Analyzing Impact of Data Uncertainty in Distributed Energy Resources using Bayesian Networks

With the high penetration of distributed energy resources (DERs), distribution networks have become more prone to uncertainties associated with renewable energy sources (RESs). If not handled judiciously, these uncertainties may lead to interruption in power supply and even failure of the entire power system in the long run. In this paper, a Bayesian Network (BN) approach is used to find the hidden inter-dependencies among the various weather parameters and how these affect renewable energy generation. A heuristic algorithm is then proposed to identify the root-cause of the uncertainty which increases the overall grid dependency and thereby, tackling its associated carbon emissions. To check the efficacy of the proposed approach, the effect of data uncertainty in the distribution network with DERs penetration in nine different regions of England is discussed. Furthermore, a case-study of a residential area of Newcastle upon Tyne is discussed in detail to back-trace the root-cause of the fault that occurred in one of the DERs.