Forecasting different dimensions of liquidity in the intraday electricity markets: A review

Sameer Thakare; Neeraj Dhanraj Bokde; Andrés E. Feijóo-Lorenzo; Sameer Thakare; Neeraj Dhanraj Bokde; Andrés E. Feijóo-Lorenzo

doi:10.3934/energy.2023044

AIMS Energy

2023, Volume 11, Issue 5: 918-959. doi: 10.3934/energy.2023044

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Review

Forecasting different dimensions of liquidity in the intraday electricity markets: A review

1.
Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India
2.
Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus 8000, Denmark
3.
iCLIMATE Aarhus University Interdisciplinary Centre for Climate Change, Foulum, Tjele 8830, Denmark
4.
Department of Electrical Engineering, University of Vigo, Vigo 36310, Spain

Received: 02 May 2023 Revised: 29 August 2023 Accepted: 04 September 2023 Published: 26 September 2023

Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models.
- electricity market,
- intraday market,
- forecasts,
- electricity prices,
- trading volume,
- volatility
Citation: Sameer Thakare, Neeraj Dhanraj Bokde, Andrés E. Feijóo-Lorenzo. Forecasting different dimensions of liquidity in the intraday electricity markets: A review[J]. AIMS Energy, 2023, 11(5): 918-959. doi: 10.3934/energy.2023044

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Abstract

Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models.

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