Performance Enhancement for Microgrids Under the Demand Uncertainties with the Presence of Multiple DGs Through Stochastic Ranking Algorithm

Abstract

The integration of Distributed Generators (DGs) in the reconfigurable microgrid is evolved to enhance the power delivery performance. The DGs are categorized as controllable and uncontrollable based on their power generation control behaviors. In addition to the control behavior, under the uncertain conditions of the microgrid, the performance of DGs is limited due to their respective drawbacks. Therefore, the identification of the most suitable DG under uncertain situation becomes more critical. This paper investigates the performance behavior of the hybrid DG, which uses the benefits of two kinds of DGs and overcomes their limitations. This work addresses five objective functions namely minimization of power loss, total generation cost, total emission cost and voltage deviation, and the maximization of the percentage DG penetration. The performance investigation of DGs is carried out for the system under five different test cases, including the uncertainty in power supply and demand. The modeling of the demand variation is done with the support of Probability Density Function (PDF). The many objective multi-indicator Stochastic Ranking Algorithm (SRA) is used for the optimization purposes. The simulations are performed on IEEE 33-bus Radial Distribution System (RDS) to assess the capability of the proposed investigation. Furthermore, the proposed strategy is applied under 24-h timely varying load pattern of IEEE 33-bus RDS to verify its applicability in uncertain load conditions. Moreover, the performance enhancement realization with the presence of hybrid DG is observed from the obtained simulation results.