Energies

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16 pages, 1785 KiB

Open AccessArticle

Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts

by Grzegorz Marcjasz, Bartosz Uniejewski and Rafał Weron

Energies 2020, 13(7), 1667; https://doi.org/10.3390/en13071667 - 3 Apr 2020

Cited by 29 | Viewed by 4211

In the last three decades the vast majority of electricity price forecasting (EPF) research has concerned day-ahead markets. However, the rapid expansion of renewable generation—mostly wind and solar—have shifted the focus to intraday markets, which can be used to balance the deviations between [...] Read more.

In the last three decades the vast majority of electricity price forecasting (EPF) research has concerned day-ahead markets. However, the rapid expansion of renewable generation—mostly wind and solar—have shifted the focus to intraday markets, which can be used to balance the deviations between positions taken in the day-ahead market and the actual demand and renewable generation. A recent EPF study claims that the German intraday, continuous-time market for hourly products is weak-form efficient, that is, that the best predictor for the so-called ID3-Price index is the most recent transaction price. Here, we undermine this claim and show that we can beat the naïve forecast by combining it with a prediction of a parameter-rich model estimated using the least absolute shrinkage and selection operator (LASSO). We further argue, that that if augmented with timely predictions of fundamental variables for the coming hours, the LASSO-estimated model itself can significantly outperform the naïve forecast. Full article

(This article belongs to the Special Issue Modeling and Forecasting Intraday Electricity Markets)

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31 pages, 45790 KiB

Open AccessFeature PaperArticle

Forecasting the Intra-Day Spread Densities of Electricity Prices

by Ekaterina Abramova and Derek Bunn

Energies 2020, 13(3), 687; https://doi.org/10.3390/en13030687 - 5 Feb 2020

Cited by 12 | Viewed by 3463

Abstract

Intra-day price spreads are of interest to electricity traders, storage and electric vehicle operators. This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast the German electricity price spreads between different hours of the day, as revealed [...] Read more.

Intra-day price spreads are of interest to electricity traders, storage and electric vehicle operators. This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast the German electricity price spreads between different hours of the day, as revealed in the day-ahead auctions. The four specifications of the density functions are dynamic and conditional upon exogenous drivers, thereby permitting the location, scale and shape parameters of the densities to respond hourly to such factors as weather and demand forecasts. The best fitting and forecasting specifications for each spread are selected based on the Pinball Loss function, following the closed-form analytical solutions of the cumulative distribution functions. Full article

(This article belongs to the Special Issue Modeling and Forecasting Intraday Electricity Markets)

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15 pages, 740 KiB

Open AccessArticle

Balancing Generation from Renewable Energy Sources: Profitability of an Energy Trader

by Christopher Kath, Weronika Nitka, Tomasz Serafin, Tomasz Weron, Przemysław Zaleski and Rafał Weron

Energies 2020, 13(1), 205; https://doi.org/10.3390/en13010205 - 1 Jan 2020

Cited by 25 | Viewed by 4868

Abstract

Motivated by a practical problem faced by an energy trading company in Poland, we investigate the profitability of balancing intermittent generation from renewable energy sources (RES). We consider a company that buys electricity generated by a pool of wind farms and pays their [...] Read more.

Motivated by a practical problem faced by an energy trading company in Poland, we investigate the profitability of balancing intermittent generation from renewable energy sources (RES). We consider a company that buys electricity generated by a pool of wind farms and pays their owners the day-ahead system price minus a commission, then sells the actually generated volume in the day-ahead and balancing markets. We evaluate the profitability (measured by the Sharpe ratio) and market risk faced by the energy trader as a function of the commission charged and the adopted trading strategy. We show that publicly available, country-wide RES generation forecasts can be significantly improved using a relatively simple regression model and that trading on this information yields significantly higher profits for the company. Moreover, we address the issue of contract design as a key performance driver. We argue that by offering tolerance range contracts, which transfer some of the risk to wind farm owners, both parties can bilaterally agree on a suitable framework that meets individual risk appetite and profitability expectations. Full article

(This article belongs to the Special Issue Modeling and Forecasting Intraday Electricity Markets)

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14 pages, 438 KiB

Open AccessArticle

Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

by Ilkay Oksuz and Umut Ugurlu

Energies 2019, 12(23), 4557; https://doi.org/10.3390/en12234557 - 29 Nov 2019

Cited by 32 | Viewed by 4762

Abstract

The intraday electricity markets are continuous trade platforms for each hour of the day and have specific characteristics. These markets have shown an increasing number of transactions due to the requirement of close to delivery electricity trade. Recently, intraday electricity price market research [...] Read more.

The intraday electricity markets are continuous trade platforms for each hour of the day and have specific characteristics. These markets have shown an increasing number of transactions due to the requirement of close to delivery electricity trade. Recently, intraday electricity price market research has seen a rapid increase in a number of works for price prediction. However, most of these works focus on the features and descriptive statistics of the intraday electricity markets and overlook the comparison of different available models. In this paper, we compare a variety of methods including neural networks to predict intraday electricity market prices in Turkish intraday market. The recurrent neural networks methods outperform the classical methods. Furthermore, gated recurrent unit network architecture achieves the best results with a mean absolute error of 0.978 and a root mean square error of 1.302. Moreover, our results indicate that day-ahead market price of the corresponding hour is a key feature for intraday price forecasting and estimating spread values with day-ahead prices proves to be a more efficient method for prediction. Full article

(This article belongs to the Special Issue Modeling and Forecasting Intraday Electricity Markets)

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16 pages, 898 KiB

Open AccessArticle

Estimation and Simulation of the Transaction Arrival Process in Intraday Electricity Markets

by Michał Narajewski and Florian Ziel

Energies 2019, 12(23), 4518; https://doi.org/10.3390/en12234518 - 27 Nov 2019

Cited by 13 | Viewed by 3340

Abstract

We examine the novel problem of the estimation of transaction arrival processes in the intraday electricity markets. We model the inter-arrivals using multiple time-varying parametric densities based on the generalized F distribution estimated by maximum likelihood. We analyse both the in-sample characteristics and [...] Read more.

We examine the novel problem of the estimation of transaction arrival processes in the intraday electricity markets. We model the inter-arrivals using multiple time-varying parametric densities based on the generalized F distribution estimated by maximum likelihood. We analyse both the in-sample characteristics and the probabilistic forecasting performance. In a rolling window forecasting study, we simulate many trajectories to evaluate the forecasts and gain significant insights into the model fit. The prediction accuracy is evaluated by a functional version of the MAE (mean absolute error), RMSE (root mean squared error) and CRPS (continuous ranked probability score) for the simulated count processes. This paper fills the gap in the literature regarding the intensity estimation of transaction arrivals and is a major contribution to the topic, yet leaves much of the field for further development. The study presented in this paper is conducted based on the German Intraday Continuous electricity market data, but this method can be easily applied to any other continuous intraday electricity market. For the German market, a specific generalized gamma distribution setup explains the overall behaviour significantly best, especially as the tail behaviour of the process is well covered. Full article

(This article belongs to the Special Issue Modeling and Forecasting Intraday Electricity Markets)

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35 pages, 4669 KiB

Open AccessArticle

Modeling Intraday Markets under the New Advances of the Cross-Border Intraday Project (XBID): Evidence from the German Intraday Market

by Christopher Kath

Energies 2019, 12(22), 4339; https://doi.org/10.3390/en12224339 - 14 Nov 2019

Cited by 25 | Viewed by 5229

Abstract

The intraday cross-border project (XBID) allows intraday market participants to trade based on a shared order book independent of countries or local energy exchanges. This theoretically leads to an efficient allocation of cross-border capacities and ensures maximum market liquidity across European intraday markets. [...] Read more.

The intraday cross-border project (XBID) allows intraday market participants to trade based on a shared order book independent of countries or local energy exchanges. This theoretically leads to an efficient allocation of cross-border capacities and ensures maximum market liquidity across European intraday markets. If this postulation holds, the technical implementation of XBID might mark a regime switch in any intraday price series. We present a regression-based model for intraday markets with a particular focus on the German European Power Exchange (EPEX) intraday market and evaluate if the introduction of XBID influence prices, volume or volatility. We analyze partial volume-weighted average prices and standard deviations as well as cross-border volumes at different trading times. We are able to falsify our initial hypothesis assuming a measurable influence of changes caused by XBID. Thus, this paper contributes to the ongoing discussion on appropriate modeling of intraday markets and demonstrates that XBID does not necessarily need to be included in any model. Full article

(This article belongs to the Special Issue Modeling and Forecasting Intraday Electricity Markets)

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14 pages, 2262 KiB

Open AccessArticle

Forecasting the Price Distribution of Continuous Intraday Electricity Trading

by Tim Janke and Florian Steinke

Energies 2019, 12(22), 4262; https://doi.org/10.3390/en12224262 - 8 Nov 2019

Cited by 34 | Viewed by 5220

Abstract

The forecasting literature on intraday electricity markets is scarce and restricted to the analysis of volume-weighted average prices. These only admit a highly aggregated representation of the market. Instead, we propose to forecast the entire volume-weighted price distribution. We approximate this distribution in [...] Read more.

The forecasting literature on intraday electricity markets is scarce and restricted to the analysis of volume-weighted average prices. These only admit a highly aggregated representation of the market. Instead, we propose to forecast the entire volume-weighted price distribution. We approximate this distribution in a non-parametric way using a dense grid of quantiles. We conduct a forecasting study on data from the German intraday market and aim to forecast the quantiles for the last three hours before delivery. We compare the performance of several linear regression models and an ensemble of neural networks to several well designed naive benchmarks. The forecasts only improve marginally over the naive benchmarks for the central quantiles of the distribution which is in line with the latest empirical results in the literature. However, we are able to significantly outperform all benchmarks for the tails of the price distribution. Full article

(This article belongs to the Special Issue Modeling and Forecasting Intraday Electricity Markets)

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27 pages, 1649 KiB

Open AccessFeature PaperArticle

Forecasting in Blockchain-Based Local Energy Markets

by Michael Kostmann and Wolfgang K. Härdle

Energies 2019, 12(14), 2718; https://doi.org/10.3390/en12142718 - 16 Jul 2019

Cited by 13 | Viewed by 4226

Abstract

Increasingly volatile and distributed energy production challenges traditional mechanisms to manage grid loads and price energy. Local energy markets (LEMs) may be a response to those challenges as they can balance energy production and consumption locally and may lower energy costs for consumers. [...] Read more.

Increasingly volatile and distributed energy production challenges traditional mechanisms to manage grid loads and price energy. Local energy markets (LEMs) may be a response to those challenges as they can balance energy production and consumption locally and may lower energy costs for consumers. Blockchain-based LEMs provide a decentralized market to local energy consumer and prosumers. They implement a market mechanism in the form of a smart contract without the need for a central authority coordinating the market. Recently proposed blockchain-based LEMs use auction designs to match future demand and supply. Thus, such blockchain-based LEMs rely on accurate short-term forecasts of individual households’ energy consumption and production. Often, such accurate forecasts are simply assumed to be given. The present research tested this assumption by first evaluating the forecast accuracy achievable with state-of-the-art energy forecasting techniques for individual households and then, assessing the effect of prediction errors on market outcomes in three different supply scenarios. The evaluation showed that, although a LASSO regression model is capable of achieving reasonably low forecasting errors, the costly settlement of prediction errors can offset and even surpass the savings brought to consumers by a blockchain-based LEM. This shows that, due to prediction errors, participation in LEMs may be uneconomical for consumers, and thus, has to be taken into consideration for pricing mechanisms in blockchain-based LEMs. Full article

(This article belongs to the Special Issue Modeling and Forecasting Intraday Electricity Markets)

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26 pages, 4491 KiB

Open AccessArticle

Intraday Load Forecasts with Uncertainty

by David Kozak, Scott Holladay and Gregory E. Fasshauer

Energies 2019, 12(10), 1833; https://doi.org/10.3390/en12101833 - 14 May 2019

Cited by 5 | Viewed by 3337

Abstract

We provide a comprehensive framework for forecasting five minute load using Gaussian processes with a positive definite kernel specifically designed for load forecasts. Gaussian processes are probabilistic, enabling us to draw samples from a posterior distribution and provide rigorous uncertainty estimates to complement [...] Read more.

We provide a comprehensive framework for forecasting five minute load using Gaussian processes with a positive definite kernel specifically designed for load forecasts. Gaussian processes are probabilistic, enabling us to draw samples from a posterior distribution and provide rigorous uncertainty estimates to complement the point forecast, an important benefit for forecast consumers. As part of the modeling process, we discuss various methods for dimension reduction and explore their use in effectively incorporating weather data to the load forecast. We provide guidance for every step of the modeling process, from model construction through optimization and model combination. We provide results on data from the largest deregulated wholesale U.S. electricity market for various periods in 2018. The process is transparent, mathematically motivated, and reproducible. The resulting model provides a probability density of five minute forecasts for 24 h. Full article

(This article belongs to the Special Issue Modeling and Forecasting Intraday Electricity Markets)

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15 pages, 346 KiB

Open AccessFeature PaperArticle

Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits

by Katarzyna Maciejowska, Weronika Nitka and Tomasz Weron

Energies 2019, 12(4), 631; https://doi.org/10.3390/en12040631 - 16 Feb 2019

Cited by 64 | Viewed by 8816

Abstract

Recently, a dynamic development of intermittent renewable energy sources (RES) has been observed. In order to allow for the adoption of trading contracts for unplanned events and changing weather conditions, the day-ahead markets have been complemented by intraday markets; in some countries, such [...] Read more.

Recently, a dynamic development of intermittent renewable energy sources (RES) has been observed. In order to allow for the adoption of trading contracts for unplanned events and changing weather conditions, the day-ahead markets have been complemented by intraday markets; in some countries, such as Poland, balancing markets are used for this purpose. This research focuses on a small RES generator, which has no market power and sells electricity through a larger trading company. The generator needs to decide, in advance, how much electricity is sold in the day-ahead market. The optimal decision of the generator on where to sell the production depends on the relation between prices in different markets. Unfortunately, when making the decision, the generator is not sure which market will offer a higher price. This article investigates the possible gains from utilizing forecasts of the price spread between the intraday/balancing and day-ahead markets in the decision process. It shows that the sign of the price spread can be successfully predicted with econometric models, such as ARX and probit. Moreover, our research demonstrates that the statistical measures of forecast accuracy, such as the percentage of correct sign classifications, do not necessarily coincide with economic benefits. Full article

(This article belongs to the Special Issue Modeling and Forecasting Intraday Electricity Markets)

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