Investigating data standardisation and modelling challenges to enable advanced power systems analysis

Abstract

As the power industry moves towards more active distribution networks there is an increased requirement for greater analysis and observability of the current state of the network. There are a number of challenges for utilities in realising this including the quality and accuracy of their network models; the lack of integration between network models and the large quantities of sensor data being collected; the security and communication challenges posed when installing large numbers of sophisticated sensors across distribution networks; and the exponential increase in computing power required to fully analyse modern network configurations. This thesis will look at these challenges and how cloud computing can be used to provide novel solutions by providing secure platforms on which to deploy complex data collection and network analysis applications. One of the main research contributions is the use of remote data collection from Micro Phasor Measurement Units (μPMUs), which collect synchronised information about the state of the distribution network. Impedance equations are applied to network data recorded from μPMUs and the results are compared to network models. This identifies areas of the distribution network as requiring resurveying or upgrading, potentially impacting planning for installation of generation or load. Triggers can be used to reduce the bandwidth of data being sent by a μPMU; these were tested with real world data to highlight how a combination of local intelligence and cloud-based analysis can be used to reduce bandwidth requirements while supporting the use of detailed measurement data for cloud-based analysis in a fault detection system. Power flow analysis is an important tool for both operations and planning engineers, and as computing power has increased the time required to run individual power flow analysis cases has decreased rapidly. However there has also been a corresponding increase in the complexity of the data as utilities seek to model and analyse distributed energy resources attached on the medium and low voltage networks. This has made network models more complex, exponentially increasing the number of contingencies that need to be analysed in an emergency situation. Another main research contribution is a demonstration of the challenges faced when using a commercial cloud platform to inexpensively solve computationally intensive power flow problems and the time, costs and feasibility of performing N-1 and N-2 analysis on a 21,000-bus network. It includes a full analysis and comparison of execution times and costs for different commercial cloud system configurations as well as the extrapolated costs required to run a full N-2 analysis of over 420 million contingencies in under 10 minutes. This includes a demonstration of a cloud client and server application developed as part of this research that leverages a commercial power flow engine. Finally, this thesis will summarise how each of these research outputs can be combined to provide utilities with a commercial, open, standards-based cloud platform for continuous, automated contingency analysis using real-time sensor data based on current network conditions. This would better inform control engineers about areas of vulnerability and help them identify and counter these in real-time.