Optimal Placement of Dispersed Generators for Practical Distribution Network

Abstract

The usage of dispersed generators (DGs) are proving a promising and environmental friendly sources to supply electrical energy near to load centers. The interest in assimilation of DGs in distribution networks (DN) is because of increasing load demand and availability of renewable generation. However, the concept of integrating DGs to low voltage DN is a paradigm that differs from conventional central generation based paradigm. The integration of dispersed generation is directed to complement the efforts in improving the energy efficiency and reliability of power systems. With the usage of DGs, the increasing load demand can be met without expanding the existing transmission systems. The DNs are characterized by large number of buses and lines with a high R/X ratio and the power flow from the higher to lower voltage levels to supply loads. In radial distribution network (RDN), the voltage of buses drop when moved away from the source and results into high losses. Moreover, the DNs are mostly subjected to growing or varying load demand. The integration of DGs benefits the DNs by diminishing losses and enhancing the voltage of all buses while meeting the load demand. Such integration is beneficial only, if DGs of appropriate sizes are placed at suitable locations. The use of shunt capacitors in DNs is expected to reduce losses and yield lower reactive burden. The optimal placement of DG and capacitor simultaneously can be investigated to supply load demand. For the practical DNs, the load cannot be assumed simply as constant power (CP) load. The bus voltage has strong influence on load associated with that bus. The prevalent practical loads can be voltage dependent as found in commercial, residential and industrial loads. Further, the inherent uncertainty in practical DNs shall be analyzed by some probabilistic approach in modeling and analysis. In this research work, the effectiveness of DG integration is investigated for diminution of losses in DNs using analytical expressions based approach and Particle Swarm Op