As concerns about environmental sustainability continue to grow, the demand for effective low-carbon energy management becomes increasingly pressing. This study presents a novel framework for multi-temporal multi-energy microgrids (MMGs), integrating advanced low-carbon technologies to meet this imperative. The framework ensures flexible operations to navigate uncertainties stemming from renewable energy sources (RES) and fluctuating energy demand. Facilitating multi-energy transactions, encompassing gas and power exchanges in both markets, the model accommodates uncertainties from RES and demand fluctuations. Objectives include reducing carbon emissions and improving economic efficiency. To address uncertainties in the MMG system, a data-driven distributionally robust optimization (DRO) method is employed. Day-ahead scheduling utilizes a two-stage three-level approach, deploying the column-and-constraints generation (C&CG) algorithm, showcasing the efficiency of DRO in minimizing energy waste and carbon emissions while remaining cost-effective. Practicality is demonstrated through real-time intra-day scheduling using the model predictive control (MPC) algorithm, building upon hourly day-ahead results. The effectiveness of both strategies is evaluated using empirical data from an MMG based on the IEEE 33-bus test system. This cost-saving framework not only achieves a significant carbon reduction of 10.6 % but also provides reliable and adaptable solutions, effectively addressing real-world variations in renewable energy and mitigating potential risks.

中文翻译：

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考虑数据驱动模糊集的多时态多能源微电网运行的分布式鲁棒脱碳调度

随着对环境可持续性的关注不断增加，对有效的低碳能源管理的需求变得越来越紧迫。这项研究提出了一种新的多时态多能源微电网（MMG）框架，整合了先进的低碳技术来满足这一要求。该框架确保灵活运营，以应对可再生能源 (RES) 和能源需求波动带来的不确定性。该模型促进了多能源交易，包括两个市场的天然气和电力交易，适应了可再生能源和需求波动的不确定性。目标包括减少碳排放和提高经济效率。为了解决 MMG 系统中的不确定性，采用了数据驱动的分布式鲁棒优化 (DRO) 方法。日前调度采用两阶段三级方法，部署列和约束生成 (C&CG) 算法，展示了 DRO 在最大限度减少能源浪费和碳排放方面的效率，同时保持成本效益。实用性通过使用模型预测控制 (MPC) 算法的实时日内调度（以每小时的日前结果为基础）得到证明。使用来自基于 IEEE 33 总线测试系统的 MMG 的经验数据来评估这两种策略的有效性。这一节省成本的框架不仅实现了 10.6% 的显着碳减排，而且提供了可靠且适应性强的解决方案，有效解决可再生能源的现实变化并降低潜在风险。