Distributed power generation has unparalleled advantages in energy substitution, environmental protection, improving system power supply reliability and reducing power supply costs. The purpose of this paper is to study the application of the improved particle swarm optimization algorithm(IPSO) in the location selection and sizing of distributed power generation. This paper first briefly introduces the characteristics of today's main distributed energy resources, and empirically analyzes the impact of distributed energy access on the distribution network. On this basis, a single-objective optimization model considering several constraints is established for the minimum investment cost and net loss of distributed generation. By examining the shortcomings of the standard particle swarm optimization algorithm, combining the optimization algorithm with various optimization techniques, a hybrid particle swarm optimization algorithm is proposed, and the effectiveness of the algorithm is studied. The optimization model is determined through the algorithm improvement, which further confirms the feasibility and effectiveness of the model and algorithm improvement. Then, in view of the environmental problems most concerned by modern society, advanced multi-objective algorithms are used to realize the overall optimization of power station location and power for distributed generation, and to provide decision makers with a variety of solutions. Experiments have shown that the overall optimization scheme in this paper can save about 20% of the cost, and through optimization, the network loss voltage can be reduced by about 30%.

Iqbal Rahat. Improved Particle Swarm Optimization Algorithm in Site Selection and Capacity of Distributed Power Supply. Distributed Processing System (2022), Vol. 3, Issue 1: 19-27. https://doi.org/10.38007/DPS.2022.030103.

[1] Yuki K, Shibamura-Fujiogi M. Surgical Site Infections and Perioperative Optimization of Host Immunity by Selection of Anesthetics. BioMed Research International, 2021, 2021(4):1-9. https://doi.org/10.1155/2021/5576959

[2] Dhruv B, Modi M, Mittal N. Improved Particle Swarm Optimization for Detection of Pancreatic Tumor using Split and Merge Algorithm. Computer Methods in Biomechanics and Biomedical Engineering: Imaging And Visualization, 2022, 10(1):38-47.

[3] Khodayari H, Pazooki F, Khodayari A R. Motion optimization algorithm designing for swarm quadrotors in application of grasping objects. Proceedings of the Institution of Mechanical Engineers, 2019, 233(11):3938-3951. https://doi.org/10.1177/0954410018812615

[4] Vafaeinejad A, Mahmoudi Jam S. Using particle swarm optimization algorithm and geospatial information system for potential evaluating of groundwater (case study: Mehran, Iran). Arabian Journal of Geosciences, 2021, 14(12):1-16.

[5] Naderipour A, Abdul-Malek Z, Nowdeh S A, et al. A Multi-Objective Optimization Problem for Optimal Site Selection of Wind Turbines for Reduce Losses and Improve Voltage Profile of Distribution Grids. Energies, 2019, 2(12):1-15. https://doi.org/10.3390/en12132621

[6] Vikram A, Karna D, Kumar A, et al. An Analytical Approach of Integrating Automated Load Scheduling to a Smart Energy Meter using Differential Evolution Algorithm. IOP Conference Series: Materials Science and Engineering, 2020, 946(1):012007 (5pp).

[7] Zhang S, Yuan X, Docherty P D, et al. An IPSO and its application in solving forward kinematics of a 3-DoF parallel manipulator:. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2021, 235(5):896-907. https://doi.org/10.1177/0954406220939109

[8] Ouyang C, Zhu D, Wang F. Application of improved sparrow search algorithm in SVM optimization. Journal of Physics Conference Series, 2021, 1966(1):012008.

[9] Lv X, Zhou D, Ma L, et al. An Improved Lagrange Particle Swarm Optimization Algorithm and Its Application in Multiple Fault Diagnosis. Shock and Vibration, 2020, 2020(3):1-8. https://doi.org/10.1155/2020/1091548

[10] Akboy E, Dagal I, Akn B. Improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems. International Journal of Energy Research, 2022, 46(7):8742-8759. https://doi.org/10.1002/er.7753

[11] Ariyati I, Rosyida S, Ramanda K, et al. Optimization Of The Decision Tree Algorithm Used Particle Swarm Optimization In The Selection Of Digital Payments. Journal of Physics: Conference Series, 2020, 1641(1):012090 (6pp).

[12] Mohammadian M. Application of the modified Fourier series method and the genetic algorithm for calibration of small‐scale parameters in the nonlocal strain gradient nanobeams. Mathematical Methods in the Applied Sciences, 2022, 45(10):6325-6345. https://doi.org/10.1002/mma.8173

[13] Ghazvinian H, Mousavi S F, Karami H, et al. Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction. PLoS ONE, 2019, 14(5):e0217634.

[14] Ouyang Z, Liu Y, Ruan S J, et al. An improved particle swarm optimization algorithm for reliability-redundancy allocation problem with mixed redundancy strategy and heterogeneous components. Reliability Engineering & System Safety, 2019, 181(JAN.):62-74. https://doi.org/10.1016/j.ress.2018.09.005

[15] Rauf H T, Bangyal W, Pervaiz S, et al. An Improved Particle Swarm Optimization Algorithm with Chi-Square Mutation Strategy. International Journal of Advanced Computer Science and Applications, 2019, 10(3):481-491.

[16] Madhumala R B, Tiwari H, Devaraj V C. An Improved Virtual Machine Placement In Cloud Data Center Using Particle Swarm Optimization Algorithm. International Journal Of Advanced Research In Engineering & Technology, 2020, 11(8):760-768.

[17] Dziwinski P, Bartczuk L. A New Hybrid Particle Swarm Optimization and Genetic Algorithm Method Controlled by Fuzzy Logic. IEEE Transactions on Fuzzy Systems, 2019, PP(99):1-1.

[18] Mortazavi A, Togan V, Moloodpoor M. Solution of structural and mathematical optimization problems using a new hybrid swarm intelligence optimization algorithm. Advances in Engineering Software, 2019, 127(JAN.):106-123. https://doi.org/10.1016/j.advengsoft.2018.11.004