Vikram Kumar Kamboj

Power system network is growing day by day as per the increased demand due to technological development around the globe. The increased power demand needs optimized planning for all the sections of power system. Economic dispatch is one of the important areas in which new optimization techniques can add to the overall improvement in the power system planning. Recent technological improvement in microgrids results in economic and efficient operation considering both conventional and non-conventional methods of generation. This paper highlights the optimal scheduling requirement in the advanced power system control by introducing a new proposed metaheuristic hybrid approach, i.e., Chaotic Spotted Hyena Optimization (CSHO). The proposed approach improves and intensifies the conventional approach and the results obtained are compared with some well-known published approaches using standard data. The CSHO is implemented on an islanded microgrid having five distributed generators out of which three are conventional generators and two are renewable sources. Three objectives are tested on the microgrid-(1) Economic scheduling, (2) Environmental emission and (3) CEED for Microgrid and the results of proposed approach are tested in four scenarios and compared with the PSO, DE, SOS, GWO and WOA approaches. The result achieved from the proposed approach is remarkable over previous approaches and showing enhanced economic operation, which are verified and presented in different sections of paper.

Chimp optimization algorithm (ChoA) has a wholesome attitude roused by chimp’s amazing thinking and hunting ability with a sensual movement for finding the optimal solution in the global search space. Classical Chimps optimizer algorithm has poor convergence and has problem to stuck into local minima for high-dimensional problems. This research focuses on the improved variants of the chimp optimizer algorithm and named as Boosted chimp optimizer algorithms. In one of the proposed variants, the existing chimp optimizer algorithm has been combined with SHO algorithm to improve the exploration phase of the existing chimp optimizer and named as IChoA-SHO and other variant is proposed to improve the exploitation search capability of the existing ChoA. The testing and validation of the proposed optimizer has been done for various standard benchmarks and Non-convex, Non-linear, and typical engineering design problems. The proposed variants have been evaluated for seven standard uni-modal benchmark functions, six standard multi-modal benchmark functions, ten standard fixed-dimension benchmark functions, and 11 types of multidisciplinary engineering design problems. The outcomes of this method have been compared with other existing optimization methods considering convergence speed as well as for searching local and global optimal solutions. The testing results show the better performance of the proposed methods excel than the other existing optimization methods.

Slime Mould Algorithm (SMA) is a newly designed meat-heuristic search that mimics the nature of slime mould during the oscillation phase. This is demonstrated in a unique mathematical formulation that utilizes adjustable weights to influence the sequence of both negative and positive propagation waves to develop a method to link food supply with intensive exploration capacity and exploitation affinity. The study shows the usage of the SM algorithm to solve a non-convex and cost-effective Load Dispatch Problem (ELD) in an electric power system. The effectiveness of SMA is investigated for single area economic load dispatch on large-, medium-, and small-scale power systems, with 3-, 5-, 6-, 10-, 13-, 15-, 20-, 38-, and 40-unit test systems, and the results are substantiated by finding the difference between other well-known meta-heuristic algorithms. The SMA is more efficient than other standard, heuristic, and meta-heuristic search strategies in granting extremely ambitious outputs according to the comparison records.

Abstract: The improved variants of Grey wolf optimizer has good exploration capability for global optimum solution. However, the exploitation competence of the existing variants of grey wolf optimizer is unfortunate. Researchers are continuously trying to improve the exploitation phase of the existing grey wolf optimizer, but still the improved variants of grey wolf optimizer are lacking in local search capability. In the proposed research, the exploitation phase of the existing grey wolf optimizer has been further improved using simulated annealing algorithm and the proposed hybrid optimizer has been named as hGWO-SA algorithm. The effectiveness of the proposed hybrid variant has been tested for various benchmark problems including multi-disciplinary optimization and design engineering problems and unit commitment problem of electric power system and it has been experimentally found that the proposed optimizer performs much better than existing variants of grey wolf optimizer. The feasibility of hGWO-SA algorithm has been tested for small & medium scale power systems unit commitment problem. In which, the results for 4 unit, 5 unit, 6 unit, 7 unit, 10 units, 19 unit, 20 unit, 40 unit and 60 units are evaluated. The 10-generating units are evaluated with 5% and 10% spinning reserve. The results obviously show that the suggested method gives the superior type of solutions as compared to other algorithms.

Recently established Harris hawks optimization (HHO) has natural behavior for finding an optimum solution in global search space without getting trapped in previous convergence. However, the exploitation phase of the current Harris hawks optimizer algorithm is poor. In the present research, an improved version of the HHO algorithm, which combines Harris hawks optimizer withCanis lupusinspire grey wolf optimizer (GWO) and named as hHHO‐GWO algorithm, has been proposed to find the solution of various optimization problems such as nonlinear, nonconvex, and highly constrained engineering design problem. In the proposed research, the phase of exploration and exploitation of the existing HHO algorithm has been further improved using GWO algorithm and its performance has been tested for various benchmarks problems including CEC2005 (unimodal, multimodal, and fixed dimensions functions), multimodal functions with variable dimensions, and CEC‐BC‐2017 test functions. Further, the developed hybrid optimizer has been tested for 11 different engineering design and optimization problems and experimental results of hHHO‐GWO have been compared with other optimizer.

The spread of COVID-19 is incearsing day by day and it has put the entire world and the whole humankind at the stack. The assets of probably the biggest economies are worried because of the enormous infectivity, and transmissibility of this ailment. Because of the developing extent of the number of cases and its ensuing weight on the organization and wellbeing experts, some expectation strategies would be required to anticipate the quantity of evidence in the future. In this paper, we have utilized time series forecasting approach entitled autoregressive integrated moving average, and bend fitting for the forecast of the quantity of COVID-19 cases in Canadian Province for 30 days ahead. The estimates of different parameters (number of positive cases, number of recouped cases, and decrease cases) got by the proposed strategy is exact inside a specific range, and will be a beneficial apparatus for overseers, and wellbeing officials to organize the clinical office in the distinctive Canadian Province.

Daily load demand for industrial, residential and commercial sectors are changing day by day. Also, inclusion of e-mobility has totally effected the operations of realistic power sector. Hence, to meet this time varying load demand with minimum production cost is very challenging. The proposed research work focuses on the mathematical formulation of profit based unit commitment problem of realistic power system considering the impact of battery electric vehicles, hybrid electric vehicles and plug in electric vehicles and its solution using Intensify Harris Hawks Optimizer (IHHO). The coordination of plants with each other is named as Unit commitment of plants in which the most economical patterns of the generating station is taken so as to gain low production cost with higher reliability. But with the increase in industrialization has affected the environment badly so to maintain the balance between the generation and environment a new thinking of generating low cost power with high reliability by causing less harm to environment i.e. less emission of flue gases is adopted by considering renewable energy sources.