Direct Current Power Flow: Computational methods and low voltage applications

Technical developments in generation and demand of energy will motivate significant change in the electric power grid, both on the transmission and the distribution level. A major innovation would be the successful transformation of the current passive power grid towards an active and ICT-based smart grid. Among the technical efforts that will help to pursue this goal, the renewed interest in DC (direct current) distribution and transmission applications is playing an important role. In particular, the interest in the DC universal distribution networks is renewed since most of the renewable energy generation technologies (e.g. PV modules, fuel cells) and loads (e.g. LED lighting, electric vehicles) are DC-native. Their direct connection would allow to skip conversion steps, thus providing higher efficiency.The focus of this thesis lies on the steady-state power flow analysis, a numerical study used in electrical engineering to assess the flows of power in the network. The aim of the thesis is to review the state of the art in computational methods for AC and DC power flow analysis and to determine a suitable method to develop a power flow tool for the DC framework.The literature study revealed that most algorithms aim to solve the non-linear power flow problem without taking into account characteristics typical of future DC networks, such as highly meshed topologies and constant power converters. An innovative power flow method has there- fore been developed in order to include different node behaviours, such as constant voltage, constant current, constant impedance, constant power and I-V droop control. A case study based on the IEEE European Low-Voltage Test Feeder is analysed to provide an example of the application of the power flow tool.The thesis shows that it is possible to linearize the system equations considering the constant power node either as a current source or as a parallel of current source and impedance. Both methods allow very fast convergence for complex meshed networks, and can therefore be adopted for diverse studies such as market analysis and N-1 redundancy analysis, among others