Electrical and Electronic Engineering - Theses
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ItemRisk Management Frameworks and Methodologies for Modern and Resilient Power Systems Planning Using Machine Learning Techniques( 2020)Renewable energy technologies, customer behaviour, and new regulations are key factors contributing to a change in power generation paradigm that is becoming increasingly decentralized and embedded in the distribution network. The new paradigm, together with strong opportunities, is bringing challenges for power networks that must be adequately anticipated and planned to maintain the security and reliability of the power supply. This research addresses two key challenges for developed power networks and one challenge for developing networks located in countries vulnerable to extreme weather events. For developed power networks, this research formulated risk assessment models based on Artificial Intelligence techniques that enable power system planners to analyse vast numbers of scenarios and assess the impact of voltage excursions and reverse power flows as a result of elevated penetration of distributed energy resources. The novelty of the work is derived from the scalability of the proposed models and its end-to-end approach that includes financial modelling of the impacts. For the developing network, this research developed one risk-based methodology to assess resilience to extreme weather events that is linked to power system planning. The novelty of the proposed methodology is derived from the problem formulation that explicitly considers both the technical power system resilience and the social community energy resilience in quantifiable terms that are linked to power system planning via an optimization problem.