Optimal allocation of renewable distributed generators and electric vehicles in a distribution system using the political optimization algorithm

his 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 (PLoss) 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 PLoss 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

Techno-economic analysis of the distribution system with integration of distributed generators and electric vehicles

Electric vehicles (EVs) have become a feasible alternative to conventional vehicles due to their technical and environmental benefits. The rapid penetration of EVs might cause a significant impact on the distribution system (DS) due to the adverse effects of charging the EVs and grid integration technologies. In order to compensate for an additional EV load to the existing load demand on the DS, the distributed generators (DGs) are integrated into the grid system. Due to the stochastic nature of the DGs and EV load, the integration of DGs alone with the DS can minimize the power losses and increase the voltage level but not to the extent that might not improve the system stability. Here, the EV that acts as a load in the grid-to-vehicle (G2V) mode during charging can act as an energy source with its bidirectional mode of operation as vehicle-to-grid (V2G) while in the discharging mode. V2G is a novel resource for energy storage and provision of high and low regulations. The article proposes a smart charging model of EVs, estimates the off-load and peak load times over a period of time, and allocates charging and discharging based on the constraints of the state of charge (SoC), power, and intermittent load demand. A standardized IEEE 33-node DS integrated with an EV charging station (EVCS) and DGs is used to reduce the losses and improve the voltage profile of the proposed system. Simulation results are carried out for various possible cases to assess the effective utilization of V2G for stable operation of the DS. The cost–benefit analysis (CBA) is also determined for the G2V and V2G modes of operation for a 24-h horizon

Static economic dispatch incorporating wind farm using Flower pollination algorithm

Renewable energy is one of the clean and cheapest forms of energy which helps in minimizing the carbon foot print. Due to the less environmental impact and economic issues integration of renewable energy sources with the existing network gained attention. In this paper, the impact of wind energy is analysed in a power system network using static economic dispatch (SED). The wind energy is integrated with the existing thermal systems. Here, the generation scheduling is optimized using Flower pollination algorithm (FPA) due to its robustness in solving nonlinear problems. Integration of wind power in the existing system increases the complexity due to its stochastic nature. Weibull distribution function is used for solving the stochastic nature of wind. Scenarios without and with wind power penetration are discussed in detail. The analysis is carried out by considering the losses and installing the wind farm at different locations in the system. The proposed methodology is tested and validated on a standard IEEE 30 bus system

Multi-Drug Scheduling for Chemotherapy Using Fractional Order Internal Model Controller

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 VX, 54% and 48 % for VY, and 48% and 40% for VZ, 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