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 article presents a new cascaded control strategy to control the power flow in a renewable-energy-based microgrid operating in grid-connected mode. The microgrid model is composed of an AC utility grid interfaced with a multi-functional grid interactive converter (MF-GIC) acting as a grid-forming converter, a photovoltaic (PV) power-generation system acting as grid-feeding distributed generation unit, and various sensitive/non-sensitive customer loads. The proposed control strategy consists of a fractional order PI (FO-PI) controller to smoothly regulate the power flow between the utility grid, distributed generation unit, and the customers. The proposed controller exploits the advantages of FO (Fractional Order) calculus in improving the steady-state and dynamic performance of the renewable-energy-based microgrid under various operating conditions and during system uncertainties. To tune the control parameters of the proposed controller, a recently developed evaporation-rate-based water-cycle algorithm (ERWCA) is utilized. The performance of the proposed control strategy is tested under various operating conditions to show its efficacy over the conventional controller. The result shows that the proposed controller is effective and robust in maintaining all the system parameters within limits under all operating conditions, including system uncertainties.

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.

Chemotherapy is a widely used cancer treatment method globally. However, cancer cells can develop resistance towards single-drug-based chemotherapy if it is infused for extended periods, resulting in treatment failure in many cases. To address this issue, oncologists have progressed towards using multi-drug chemotherapy (MDC). This method considers different drug concentrations for cancer treatment, but choosing incorrect drug concentrations can adversely affect the patient’s body. Therefore, it is crucial to recognize the trade-off between drug concentrations and their adverse effects. To address this issue, a closed-loop multi-drug scheduling based on Fractional Order Internal-Model-Control Proportional Integral (IMC-FOPI) Control is proposed. The proposed scheme combines the benefits of fractional PI and internal model controllers. Additionally, the parameters of IMC-FOPI are optimally tuned using a random walk-based Moth-flame optimization. The performance of the proposed controller is compared with PI and Two degrees of freedom PI (2PI) controllers for drug concentration control at the tumor site. The results reveal that the proposed control scheme improves the settling time by 43% and 21% for V_X, 54% and 48 % for V_Y, and 48% and 40% for V_Z, respectively, compared to PI and 2PI. Therefore, it can be concluded that the proposed control scheme is more efficient in scheduling multi-drug than conventional controllers.