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A Multiparadigm Approach for Generation Dispatch Optimization in a Regulated Electricity Market towards Clean Energy Transition

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  • Suroso Isnandar

    (School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia
    Perusahaan Listrik Negara, PT PLN Persero, Jakarta 12160, Indonesia)

  • Jonathan F. Simorangkir

    (School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia)

  • Kevin M. Banjar-Nahor

    (School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia)

  • Hendry Timotiyas Paradongan

    (School of Business and Management, Bandung Institute of Technology, Bandung 40132, Indonesia)

  • Nanang Hariyanto

    (School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia)

Abstract

In Indonesia, the power generation sector is the primary source of carbon emissions, largely due to the heavy reliance on coal-fired power plants, which account for 60% of electricity production. Reducing these emissions is essential to achieve national clean energy transition goals. However, achieving this initiative requires careful consideration, especially regarding the complex interactions among multiple stakeholders in the Indonesian electricity market. The electricity market in Indonesia is characterized by its non-competitive and heavily regulated structure. This market condition often requires the PLN, as the system operator, to address multi-objective and multi-constraint problems, necessitating optimization in the generation dispatch scheduling scheme to ensure a secure, economical, and low-carbon power system operation. This research introduces a multiparadigm approach for GS optimization in a regulated electricity market to support the transition to clean energy. The multiparadigm integrates multi-agent system and system dynamic paradigms to model, simulate, and quantitatively analyze the complex interactions among multiple stakeholders in the Indonesian regulated electricity market. The research was implemented on the Java–Madura–Bali power system using AnyLogic 8 University Researcher Software. The simulation results demonstrate that the carbon policy scheme reduces the system’s carbon emissions while increasing the system’s cost of electricity. A linear regression for sensitivity analysis was conducted to determine the relationship between carbon policies and the system’s cost of electricity. This research offers valuable insights for policymakers to develop an optimal, acceptable, and reasonable power system operation scheme for all stakeholders in the Indonesian electricity market.

Suggested Citation

  • Suroso Isnandar & Jonathan F. Simorangkir & Kevin M. Banjar-Nahor & Hendry Timotiyas Paradongan & Nanang Hariyanto, 2024. "A Multiparadigm Approach for Generation Dispatch Optimization in a Regulated Electricity Market towards Clean Energy Transition," Energies, MDPI, vol. 17(15), pages 1-28, August.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3807-:d:1448593
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    References listed on IDEAS

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    1. Dongfang Ren & Xiaopeng Guo, 2023. "Simulation modeling and analysis of carbon emission reduction potential of multi-energy generation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 11823-11845, October.
    2. Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
    3. Wang, Tonghe & Hua, Haochen & Shi, Tianying & Wang, Rui & Sun, Yizhong & Naidoo, Pathmanathan, 2024. "A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy," Applied Energy, Elsevier, vol. 356(C).
    4. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    5. Salkuti, Surender Reddy, 2019. "Day-ahead thermal and renewable power generation scheduling considering uncertainty," Renewable Energy, Elsevier, vol. 131(C), pages 956-965.
    6. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
    7. Zhao Luo & Jinghui Wang & Ni Xiao & Linyan Yang & Weijie Zhao & Jialu Geng & Tao Lu & Mengshun Luo & Chenming Dong, 2022. "Low Carbon Economic Dispatch Optimization of Regional Integrated Energy Systems Considering Heating Network and P2G," Energies, MDPI, vol. 15(15), pages 1-14, July.
    8. Psarros, Georgios N. & Papathanassiou, Stavros A., 2023. "Generation scheduling in island systems with variable renewable energy sources: A literature review," Renewable Energy, Elsevier, vol. 205(C), pages 1105-1124.
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