Agent-based model for spot and balancing electricity markets.
problem Accurately modeling the complex dynamics of electricity markets.
method Agent-based model with one-minute resolution.
result Simulation results match Nord Pool data.
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48 results for “electricity balancing”
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.
This review analyzes deep learning methods for electricity price forecasting across different markets.
problem Insufficient analysis of deep learning methods in electricity price forecasting.
method Unified taxonomy of deep learning components, analysis of trends across markets.
result Shift toward probabilistic, microstructure-centric, and market-aware designs.
Risk Constrained Trading Strategies for Stochastic Generation with a Single-Price Balancing Marketq-fin.GN
Paper develops trading strategies for wind power to balance risk and revenue.
problem Challenges in trading stochastic generation like wind power in competitive markets.
method Develops risk-constrained trading strategies using probabilistic framework and system information.
result Strategies increase revenue and reduce risk for stochastic generators.
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.
Study shows increased renewables lead to more extreme price spikes in balancing markets.
problem Higher variability of renewable energy leads to increased ancillary service costs, especially extreme price spikes.
method Agent-based modeling to simulate fluctuations in load and renewable energy production, forecasting resulting energy prices and costs.
result The probability of extreme price events increases with an increased share of renewable energy sources.
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.
The paper uses Frenet frame to unify electrical and geometric quantities.
problem Defining time derivatives of electrical quantities in various conditions.
method Utilizes Frenet frame from differential geometry to define time derivatives in both stationary and transient conditions.
result Unifies and generalizes time- and phasor-domain frameworks.
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.
Scenario Forecast of Cross-border Electric Interconnection towards Renewables in South Americaeess.SY
This paper forecasts renewable energy prospects in South America through cross-border interconnection.
problem Lack of renewable energy integration across South American countries.
method Long-term scenario forecasting methodology applied to raw data from typical countries.
result Promoting cross-border interconnection towards renewables can optimize energy supply, reduce costs, and balance the energy matrix.
Proposes a new method for predicting uncertain net electricity demand.
problem Uncertain net electricity demand and asymmetric cost structure.
method Bilevel program using clustering to tailor prescriptions.
result Substantial cost savings compared to standard methods.
The study finds that real-time prices can lead to higher costs due to consumer flexibility.
problem The integration of consumer flexibility into electricity markets leads to higher costs.
method Agent-based model and multi-agent simulation to study market integration.
result Real-time prices can lead to higher costs due to consumer flexibility and market structure.
Paper introduces reinforcement learning for managing power grids.
problem Balancing power flows and maintaining grid stability in real-time.
method Reinforcement Learning applied to power network operations.
result Demonstrates feasibility of machine learning in power grid management.
Study on price fluctuations and persistence in European electricity spot markets.
problem Analyzing variability and persistence of electricity prices in European spot markets.
method Analysis of hourly, intraday, and 15-min intraday market prices; quantification of fluctuations, correlations, and extreme events; classification into circulation weather types.
result Different time scales in market dynamics; multifractal behavior below 12 hours; anti-correlation and mean reversion above 12 hours; long-term behavior influenced by four-day weather patterns; -Gaussian distributions as best fit.
Multiple split approach -- multidimensional probabilistic forecasting of electricity marketsq-fin.RM
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.
Study examines economic impact of wind energy on Colombian electricity market.
problem Impact of wind energy on Colombian electricity market pricing and conventional generation.
method Built a unit commitment model to simulate market legislation and system data.
result Wind energy reduces the operation of gas-fired plants by up to 20%.
This review explores probabilistic forecasting methods in evolving energy markets.
problem Volatility and uncertainty in renewable energy markets require probabilistic forecasting for risk assessment.
method Traces evolution from Bayesian and distribution-based approaches to conformal prediction.
result Probabilistic forecasting offers a more comprehensive approach to risk assessment and market participation.
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.
Forecasting Electricity Pricesq-fin.ST
Research focuses on predicting electricity prices with complex models considering probabilistic forecasts.
problem Challenging task due to market dynamics and weather and business activity dependence.
method Shift from econometric to statistical/machine learning models, considering probabilistic forecasts.
result More accurate predictions with complex models and probabilistic measures.
Game theory models storage investment to balance market competition and profits.
problem Strategic storage investment impacts electricity market prices and revenues.
method Formulated a non-cooperative game between investors to model strategic storage decisions.
result Increasing storage capacity reduces individual profits but increases total investment.
Deep learning improves weather modeling for electricity load forecasting.
problem Accurate load and renewable energy forecasting requires complex spatio-temporal weather modeling.
method Automated spatio-temporal feature extraction using deep neural networks.
result Deep learning outperforms traditional methods in French national load 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.
Proposes a neural network for efficient imbalance electricity price forecasting.
problem Accurate and efficient imbalance electricity price forecasting in industrial energy trading systems.
method Market-rule-informed neural network framework.
result The proposed model achieves competitive forecasting performance with fewer parameters and shorter training time.
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.
New principle reduces load imbalance in LLM serving systems, saving up to 52% energy.
problem Wasted computational power due to load imbalance in LLM serving systems.
method Developed a universal load-balancing principle for barrier-synchronized systems with non-migratable state.
result Proves worst-case theoretical guarantees for imbalance reduction and energy savings.
Study electric field and potential of torus knots, focusing on z-axis.
problem Analyze electric field and potential of torus knots.
method Parametrize torus knots, use symmetry, numerical methods, contour integration.
result Electric field is zero only at the origin, extreme points analyzed.
Integrated analysis of the urban water-electricity demand nexus in the Midwestern United Statescs.LG
Model predicts climate-sensitive water and electricity use in Midwestern cities.
problem Ensuring conservation measures in growing cities under climate change.
method Statistical learning theory-based modeling framework for predicting climate-sensitive water-electricity demand nexus.
result Water use is slightly more sensitive to climate than electricity use.
Method minimizes electricity procurement cost based on demand prediction errors.
problem Minimizing electricity procurement cost in spot markets.
method Formulate method to minimize procurement cost over two parameters.
result Minimizes total electricity cost with known unit prices and prediction errors.
Deep neural networks predict electricity consumption accurately.
problem Predicting future electricity consumption for better management.
method Used Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) networks to predict electricity consumption based on past data.
result Both RNN and LSTM achieved an average Root Mean Square error of 0.1.
Discrete Green's functions are the inverses or pseudo-inverses of combinatorial Laplacians. We present compact formulas for discrete Green's functions, in terms of the eigensystems of corresponding Laplacians, for products of regular graphs with or without boundary. Explicit formulas are derived for the cycle, torus, a…
This study optimizes energy storage scheduling under price uncertainty, balancing risk and reward.
problem Optimizing energy storage operation under price uncertainty and risk.
method Two-stage stochastic risk-constrained approach using conditional value-at-risk.
result Increasing risk aversion leads to substantial benefits in terms of risk reduction and expected reward.
New method tracks electricity flows to measure carbon emissions in real-time.
problem Accurate measurement of carbon emissions from electricity consumption.
method Real-time consumption-based accounting using flow tracing.
result Substantial differences found between production and consumption intensities.
Regression decision trees outperform other methods in predicting electricity prices.
problem Short-term forecasting of electricity prices to manage risk and strategy.
method Comparison of regression decision trees and recurrent neural networks (RNNs) with ARIMA.
result Regression decision trees achieve high performance compared to other methods.
Study shows Bitcoin mining with surplus electricity can boost KEPCO's financial stability.
problem Improving energy resource efficiency and reducing KEPCO's debt.
method Utilized surplus electricity for Bitcoin mining using Antminer S21 XP Hyd, analyzed with Random Forest Regressor and Long Short-Term Memory models.
result Bitcoin mining with surplus electricity generates economic revenue, minimizes energy loss, and resolves payment issues for KEPCO.
Paper provides a method to price electricity storage contracts using COS technique.
problem Valuation of electricity storage contracts considering physical and operational constraints.
method Uses Fourier-based COS method to price contracts based on stochastic polynomial process.
result The COS method accurately and efficiently prices electricity storage contracts.
Exploring Strategies for Classification of External Stimuli Using Statistical Features of the Plant Electrical Responsephysics.bio-ph
Paper classifies plant electrical signals to identify external stimuli.
problem Classifying external stimuli using plant electrical response.
method Computed 11 statistical features from plant electrical signals and used discriminant analysis.
result Raw electrical signals contain enough information for stimulus classification.
Panel semiparametric quantile regression neural network for electricity consumption forecastingstat.ML
PSQRNN model forecasts electricity consumption in China by integrating neural networks and quantile regression.
problem Electricity forecasting in China due to regional economic, social, and natural conditions.
method PSQRNN combines neural networks and semiparametric quantile regression to model electricity consumption.
result PSQRNN model outperforms traditional methods in forecasting electricity consumption in China.
Algorithm optimizes electricity procurement costs by 1.65%.
problem Minimizing energy cost while covering forecast consumption.
method Deep learning forecasting and deviation indicator.
result Reduction of 1.65% in costs compared to uniform policy.
New approach predicts electricity prices for months to years with probabilistic forecasts.
problem Uncertainty in long-term electricity price forecasting.
method Extends X-Model using supply and demand curve for hourly electricity prices.
result Probabilistic forecasts detect long-term price spikes.
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…
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…
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.
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.
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 …
Deep Recurrent Electricity Theft Detection in AMI Networks with Random Tuning of Hyper-parameterscs.LG
Deep RNN detects electricity theft in smart grids.
problem Electricity theft in smart grids.
method Generalized deep recurrent neural network (RNN) with gated recurrent unit (GRU) and random hyper-parameter tuning.
result Superior performance compared to existing detectors.
Algorithm optimizes hedging in electricity markets by minimizing variance risk.
problem Hedging contracts in electricity markets due to liquidity and market characteristics.
method Developed an algorithm for mean variance hedging considering transaction costs and market depth.
result Algorithm effectively reduces residual risk in electricity market positions.
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…
This paper improves electricity price forecasting and analyzes economic benefits.
problem Improving accuracy of quarter-hourly electricity price forecasts.
method Proposes a multivariate elastic net regression model for German spot markets.
result Simple trading strategies with accurate forecasts can lead to substantial economic impact.