This paper proposes a joint energy and data market to handle uncertainty in energy procurement.
problem Handling uncertainty in energy markets through data markets.
method Modeling a day-ahead retailer energy procurement problem with uncertain demand, integrating forecasting and optimisation, and using differential privacy.
result The value of joint energy and data clearing is highlighted through numerical case studies.
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.
Recent innovations in Information and Communication Technologies (ICT) provide new opportunities and challenges for integration of distributed energy resources (DERs) into the energy supply system as active market players. By increasing integration of DERs, novel market platform should be designed for these new market …
Paper optimizes energy trading on DA markets using RL.
problem Volatility and randomness in renewable energy sources.
method Markov Decision Process, reinforcement learning, evolutionary algorithm.
result RL-based strategy generates highest market profits.
This paper applies Heath-Jarrow-Morton framework to energy markets for practical use.
problem Applying complex financial models to energy markets for practical use.
method Calibration by PCA, Monte Carlo simulations, derivatives pricing.
result Calibrated model accurately simulates European power and gas markets.
New model predicts energy prices under different scenarios.
problem Complex causal relationships in energy markets with continuous regime changes.
method Augmented Time Series Structural Causal Models (ATSCM) integrating neural causal discovery.
result Enables novel counterfactual queries in energy markets.
Optimizes BESS for cross-market energy arbitrage to boost revenues.
problem Charging and discharging BESS at optimal times to maximize profits.
method Developed a generic framework, backtest engine, and optimization strategy.
result Boosted revenues by 10% through strategic BESS operation.
Paper explores balancing market dynamics and interpretable forecasting models for energy prices.
problem Tackles the challenge of accurately predicting mFRR price and understanding market dynamics.
method Compares XGBoost and EBM for forecasting mFRR activation price in the balancing market.
result EBM provides comparable forecasting accuracy to XGBoost but with higher interpretability.
This paper studies the extreme dependencies between energy, agriculture and metal commodity markets, with a focus on local co-movements, allowing the identification of asymmetries and changing trend in the degree of co-movements. More precisely, starting from a non-parametric mixture copula, we use a novel copula-based…
Paper models uncertainty in electricity and gas markets to assess its impact.
problem Addressing uncertainties in coupled electricity and gas markets.
method Integrated and stochastic optimisation approaches for large-scale energy systems.
result Quantifies the value of encoding uncertainty in models.
Hybrid models forecast EPEC energy spot prices.
problem Forecasting energy spot prices in EPEC markets.
method Combining Naive, Fourier, ARMA/GARCH, mean-reversion, jump-diffusion, and RNN models.
result Improved accuracy in forecasting compared to individual models.
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…
Deep reinforcement learning improves trading performance in volatile energy markets.
problem Volatility and low signal-to-noise ratios in energy markets.
method Formalized trading as a stochastic system, developed reactive and adaptive algorithms, used deep neural networks.
result Deep reinforcement learning models outperform buy-and-hold strategy with an 83% higher Sharpe ratio.
Study extends Lévy models to capture market propagation delays.
problem Capturing sudden events in related markets with stochastic delays.
method Extend multivariate Lévy models using self-decomposability and multivariate subordination.
result Derived closed-form expressions for characteristic function and implemented Monte Carlo scheme.
A framework uses deep reinforcement learning to optimize energy storage in intraday markets.
problem Optimizing energy storage in intraday markets for renewable energy integration.
method Markov Decision Process, asynchronous distributed fitted Q iteration algorithm, artificial trajectories.
result The agent converges to a policy that achieves higher total revenues than the benchmark strategy.
Study examines barriers to grid-connected battery systems in Spain, finding high cycle cost remains main obstacle.
problem Barriers to grid-connected battery systems in Spain's deregulated electricity market.
method Utilization analysis and concept of 'potentially profitable utilization time' introduced.
result High cycle cost remains the main barrier for grid-connected battery systems in Spain.
Distributed, controllable energy storage devices offer several benefits to electric power system operation. Three such benefits include reducing peak load, providing standby power, and enhancing power quality. These benefits, however, are only realized during peak load or during an outage, events that are infrequent. T…
BESS shows potential in European markets for frequency support, but not for energy arbitrage.
problem Lack of profitability for BESS in energy arbitrage in most European markets.
method Proposed a general payoff model for BESS operation and calculated utilization factors for common applications.
result BESS shows higher potential in providing frequency support services, especially in Central Western and Northern Europe.
Case study shows impact of co-optimizing energy and reserve for wind energy.
problem Impact of lack of co-optimization of energy and reserve in high wind penetration scenarios.
method Developed two models with and without co-optimization, calibrated with Spanish market parameters.
result Models show significant differences in energy and reserve management.
Energy markets are strategic to governments and economic development. Several commodities compete as substitutable energy sources and energy diversifiers. Such competition reduces the energy vulnerability of countries as well as portfolios' risk exposure. Vulnerability results mainly from price trends and fluctuations,…
In this paper we look at the efficacy of different risk measures on energy markets and across several different stock market indices. We use both the Value at Risk and the Tail Conditional Expectation on each of these data sets. We also consider several different durations and levels for historical risk measures. Throu…
Solar Renewable Energy Certificate (SREC) markets are a market-based system that incentivizes solar energy generation. A regulatory body imposes a lower bound on the amount of energy each regulated firm must generate via solar means, providing them with a tradeable certificate for each MWh generated. Firms seek to navi…
New method assesses energy storage value beyond cost reduction.
problem Improving energy storage value beyond cost reduction.
method Market potential method to evaluate and compare energy storage technologies.
result High-cost hydrogen storage can be more valuable than low-cost hydrogen storage.
Model predicts BESS interactions and price impacts in energy markets.
problem Understanding BESS interactions and price formation in energy markets.
method Stochastic game-theoretic model with linear-quadratic differential game.
result Equilibrium controls and prices derived for BESSs in both heterogeneous and homogeneous settings.
Develops a new bivariate process for energy markets with improved simulation methods.
problem Modelling energy markets with stochastic delays and efficient simulations.
method Introduces a novel bivariate Normal Inverse Gaussian process and a path simulation scheme.
result Improves simulation efficiency for energy market models.
In recent years there has been an advent of quanto options in energy markets. The structure of the payoff is rather a different type from other markets since it is written as a product of an underlying energy index and a measure of temperature. In the HJM framework, by adopting the futures energy dynamics, we use the M…
The paper analyzes profitable bidding strategies for BESS in day-ahead and intraday markets.
problem Optimizing profitability of Battery Energy Storage Systems (BESS) in day-ahead and intraday markets.
method Employing the rolling intrinsic approach to model continuous intraday markets, accounting for bid-ask spreads and liquidity constraints.
result Multi-market bidding strategies outperform single-market participation, and relaxing daily cycling constraints can unlock additional value.
The article describes the algorithm used to define the electricity price in day-ahead and itraday energy markets in Italy. Details of Matlab implementation of one of its simplified versions, capable of producing good results in a extremely short time, are then provided and numerical results are discussed.
Framework for energy markets using measure-valued processes.
problem Arbitrage-free modeling of energy futures markets.
method Translation of Heath-Jarrow-Morton approach to measure-valued processes, derivation of HJM-drift condition, analysis of measure-valued diffusions.
result Existence of non-negative measure-valued diffusions satisfying the HJM-drift condition.
The paper investigates non-linear and heavy-tailed predictability in transition-energy financial markets.
problem Incomplete representation of dependence structure in Gaussian-linear forecasting frameworks.
method Develops a hybrid forecasting framework combining Student-t Vector Autoregressions with nonlinear recurrent residual learning architectures.
result The proposed framework consistently improves predictive accuracy relative to conventional models, especially during macro-financial stress.
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.
Study on energy storage's impact on electricity prices and profitability.
problem Analyzing the profitability of energy storage in electricity markets.
method Characterized optimal operating strategy for storage systems, determined equilibrium price in a market with storage, renewables, and conventional producers, and characterized price process using stochastic differential equations.
result Increased average revenues and interquantile ranges for storage assets in energy transition scenarios.
The authors characterize flexibility in power and energy markets considering time, spatiality, resource, and risk.
problem Evaluating and maximizing flexibility in power systems and markets.
method Characterization of flexibility dimensions (time, spatiality, resource, risk) and their interrelations with flexibility assets, products, and services.
result Flexibility should be evaluated based on multiple dimensions for efficient power systems and markets.
Calibrates historical and implied correlations in energy markets.
problem Challenges in aligning historical correlations of futures contracts with implied volatility smiles.
method Multiplicative multi-factor Heath-Jarrow-Morton model combined with stochastic volatility from lifted Heston model, using Kemna-Vorst approximation and Fourier-based techniques.
result Remarkable joint historical and implied calibration fits on the German power market.
Grid-scale batteries' bid patterns in price uncertainty markets
problem Interpreting bids from grid-scale batteries in wholesale electricity markets under price uncertainty
method Developing an asset-level model of a price-taking battery
result Empirical results deliver insights into withholding behavior, uncertainty effects, and risk management reshaping bid curves
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.
An increase in energy production from renewable energy sources is viewed as a crucial achievement in most industrialized countries. The higher variability of power production via renewables leads to a rise in ancillary service costs over the power system, in particular costs within the electricity balancing markets, ma…
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.
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.
Paper uses news data to model asset correlations without market data.
problem Traditional risk models rely on market data; this paper offers an alternative.
method Uses encoder-only language models to embed news data, then calculates asset return distributions and covariance through Energy Distance.
result Established connections between distributional differences and excess returns co-movements using Energy Distance.
Framework optimizes battery storage for markets by separating long-term degradation from short-term market dynamics.
problem Intractable computation due to timescale mismatch between battery degradation and market dynamics.
method Approximate dynamic programming with value function approximation and pseudo-time encoding.
result Policy outperforms benchmarks in real-time market scenarios.
Grid security and open markets are two major smart grid goals. Transparency of market data facilitates a competitive and efficient energy environment, yet it may also reveal critical physical system information. Recovering the grid topology based solely on publicly available market data is explored here. Real-time ener…
In this article we present a continuous time model for natural gas and crude oil future prices. Its main feature is the possibility to link both energies in the long term and in the short term. For each energy, the future returns are represented as the sum of volatility functions driven by motions. Under the risk neutr…
Study analyzes European energy markets' reactions to 2022 events using Bayesian methods.
problem Detecting structural breakpoints in energy and financial markets during turbulent times.
method Combines Hurst exponent for market efficiency, BEAST for abrupt changes, and seasonal/trend analysis.
result Markets exhibit varied reactions to critical events, affecting their trends and breakpoints.
The creation of the Renewable Energy Law (Law 1715 of 2014) promotes the introduction of large-scale renewable energy generation in the Colombian electricity market. The new legislation aims to diversify the country's generation matrix, mainly composed of hydro and fuel-based generation, with a share of 66% and 34% res…
Investigates how 'green' labels affect bond market dynamics.
problem Understanding the impact of 'green' labels on bond market trading activity.
method Used Hawkes processes and a moving average model to analyze high-frequency bond price dynamics.
result Differences in bond market dynamics emerge during periods with interest rate announcements, especially for energy market issuers.
Study optimal incentives for cleaner energy production.
problem Accelerate transition to cleaner technologies in energy market.
method Stochastic control models for three scenarios: single firm, two firms, and two firms without incentives.
result Optimal strategies for investment and production emerge, highlighting firm interactions and incentive effects.
Based on forward curves modelled as Hilbert-space valued processes, we analyse the pricing of various options relevant in energy markets. In particular, we connect empirical evidence about energy forward prices known from the literature to propose stochastic models. Forward prices can be represented as linear functions…