Research forecasts electricity spot prices using stochastic volatility models.
problem Forecasting day-ahead electricity prices in a spot market.
method Exploring and enriching a baseline stochastic volatility model with exogenous regressors.
result A better fitting model confirmed by out-of-sample forecasts.
Hydropower reduces system electricity price and volatility, especially at extreme levels.
problem Impact of hydropower on system electricity price and volatility.
method Robust statistical analysis using multiple linear regression and quantile regression.
result Hydropower reduces system electricity price and volatility, especially at extreme levels.
Study shows increased VRE penetration reduces electricity prices and volatility.
problem Impact of increased variable renewable energy on electricity prices and volatility.
method Hourly, real-time data from six ISOs, quantile and skew t-distribution regressions.
result Increased VRE penetration is associated with decreased system electricity price and volatility in most ISOs.
Study reveals different drivers of electricity price volatility across Europe.
problem Understanding the drivers of electricity price volatility across different European zones.
method Developed estimators of weekly integrated variance using a stochastic partial differential equation approach, accounting for mean-reversion and semigroup-smoothing.
result Each European generation zone has distinct drivers of volatility, and leverage effects are not generally asymmetric.
The price of electricity is far more volatile than that of other commodities normally noted for extreme volatility. The possibility of extreme price movements increases the risk of trading in electricity markets. However, underlying the process of price returns is a strong mean-reverting mechanism. We study this featur…
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.
Study examines dependence of extreme electricity prices in Australian markets.
problem Understanding and managing risks of extreme price outcomes in Australian electricity markets.
method Examined extremal dependence using extremograms for 5-minute and 30-minute price data.
result Persistence and dependence of extreme prices are influenced by market structure and renewable energy share.
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.
Paper introduces a new pricing method for electricity swaps and options.
problem Pricing electricity swaps and options in markets with varying delivery periods.
method Introduces a weighted geometric averaging of futures prices over delivery periods.
result Arbitrage-free pricing framework for derivatives in electricity markets.
Reliability Options are capacity remuneration mechanisms aimed at enhancing security of supply in electricity systems. They can be framed as call options on electricity sold by power producers to System Operators. This paper provides a comprehensive mathematical treatment of Reliability Options. Their value is first de…
Modeling price formation in intraday electricity markets with renewable generation.
problem Price formation and optimal trading strategies in intraday electricity markets with intermittent renewable generation.
method Developed a tractable equilibrium model using stochastic control theory to identify optimal strategies and exhibit Nash equilibrium.
result Identified optimal trading strategies and exhibited Nash equilibrium in closed form for a finite number of agents and in the asymptotic framework of mean field games.
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.
In this paper we investigate predictability of electricity prices in the Canadian provinces of Alberta and Ontario, as well as in the US Mid-C market. Using scale-dependent detrended fluctuation analysis, spectral analysis, and the probability distribution analysis we show that the studied markets exhibit strongly anti…
Transformer model forecasts electricity price spread for virtual bidding.
problem Volatility in renewable energy causes price forecasting challenges.
method Transformer-based deep learning model using various time-series features.
result Trading strategy at peak hour yields nearly consistent profit.
Study shows different price correlations in European electricity markets.
problem Stochastic variability and temporal correlation in electricity prices.
method Comparison of Detrended Fluctuation Analysis (DFA) and Kramers--Moyal equation.
result Intraday 15 minutes spot markets show strong negative correlations, unlike other markets.
Proposes a new model for simulating electricity prices and their correlation structure.
problem Simulating and understanding the complex dynamics of intraday electricity prices.
method Develops a multidimensional statistical model based on Poisson measures, estimating three key parameters.
result Demonstrates the model's effectiveness in battery valuation through dynamic programming.
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.
Paper proposes a risk-averse approach to energy storage price arbitrage using conformal uncertainty quantification.
problem Inherent volatility and uncertainty of real-time electricity prices create financial risks for storage arbitrage.
method Two-layer prediction model with conformal uncertainty quantification for high coverage of real-time price uncertainty.
result The framework achieves good profit margins with minimal losses, demonstrating effectiveness in real-time market.
Model equilibrium price in intraday electricity markets with uncertainty.
problem Formulate equilibrium model for intraday electricity trading with balancing constraints and uncertainty.
method Develop equilibrium model with agents' balancing constraints, forecasted consumption, production uncertainties, and Markov chain outages.
result Existence and uniqueness of equilibrium price as a martingale, with insights into price formation and impact of uncertainty.
We discuss stochastic modeling of volatility persistence and anti-correlations in electricity spot prices, and for this purpose we present two mean-reverting versions of the multifractal random walk (MRW). In the first model the anti-correlations are modeled in the same way as in an Ornstein-Uhlenbeck process, i.e. via…
Develops a new model for day-ahead electricity prices using ambit fields.
problem The high-dimensional panel structure of electricity spot prices in European zones.
method Formulates a continuous time framework as an ambit field indexed by a cylinder surface, embedding intrinsic dependence structures.
result The model allows for pricing of derivatives on individual delivery periods, making products like spreads analytically tractable.
This work presents the results of an empirical research with the target of modeling the stylized facts of the daily expost System Marginal Price (SMP) of the Greek wholesale electricity market, using data from January 2004 to December of 2011. SMP is considered here as the footprint of an underline stochastic and nonli…
This study improves electricity price forecasting in the Irish balancing market.
problem Limited and inconsistent research on short-term price forecasting in volatile balancing markets.
method Compared statistical, machine learning, and deep learning models using a public dataset and framework.
result LEAR, a statistical approach, outperforms complex models in the balancing market.
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.
Utilization of non-linear tools to characterize the state of development of the electricity markets in Italy and Greece. This is equivalent to testing the Efficient Market Hypothesis on these markets. The tools include a variety of complexity measures like Maximal Lyapunov and Hurst exponents and HHI index for market c…
Proposes a virtual bidding strategy for electricity markets using stochastic control.
problem Optimizing electricity prices in day-ahead and real-time markets.
method Modeling price differences as Brownian motion with meteorological variables, transforming into portfolio management problem.
result Developed a strategy to manage electricity prices efficiently.
The electricity market is a very peculiar market due to the large variety of phenomena that can affect the spot price. However, this market still shows many typical features of other speculative (commodity) markets like, for instance, data clustering and mean reversion. We apply the diffusion entropy analysis (DEA) to …
The paper proposes a framework for modeling and analysis of the dynamics of supply, demand, and clearing prices in power system with real-time retail pricing and information asymmetry. Real-time retail pricing is characterized by passing on the real-time wholesale electricity prices to the end consumers, and is shown t…
This paper introduces the class of volatility modulated Lévy-driven Volterra (VMLV) processes and their important subclass of Lévy semistationary (LSS) processes as a new framework for modelling energy spot prices. The main modelling idea consists of four principles: First, deseasonalised spot prices can be modelled di…
We empirically analyze the most volatile component of the electricity price time series from two North-American wholesale electricity markets. We show that these time series exhibit fluctuations which are not described by a Brownian Motion, as they show multi-scaling, high Hurst exponents and sharp price movements. We …
Study forecasts volatility and risk in electricity markets using matrix-HAR models.
problem Forecasting volatility and risk in electricity markets.
method Constructed a parsimonious matrix-HAR type model to estimate realized covariation and risk premia in electricity markets.
result Inclusion of longer time horizons and renewable generation information improves forecasts.
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.
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…
The non-storability of electricity makes it unique among commodity assets, and it is an important driver of its price behaviour in secondary financial markets. The instantaneous and continuous matching of power supply with demand is a key factor explaining its volatility. During periods of high demand, costlier generat…
In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market…
DMIDAS improves long-term forecasting accuracy in healthcare and electricity data.
problem Challenging long-term forecasting accuracy and computational complexity.
method Smoothness regularization and mixed data sampling techniques integrated into NBEATS architecture.
result Improves prediction accuracy by 5% on long forecasting horizons (1000 timestamps) compared to state-of-the-art models.
Electricity is bought and sold in wholesale markets at prices that fluctuate significantly. Short-term forecasting of electricity prices is an important endeavor because it helps electric utilities control risk and because it influences competitive strategy for generators. As the "smart grid" grows, short-term price fo…
In this paper we introduce an additive two-factor model for electricity futures prices based on Normal Inverse Gaussian Lévy processes, that fulfills a no-overlapping-arbitrage (NOA) condition. We compute European option prices by Fourier transform methods, introduce a specific calibration procedure that takes into acc…
Introduces an unobservable intrinsic electricity price to link storage theory with risk premium.
problem Connecting storage theory with risk premium in electricity markets.
method Introduces an unobservable intrinsic electricity price and derives prices for various contracts.
result Finds an overall negative risk premium in empirical analysis.
We analyze long-term memory properties of hourly prices of electricity in the Czech Republic between 2009 and 2012. As the dynamics of the electricity prices is dominated by cycles -- mainly intraday and daily -- we opt for the detrended fluctuation analysis, which is well suited for such specific series. We find that …
Proposes a pricing agent using reinforcement learning to balance renewable energy demand.
problem Intermittent renewable energy sources challenge carbon-free electricity generation.
method Reinforcement learning approach to balance customer demand with renewable energy generation.
result Demonstrates improved electricity pricing strategy for renewable energy integration.
Transfer learning improves electricity price forecasting accuracy.
problem Accurate day-ahead electricity price prediction using available data.
method Pre-train a neural network on source markets and fine-tune for target market.
result Transfer learning significantly improves forecasting performance.
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.
Model for hedging price and quantity risks in electricity markets.
problem Hedging risks for energy retailers in a regulated electricity market.
method Closed-form solution for optimal portfolio using financial instruments based on price and weather indexes.
result Closed-form solution for mean-var model in discrete setting without distributional assumptions.
This review classifies electricity price models for risk management.
problem Choosing suitable models for risk management in electricity markets.
method Classification of models based on their ability to represent price behavior.
result Helps users select appropriate models for risk management.
Paper models and forecasts intra-day electricity price spreads.
problem Forecasting intra-day price spreads for electricity traders and operators.
method Dynamic density functions based on skewed-t distributions, conditional on exogenous drivers.
result Best fitting and forecasting specifications selected using Pinball Loss function.
The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity pri…
CapOptix uses options theory to price capacity in electricity markets.
problem Traditional capacity market designs fail to account for risk and price shocks.
method Interprets capacity commitments as reliability options and uses Markov Regime Switching Process.
result CapOptix provides more accurate pricing of capacity premia compared to existing mechanisms.