This paper proposes an effective approach to solve renewable distributed generators (RDGs) and electric vehicle charging station (EVCS) allocation problems in the distribution system (DS) to reduce power loss (P_Loss) and enhance voltage profile. The RDGs considered for this work are solar, wind and fuel cell. The uncertainties related to RDGs are modelled using probability distribution functions (PDF). These sources’ best locations and sizes are identified by the voltage stability index (VSI) and political optimization algorithm (POA). Furthermore, EV charging strategies such as the conventional charging method (CCM) and optimized charging method (OCM) are considered to study the method’s efficacy. The developed approach is studied on Indian 28 bus DS. Different cases are considered, such as a single DG, multiple DGs and a combination of DGs and EVs. This placement of multiple DGs along with EVs, considering proper scheduling patterns, minimizes P_Loss and considerably improves the voltage profile. Finally, the proposed method is compared with other algorithms, and simulated results show that the POA method produces better results in all aspects.

Increased electric vehicles (EVs) penetration into the distribution network burdens the system during the charging process. But the same turns out to be advantageous if the EVs are used to support the utility based on the requirement. In this work, an attempt is made to efficiently schedule both the grid-to-vehicle and vehicle-to-grid operational modes of EVs with the objective of reducing power loss in the distribution system in presence of distributed generation (DG). The EVs are modeled by considering the dominating factors, such as EV State of Charge (SoC), trip conditions, EV battery capacity, charging/discharging levels. An improvement to the existing grasshopper optimization technique is also suggested in this article, which enhances the exploring capability of the metaheuristic algorithm. The presented methodology is a combination of smart charging method, voltage stability index, and enhanced grasshopper optimization algorithm (EGOA), which decides the size of DGs to be placed in the system. The proposed hybrid approach tries to schedule both the EVs and DGs so as to achieve reduced power loss and improved voltage profile. In order to test the robustness of the proposed strategy, various analyses considering uncertainty in EVs and load conditions are performed.

In recent years the demand of electrical energy increases and limited availability of conventional generation sources, it is very important to use renewable energy resources in the power system network. Optimal location of renewable based distributed generators in distribution system is a challenging issue in recent years. In this paper an effective technique based on the cuckoo search algorithm is proposed to determine optimal allocation of wind based distributed generators in the distribution system. The objective is to reduce power loss of the distribution system. The proposed method is tested on IEEE 69 bus test system and the obtained results are compared with other methods for validation

This research takes on a crucial task- exploring the optimal placement of Renewable Distributed Generators such as Solar Photovoltaic, wind turbines and Electric Vehicles into the Radial Distribution System (RDS). This is a strategic move aimed at minimising power loss (P_Loss) and improving the voltage profile and stability index. The RDGs are integrated into RDS with and without considering the uncertainty of the different load demands for 24 h. The probability function of Beta and Weibull distribution functions are employed to attain the solar irradiance and wind speed in a particular region. In addition, EVs are also integrated into RDS, employing meta-heuristic algorithms intended to reduce power loss (PLoss) and improve the voltage profile. The study uses an Indian 28-bus test system mimicking a balanced radial distribution network to integrate distributed generators (DGs) and EV charging stations. The simulated results demonstrate that integrating DGs into power systems has offered considerable benefits, including reduced PLoss, heightened efficiency, decreased dependency on centralised generation, and improved environmental sustainability. It is discovered that the Multi-population Evolution Whale Optimization Algorithm (MEWOA) produces better results than other methods in the literature and is valuable and practical for handling these nonlinear optimisation situations.

Better integration of capacitors and renewable distributed generators (DGs) in distribution system is a prominent problem in present era. In this paper a systematic approach is presented to solve the DER allocation problem with an aim of improving voltage profile as well as power loss reduction. An integrated approach of loss sensitivity factor and dragonfly algorithm is presented to solve the problem. Developed approach is tested on large scale 119 bus system. Different cases are considered for analysis. Further, the obtained result of developed method is compared with simulated annealing technique for validation.