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.

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.

The United Nations has set an ambitious goal to achieve net zero carbon emissions by 2050. This objective requires shifting towards green and renewable energy sources instead of conventional fossil fuels to address the global energy crisis without emitting greenhouse gases. While the energy radiated by the sun is one of the most abundant sources of energy available, its efficient and optimal use remains a significant challenge. To facilitate solar-energy-based applications, estimating the amount of solar energy available is crucial. Empirical and soft computing is the most-used method to estimate solar energy. This paper aims to analyze the existing techniques used in various models for estimating and predicting the quantity and quality of solar radiation using readily available data. Additionally, the study aims to identify the most appropriate techniques for developing prediction models using available explanatory variables. To fully harness the potential of solar energy, it is necessary to limit the terrestrial loss of solar radiation by minimizing the harmful effects of anthropogenic factors that reduce the quantity and quality of solar radiation in the area. This paper provides valuable insights to identify opportunities to maximize the potential of solar energy in different locations.

In response to the market's increasing need for electricity and the escalating technical and environmental challenges, the Power System (PS) sector has strongly emphasised the escalating technical and ecological challenges, and the PS sector has placed a strong emphasis on integrating distributed energy resources (DERs) into distribution systems (DS). However, if not allocated optimally, integrating DERs can provide various technical topics such as power quality, stability, reliability, and voltage management concerns. Therefore, creating effective and efficient optimisation techniques to solve issues of DER integration is crucial. In this paper, the integration of DERs into DS is utilised by the Multi-Objective Evolution of the Whale Optimisation Algorithm (MEWOA). MEWOA is an optimisation method inspired by humpback whales' hunting strategies and has demonstrated promising results in solving challenging optimisation issues. The suggested approach tries to optimise the location and sizing of DERs in DS while considering several factors, including voltage variation, Power loss (P_Loss) reduction, and operational cost. The extensive simulations are run on the Indian 28-bus and IEEE 69-bus distribution systems to show the viability of the suggested approach. The findings demonstrate that the proposed method can significantly enhance the voltage profile, lessen P_Loss, lower the annual operating expenses and save revenue from P_Loss minimisation. The results also show that MEWOA outperforms other optimisation techniques like the Grasshopper Optimisation Algorithm (GOA), the Dragonfly Algorithm (DA), and the Whale Optimisation Algorithm (WOA) in terms of convergence speed and solution quality. As a result, the suggested way for integrating DERs into DS utilising MEWOA is a successful and efficient optimisation technique. The results demonstrate that the proposed approach can enhance distribution system performance while lowering operational expenses and environmental impact.