Demand side management for wind integrated systems using genetic algorithm

Joint Authors

Grimaccia, Francesco
Chandra, D. Rakesh
Kumari, M. Sailaja
Mussetta, M. Sydulu Marco

Source

Journal of Electrical Systems

Issue

Vol. 14, Issue 4 (31 Dec. 2018), pp.217-230, 14 p.

Publisher

Piercing Star House

Publication Date

2018-12-31

Country of Publication

Algeria

No. of Pages

14

Main Subjects

Earth Sciences, Water and Environment
Electronic engineering

Topics

Abstract EN

Demand Side Management (DSM) is the most intelligent way to arrange loads for the future events.

The main intention of DSM is reducing the peak load, improving the system load factor and efficiency.

It is one of the key issues in present day power system operation.

As wind power generation is variable and intermittent in nature, using wind power in demand side management is a critical task.

This paper aims at maximum wind power utilization and improvement of system load factor.

The paper presents demand side management incorporating wind generation into the system using a Genetic Algorithm (GA) approach.

RTS (Reliability Test System) 24 bus and IEEE 14 bus systems are used to demonstrate day ahead demand side management with wind power integration.

It uses load shifting Demand Side Management technique to improve load factor of the system.

American Psychological Association (APA)

Chandra, D. Rakesh& Kumari, M. Sailaja& Grimaccia, Francesco& Mussetta, M. Sydulu Marco. 2018. Demand side management for wind integrated systems using genetic algorithm. Journal of Electrical Systems،Vol. 14, no. 4, pp.217-230.
https://search.emarefa.net/detail/BIM-833611

Modern Language Association (MLA)

Chandra, D. Rakesh…[et al.]. Demand side management for wind integrated systems using genetic algorithm. Journal of Electrical Systems Vol. 14, no. 4 (2018), pp.217-230.
https://search.emarefa.net/detail/BIM-833611

American Medical Association (AMA)

Chandra, D. Rakesh& Kumari, M. Sailaja& Grimaccia, Francesco& Mussetta, M. Sydulu Marco. Demand side management for wind integrated systems using genetic algorithm. Journal of Electrical Systems. 2018. Vol. 14, no. 4, pp.217-230.
https://search.emarefa.net/detail/BIM-833611

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references : p. 229-230

Record ID

BIM-833611