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<p>The growing use of renewable energy sources (RES), particularly wind power (WP), in deregulated electricity markets has resulted in large operating uncertainties because of the erratic and uncertain behavior of wind resources. Differences between actual wind velocity (AWV) and forecasted wind velocity (FWV) cause a large price imbalance (P<sub>IMB</sub>) that has implications for market execution scheduling, operational reliability, and economic profitability. To solve these problems, this paper presents a comprehensive techno-economic model comprising Long Short-Term Memory (LSTM)-based wind forecasting coupled with an American Zebra Optimization (AZO) algorithm for profit maximization in wind-integrated deregulated power systems. The proposed approach is validated on the IEEE 14-bus test system by collecting real wind datasets from Mina Sultan Qaboos, Muscat, Oman, and Duqm, Oman. This work shows that the LSTM model works in modelling nonlinear temporal dependencies in wind speed data, leading to a significant improvement in forecasting accuracy relative to traditional forecasting techniques and Random Forest (RF)-based approaches. Using Locational Marginal Pricing (LMP), imbalance settlement mechanisms, generator operational limits, and transmission constraints, the forecasted outputs are integrated into an Optimal Power Flow (OPF)-based market framework. The proposed LSTM-based framework has achieved a significant reduction in imbalance pricing by 30–35% in Muscat and 28–29% in Duqm when compared to traditional forecasting techniques. Moreover, the AZO algorithm outperforms Sequential Quadratic Programming (SQP) in convergence performance and overall system profitability by approximately 6%. Profit improvement of the obtained result of wind potential is 5.9% for Muscat and 6.4% for Duqm. The proposed framework successfully reduces uncertainty in the market and enhances renewables integration capacity, as well as offers a robust decision-support framework for the electricity market operation and a decision support tool under the RES-driven high-level market conditions to ensure economic competition for competitive electricity market operations.</p>

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Wind forecasting-driven profit optimization in deregulated energy markets using long short-term memory and American zebra optimization algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • Shreya Shree Das,
  • Muhammad Waseem Khan,
  • Subhojit Dawn,
  • Ahmet Onen,
  • Taha Selim Ustun,
  • Saleh Al-Saadi

摘要

The growing use of renewable energy sources (RES), particularly wind power (WP), in deregulated electricity markets has resulted in large operating uncertainties because of the erratic and uncertain behavior of wind resources. Differences between actual wind velocity (AWV) and forecasted wind velocity (FWV) cause a large price imbalance (PIMB) that has implications for market execution scheduling, operational reliability, and economic profitability. To solve these problems, this paper presents a comprehensive techno-economic model comprising Long Short-Term Memory (LSTM)-based wind forecasting coupled with an American Zebra Optimization (AZO) algorithm for profit maximization in wind-integrated deregulated power systems. The proposed approach is validated on the IEEE 14-bus test system by collecting real wind datasets from Mina Sultan Qaboos, Muscat, Oman, and Duqm, Oman. This work shows that the LSTM model works in modelling nonlinear temporal dependencies in wind speed data, leading to a significant improvement in forecasting accuracy relative to traditional forecasting techniques and Random Forest (RF)-based approaches. Using Locational Marginal Pricing (LMP), imbalance settlement mechanisms, generator operational limits, and transmission constraints, the forecasted outputs are integrated into an Optimal Power Flow (OPF)-based market framework. The proposed LSTM-based framework has achieved a significant reduction in imbalance pricing by 30–35% in Muscat and 28–29% in Duqm when compared to traditional forecasting techniques. Moreover, the AZO algorithm outperforms Sequential Quadratic Programming (SQP) in convergence performance and overall system profitability by approximately 6%. Profit improvement of the obtained result of wind potential is 5.9% for Muscat and 6.4% for Duqm. The proposed framework successfully reduces uncertainty in the market and enhances renewables integration capacity, as well as offers a robust decision-support framework for the electricity market operation and a decision support tool under the RES-driven high-level market conditions to ensure economic competition for competitive electricity market operations.