Reliability analysis of power distribution system in presence of distributed generation units

One of the primary emphases in planning and designing distribution networks is the reliability evaluation considering the economic impact of minimizing theinterruptions for the end users in the networks. An effective way to minimize the customer interruptions is to use protective devices (fuses and CBs) andsectionalizing switches. In power distribution reliability analysis, the optimal placement of switches and protective devices can reduce the annual failure rate andoutage duration of the customers and consequently improves the reliability indices of the networks. This research presents a new mixed-integer nonlinearprogramming model aiming to identify the type, optimum number and location of protective devices and sectionalizing switches in distribution networks moreaccurately. A value-based reliability optimization formulation is derived from the proposed model. To ensure the effectiveness of the proposed formulation bothtechnical and economic constraints are considered.In power distribution reliability analysis, distributed generation (DG units) can potentially improve network reliability by reducing interruption duration andrestoration time after a given fault. However, such improvement depends on the operation mode of the DG units. An island can be formed when sufficient localgeneration exists to avoid load prioritization and/or shedding.The problem of optimum switch and protective device placement becomes more complicated when DG units are present in distribution networks. A method forsolving the problem of optimal placement of sectionalizing switches with high penetration of DG is also presented in this research. The primary objective is tominimize the total cost of achieving a certain level of reliability in presence of DG. The problem is formulated using mixed-integer linear programming todetermine the optimal number and location of sectionalizing switches.The accuracy of the reliability analysis can be affected directly by the customer interruption cost model. A probability distribution cost model (PDM) isdeveloped based on a cascade correlation neural network to have a more accurate reliability assessment. The performances of the proposed approaches areassessed and illustrated by studying on the RBTS test system and a typical real size network. Simulation results verify the capability and accuracy of the proposedapproaches