An integrated approach for distributed resource allocation and network reconfiguration considering load diversity among customers

In this paper a new dedicated search teaching learning based optimization (DSTLBO) method is proposed for simultaneous allocation of distributed resources keeping in view the practical aspect of network reconfiguration to maximize annual energy loss reduction while maintaining a better node voltage profiles. The accuracy of distributed resource allocation and their benefits depend on proper modeling of system loads. Distribution system planners usually provide dedicated feeders to different classes of customers; each has its own characteristic load pattern which varies hourly and seasonally. There exists diversity in customers' load demands. Therefore proper modeling of annual load profile is crucial to obtain realistic allocation of distribution resources (shunt capacitors and distributed generations). In the proposed formulation of the optimization problems the diversity in customers' load patterns has been taken into account. In the proposed dedicated search teaching learning based optimization (DSTLBO), new learning approaches are suggested to enhance convergence, accuracy and efficiency of the standard TLBO. The proposed method is investigated on the benchmark IEEE 33-bus test distribution system. The application results are presented.