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Multi-objective stochastic economic dispatch with maximal renewable penetration under renewable obligation

Author

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  • Hlalele, Thabo G.
  • Naidoo, Raj M.
  • Bansal, Ramesh C.
  • Zhang, Jiangfeng

Abstract

In this paper, a stochastic multi-objective economic dispatch model is presented under renewable obligation policy framework. This proposed model minimises the total operating costs of generators and spinning reserves under renewable obligation while maximising renewable penetration. The intermittent nature of the wind and photovoltaic power plants is incorporated into the renewable obligation model. In order to minimise the cycling costs associated with ramping the thermal generators, the battery energy storage system units are included in the model to assist the system spinning reserves. Dynamic scenarios are created to deal with the intermittency of renewable energy sources. Due to the computational complexity of all possible scenarios, a scenario reduction method is applied to reduce the number of scenarios and solve the proposed stochastic renewable obligation model. A Pareto optimal solution is presented for the renewable obligation, and further decision making is conducted to assess the trade-offs associated with the Pareto front. To show the effectiveness of the proposed stochastic renewable obligation model, two IEEE test systems are used, i.e., the modified IEEE 30-bus and IEEE 118-bus system. In both test systems, the proposed model can attain high renewable penetration while minimising the expected operating cost. In the large IEEE 118-bus test system, the computational efficiency of the renewable obligation model is demonstrated by reducing the line constraints by 87% which minimises the computing time. A comparative study evaluates the impact of the stochastic model to the deterministic one, and it shows that the stochastic model can achieve high renewable penetration.

Suggested Citation

  • Hlalele, Thabo G. & Naidoo, Raj M. & Bansal, Ramesh C. & Zhang, Jiangfeng, 2020. "Multi-objective stochastic economic dispatch with maximal renewable penetration under renewable obligation," Applied Energy, Elsevier, vol. 270(C).
    • Handle: RePEc:eee:appene:v:270:y:2020:i:c:s0306261920306322
    DOI: 10.1016/j.apenergy.2020.115120
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    Cited by:

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    2. Yin, Linfei & Zhao, Lulin, 2021. "Rejectable deep differential dynamic programming for real-time integrated generation dispatch and control of micro-grids," Energy, Elsevier, vol. 225(C).
    3. Mohammad Abdul Baseer & Venkatesan Vinoth Kumar & Ivan Izonin & Ivanna Dronyuk & Athyoor Kannan Velmurugan & Babu Swapna, 2023. "Novel Hybrid Optimization Techniques to Enhance Reliability from Reverse Osmosis Desalination Process," Energies, MDPI, vol. 16(2), pages 1-15, January.
    4. Hlalele, Thabo G. & Zhang, Jiangfeng & Naidoo, Raj M. & Bansal, Ramesh C., 2021. "Multi-objective economic dispatch with residential demand response programme under renewable obligation," Energy, Elsevier, vol. 218(C).
    5. Fatemeh Marzbani & Akmal Abdelfatah, 2024. "Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review," Energies, MDPI, vol. 17(3), pages 1-31, January.
    6. Mo, Qiu & Liu, Fang, 2020. "Modeling and optimization for distributed microgrid based on Modelica language," Applied Energy, Elsevier, vol. 279(C).
    7. Yang, Wenqiang & Zhu, Xinxin & Xiao, Qinge & Yang, Zhile, 2023. "Enhanced multi-objective marine predator algorithm for dynamic economic-grid fluctuation dispatch with plug-in electric vehicles," Energy, Elsevier, vol. 282(C).

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