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The advanced ageing of the electrical power network combined with the inevitable increase of renewable energy utilization and the constant increase of power consumption over the last decades call for a change in the current power network paradigm. Electrical world stakeholders have to embrace the situation and come up with methods of better utilizing the existing, but also new, network resources. The ongoing financial crisis also acted as an eye opener for new concepts and smart opportunities. This project’s approach is to create a modern and automated layer on top of the existing power network physical layer which makes use of several physical models to interpret the current and future expected health status of the network’s components. With this automated layer, the network operator can configure the network to be a self-sustainable, or at least more independent, and a more insightful system. Real-time and projections of the network’s health status can be used as power flow optimization factors. Simulations based on real power flow data have shown that distributing thermal loading throughout neighbouring power components results in a more efficient resource utilization as it lowers the overall network accelerated ageing factor, while keeping the power flow characteristics within the legislated integrity limits. It has been shown that, by applying this health prediction framework to the IEEE-14 bus network and allowing the layer to act in a decentralized fashion independently of the network operator, the overall network lifetime utilization can be reduced by 80% by rerouteing only 13% of the overall power flow. This rerouteing of power flow does not compromise the load requirements, nor does it require more power components than the ones present in the IEEE-14 bus network configuration. Although it is difficult to implement this framework layer on top of the electrical power network, due to its well-known structural inertia, it has been shown that this system can e, EPE, High Voltage Technology and Asset Management, Electrical Engineering, Mathematics and Computer Science

The advanced ageing of the electrical power network combined with the inevitable increase of renewable energy utilization and the constant increase of power consumption over the last decades call for a change in the current power network paradigm. Electrical world stakeholders have to embrace the situation and come up with methods of better utilizing the existing, but also new, network resources. The ongoing financial crisis also acted as an eye opener for new concepts and smart opportunities. This project’s approach is to create a modern and automated layer on top of the existing power network physical layer which makes use of several physical models to interpret the current and future expected health status of the network’s components. With this automated layer, the network operator can configure the network to be a self-sustainable, or at least more independent, and a more insightful system. Real-time and projections of the network’s health status can be used as power flow optimization factors. Simulations based on real power flow data have shown that distributing thermal loading throughout neighbouring power components results in a more efficient resource utilization as it lowers the overall network accelerated ageing factor, while keeping the power flow characteristics within the legislated integrity limits. It has been shown that, by applying this health prediction framework to the IEEE-14 bus network and allowing the layer to act in a decentralized fashion independently of the network operator, the overall network lifetime utilization can be reduced by 80% by rerouteing only 13% of the overall power flow. This rerouteing of power flow does not compromise the load requirements, nor does it require more power components than the ones present in the IEEE-14 bus network configuration. Although it is difficult to implement this framework layer on top of the electrical power network, due to its well-known structural inertia, it has been shown that this system can e, EPE, High Voltage Technology and Asset Management, Electrical Engineering, Mathematics and Computer Science