A Novel Genetic Algorithm Based Dynamic Economic Dispatch with Short-Term Load Forecasting
Kalakova A. Kumar Nunna H.S.V.S. Jamwal P.K. Doolla S.
May-June 2021Institute of Electrical and Electronics Engineers Inc.
IEEE Transactions on Industry Applications
2021#57Issue 32972 - 2982 pp.
This article proposes an optimal energy scheduling method for power transmission networks using novel genetic algorithm (nGA) 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 multilayer 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.
Distributed generator (DG) , dynamic economic dispatch (DED) , optimal power flow (OPF) , short-term load forecasting (STLF)
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Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
Indian Institute of Technology, Mumbai, 400076, India
Nazarbayev University
Indian Institute of Technology
10 лет помогаем публиковать статьи Международный издатель
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