Improved genetic algorithm for optimal demand response in smart grid

被引：19

作者：

Jeyaranjani, J. ^{[1
]}

Devaraj, D. ^{[2
]}

机构：

[1] Kalasalingam Acad Res & Educ, Dept Comp Sci & Engn, Srivilliputhur, Tamil Nadu, India

[2] Kalasalingam Acad Res & Educ, Dept Elect & Elect Engn, Srivilliputhur, Tamil Nadu, India

来源：

SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS | 2022年 / 35卷

关键词：

Energy consumption; Genetic Algorithm; Load scheduling; Demand Response; Optimization algorithm; Energy management system; Real time pricing; ENERGY MANAGEMENT;

D O I：

10.1016/j.suscom.2022.100710

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In Smart Grid system, the consumers are provided with the opportunity to schedule their home appliances in response to variations in electricity price over time. This paper presents the optimal scheduling of resident ap-pliances. This optimal scheduling is formulated as an optimization problem and is solved by applying improved Genetic Algorithm (GA). In this improved GA, the selection of chromosomes is carried out using entropy method, blended crossover and mutation is performed using correlation coefficient. The simulation is performed for a single resident load profile for a set of appliances using Python programming. The result shows the reduction in the electricity cost to that of the original. The number of iterations taken by the improved Genetic Algorithm (GA) is comparatively lesser than the standard GA and the execution time is reduced by 2.76 s. Thus the result proves the effectiveness of the proposed improvisation in Genetic Algorithm for optimal load scheduling.

引用

页数：8

共 22 条

[1] Energy storage system scheduling for peak demand reduction using evolutionary combinatorial optimisation [J].

Agamah, Simon U. ;

Ekonomou, Lambros .

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, 2017, 23 :73-82

[2] An Online Learning Algorithm for Demand Response in Smart Grid [J].

Bahraini, Shahab ;

Wong, Vincent W. S. ;

Huang, Jianwei .

IEEE TRANSACTIONS ON SMART GRID, 2018, 9 (05) :4712-4725

[3] Appliances Scheduling using Hybrid Scheme of Genetic Algorithm and Elephant Herd Optimization for Residential Demand Response [J].

Bukhsh, Rasool ;

Javaid, Nadeem ;

Iqbal, Zafar ;

Ahmed, Usman ;

Ahmad, Zeeshan ;

Iqbal, Muhammad Nadeem .

2018 32ND INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS WORKSHOPS (WAINA), 2018, :210-217

[4]

Cakmak R, 2016, 4 INT IST SMART GRID, P16, DOI [10.1109/SGCF.2016.7492435, DOI 10.1109/SGCF.2016.7492435]

[5] Genetic-algorithm-based optimal power flow for security enhancement [J].

Devaraj, D ;

Yegnanarayana, B .

IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION, 2005, 152 (06) :899-905

[6] A Robust Optimization Approach for Demand Side Scheduling Considering Uncertainty of Manually Operated Appliances [J].

Du, Yuefang F. ;

Jiang, Lin ;

Li, Yuanzheng ;

Wu, Qinghua .

IEEE TRANSACTIONS ON SMART GRID, 2018, 9 (02) :743-755

[7] Optimized energy consumption model for smart home using improved differential evolution algorithm [J].

Essiet, Ima O. ;

Sun, Yanxia ;

Wang, Zenghui .

ENERGY, 2019, 172 :354-365

[8] Demand response implementation in smart households [J].

Ghazvini, Mohammad Ali Fotouhi ;

Soares, Joao ;

Abrishambaf, Omid ;

Castro, Rui ;

Vale, Zita .

ENERGY AND BUILDINGS, 2017, 143 :129-148

[9]

Kakran S., 2019, Int J Electr Eng Inform, V11, P18, DOI [10.15676/ijeei.2019.11.1.2, DOI 10.15676/IJEEI.2019.11.1.2]

[10] Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions [J].

Kampelis, Nikos ;

Tsekeri, Elisavet ;

Kolokotsa, Dionysia ;

Kalaitzakis, Kostas ;

Isidori, Daniela ;

Cristalli, Cristina .

ENERGIES, 2018, 11 (11)

← 1 2 3 →