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Application of Metaheuristic Algorithms for Solving Real-World Electricity Demand Forecasting and Generation Expansion Planning Problems

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Iranian Journal of Science and Technology, Transactions of Electrical Engineering Aims and scope Submit manuscript

Abstract

An expansion plan of a real-world power system will be more optimal when it combines the Electricity Demand Forecasting (EDF) and Generation Expansion Planning (GEP) problem. It should consider minimum error and cost; more reliability and environment concern. In this study, the EDF problem of Tamil Nadu, an Indian state, is handled by having the input data for instance Gross State Domestic Product (GSDP), Per Capita Income (PCI) and population. The electricity demand has been estimated and validated through Mean Absolute Percentage Error (MAPE) using the optimization techniques such as Artificial Immune System (AIS), Genetic Algorithm (GA) and Differential Evolution (DE). The outcomes reveal that DE outdid other algorithms. The future demands which are estimated from the EDF problem are provided as input to the GEP problem. From the results of the EDF problem, three different cases, for instance low, average and high growth rates, are framed to solve the GEP problem by minimizing the cost, environmental pollution and by enhancing the reliability. The uncertainties in demand, cost, Forced Outage Rate (FOR) and capacity credit of the power plants are considered when handling the GEP problem for short-term (6-year) and long-term (12-year) planning horizons using DE, Self-adaptive Differential Evolution (SaDE) and Opposition-based Differential Evolution (ODE). The simulation results reveal that SaDE outdid other techniques in estimating the ideal solution.

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Authors and Affiliations

  1. PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India

    Amosedinakaran Samuel & Bhuvanesh Ananthan

  2. Easwari Engineering College, Chennai, Tamil Nadu, India

    Mala Krishnamoorthy

  3. Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India

    Kannan Subramanian

  4. Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar, Tamil Nadu, India

    Karuppasamy Pandiyan Murugesan

Authors
  1. Amosedinakaran Samuel
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  2. Mala Krishnamoorthy
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  3. Bhuvanesh Ananthan
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  4. Kannan Subramanian
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  5. Karuppasamy Pandiyan Murugesan
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Corresponding author

Correspondence to Amosedinakaran Samuel.

Appendix

Appendix

See Tables

Table 19 Data of prevailing units (as on March 31, 2019)
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Table 20 Data of candidate plants
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Table 21 Technical details of coal and gas plants with uncertainties
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Table 22 Technical details of diesel and nuclear plants with uncertainties
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Table 23 Technical details solar and wind plants with uncertainties
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23

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Samuel, A., Krishnamoorthy, M., Ananthan, B. et al. Application of Metaheuristic Algorithms for Solving Real-World Electricity Demand Forecasting and Generation Expansion Planning Problems. Iran J Sci Technol Trans Electr Eng 46, 413–439 (2022). https://doi.org/10.1007/s40998-022-00480-x

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