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<p>The sustainability of the electricity market relies on enhanced forecasting of renewable energy sources combined with energy storage arbitrage. This paper introduces a bi-level approach with a novel contribution to wind farms and energy storage to generate maximum revenue in the day-ahead (DA) electricity market. In the first stage, the maximum revenue of the wind farm is estimated by forecasting wind power and market prices. The second stage models the optimal energy arbitrage strategy for storage devices. To solve the first problem, a well-organized Long-Short-Term-Memory (LSTM) combined with recurrent neural network (RNN)—Adam optimizer model is used, and for the second level by using Monte-Carlo optimization, the superior frontier of the revenue is estimated with locational based marginal prices (LBMPs). The solution to the second-level problem delivers a solitary value of the foremost boundary of the revenue and the equivalent charging/discharging schedule. An effective and feasible solution for improving the financial performance of renewable energy and storage systems in the DA market is offered by the suggested framework, which presents a novel approach to incorporating analytical prediction and optimization methodologies.</p>

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Wind power forecasting and economics of energy arbitrage in electricity market using machine learning techniques

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月30日

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  • Tirunagaru V. Sarathkumar,
  • Arup Kumar Goswami,
  • Hassan Abdurrahman Shuaibu,
  • Taha Selim Ustun

摘要

The sustainability of the electricity market relies on enhanced forecasting of renewable energy sources combined with energy storage arbitrage. This paper introduces a bi-level approach with a novel contribution to wind farms and energy storage to generate maximum revenue in the day-ahead (DA) electricity market. In the first stage, the maximum revenue of the wind farm is estimated by forecasting wind power and market prices. The second stage models the optimal energy arbitrage strategy for storage devices. To solve the first problem, a well-organized Long-Short-Term-Memory (LSTM) combined with recurrent neural network (RNN)—Adam optimizer model is used, and for the second level by using Monte-Carlo optimization, the superior frontier of the revenue is estimated with locational based marginal prices (LBMPs). The solution to the second-level problem delivers a solitary value of the foremost boundary of the revenue and the equivalent charging/discharging schedule. An effective and feasible solution for improving the financial performance of renewable energy and storage systems in the DA market is offered by the suggested framework, which presents a novel approach to incorporating analytical prediction and optimization methodologies.