Spider monkey optimization technique–based allocation of distributed generation for demand side management

2019 ◽  
Vol 29 (5) ◽  
pp. e12009 ◽  
Author(s):  
Gagari Deb ◽  
Kabir Chakraborty ◽  
Sumita Deb
Keyword(s):  
Distributed Generation ◽  
Optimization Technique ◽  
Demand Side Management ◽  
Spider Monkey ◽  
Demand Side ◽  
Spider Monkey Optimization

Efficient power scheduling in smart homes using a novel artificial ecosystem optimization technique considering two pricing schemes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Souhil Mouassa ◽  
Marcos Tostado-Véliz ◽  
Francisco Jurado
Keyword(s):  
Energy Demand ◽  
Supply And Demand ◽  
Optimization Technique ◽  
Smart Homes ◽  
Demand Side Management ◽  
Time Interval ◽  
Demand Side ◽  
Artificial Ecosystem ◽  
Pricing Schemes ◽  
Operational Time

Abstract With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificial ecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. Artificial ecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff.

scholarly journals Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems

2020 ◽  
Vol 10 (21) ◽  
pp. 7551
Author(s):  
Jaser A. Sa’ed ◽  
Zakariya Wari ◽  
Fadi Abughazaleh ◽  
Jafar Dawud ◽  
Salvatore Favuzza ◽  
...  
Keyword(s):  
Distributed Generation ◽  
Smart Grids ◽  
Distribution Systems ◽  
Electrical Energy ◽  
Demand Side Management ◽  
Test System ◽  
Demand Side ◽  
Direct Load Control ◽  
Wide Range ◽  
Load Shape

In this new era of high electrical energy dependency, electrical energy must be abundant and reliable, thus smart grids are conducted to deliver load demands. Hence, smart grids are implemented alongside distributed generation of renewable energies to increase the reliability and controllability of the grid, but, with the very volatile nature of the Distributed Generation (DG), Demand Side Management (DSM) helps monitor and control the load shape of the consumed power. The interaction of DSM with the grid provides a wide range of mutual benefits to the user, the utility and the market. DSM methodologies such as Conservation Voltage Reduction (CVR) and Direct Load Control (DLC) collaborate in the reduction of plant generation and reciprocally to the comprehensive cost. The aim of this paper is to investigate the effects caused by the implementation of DSM on the operation of PV-integrated distribution systems. The algorithms of CVR, DLC and the combination of CVR and DLC were implemented using OpenDSS and MATLAB. The effectiveness of the aforementioned schemes was verified on IEEE 30-Bus test system. Various possible integration scenarios between Photovoltaic (PV) and DSM schemes are illustrated. The optimal integration of such schemes constraining the reduction of energy consumed by the user and utility is presented. The results show that the implemented DSM algorithms provide a noticeable reduction in energy losses and reduction in consumed energy.

Comparative Study of Fuzzy PID and PID controller optimized with Spider Monkey Optimization for a Robotic Manipulator System

2019 ◽  
Vol 12 ◽  
Author(s):  
Alka Agrawal ◽  
Vishal Goyal ◽  
Puneet Mishra
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Optimization Technique ◽  
Absolute Error ◽  
Robotic Manipulator ◽  
Spider Monkey ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Spider Monkey Optimization

Background: Robotic manipulator system has been useful in many areas like chemical industries, automobile, medical fields etc. Therefore, it is essential to implement a controller for controlling the end position of a robotic armeffectively. However, with the increasing non-linearity and the complexities of a robotic manipulator system, a conventional Proportional-Integral-Derivative controller has become ineffective. Nowadays, intelligent techniques like fuzzy logic, neural network and optimization algorithms has emerged as an efficient tool for controlling the highly complex non-linear functions with uncertain dynamics. Objective: To implement an efficient and robustcontroller using Fuzzy Logic to effectively control the end position of Single link Robotic Manipulator to follow the desired trajectory. Methods: In this paper, a Fuzzy Proportional-Integral-Derivativecontroller is implemented whose parameters are obtainedwith the Spider Monkey Optimization technique taking Integral of Absolute Error as an objective function. Results: Simulated results ofoutput of the plants controlled byFuzzy Proportional-Integral-Derivative controller have been shown in this paper and the superiority of the implemented controller has also been described by comparing itwith the conventional Proportional-Integral-Derivative controller and Genetic Algorithm optimization technique. Conclusion: From results, it is clear that the FuzzyProportional-Integral-Derivativeoptimized with the Spider monkey optimization technique is more accurate, fast and robust as compared to the Proportional-Integral-Derivativecontroller as well as the controllers optimized with the Genetic algorithm techniques.Also, by comparing the integral absolute error values of all the controllers, it has been found that the controller optimized with the Spider Monkey Optimization technique shows 99% better efficacy than the genetic algorithm technique.

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