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Environmental economic dispatch method of power system based on multiobjective artificial bee colony algorithm

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Abstract

A multiobjective environmental economic dispatching model of power system is established with minimum economic cost and pollution emission as the optimization objectives to meet the challenges of power system dispatching caused by the global energy crisis and climate warming and promote the realization of the double carbon goal. A multiobjective artificial bee colony algorithm (MOABC) based on nondominant sorting and improved greedy criterion is designed according to the characteristics of the model. In the design of the algorithm, the Taguchi method is used to optimize the parameters, the heuristic method is used to process the constraints dynamically, and a variety of comprehensive evaluation indexes are used to evaluate the algorithm's performance. In the simulation analysis, a six-machine power system and a ten-machine power system are simulated and compared with different algorithms to verify the proposed model's rationality and effectiveness. Finally, the technique for order preference by similarity to the ideal solution (TOPSIS) method is used to determine the optimal compromise solution to provide a reference for the scientific decision-making of dispatchers. The simulation results show that the MOABC algorithm achieved the lowest economic cost (161.159 × 103 yuan for the 6-machine system and 2.771 × 104 $ for the 10-machine system) and pollution emission (194.202 kg for the 6-machine system and 3.639 × 103 kg for the 10-machine system) compared to the multiobjective wind driven optimization, multiobjective particle swarm optimization, and NSGA-II algorithms.

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Abbreviations

EED:

Environmental economic dispatch

TOPSIS:

Technique for order preference by similarity to the ideal solution

MOABC:

Multiobjective artificial bee colony

MOWDO:

Multiobjective wind driven optimization

MOPSO:

Multiobjective particle swarm optimization

NSGA:

Nondominated sorting genetic algorithm

IGD:

Inverted generational distance

HV:

Hypervolume

MS:

Maximum spread

PF:

Pareto optimal front

RV:

Response variable

EGO:

Efficient global optimization

MODE:

Multiobjective differential evolution

MOEA:

Multiobjective evolutionary algorithm

MOIA:

Multiobjective immune algorithm

BBPSO:

Backbone particle swarm optimization

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (52007124), the Liaoning Province Applied Basic Research Project (2022JH2/101300134), the Shenyang Science and Technology Plan Project (20-203-5-51), the Liaoning Province “JIE BANG GUA SHUAI” Science and Technology Plan Project (2021JH1/10400009), and the Natural Science Foundation of Liaoning Province of China (2023-MS-247).

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Authors and Affiliations

  1. School of Electrical Engineering, Shenyang University of Technology, Shenyang, 110870, China

    Liming Wang, Yingming Liu & Xiaodong Wang

  2. College of Information, Shenyang Institute of Engineering, Shenyang, 110136, China

    Liming Wang

  3. School of Computer Science and Informatics, De Montfort University, Leicester, LE1 9BH, UK

    Xinfu Pang

  4. College of Energy and Power, Shenyang Institute of Engineering, Shenyang, 110136, China

    Qimin Wang

Authors
  1. Liming Wang
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  5. Xiaodong Wang
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Contributions

Liming Wang contributed to methodology, software, investigation, and writing—original draft. Yingming Liu contributed to conceptualization, methodology, and data curation. Xinfu Pang contributed to methodology, writing—review and editing. Qimin Wang contributed to supervision. Xiaodong Wang contributed to writing—review and editing.

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Correspondence to Liming Wang.

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Wang, L., Liu, Y., Pang, X. et al. Environmental economic dispatch method of power system based on multiobjective artificial bee colony algorithm. Electr Eng 106, 567–579 (2024). https://doi.org/10.1007/s00202-023-01988-z

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