UGC APPROVED ISSN 2278-1412

Archive

  Volume 14 | Issue 4

  Volume 14 | Issue 3

  Volume 14 | Issue 2

  Volume 14 | Issue 1

  Volume 13 | Issue 12

  Volume 13 | Issue 11

  Volume 13 | Issue 10

  Volume 13 | Issue 9

  Volume 13 | Issue 8

  Volume 13 | Issue 7

  Volume 13 | Issue 6

  Volume 13 | Issue 5

  Volume 13 | Issue 3

  Volume 13 | Issue 1

  Volume 12 | Issue 12

  Volume 12 | Issue 11

  Volume 12 | Issue 10

  Volume 12 | Issue 9

  Volume 12 | Issue 8

  Volume 12 | Issue 7

  Volume 12 | Issue 6

  Volume 12 | Issue 5

  Volume 12 | Issue 4

  Volume 12 | Issue 3

  Volume 12 | Issue 2

  Volume 12 | Issue 1

  Volume 11 | Issue 12

  Volume 11 | Issue 11

  Volume 11 | Issue 10

  Volume 11 | Issue 8

  Volume 11 | Issue 7

  Volume 11 | Issue 6

  Volume 11 | Issue 3

  Volume 11 | Issue 1

  Volume 10 | Issue 12

  Volume 10 | Issue 8

  Volume 10 | Issue 6

  Volume 10 | Issue 3

  Volume 10 | Issue 2

  Volume 10 | Issue 1

  Volume 9 | Issue 10

  Volume 8 | Issue 11

  Volume 8 | Issue 7

  Volume 7 | Issue 8

  Volume 7 | Issue 7

  Volume 7 | Issue 6

  Volume 7 | Issue 5

  Volume 7 | Issue 4

  Volume 7 | Issue 3

  Volume 7 | Issue 2

  Volume 7 | Issue 1

  Volume 6 | Issue 12

  Volume 6 | Issue 10

  Volume 6 | Issue 9

  Volume 6 | Issue 8

  Volume 6 | Issue 7

  Volume 6 | Issue 6

  Volume 6 | Issue 5

  Volume 6 | Issue 4

  Volume 6 | Issue 3

  Volume 6 | Issue 2

  Volume 6 | Issue 1

  Volume 5 | Issue 12

  Volume 5 | Issue 11

  Volume 5 | Issue 10

  Volume 5 | Issue 9

  Volume 5 | Issue 8

  Volume 5 | Issue 7

  Volume 5 | Issue 6

  Volume 5 | Issue 5

  Volume 5 | Issue 4

  Volume 5 | Issue 3

  Volume 5 | Issue 2

  Volume 5 | Issue 1

  Volume 4 | Issue 12

  Volume 4 | Issue 10

  Volume 4 | Issue 8

  Volume 4 | Issue 7

  Volume 4 | Issue 6

  Volume 4 | Issue 5

  Volume 4 | Issue 4

  Volume 4 | Issue 2

  Volume 4 | Issue 1

  Volume 3 | Issue 10

  Volume 3 | Issue 8

  Volume 3 | Issue 6

  Volume 3 | Issue 5

  Volume 3 | Issue 4

  Volume 3 | Issue 3

  Volume 3 | Issue 2

  Volume 3 | Issue 1

  Volume 2 | Issue 12

  Volume 2 | Issue 11

  Volume 2 | Issue 10

  Volume 2 | Issue 9

  Volume 2 | Issue 8

  Volume 2 | Issue 7

  Volume 2 | Issue 2

  Volume 1 | Issue 9

  Volume 1 | Issue 8

  Volume 1 | Issue 7

  Volume 1 | Issue 6

  Volume 1 | Issue 4

  Volume 1 | Issue 3

  Volume 1 | Issue 2

  Volume 1 | Issue 1

Current Volume 14 | Issue 05

ANN-Based Solutions for Dynamic Economic Load Dispatch: A Review


Volume:  14 - Issue: 02 - Date: 01-02-2025
Approved ISSN:  2278-1412
Published Id:  IJAECESTU448 |  Page No.: 107-112
Author: Priyanka Sharma
Co- Author: Krishna Teerth Chaturvedi,,,
Abstract:-Dynamic Economic Load Dispatch (DELD) is a critical optimization problem in modern power systems, aiming to schedule generator outputs over multiple time periods at minimum cost while obeying operational constraints. This review examines recent developments in applying Artificial Neural Networks (ANN) to solve the DELD problem. We first outline the DELD formulation and its dynamic constraints (like generator ramp limits). We then discuss ANN-based solution techniques, highlighting their strengths (such as handling nonlinearity and fast computation) and limitations (such as training data requirements and constraint enforcement). A comparative analysis is presented between ANN approaches and other AI-based methods – notably Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and hybrid techniques – to underscore performance differences in convergence speed, accuracy, and practicality. Also explore applications of ANN-driven DELD in smart grids, including integration of renewable energy sources and electric vehicles, where ANN models facilitate real-time dispatch under uncertainty. Recent simulation studies (primarily in MATLAB) are reviewed to demonstrate the effectiveness of ANN models, and key challenges (convergence reliability, solution accuracy, real-time performance) are identified alongside trends from current literature. The paper is organized into sections covering introduction, literature survey of ANN in DELD, comparative analysis with other methods, simulation tools and case studies, and conclusions.
Key Words:-Dynamic Economic Load Dispatch (DELD), Artificial Neural Networks (ANN), Smart Grid, Load Forecasting, Optimization Techniques, Fuel Cost Minimization, Renewable Energy Integration, Hybrid Neural Models, Transmission Loss Modeling.
Area:-Engineering
Download Paper: 
Preview This Article

Unable to display PDF file. Download instead.


Download Paper

Downlaod Paper

No. of Download

0001

Impact Factor

7.4


ijaece

Upcoming Events


Special Issue For Paper


Upcoming Conference


Call For Paper