This paper proposes a multi-objective optimization method to determine the optimal size allocation of four different types of distributed generators, that is, TYPE-1, TYPE-2, TYPE-3, and TYPE-4 in transmission networks. In one such optimization strategy, distributed generators are injected after the Newton–Raphson method, a popular power flow analysis tool, has been applied first. A power system’s steady-state operating conditions, including voltage magnitudes, phase angles, line flows, and power losses, can be ascertained using the Newton–Raphson approach. It is possible to determine the transmission system’s baseline operating conditions by conducting a power flow analysis using the Newton–Raphson approach. The multi-objective function to be optimized includes two objective functions: reduction of real power losses and improvement of the voltage profile. The multi-objective functions are optimized using the particle swarm optimization algorithm in the suggested methodology. This method is tested on standard IEEE 3 bus,14 bus, and 30 bus systems using MATLAB. We considered four different types of distributed generator units and aimed to find the optimal size for each of them. The results illustrate the effectiveness of this approach in achieving optimal distributed generator allocation, leading to reduced power losses and minimized voltage deviations.

Keywords:Multi-objective optimization, distributed generator allocation, transmission networks, particle swarm optimization, MATLAB software.

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1. Del Valle Y, Perkel J, Venayagamoorthy GK, Harley RG. Optimal allocation of facts devices: classical versus metaheuristc approaches. SAIEE Africa Res J. 2009;100(1):12–23.
2. Xiong X, Wu W, Li N, Yang L, Zhang J, Wei Z. Risk-based multi-objective optimization of distributed generation based on GPSO-BFA algorithm. IEEE Access. 2019;7:30563–72.
3. Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO TechniquesLuis Fernando Grisales-Norena, Daniel Gonzalez Montoya and Carlos Andres Ramos-Paja.
4. Eladany MM, Eldesouky AA, Sallam AA. Power system transient stability: an algorithm for assessment and enhancement based on catastrophe theory and FACTS devices. IEEE Access. 2018;6:26424–37.
5. Payasi RP, Singh AK, Singh D, Singh NK. Multi-objective optimization of distributed generation with voltage step constraint. Int J Eng Sci Tech. 2015;7(3):33–41.
6. Gallano RJ, Nerves AC. Multi-objective optimization of distribution network reconfiguration with capacitor and distributed generator placement. In: TENCON 2014 IEEE Region 10 Conference, Bangkok, Thailand. 2014. pp. 1–6.
7. D Patel T, Acharya AG. Minimize power loss using particle swarm optimization technique. Int J Electr Eng Tech. 2019;10(2):69–80.
8. Multi-objective optimal allocation of distributed generation unit in distribution network using PSO by Mruthi prasanna, Likith kumar M V, Ananthapadmanabha Thammah.
9. Adepoju GA, Aderemi BA, Salimon SA, Alabi OJ. Optimal placement and sizing of distributed generation for power loss minimization in distribution network using particle swarm optimization technique. Europe J Eng Tech Res. 2023;8(1):19–25.
10. Asgharian V, Genc VI. Multi-objective optimization for voltage regulation in distribution systems with distributed generators. In: 2016 IEEE Electrical Power and Energy Conference (EPEC), Ottawa, ON, Canada. IEEE. pp. 1–6
11. Mostafa HA, El-Shatshat R, Salama MM. Multi-objective optimization for the operation of an electric distribution system with a large number of single phase solar generators. IEEE Trans Smart Grid. 2013;4(2):1038–47.
12. Pon Ragothama Priya P, Baskar S, Tamil Selvi S, Babulal CK. Optimal allocation of distributed generation using evolutionary multi-objective optimization. J Electr Eng Tech. 2023;18(2):869–86.

This paper proposes a multi-objective optimization method to determine the optimal size allocation of four different types of distributed generators, that is, TYPE-1, TYPE-2, TYPE-3, and TYPE-4 in transmission networks. In one such optimization strategy, distributed generators are injected after the Newton–Raphson method, a popular power flow analysis tool, has been applied first. A power system’s steady-state operating conditions, including voltage magnitudes, phase angles, line flows, and power losses, can be ascertained using the Newton–Raphson approach. It is possible to determine the transmission system’s baseline operating conditions by conducting a power flow analysis using the Newton–Raphson approach. The multi-objective function to be optimized includes two objective functions: reduction of real power losses and improvement of the voltage profile. The multi-objective functions are optimized using the particle swarm optimization algorithm in the suggested methodology. This method is tested on standard IEEE 3 bus,14 bus, and 30 bus systems using MATLAB. We considered four different types of distributed generator units and aimed to find the optimal size for each of them. The results illustrate the effectiveness of this approach in achieving optimal distributed generator allocation, leading to reduced power losses and minimized voltage deviations.

Keywords:Multi-objective optimization, distributed generator allocation, transmission networks, particle swarm optimization, MATLAB software.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Del Valle Y, Perkel J, Venayagamoorthy GK, Harley RG. Optimal allocation of facts devices: classical versus metaheuristc approaches. SAIEE Africa Res J. 2009;100(1):12–23.
2. Xiong X, Wu W, Li N, Yang L, Zhang J, Wei Z. Risk-based multi-objective optimization of distributed generation based on GPSO-BFA algorithm. IEEE Access. 2019;7:30563–72.
3. Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO TechniquesLuis Fernando Grisales-Norena, Daniel Gonzalez Montoya and Carlos Andres Ramos-Paja.
4. Eladany MM, Eldesouky AA, Sallam AA. Power system transient stability: an algorithm for assessment and enhancement based on catastrophe theory and FACTS devices. IEEE Access. 2018;6:26424–37.
5. Payasi RP, Singh AK, Singh D, Singh NK. Multi-objective optimization of distributed generation with voltage step constraint. Int J Eng Sci Tech. 2015;7(3):33–41.
6. Gallano RJ, Nerves AC. Multi-objective optimization of distribution network reconfiguration with capacitor and distributed generator placement. In: TENCON 2014 IEEE Region 10 Conference, Bangkok, Thailand. 2014. pp. 1–6.
7. D Patel T, Acharya AG. Minimize power loss using particle swarm optimization technique. Int J Electr Eng Tech. 2019;10(2):69–80.
8. Multi-objective optimal allocation of distributed generation unit in distribution network using PSO by Mruthi prasanna, Likith kumar M V, Ananthapadmanabha Thammah.
9. Adepoju GA, Aderemi BA, Salimon SA, Alabi OJ. Optimal placement and sizing of distributed generation for power loss minimization in distribution network using particle swarm optimization technique. Europe J Eng Tech Res. 2023;8(1):19–25.
10. Asgharian V, Genc VI. Multi-objective optimization for voltage regulation in distribution systems with distributed generators. In: 2016 IEEE Electrical Power and Energy Conference (EPEC), Ottawa, ON, Canada. IEEE. pp. 1–6
11. Mostafa HA, El-Shatshat R, Salama MM. Multi-objective optimization for the operation of an electric distribution system with a large number of single phase solar generators. IEEE Trans Smart Grid. 2013;4(2):1038–47.
12. Pon Ragothama Priya P, Baskar S, Tamil Selvi S, Babulal CK. Optimal allocation of distributed generation using evolutionary multi-objective optimization. J Electr Eng Tech. 2023;18(2):869–86.