Forecasting Day-ahead Electricity Prices with A SARIMAX Model

McHugh, Catherine; Coleman, Sonya; Kerr, Dermot; McGlynn, Daniel

doi:10.1109/ssci44817.2019.9002930

Cited by 25 publications

(11 citation statements)

References 14 publications

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“…Day-ahead market modelling using both different versions of ANN [1,12,20] as well as other modelling methods [5,8,9,10,11,13] is not a new issue and has been descripted in many papers. They referred both to finding the better model than the previous ones for classic power plants as well as for photovoltaic or wind farms.…”

Section: Description Of the Modelled Systemmentioning

confidence: 99%

Neural modelling of electricity prices quoted on the Day-Ahead Market of TGE S.A. shaped by environmental and economic factors

Ruciński

2021

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The paper contains the results of research on the impact of the number of factors used to build the Day-Ahead Market model at Polish Power Exchange S.A. Five models with a different number of factors influencing the model were tested. To test the quality of models according to the adopted evaluation criteria, i.e., mean square error and the coefficient of determination for the weighted average prices sold in a given hour of the day, the influence of weather factors, socio-economic factors and energy demand were adopted. The results obtained from the analysis show a relatively high correctness of the simplest of the adopted models, which differs slightly from the best model.

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Section: Description Of the Modelled Systemmentioning

confidence: 99%

Neural modelling of electricity prices quoted on the Day-Ahead Market of TGE S.A. shaped by environmental and economic factors

Ruciński

2021

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“…Historical electricity prices in DKK are used as input for the forecasting model. The authors in [42] statistically show that a SARIMA with order (2, 1, 3)(1, 0, 1) 24 is appropriate for forecasting such prices, and the same order is used here. The previous 100 days of hourly electricity prices are used as training data.…”

Section: Single-day Demonstrationmentioning

confidence: 99%

Optimal Operation and Market Integration of a Hybrid Farm with Green Hydrogen and Energy Storage: A Stochastic Approach Considering Wind and Electricity Price Uncertainties

García-Miguel,

Zarilli,

Alonso-Martinez

et al. 2024

Sustainability

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In recent years, growing interest has emerged in investigating the integration of energy storage and green hydrogen production systems with renewable energy generators. These integrated systems address uncertainties related to renewable resource availability and electricity prices, mitigating profit loss caused by forecasting errors. This paper focuses on the operation of a hybrid farm (HF), combining an alkaline electrolyzer (AEL) and a battery energy storage system (BESS) with a wind turbine to form a comprehensive HF. The HF operates in both hydrogen and day-ahead electricity markets. A linear mathematical model is proposed to optimize energy management, considering electrolyzer operation at partial loads and accounting for degradation costs while maintaining a straightforward formulation for power system optimization. Day-ahead market scheduling and real-time operation are formulated as a progressive mixed-integer linear program (MILP), extended to address uncertainties in wind speed and electricity prices through a two-stage stochastic optimization model. A bootstrap sampling strategy is introduced to enhance the stochastic model’s performance using the same sampled data. Results demonstrate how the strategies outperform traditional Monte Carlo and deterministic approaches in handling uncertainties, increasing profits up to 4% per year. Additionally, a simulation framework has been developed for validating this approach and conducting different case studies.

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“…Time-series prediction of the DAM price is a popular research topic, and can be done by applying various methods such as (S)ARIMA(X) models [21][22][23], artificial neural networks [24,25] or deep learning [26]. The DAM has been an established electricity trading platform for many years, hence leading to a large available dataset on the historical prices which can serve as input to a prediction algorithm.…”

Section: Day-ahead Market Price Prediction Modelmentioning

confidence: 99%

A Two-Stage Stochastic Optimisation Methodology for the Operation of a Chlor-Alkali Electrolyser under Variable DAM and FCR Market Prices

et al. 2020

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The increased penetration of renewable energy sources in the electrical grid raises the need for more power system flexibility. One of the high potential groups to provide such flexibility is the industry. Incentives to do so are provided by variable pricing and remuneration of supplied ancillary services. The operational flexibility of a chlor-alkali electrolysis process shows opportunities in the current energy and ancillary services markets. A co-optimisation of operating the chlor-alkali process under an hourly variable priced electricity sourcing strategy and the delivery of Frequency Containment Reserve (FCR) is the core of this work. A short term price prediction for the Day-Ahead Market (DAM) and FCR market as input for a deterministic optimisation shows good results under standard DAM price patterns, but leaves room for improvement in case of price fluctuations, e.g., as caused by Renewable Energy Sources (RES). A two-stage stochastic optimisation is considered to cope with the uncertainties introduced by the exogenous parameters. An improvement of the stochastic solution over the deterministic Expected Value (EV) solution is shown.

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Forecasting Day-ahead Electricity Prices with A SARIMAX Model

Cited by 25 publications

References 14 publications

Neural modelling of electricity prices quoted on the Day-Ahead Market of TGE S.A. shaped by environmental and economic factors

Neural modelling of electricity prices quoted on the Day-Ahead Market of TGE S.A. shaped by environmental and economic factors

Optimal Operation and Market Integration of a Hybrid Farm with Green Hydrogen and Energy Storage: A Stochastic Approach Considering Wind and Electricity Price Uncertainties

A Two-Stage Stochastic Optimisation Methodology for the Operation of a Chlor-Alkali Electrolyser under Variable DAM and FCR Market Prices

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