Simultaneous allocation of distributed generation and shunt capacitor using multi-objective mountain gazelle optimizer for technical and economic benefits

Abstract

Abstract A technically and economically efficient distribution network is paramount in the modern world, and the leading causes of compromise on these are increased system losses and poor voltage profiles. Numerous techniques have been put forth to improve the efficiency of distribution networks, including the strategic allotment of distributed generation (DG) and shunt capacitors (SCs), which provide active and reactive power compensation at their allocated buses. While researchers have explored this solution, there have been drawbacks due to time (and memory) complexity regarding the vast search space the optimization process encounters. Additionally, the balance between technical benefits and economic viability of this approach were not vividly taken into consideration in the optimization processes. Hence, this paper suggests the simultaneous allotment of DG and SC into radial distribution networks (RDN) utilizing a multi-objective mount gazelle optimization (MGO) algorithm. The multi-objective function is formulated to minimize systems’ active power losses, maximize voltage profile index (VPI), maximize systems’ average voltage stability index (AVSI), and minimize the total operating costs. To also streamline the search process, reduce repetitive load analysis, simplify the optimization procedure, and reduce computation time, we employ two novel indices: the effective active power voltage stability index (EPVSI) and the new voltage stability index (NVSI) to pinpoint the optimal locations for DG and SC, respectively. The MGO is then used to obtain the most suitable sizes of the DG and SC. The efficacy of the proposed method is validated on the standard IEEE 33-bus network and further ascertain its practical applicability, it is tested on the practical Nigerian Ayepe 34-bus system. The optimal solution was achieved by simultaneously integrating DGs and SCs into the networks. For the 33-bus system, this resulted in power loss reduction, AVSI, VPI, and cost savings percentage values of 73.23%, 0.9401 p.u., 1.7598 p.u., and 68.85%, respectively, while for the Ayepe 34-bus network, the corresponding values were 62.17%, 0.7474 p.u., 1.1244 p.u., and 61.04%. By comparing the suggested MGO with other methods, its effectiveness was established, and it was shown to perform better in reducing power loss.