With the emergence of the smart grid, the distribution network is facing various problems, such as power limitations, voltage uncertainty, and many others. Apart from the power sector, the growth of electric vehicles (EVs) is leading to a rising power demand. These problems can potentially lead to blackouts. This paper presents three meta-heuristic techniques: grey wolf optimization (GWO), whale optimization algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and sizing) of solar photovoltaic (SPV), wind turbine generation (WTG), and electric vehicle charging stations (EVCSs). The aim of implementing these techniques is to optimize allocation of renewable energy distributed generation (RE-DG) for reducing active power losses, reactive power losses, and total voltage deviation, and to improve the voltage stability index in radial distribution networks (RDNs). MATLAB 2022a was used for the simulation of meta-heuristic techniques. The proposed techniques were implemented on IEEE 33-bus RDN for optimal allocation of RE-DGs and EVCSs while considering seasonal variations and uncertainty modeling. The results validate the efficiency of meta-heuristic techniques with a substantial reduction in active power loss, reactive power loss, and an improvement in the voltage profile with optimal allocation across all considered scenarios.

The availability of sustainable, efficient electricity access is critical for rural communities as it can facilitate economic development and improve the quality of life for residents. Isolated microgrids can provide a solution for rural electrification, as they can generate electricity from local renewable energy sources and can operate independently from the central grid. Residential load scheduling is also an important aspect of energy management in isolated microgrids. However, effective management of the microgrid’s energy resources and load scheduling is essential for ensuring the reliability and cost-effectiveness of the system. To cope with the stochastic nature of RERs, the idea of an optimal energy management system (EMS) with a local energy transactive market (LETM) in an isolated multi-microgrid system is proposed in this work. Nature-inspired algorithms such as JAYA (Sanskrit word meaning victory) and teaching–learning based optimization algorithm (TLBO) can get stuck in local optima, thus reducing the effectiveness of EMS. For this purpose, a modified hybrid version of the JAYA and TLBO algorithm, namely, the modified JAYA learning-based optimization (MJLBO), is proposed in this work. The prosumers can sell their surplus power or buy power to meet their load demand from LETM enabling a higher load serving as compared to a single isolated microgrid with multi-objectives, resulting in a reduced electricity bill, increased revenue, peak-average ratio, and user discomfort. The proposed system is evaluated against three other algorithms TLBO, JAYA, and JAYA learning-based optimization (JLBO). The result of this work shows that MJLBO outperforms other algorithms in achieving the best numerical value for all objectives. The simulation results validate that MJLBO achieves a peak-to-average ratio (PAR) reduction of 65.38% while there is a PAR reduction of 51.4%, 52.53%, and 51.2% for TLBO, JLBO, and JAYA as compared to the unscheduled load.

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96.87% of the initial value for the 33-bus and 69-bus RDNs, respectively, while the other parameters, i.e., operating cost index, voltage deviation, and voltage stability index, are also improved. This validates the efficiency of the algorithms. The proposed study is also carried out by considering different voltage-dependent load models, including industrial, residential, and commercial types.

Objective: To investigate the efficacy of Ketorolac with and without venous occlusion to relieve pain associated with Propofol injection. Study Design: Quasi-experimental study. Place and Duration of Study: Anesthesiology Department, Combined Military Hospital Multan, from Jan 2020 to Mar 2021. Methodology: One hundred and twenty patients of age more than 16 years with ASA physical status 1 and 2, undergoing elective surgery at Combined Military Hospital Multan were selected. Patients were allocated into the groups to receive Saline with sham occlusion (group-A), 10 mg Ketorolac with sham occlusion (Group-B), or 10 mg Ketorolac with full venous occlusion for 120 seconds (Group-C). Before surgery, the patients were asked to rate any local discomfort on a scale of 0-3 ten seconds after receiving a Propofol injection. On the seventh post-operative day, all the patients were handed a questionnaire to describe any untoward symptoms. Results: The mean age of the patients was 48.3 ± 2.8 years (range: 16 to 80 years). Mild discomfort was experienced by 4 (10%) patients, while 12 (30%) patients had moderate pain and 5 (12.5%) patients experienced severe pain in Group-A. In group- B, 16 (40%) patients had mild discomfort, 7 (17.5%) had moderate pain, and 5 (12.5%) had severe pain. In Group-C, 10 (25%) individuals experienced mild discomfort, 5 (12.5%) patients experienced moderate pain, whereas none of the patients experienced severe pain (p<0.001). Patients among the three groups reported no significant difference in post-injection venous sequelae. Conclusion: Pre-treatment with 10 mg Ketorolac and venous occlusion for 120 seconds...

Objectives: To compare the effects of Norepinephrine and Phenylephrine on blood pressure and heart rate during spinal anaesthesia for caesarean section. Study Design: Quasi-experimental study. Place and Duration of Study: Department of Anaesthesia Combined Military Hospital, Multan Pakistan, from May to Nov 2019. Methodology: Women (age: 18 to 45 years) with singleton pregnancy planned for caesarean section in spinal anaesthesia were included. Women with pregnancy-induced hypertension, placenta previa, placenta accreta, diabetes mellitus, and any other cardiovascular disease were excluded. After distribution into groups, Group-A was given a 20μg Norepinephrine bolus, and Group-B was given a 50μg bolus of Phenylephrine just after spinal anaesthesia. At five time points, systolic, diastolic, and mean blood pressures and heart rate were measured: baseline, block of the highest sensory level, oxytocin injection, delivery, and operation completion. If hypotension developed, the same rescue drug was repeated. Bradycardia was countered by 0.5 mg of Atropine. Results: The frequency of hypotension in the Norepinephrine group was 5 (16.67%), and in the Phenylephrine Group, it was 19 (63.33%) (p=0.0001). The frequency of bradycardia in the Norepinephrine Group was 6 (20%), and in the Phenylephrine Group, it was 17 (56.67%) (p=0.003). Conclusion: The frequency of hypotension and bradycardia is less after prophylactic 20 μg of Norepinephrine during spinal anaesthesia for a caesarean section than 50 μg of Phenylephrine.