Power system operation is gaining complexity due to the changes imposed by the energy transition. Especially, the increased share of intermittent and decentralized renewable generation units in the energy mix, an increased uncertainty regarding the supply of energy, and the predominantly market-driven cross-region and cross-border transport of electricity impose new challenges on the operation of power systems in Europe. In particular, power system operators must facilitate higher utilization of the grid capacity and coordinate more with neighboring transmission system operators (TSOs) and distribution system operators (DSOs). To deal with these new challenges, there is a pressing need to improve the observability and controllability of key system parameters to safeguard the reliability of power systems. Furthermore, the aforementioned developments and challenges go hand in hand with the need to improve the system resilience from the cybersecurity and system stability points of view. In the future, these challenges cannot be met without innovation towards intelligent decision support systems and assistant functions, which allow a look ahead combined with fast response and proactive actions. Here, the rather novel digital twin (DT) concept in combination with data-driven (i.e., machine learning) applications can be purposefully applied.

中文翻译：

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您可以信赖的幸运决定：数字孪生作为下一代能源管理系统和复杂操作员辅助系统的推动者

由于能源转型带来的变化，电力系统运行变得越来越复杂。特别是间歇性、分散式可再生能源发电在能源结构中所占比重的增加，能源供应的不确定性增加，以及以市场为主导的跨地区、跨境电力输送，给电力运营带来了新的挑战。欧洲的系统。特别是，电力系统运营商必须促进电网容量的更高利用率，并与邻近的输电系统运营商（TSO）和配电系统运营商（DSO）进行更多协调。应对这些新挑战，迫切需要提高关键系统参数的可观测性和可控性，保障电力系统的可靠性。此外，从网络安全和系统稳定性的角度来看，上述发展和挑战与提高系统弹性的需求是相辅相成的。未来，如果没有智能决策支持系统和辅助功能的创新，就无法应对这些挑战，这些创新可以将前瞻性与快速响应和主动行动相结合。在这里，可以有目的地应用相当新颖的数字孪生（DT）概念与数据驱动（即机器学习）应用程序的结合。