A Novel Genetic Algorithm based Dynamic Economic Dispatch with Short-Term Load Forecasting

Aidana Kalakova, Sivanand Kumar, Prashant K. Jamwal, Suryanarayana Doolla

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes an optimal energy scheduling method for power transmission networks using Novel Genetic Algorithm (ηGA) for solving the Dynamic Economic Dispatch (DED) problem combined with machine learning based Short-Term Load Forecasting (STLF). The STLF is implemented based on a Multi-layer Artificial Neural Network (MANN) to estimate the day-ahead variations in the demand. The efficacy of the proposed energy scheduling model together with the STLF is verified using a modified IEEE 30-bus system using real data of the power plants located in the Ereymentau region of Kazakhstan. The simulation results suggest that the proposed model offers a cost effective, reliable and efficient dynamic energy scheduling in power transmission systems.

Original languageEnglish
JournalIEEE Transactions on Industry Applications
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Artificial intelligence
  • Distributed Generator (DG)
  • Dynamic Economic Dispatch (DED)
  • Economics
  • Forecasting
  • Load forecasting
  • Optimal Power Flow (OPF)
  • Predictive models
  • Reliability
  • Short-Term Load Forecasting (STLF)
  • Wind turbines

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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