Asset management attempts to keep the power system in working conditions. It requires much coordination between multiple entities and long term planning often months in advance. In this work we introduce a mid-term asset management formulation as a stochastic optimization problem, that includes three hierarchical layers of decision making, namely the mid-term, short-term and real-time. We devise a tractable scenario approximation technique for efficiently assessing the complex implications a maintenance schedule inflicts on a power system. This is done using efficient Monte-Carlo simulations that trade-off between accuracy and tractability. We then present our implementation of a distributed scenario-based optimization algorithm for solving our formulation, and use an updated PJM 5-bus system to show a solution that is cheaper than other maintenance heuristics that are likely to be considered by TSOs.
A Novel Context driven Critical Integrative Levels (CIL) Approach: Advancing Human-Centric and Integrative Lighting Asset Management in Public Libraries with Practical Thresholds
A Sensor-Driven Optimization Framework for Asset Management in Energy Systems: Implications for Full and Partial Digital Transformation in Hydro Fleets
An Overview of Digital Twins Application Domains in Smart Energy Grid
Digital Twins为智能电网提供了有前途的解决方案，这些解决方案涉及能源资产的优化运营、管理和控制，以及安全可靠的能源分配。由于在电网边缘大规模采用分布式可再生资源，这些挑战比以往任何时候都更加紧迫。数字双胞胎利用物联网、大数据分析、机器学习和云计算等技术，分析来自不同能源传感器的数据，查看和验证物理能源资产的状态，并提取有用的信息来预测和优化资产性能。在本文中，我们将提供一个概述数字孪生应用领域的智能电网，同时分析现有的最先进的文献。我们专注于以下应用领域：能源资产建模，故障和安全诊断，运营优化和商业模型。所发现的大多数相关文献方法都是在最近三年内发表的，表明数字孪生在智能电网中的应用领域是热门的，并正在逐步发展。无论如何，对于数字孪生系统的实施以及与能源管理流程的集成还没有统一的看法，因此，仍然需要做很多工作来理解和自动化智能电网管理。
