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<p>This paper proposes a sustainable computing–oriented framework for economic energy scheduling in renewable energy hubs equipped with stationary storage systems within coupled electrical and thermal microgrid environments. The proposed formulation aims to simultaneously reduce operational expenditures and energy loss–related costs by defining a unified optimization objective. The framework integrates detailed hub operation models with optimal power flow analysis to ensure coordinated and efficient microgrid operation. The considered energy hubs include wind turbines, photovoltaic units, bio-waste conversion systems, hydrogen storage facilities, and thermal energy storage units, each subject to their respective technical and operational constraints. The bio-waste subsystem is modeled as a combined producer of electrical and thermal outputs. Multiple sources of uncertainty, namely energy market prices, loss cost coefficients, renewable generation variability, and load demand fluctuations, are explicitly addressed through a stochastic optimization approach. This enables robust and reliable hub operation under prediction inaccuracies and uncertain operating conditions. To derive an optimal and computationally efficient solution, the study employs an artificial intelligence (AI)–based hybrid optimization technique that combines the Ant Lion Optimizer with the Artificial Bee Colony algorithm. The hybrid solver demonstrates strong convergence characteristics and achieves high-quality solutions with reduced computational burden. Simulation results validate the proposed strategy’s capability to substantially enhance both economic efficiency and operational reliability of renewable-based microgrids through advanced energy management practices. In particular, the sustainable computing–based optimal scheduling of stationary storage assets yields notable improvements, achieving operational performance gains ranging from 21.7% to 47.6% and an economic cost reduction of approximately 48.8% compared with conventional power flow–based analyses.</p>

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Minimization of operation and energy loss costs to improve economic and operation objectives of micro-grids manger considering sustainable computing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月28日

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  • Mohamed Bechir Ben Hamida,
  • Ehsan Akbari,
  • Sasan Pirouzi

摘要

This paper proposes a sustainable computing–oriented framework for economic energy scheduling in renewable energy hubs equipped with stationary storage systems within coupled electrical and thermal microgrid environments. The proposed formulation aims to simultaneously reduce operational expenditures and energy loss–related costs by defining a unified optimization objective. The framework integrates detailed hub operation models with optimal power flow analysis to ensure coordinated and efficient microgrid operation. The considered energy hubs include wind turbines, photovoltaic units, bio-waste conversion systems, hydrogen storage facilities, and thermal energy storage units, each subject to their respective technical and operational constraints. The bio-waste subsystem is modeled as a combined producer of electrical and thermal outputs. Multiple sources of uncertainty, namely energy market prices, loss cost coefficients, renewable generation variability, and load demand fluctuations, are explicitly addressed through a stochastic optimization approach. This enables robust and reliable hub operation under prediction inaccuracies and uncertain operating conditions. To derive an optimal and computationally efficient solution, the study employs an artificial intelligence (AI)–based hybrid optimization technique that combines the Ant Lion Optimizer with the Artificial Bee Colony algorithm. The hybrid solver demonstrates strong convergence characteristics and achieves high-quality solutions with reduced computational burden. Simulation results validate the proposed strategy’s capability to substantially enhance both economic efficiency and operational reliability of renewable-based microgrids through advanced energy management practices. In particular, the sustainable computing–based optimal scheduling of stationary storage assets yields notable improvements, achieving operational performance gains ranging from 21.7% to 47.6% and an economic cost reduction of approximately 48.8% compared with conventional power flow–based analyses.