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<p>Multi-energy microgrid networks have developed into an efficient solution for accommodating different energy sources and increasing energy efficiency, particularly as carbon emission constraints become a growing concern in energy management. In this paper, a carbon-conscious and sustainable energy optimization structure is proposed for multi-microgrid (MMG) systems with various energy sources, aiming to reduce operational costs and carbon footprints. The structure makes use of renewable resources, energy storage devices, and electric vehicles, adopting a Monte Carlo approach with k-means clustering-based scenario reduction to cope with uncertainties in energy generation and load demand. The Holistic Swarm Optimization (HSO) is adopted to solve the multi-objective optimization issue, enhancing efficiency and precision over traditional methods. The method involves day-ahead scheduling for optimal resource coordination and intra-day rescheduling for coping with unexpected variations, complemented by demand response (DR) strategies and bi-directional energy sharing between microgrids. The objective function aims for minimizing operational costs, carbon emissions, and penalties for scheduling deviations across stochastic scenarios. Simulation outcomes reveal substantial decreases in renewable energy curtailment (up to 36%), carbon emissions (up to 46%), and operational costs (up to 15%) over baseline isolated operation. Nevertheless, consideration of production and consumption uncertainties adds up to 13% to costs and up to 20% to carbon emissions in the most problematic subsystems relative to the deterministic collaborative case; such impacts are effectively alleviated through the proposed stochastic framework and advanced demand response measures. This paper presents an integrated and adaptive system, opening the way towards developing sustainable and efficient energy networks.</p>

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Sustainable operation of multi-energy microgrids under carbon-restriction with stochastic resource and storage management

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Fazel Rostamzadeh,
  • Fardin Hashemzadeh

摘要

Multi-energy microgrid networks have developed into an efficient solution for accommodating different energy sources and increasing energy efficiency, particularly as carbon emission constraints become a growing concern in energy management. In this paper, a carbon-conscious and sustainable energy optimization structure is proposed for multi-microgrid (MMG) systems with various energy sources, aiming to reduce operational costs and carbon footprints. The structure makes use of renewable resources, energy storage devices, and electric vehicles, adopting a Monte Carlo approach with k-means clustering-based scenario reduction to cope with uncertainties in energy generation and load demand. The Holistic Swarm Optimization (HSO) is adopted to solve the multi-objective optimization issue, enhancing efficiency and precision over traditional methods. The method involves day-ahead scheduling for optimal resource coordination and intra-day rescheduling for coping with unexpected variations, complemented by demand response (DR) strategies and bi-directional energy sharing between microgrids. The objective function aims for minimizing operational costs, carbon emissions, and penalties for scheduling deviations across stochastic scenarios. Simulation outcomes reveal substantial decreases in renewable energy curtailment (up to 36%), carbon emissions (up to 46%), and operational costs (up to 15%) over baseline isolated operation. Nevertheless, consideration of production and consumption uncertainties adds up to 13% to costs and up to 20% to carbon emissions in the most problematic subsystems relative to the deterministic collaborative case; such impacts are effectively alleviated through the proposed stochastic framework and advanced demand response measures. This paper presents an integrated and adaptive system, opening the way towards developing sustainable and efficient energy networks.