Dynamic equivalencing of distribution network with embedded generation

Abstract

Renewable energy generation will play an important role in solving the climate change problem. With renewable electricity generation increasing, there will be some significant changes in electric power systems, notably through smaller generators embedded in the distribution network. Historically insignificant volumes of Embedded Generation (EG) mean that traditionally it has been treated by the transmission system operator as negative load, with its impact on the dynamic behaviour of power systems neglected. However, with the penetration level increasing, EG would start to influence the dynamics and stability of the transmission network. Hence the dynamic behaviour of distribution network cannot be neglected any more. In most cases, a detailed distribution network model is not always available or necessary for the study of transmission network dynamics and stability. Thus a dynamic equivalent model of the distribution network that keeps its essential dynamic behavior, is required. Most existing dynamic equivalencing methods are based on the assumption that the detailed information of the complete power system is known. Dynamic equivalencing methods based on coherency of the machines have been applied to transmission networks but cannot be applied to distribution networks due to their radial structure. Hence an alternative methodology has been developed in this project to derive the dynamic equivalent model of the distribution network using system identification, without the detailed information of the distribution network necessarily known. Case studies have been accomplished in PSS/E on a model of the Scottish transmission network with the distribution network in Dumfries and Galloway. Embedded generation with a certain penetration level in either conventional generation or DFIG wind generation has been added to the model of the distribution network. The dynamic equivalent models of the distribution network are compared with the original distribution network model using a series of indicators. A constant power model has also been involved in the comparison to illustrate the advantage of using the dynamic equivalent to represent the distribution network. The results suggest that a proper dynamic equivalent model derived using this methodology may have better agreement to the original power system dynamic response than constant power equivalent. A discussion on factors that influence the performance of the dynamic equivalent model, is given to indicate the proper way to use this methodology. The major advantage of the dynamic equivalencing methodology developed in this project is that it can potentially use the time series obtained from measurements to derive the dynamic equivalent models without knowing detailed information on the distribution network. The derived dynamic equivalent, in a simple spate-space form, can be implemented in commercial simulation tools, such as PSS/E.