scholarly journals An Efficient Energy Management in Office Using Bio-Inspired Energy Optimization Algorithms

Processes ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 142 ◽  
Author(s):  
Ibrar Ullah ◽  
Zar Khitab ◽  
Muhammad Khan ◽  
Sajjad Hussain
Keyword(s):  
Energy Consumption ◽  
Power Flow ◽  
Average Power ◽  
Energy Optimization ◽  
Optimization Techniques ◽  
Electricity Bill ◽  
Maximum Benefit ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Smart Technologies

Energy is one of the valuable resources in this biosphere. However, with the rapid increase of the population and increasing dependency on the daily use of energy due to smart technologies and the Internet of Things (IoT), the existing resources are becoming scarce. Therefore, to have an optimum usage of the existing energy resources on the consumer side, new techniques and algorithms are being discovered and used in the energy optimization process in the smart grid (SG). In SG, because of the possibility of bi-directional power flow and communication between the utility and consumers, an active and optimized energy scheduling technique is essential, which minimizes the end-user electricity bill, reduces the peak-to-average power ratio (PAR) and reduces the frequency of interruptions. Because of the varying nature of the power consumption patterns of consumers, optimized scheduling of energy consumption is a challenging task. For the maximum benefit of both the utility and consumers, to decide whether to store, buy or sale extra energy, such active environmental features must also be taken into consideration. This paper presents two bio-inspired energy optimization techniques; the grasshopper optimization algorithm (GOA) and bacterial foraging algorithm (BFA), for power scheduling in a single office. It is clear from the simulation results that the consumer electricity bill can be reduced by more than 34.69% and 37.47%, while PAR has a reduction of 56.20% and 20.87% with GOA and BFA scheduling, respectively, as compared to unscheduled energy consumption with the day-ahead pricing (DAP) scheme.

scholarly journals An energy optimization with improved QOS approach for adaptive cloud resources

2020 ◽  
Vol 10 (5) ◽  
pp. 4881
Author(s):  
Danthuluri Sudha ◽  
Sanjay Chitnis
Keyword(s):  
Cloud Computing ◽  
Signal Processing ◽  
Energy Consumption ◽  
Average Power ◽  
Energy Optimization ◽  
Optimization Techniques ◽  
Processing Methods ◽  
Dynamic Voltage ◽  
Signal Processing Methods ◽  
Multimedia Signal Processing

In recent times, the utilization of cloud computing VMs is extremely enhanced in our day-to-day life due to the ample utilization of digital applications, network appliances, portable gadgets, and information devices etc. In this cloud computing VMs numerous different schemes can be implemented like multimedia-signal-processing-methods. Thus, efficient performance of these cloud-computing VMs becomes an obligatory constraint, precisely for these multimedia-signal-processing-methods. However, large amount of energy consumption and reduction in efficiency of these cloud-computing VMs are the key issues faced by different cloud computing organizations. Therefore, here, we have introduced a dynamic voltage and frequency scaling (DVFS) based adaptive cloud resource re-configurability (ACRR) technique for cloud computing devices, which efficiently reduces energy consumption, as well as perform operations in very less time. We have demonstrated an efficient resource allocation and utilization technique to optimize by reducing different costs of the model. We have also demonstrated efficient energy optimization techniques by reducing task loads. Our experimental outcomes shows the superiority of our proposed model ACRR in terms of average run time, power consumption and average power required than any other state-of-art techniques.

scholarly journals Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 111 ◽  
Author(s):  
Touqeer Jumani ◽  
Mohd Mustafa ◽  
Madihah Rasid ◽  
Nayyar Mirjat ◽  
Mazhar Baloch ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Power Quality ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Harmonic Distortion ◽  
Optimal Power ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Power Flow Controller

Despite the vast benefits of integrating renewable energy sources (RES) with the utility grid, they pose stability and power quality problems when interconnected with the existing power system. This is due to the production of high voltages and current overshoots/undershoots during their injection or disconnection into/from the power system. In addition, the high harmonic distortion in the output voltage and current waveforms may also be observed due to the excessive inverter switching frequencies used for controlling distributed generator’s (DG) power output. Hence, the development of a robust and intelligent controller for the grid-connected microgrid (MG) is the need of the hour. As such, this paper aims to develop a robust and intelligent optimal power flow controller using a grasshopper optimization algorithm (GOA) to optimize the dynamic response and power quality of the grid-connected MG while sharing the desired amount of power with the grid. To validate the effectiveness of proposed GOA-based controller, its performance in achieving the desired power sharing ratio with optimal dynamic response and power quality is compared with that of its precedent particle swarm optimization (PSO)-based controller under MG injection and abrupt load change conditions. The proposed controller provides tremendous system’s dynamic response with minimum current harmonic distortion even at higher DG penetration levels.

scholarly journals A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 108 ◽  
Author(s):  
Abdul Shah ◽  
Haidawati Nasir ◽  
Muhammad Fayaz ◽  
Adidah Lajis ◽  
Asadullah Shah
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Optimization Algorithms ◽  
Energy Optimization ◽  
Smart Homes ◽  
Electrical Energy ◽  
Optimization Techniques ◽  
Comfort Level ◽  
Energy Consumption Optimization ◽  
User Comfort

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

A Probabilistic Multi-Objective Approach for Power Flow Optimization in Hybrid Wind-Based Power Systems Using Grasshopper Optimization Algorithm

2020 ◽  
Vol 11 (4) ◽  
pp. 61-86
Author(s):  
Barun Mandal ◽  
Provas Kumar Roy
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Thermal Power ◽  
Optimal Power ◽  
Valve Point Effect ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Practical Constraints

This article introduces a grasshopper optimization algorithm (GOA) to efficiently prove its superiority for solving different objectives of optimal power flow (OPF) based on a mixture thermal power plant that incorporates uncertain wind energy (WE) sources. Many practical constraints of generators, valve point effect, multiple fuels, and the various scenarios incorporating several configurations of WEs are considered for both singles along with multi-objectives for the OPF issue. Within the article, the considered method is verified on two common bus experiment systems, i.e. IEEE 30-bus as well as the IEEE 57-bus. Here, the fuel amount minimization and emission minimization are studied as the primary purposes of a GOA-based OPF problem. However, the proposed algorithm yields a reasonable conclusion about the better performance of the wind turbine. Computational results express the effectiveness of the proposed GOA approach for the secure and financially viable of the power system under various uncertainties. The comparison is tabulated with the existing algorithms to provide superior results.

Grasshopper Optimization Algorithm-Based Fuzzy-2DOF-PID Controller for LFC of Interconnected System With Nonlinearities

2021 ◽  
Vol 12 (3) ◽  
pp. 11-29
Author(s):  
Debasis Tripathy ◽  
Nalin Behari Dev Choudhury ◽  
Binod Kumar Sahu
Keyword(s):  
Optimization Algorithm ◽  
Pid Controller ◽  
Firefly Algorithm ◽  
Pattern Search ◽  
Optimization Techniques ◽  
Degree Of Freedom ◽  
Pid Controllers ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Two Degree Of Freedom

The load frequency control (LFC) is an automation scheme employed for an interconnected power system to overcome the frequency deviation issue because of load variation in the most economical way. This work puts an earliest effort to study the LFC issue of a three-area power systems including nonlinearities using fuzzy-two degree of freedom-PID (F-2DOF-PID) controller optimized with grasshopper optimization algorithm (GOA). Initially, GOA optimized PID controllers are considered for a two area non-reheat thermal system including generation rate constraint to validate the superiority over PID controllers tuned with some recently reported optimization techniques, such as hybrid firefly algorithm-pattern search, firefly algorithm, bacteria foraging optimization algorithm, genetic algorithm, and conventional Ziegler Nichols technique. Then the work is reconsidered for the same system to verify the supremacy of F-2DOF-PID controller over other controllers such as fuzzy-PID, two degree of freedom-PID, and PID with GOA framework. Furthermore, the study is extended to a three-area system considering the effect of nonlinearities to verify effectiveness and robustness of proposed controller.

Sine-cosine optimization algorithm for the conceptual design of automobile components

2020 ◽  
Vol 62 (7) ◽  
pp. 744-748 ◽  
Author(s):  
A. B. S. Yıldız ◽  
N. Pholdee ◽  
S. Bureerat ◽  
A. R. Yıldız ◽  
S. M. Sait
Keyword(s):  
Shape Optimization ◽  
Optimization Algorithm ◽  
Optimization Techniques ◽  
Stress Constraint ◽  
Element Analysis ◽  
Surface Method ◽  
Grasshopper Optimization Algorithm ◽  
Sine Cosine Algorithm ◽  
Automobile Components ◽  
Grasshopper Optimization

Abstract In this paper, the sine-cosine optimization algorithm (SCO) is used to solve the shape optimization of a vehicle clutch lever. The design problem is posed for the shape optimization of a clutch lever with a mass objective function and a stress constraint. Actual function evaluations are based on finite element analysis, while the response surface method is used to obtain the equations for objective and constraint functions. Recent optimization techniques such as the salp swarm algorithm, grasshopper optimization algorithm, and sine-cosine algorithm are used for shape optimization. The results show the ability of the sine-cosine optimization algorithm to optimize automobile components in the industry.

scholarly journals On Comparing the Performance of Swarm-Based Algorithms with Human-Based Algorithm for Nonlinear Systems

2021 ◽  
Author(s):  
Vishal Srivastava ◽  
Smriti Srivastava ◽  
Gopal Chaudhary ◽  
Xiomarah Guzmán-Guzmán ◽  
Vicente García-Díaz
Keyword(s):  
Nonlinear Systems ◽  
Teacher Learning ◽  
Pid Controller ◽  
Inverted Pendulum ◽  
External Disturbance ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Parameter Perturbation ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

AbstractThis paper exploits various meta-heuristic optimization techniques to learn PID controller parameters for nonlinear systems. The nonlinear systems considered here are well known ball and beam, inverted pendulum, and robotic arm manipulator. The gain parameters of the controllers are optimized by using two categories of meta-heuristic optimization techniques—swarm-based grasshopper optimization algorithm and particle swarm optimization and human-based, i.e., teacher learning-based optimization. Mean square error has been used to measure the performance of various algorithms. Robustness of these algorithms is studied and compared using parameter perturbation and external disturbance. There are substantial improvements in the performance of these plants using the mentioned algorithms as shown in the simulation results. A detailed comparative analysis of these algorithms has also been done.

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