the day; Bordignon et al, 2013;Nowotarski et al, 2014;Uniejewski et al, 2016;Lago et al, 2018;Gianfreda et al, 2020) or a multivariate variant proposed by Ziel and Weron (2018) are performed Hubicka et al, 2019;Marcjasz et al, 2019;Maciejowska et al, 2021;Özen and Yıldırım, 2021). The latter jointly tests forecasting accuracy across all H load periods using the 'daily' or 'multivariate' loss differential series:…”

Section: Point Forecastsmentioning

confidence: 99%

Forecasting Electricity Prices

Maciejowska¹,

Uniejewski²,

Weron³

2022

Preprint

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Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. It is interdisciplinary by nature. It requires expertise in econometrics, statistics or machine learning for developing well-performing predictive models, in finance for understanding market mechanics, and in electrical engineering for comprehension of the fundamentals driving electricity prices.Although electricity price forecasting aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being weather (both demand and supply) and business activity (demand only) dependent. The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure.On the methodological side, this leads to three visible trends in electricity price forecasting research as of 2022. Firstly, there is a slow, but more noticeable with every year, tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches. Thirdly, statistical error measures are nowadays regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, more and more often, they are complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models.

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“…In addition, a reduction in forecast errors regarding wind and solar energy has led to lower price volatility. Improved forecasts of fundamentals lead to more accurate day-ahead and intraday prices, as shown by [13], and [14] used principal component analysis to forecast both prices.…”

Section: Literature Reviewmentioning

confidence: 99%

Volatility and Dispersion of Hourly Electricity Contracts on the German Continuous Intraday Market

Baule

Naumann

2021

Energies

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Intraday electricity trading on the continuous intraday market of EPEX SPOT is particularly well suited for the rebalancing of energy production. We analyzed the volatility and dispersion of individual hourly contracts, taking into account the particularities of the market, due to which the standard volatility measure from financial time series cannot be applied. We used and analyzed five measures for price fluctuations, which turned out to be similarly well suited for electricity contracts, with small differences. We then identified fundamental drivers of price fluctuations: the relative share of wind in the overall mix increased dispersion. In addition, price dispersion was positively correlated with the traded volume as well as the absolute difference between the day-ahead auction price and the volume-weighted intraday price. We furthermore analyzed the timely structure of price fluctuations to identify forecast indicators for a contract’s peak trading hour before maturity, finding that trading-related variables are more important to forecast price fluctuations than fundamental factors. With lagged realizations and additional external drivers, forecast regressions reached an adjusted R2 of 0.479 for volatility and around 0.3 for the dispersion measures.

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Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices

Cited by 48 publications

References 27 publications

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Forecasting Electricity Prices

Volatility and Dispersion of Hourly Electricity Contracts on the German Continuous Intraday Market

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