Research optimizes a small RES utility's portfolio by dynamically trading in German electricity markets.
problem Managing risks in RES producers and electricity traders in changing electricity markets.
method Uses SVAR model to estimate market relationships and data-driven trading strategies to optimize revenue and reduce risk.
result Data-driven trading strategies increase utility revenue and reduce trading risk.
Short-term probabilistic forecasting of German electricity imbalance prices.
problem Uncertainty in renewable energy capacity and electricity prices.
method Combining lasso with bootstrap, gamlss, and probabilistic neural networks for forecasting imbalance prices.
result Sophisticated methods improve empirical coverage of imbalance prices but do not substantially outperform the intraday continuous price index.
How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oi…
In the following paper, we analyse the ID3-Price in the German Intraday Continuous electricity market using an econometric time series model. A multivariate approach is conducted for hourly and quarter-hourly products separately. We estimate the model using lasso and elastic net techniques and perform an out-of-samp…
Optimizes bidding in hourly and quarter-hourly electricity markets to reduce price impact.
problem Maximizing profit in two consecutive electricity markets with market impact and transaction costs.
method Examined multiple price scenarios, estimated market impact, used trading strategies, provided theoretical results.
result Minimizing price impact is more profitable than maximizing arbitrage in the German EPEX market.
Paper proposes a new method for probabilistic electricity price forecasting.
problem Accurate estimation of forecast uncertainties for optimal decision making.
method Implicit generative ensemble post-processing using an ensemble of point forecasting models.
result Method outperforms well-established model combination benchmarks.
THieF improves day-ahead electricity price prediction accuracy by reconciling hourly and block forecasts.
problem Improving accuracy in predicting day-ahead electricity prices.
method Temporal hierarchy forecasting (THieF) reconciling hourly and block forecasts.
result THieF significantly improves accuracy (up to 13%) at all levels of prediction.
A method for multidimensional probabilistic electricity market forecasting is proposed.
problem Uncertainty in simultaneous multivariate predictions of electricity markets.
method Repeated resampling to estimate uncertainty of simultaneous multivariate predictions.
result The method provides highly accurate predictions and gains are largest when considering functions of variables.
The study analyzes how probabilistic forecasts improve battery trading strategies in electricity markets.
problem Improvements in statistical forecast quality do not directly translate to economic value in battery trading strategies.
method The study frames battery optimization as a stochastic program based on fully probabilistic forecasts and examines decision quality under different uncertainty models.
result The study identifies two critical flaws in quantile-based trading strategies and provides theoretical justification and empirical evidence.
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…
Optimizes intraday electricity trading to minimize costs.
problem Minimizing costs in intraday electricity trading.
method Derives an optimal model considering order book depth, time to delivery, and trading regimes.
result Optimal execution strategies have a significant monetary impact.
Study predicts electricity prices using LSTM models with feature selection, considering market coupling.
problem Accurate day-ahead electricity price forecasting in coupled markets.
method Hybrid LSTM-based deep learning models with feature selection algorithms.
result Proposed models achieve considerably accurate results in Nordic market.
The study forecasts hourly intraday electricity prices using ensemble methods.
problem Weak-form efficiency of hourly German Intraday Continuous Market prices.
method Probabilistic forecasting with ensemble trajectories, generalized additive model, and lasso penalty.
result The mixture model outperforms benchmarks in forecasting price distribution and volatility.
The study compares VaR and ES models for tail risk of electricity futures, finding AR(1)-GARCH(1,1) with Student-t distribution best.
problem Modeling tail risk of electricity futures contracts in various markets.
method Comparison of VaR and ES models using AR(1)-GARCH(1,1) with Student-t distribution, historical simulation, and quantile regression.
result AR(1)-GARCH(1,1) with Student-t distribution is the best-performing model for tail risk estimation.
This paper improves bidding price prediction for ancillary services markets, boosting revenues.
problem Volatility in renewable energy sources affects grid stability and revenue optimization.
method Machine learning models (SVR, DT, k-NN) and offset adjustment for pay-as-bid markets.
result The proposed approach increases potential revenues by 27.43% to 37.31% compared to baseline models.
Modeling intraday electricity prices with a Hawkes process.
problem Capturing the dynamics of intraday electricity prices, especially microstructure noise.
method 2D marked Hawkes process with increasing baseline intensity, providing analytic moments and signature plot.
result The model fits German intraday electricity data well and converges to a Brownian motion with increasing volatility.
Optimal energy trading strategy for intraday markets using Hawkes processes.
problem Optimal execution in intraday energy markets with specific trading patterns.
method Calibrated Hawkes process model with transient price impact.
result Substantial cost reductions in TWAP and VWAP benchmarks.
The paper models battery valuation in intraday electricity markets, incorporating liquidity costs.
problem Valuing batteries in intraday electricity markets considering liquidity costs.
method Stochastic model for mid-prices combined with a deterministic model for liquidity costs, using dynamic programming for optimization.
result Liquidity costs significantly impact battery valuation, especially with multiple batteries.
This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected demand. Appropriate feature extraction for the order book data is dev…
iQRA improves probabilistic forecasts of electricity prices.
problem Lack of uncertainty estimates in machine learning forecasts for volatile markets.
method Isotonic Quantile Regression Averaging (iQRA) with stochastic order constraints.
result iQRA outperforms state-of-the-art methods in reliability and sharpness.
We propose a multi-factor polynomial framework to model and hedge long-term electricity contracts with delivery period. This framework has several advantages: the computation of forwards, risk premium and correlation between different forwards are fully explicit, and the model can be calibrated to observed electricity …
Novel neural network predicts electricity prices with higher moments.
problem Probabilistic forecasting of volatile electricity prices.
method Distributional neural network with a probability layer.
result Significantly outperforms benchmarks in forecasting.
Copulas model cross-product effects in intraday power markets.
problem Intraday power markets' cross-product effects are not adequately addressed by existing univariate approaches.
method Copulas and latent beta regression for modeling high-dimensional intraday price return vector, with time-varying dependence parameter.
result Modeling cross-product effects improves forecasting performance.
New method selects recent similar periods for better electricity price forecasting.
problem Improving accuracy in forecasting electricity prices.
method Change-point detection (NOT method) to select calibration periods; estimating autoregressive models only for selected data.
result Significant improvement in forecasting accuracy compared to existing methods.
We propose a multivariate elastic net regression forecast model for German quarter-hourly electricity spot markets. While the literature is diverse on day-ahead prediction approaches, both the intraday continuous and intraday call-auction prices have not been studied intensively with a clear focus on predictive power. …
Estimates price elasticity from autocorrelated time series using causal graphs.
problem Inconsistent IV estimators in autocorrelated time series data.
method Model equilibrium with unobserved confounders, derive DAG, and use graphical inference for valid IV estimators.
result Valid IV estimators improve understanding of economic dynamics.
Novel approach predicts long-term seasonal component of electricity prices for improved forecasting.
problem Improving day-ahead electricity price forecasting accuracy.
method Extracts trend-seasonal pattern from extrapolated price series using autoregressive and LASSO models.
result Improves predictive accuracy by 3-15% in root mean squared error and 1% in profits.
A new method forecasts hourly electricity prices considering product dynamics and limit order book signals.
problem High volatility and imbalance in power systems due to renewable energy and flexible demand.
method Incorporates short-term features from hourly and quarter-hourly products, including limit order book and neighboring product signals.
result Features from the limit order book are most influential, and neighboring product signals improve forecast accuracy.
A new data-driven model forecasts electricity prices efficiently.
problem Forecasting electricity prices using traditional methods.
method Integrates data-driven and fundamental models, learns from historical data.
result Significantly improves forecasting accuracy compared to existing models.
Hybrid approach improves probabilistic forecasts for electricity trading.
problem Improving probabilistic forecasts for electricity trading markets.
method Combines QRA and factor-based averaging for probabilistic forecasting.
result The hybrid approach outperforms benchmarks in statistical measures and economic value.
New online method for multivariate probabilistic electricity price forecasting.
problem Multivariate probabilistic forecasting of electricity prices.
method Online multivariate distributional regression with LASSO regularization.
result Robust and interpretable joint prediction intervals for 24-hour electricity prices.
Develops a new trading strategy for renewable producers to manage price volatility.
problem Price volatility and imbalance risk in power markets due to renewable generation.
method Data-driven continuous-time stochastic optimal control framework using SDEs and diffusion models.
result Trading strategy outperforms benchmarks and reduces profit and loss.
Study improves forecasting of aggregated curves in electricity markets.
problem Improving accuracy in predicting aggregated curves like demand and supply in electricity markets.
method Exploits hierarchical structure of aggregated curves, uses reconciliation methods (bottom-up, top-down, linear optimal, aggregated-down).
result Hierarchical reconciliation methods can significantly improve forecast accuracy of aggregated curves.
New method improves probabilistic electricity price predictions.
problem Improving point forecasts to probabilistic distributions for better decision-making.
method Isotonic Distributional Regression combined with other postprocessing methods.
result Isotonic Distributional Regression outperforms other methods in combining probabilistic distributions.
An on-going debate in the energy economics and power market community has raised the question if energy-only power markets are increasingly failing due to growing feed-in shares from subsidized renewable energy sources (RES). The short answer to this is: No, they are not failing. Energy-based power markets are, however…
Proposes a regularization approach to model German power derivative market, identifying significant risk spillovers.
problem Large portfolio of German power derivative contracts, identifying significant risk spillovers.
method Combines high-dimensional variable selection with dynamic network analysis.
result Identifies significant risk contributors and interdependencies between contracts, especially spot contracts.
ARHNN method improves electricity price forecasting accuracy.
problem Improving accuracy in electricity price forecasting.
method Combines Autoregressive Hybrid Nearest Neighbors (ARHNN) method with calibration sample selection and forecast combination.
result ARHNN method outperforms benchmarks by up to 10% in German, Spanish, and New England markets.
Study on time-zero efficiency of European power derivatives markets using statistical tests and trading rules.
problem Assessing time-zero efficiency in European power derivatives markets.
method Statistical tests based on the law of one price and trading rules based on price differentials and no-arbitrage violations applied to daily data of three European power markets.
result Definite conclusions on time-zero efficiency are not possible for French and Spanish markets due to liquidity and representativeness challenges.
We present a new model for the electricity spot price dynamics, which is able to capture seasonality, low-frequency dynamics and the extreme spikes in the market. Instead of the usual purely deterministic trend we introduce a non-stationary independent increments process for the low-frequency dynamics, and model the la…
Study reveals stylized facts in German bond futures markets.
problem Understanding market dynamics in German bond futures.
method Analyzed tick-by-tick data of four German bond futures contracts.
result Uncovered commonalities and unique characteristics across different futures.
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-…
Study finds CRPS learning doesn't improve day-ahead bidding profits despite better accuracy.
problem Improving day-ahead bidding profits through better probabilistic price forecasting.
method CRPS learning to minimize continuous ranked probability score (CRPS) for ensemble predictions.
result Higher diversity in ensemble predictions improves accuracy but doesn't lead to higher profits.
This work forecasts electricity prices using Bayesian regime detection and conditional neural processes.
problem Forecasting electricity prices with optimal operational outcomes.
method Bayesian regime detection with conditional neural processes, integrating multi-criteria decision support.
result R-NP model outperformed other models in comprehensive operational utility assessments.
High-frequency trading strategy boosts battery storage profits.
problem Maximizing revenue for battery energy storage systems in intraday markets.
method Adapted dynamic programming for continuous intraday markets, considering limit order book dynamics.
result Dynamic programming strategy outperforms standard re-optimization methods, increasing profits by 58% and 14% respectively.
In this paper, we analyze Nash equilibria between electricity producers selling their production on an electricity market and buying CO2 emission allowances on an auction carbon market. The producers' strategies integrate the coupling of the two markets via the cost functions of the electricity production. We set out a…
Paper optimizes battery storage in multiple energy markets for better profits.
problem Optimizing battery storage participation in multiple energy markets to balance supply and demand.
method Developed a joint bidding strategy combining intraday and frequency markets using mixed integer linear programming and a learned classifier strategy.
result The LCS increases overall profits by over 4% compared to static strategies and by more than 3% over a naive dynamic benchmark.
In this note, we present an existence result of a Nash equilibrium between electricity producers selling their production on an electricity market and buying CO2 emission allowances on an auction carbon market. The producers' strategies integrate the coupling of the two markets via the cost functions of the electricity…
Competition has been introduced in the electricity markets with the goal of reducing prices and improving efficiency. The basic idea which stays behind this choice is that, in competitive markets, a greater quantity of the good is exchanged at a lower and a lower price, leading to higher market efficiency. Electricity …