Abstract Given their highly stochastic features, the hasten increase of renewable energy sources contribution in the global energy balance issues a strong impact on power systems operating conditions. In order to overcome drawbacks related to supply reliability and systems stability, real-time power management strategies able to achieve optimal targets in uncertain frameworks are currently of great interest. This paper aims to overview for the first time the latest progresses in the field of real-time power management algorithms designed for hybrid renewable energy systems. The findings of this research provide a comprehensive review of the state-of-the-art, individuating specific fields of application and focusing on the gaps that should be further investigated. Several approaches for real-time stochastic power management are presented from both theoretical and applicative points of view. A wide range of applications in terms of installed power, multi-objective optimization formulation and energy storage technologies hybridization result as the main challenges for real-time power management. To this regard, gradient-based optimization algorithms show the highest potential for real-time power management strategies implementation. In particular, their reduced computational cost if compared to other approaches, as well as the high adaptability to various configurations, make this kind of algorithms worthy of further investigation.

中文翻译：

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混合可再生能源系统中的实时随机电源管理策略：关键应用和观点回顾

摘要 鉴于可再生能源具有高度随机性的特点，可再生能源在全球能源平衡中的贡献迅速增加，对电力系统运行状况产生了强烈影响。为了克服与供电可靠性和系统稳定性相关的缺点，目前人们对能够在不确定框架中实现最佳目标的实时电源管理策略非常感兴趣。本文旨在首次概述为混合可再生能源系统设计的实时电源管理算法领域的最新进展。这项研究的结果对最先进的、个性化的特定应用领域进行了全面审查，并重点关注应进一步调查的差距。从理论和应用的角度介绍了几种实时随机电源管理方法。装机功率、多目标优化公式和储能技术混合方面的广泛应用是实时功率管理的主要挑战。在这方面，基于梯度的优化算法显示了实时电源管理策略实施的最大潜力。特别是，与其他方法相比，它们降低了计算成本，以及对各种配置的高度适应性，使得这种算法值得进一步研究。多目标优化公式和储能技术混合是实时电源管理的主要挑战。在这方面，基于梯度的优化算法显示了实时电源管理策略实施的最大潜力。特别是，与其他方法相比，它们降低了计算成本，以及对各种配置的高度适应性，使得这种算法值得进一步研究。多目标优化公式和储能技术混合是实时电源管理的主要挑战。在这方面，基于梯度的优化算法显示了实时电源管理策略实施的最大潜力。特别是，与其他方法相比，它们降低了计算成本，以及对各种配置的高度适应性，使得这种算法值得进一步研究。