IEEE transactions on industry applications, 2021-05, Vol.57 (3), p.2972-2982
2021
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- Autor(en) / Beteiligte
- Titel
- A Novel Genetic Algorithm Based Dynamic Economic Dispatch With Short-Term Load Forecasting
- Ist Teil von
- IEEE transactions on industry applications, 2021-05, Vol.57 (3), p.2972-2982
- Ort / Verlag
- New York: IEEE
- Erscheinungsjahr
- 2021
- Quelle
- IEEE Xplore
- Beschreibungen/Notizen
- 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0093-9994eISSN: 1939-9367DOI: 10.1109/TIA.2021.3065895Titel-ID: cdi_proquest_journals_2528947611
- Format
- –
- Schlagworte
- Artificial intelligence, Artificial neural networks, Distributed generator (DG), dynamic economic dispatch (DED), Economic forecasting, Economics, Electric power generation, Electrical loads, Forecasting, Genetic algorithms, Load forecasting, Machine learning, Mathematical models, Multilayers, optimal power flow (OPF), Power dispatch, Power plants, Predictive models, Reliability, Scheduling, short-term load forecasting (STLF), System effectiveness, Wind turbines