Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia

Lu, Xin; Qiu, Jing; Lei, Gang; Zhu, Jianguo

doi:10.1016/j.apenergy.2021.118296

Cited by 32 publications

(8 citation statements)

References 63 publications

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“…Existing electricity forecasting methods can be divided into the following three categories, namely, physical methods [28], statistical methods (such as seasonal autoregressive integrated moving average (SARIMA) [29]), and machine learning methods [30], [31], [32]. Recently, with the development of deep learning, new electricity price prediction models have been continuously proposed [22], [30], [33]. A long short-term memory (LSTM) network is a special kind of deep learning models that is capable of learning long-term dependencies.…”

Section: A Motivationmentioning

confidence: 99%

Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

Khodadadi

Hesamzadeh

Söder

2023

IEEE Systems Journal

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This article presents a new approach for finding the optimal multimarket trading strategy of cascaded hydropower plants (HPPs) in the sequential electricity markets. These markets are day-ahead energy market, the market for frequency containment reserve in normal mode (FCR-N), and manual frequency restoration reserve markets for both energy production and capacity reserve. The active-time duration (ATD) of an mFRR energy offer is an important required parameter and it is uncertain at the time of day-ahead offer-function submission. Hence, we suggest a distributional regression approach for ATD modeling in an optimal multimarket setup. Also, a modified machine learning approach is proposed to generate price scenarios for the mFRR energy market taking into account uncertain ATD parameters. To illustrate our proposed approach, various numerical experiments are performed. Our numerical results show how proper modeling of ATD parameters can lead to a more realistic multimarket offer-function for cascaded HPPs. Furthermore, the results show how the inclusion of FCR-N and mFRR capacity markets change the optimal day-ahead offer-function.

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Section: A Motivationmentioning

confidence: 99%

Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

Khodadadi

Hesamzadeh

Söder

2023

IEEE Systems Journal

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“…In recent years, global warming and environmental pollution have become major challenges faced by the global human society because of fossil energy consumption. Developing a low-carbon economy to realize carbon dioxide (CO 2 ) emissions reduction targets has been focused on in many countries [1,2]. Under this situation of low-carbon development demand, the research on energy system optimal operation has gradually evolved to its low-carbon economic dispatch.…”

Section: Background and Motivationsmentioning

confidence: 99%

Low‐carbon economic dispatch of the combined heat and power‐virtual power plants: A improved deep reinforcement learning‐based approach

Tan

Shen

et al. 2022

IET Renewable Power Gen

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To realize the national strategies for carbon emission reduction, electric power industries should undergo reforms to cope with the multiple challenges of decarbonization, marketization, and energy transition. How to design a dispatch strategy that considers both low‐carbon demand and economic cost has become a major concern in integrated energy systems. To realize multi‐energy complementation and low‐carbonization, a scheduling framework of combined heat and power‐virtual power plants (CHP‐VPPs) considering carbon capture and power‐to‐gas is proposed. The stochastic dynamic optimization problem is modelled as a Markov decision process. An improved deep reinforcement learning‐based algorithm named multi‐level backtracking prioritized experience replay‐twin delayed deep deterministic policy gradient (MBEPR‐TD3) is employed to solve the low‐carbon economic dispatch problem. The results show that: (1) The profits have been increased by 85.8% and carbon emissions have been reduced by 30.3% because of the addition of power‐to‐gas in CHP‐VPP; (2) the revenues have been improved by 22.24% and carbon emissions have been decreased by 37.04% owing to the introduction of carbon capture and trading; (3) compared with results using DQN, DDPG, and TD3, the optimal dispatch strategy obtained by MBEPR‐TD3 has higher reward returns and more stable convergence.

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“…In this context, accurate electricity price forecasting has an important role in decision making by various stakeholders and the development of innovative business and market models towards a future power grid [5]. Market players such as generators, retailers, and consumers require accurate price forecasting to optimize their operations, manage their energy portfolios, and make informed decisions about energy transactions according to their interests [6]. This also enables innovative business models, such as peer-to-peer energy trading, grid balancing services, and energy aggregation platforms, along with DER integration.…”

Section: Introductionmentioning

confidence: 99%

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

Cantillo-Luna,

Moreno-Chuquen,

Lopez-Sotelo

et al. 2023

Energies

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This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data’s behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector.

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Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia

Cited by 32 publications

References 63 publications

Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

Low‐carbon economic dispatch of the combined heat and power‐virtual power plants: A improved deep reinforcement learning‐based approach

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

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