Abstract

Integration of Distributed Generation (DG) has become one of the popular research interests in power system. DG used small-scale technologies to generate electricity near to the consumer and the size is normally small, range from 50 MW to 100 MW. However, in order to integrate DG into the power system distribution, it is very crucial to consider several factors such as location, size and the number of DG to maintain its benefits. This paper proposes comparison of three techniques for optimal placement of DG in distribution system. The optimal placement was done using Evolutionary Programming (EP), Ant Lion Optimizer (ALO) and Loss Sensitivity Factor (LSF) to minimize the power losses in distribution system. These techniques were tested in three conditions; base case (without loading increment), 50% loading and 100% loading to observe the performance of the techniques in various phenomena. The test was conducted using IEEE 10-bus radial distribution system and the result shows that the integration of DG does minimize the power losses with ALO provides the most promising results.

Keyword

Optimal placement distributed generation, Evolutionary programming, Ant lion optimizer, Loss sensitivity factor.

Cite this article

Rahman NA, Hamid ZA, Musirin I, Salim NA, Yusoff MF

Refference

[1][1]Gusnanda AF, Putranto LM. Effect of distributed photovoltaic generation installation on voltage profile: a case study of rural distribution system in yogyakarta Indonesia. In international conference on information and communications technology 2019 (pp. 750-5). IEEE.

[2][2]Zhong Z, Zhang Y, Shen H, Li X. Optimal planning of distributed photovoltaic generation for the traction power supply system of high-speed railway. Journal of Cleaner Production. 2020; 263:1-20.

[3][3]Ackermann T, Andersson G, Söder L. Distributed generation: a definition. Electric Power Systems Research. 2001; 57(3):195-204.

[4][4]Borges CL, Falcao DM. Optimal distributed generation allocation for reliability, losses, and voltage improvement. International Journal of Electrical Power & Energy Systems. 2006; 28(6):413-20.

[5][5]Griffin T, Tomsovic K, Secrest D, Law A. Placement of dispersed generation systems for reduced losses. In proceedings of the annual Hawaii international conference on system sciences 2000 (pp. 9-pp). IEEE.

[6][6]Schweer A, Study committee power system planning and development international conference on large high voltage electric systems. Impact of Increasing Contribution of Dispersed Generation on the Power System. Cigré; 1999:46-55.

[7]

[8][7]Maribu KM. Distributed generation in liberalised electricity markets. A Summary of PhD Projects 2002. 2005.

[9][8]Jordehi AR. Allocation of distributed generation units in electric power systems: a review. Renewable and Sustainable Energy Reviews. 2016; 56:893-905.

[10][9]Jagtap KM. Impact of different types of distributed generation on radial distribution network. In international conference on reliability optimization and information technology 2014 (pp. 473-6). IEEE.

[11][10]Abdmouleh Z, Gastli A, Ben-Brahim L, Haouari M, Al-Emadi NA. Review of optimization techniques applied for the integration of distributed generation from renewable energy sources. Renewable Energy. 2017; 113:266-80.

[12][11]Surendra K, Vyjayanthi C. Fault level analysis in modern electrical distribution system considering various distributed generations. In international conference on power, energy, control and transmission systems 2018 (pp. 36-42). IEEE.

[13][12]Hamid ZB, Jipinus S, Musirin I, Othman MM, Salimin RH. Optimal sizing of distributed generation using firefly algorithm and loss sensitivity for voltage stability improvement. Indonesian Journal of Electrical Engineering and Computer Science. 2020;17(2):720-7.

[14][13]Rueda-Medina AC, Franco JF, Rider MJ, Padilha-Feltrin A, Romero R. A mixed-integer linear programming approach for optimal type, size and allocation of distributed generation in radial distribution systems. Electric Power Systems Research. 2013; 97:133-43.

[15][14]Wang Z, Chen B, Wang J, Kim J, Begovic MM. Robust optimization based optimal DG placement in microgrids. IEEE Transactions on Smart Grid. 2014; 5(5):2173-82.

[16][15]Al Abri RS, El-Saadany EF, Atwa YM. Optimal placement and sizing method to improve the voltage stability margin in a distribution system using distributed generation. IEEE Transactions on Power Systems. 2013; 28(1):326-34.

[17][16]Kaur S, Kumbhar G, Sharma J. A MINLP technique for optimal placement of multiple DG units in distribution systems. International Journal of Electrical Power & Energy Systems. 2014; 63:609-17.

[18][17]Ehsan A, Yang Q. Optimal integration and planning of renewable distributed generation in the power distribution networks: a review of analytical techniques. Applied Energy. 2018; 210:44-59.

[19][18]Kansal S, Kumar V, Tyagi B. Optimal placement of different type of DG sources in distribution networks. International Journal of Electrical Power & Energy Systems. 2013; 53:752-60.

[20][19]Abdi S, Afshar K. Application of IPSO-Monte Carlo for optimal distributed generation allocation and sizing. International Journal of Electrical Power & Energy Systems. 2013; 44(1):786-97.

[21][20]Zeinalzadeh A, Mohammadi Y, Moradi MH. Optimal multi objective placement and sizing of multiple DGs and shunt capacitor banks simultaneously considering load uncertainty via MOPSO approach. International Journal of Electrical Power & Energy Systems. 2015; 67:336-49.

[22][21]Devi S, Geethanjali M. Optimal location and sizing determination of distributed generation and DSTATCOM using particle swarm optimization algorithm. International Journal of Electrical Power & Energy Systems. 2014; 62:562-70.

[23][22]Karimyan P, Gharehpetian GB, Abedi M, Gavili A. Long term scheduling for optimal allocation and sizing of DG unit considering load variations and DG type. International Journal of Electrical Power & Energy Systems. 2014; 54:277-87.

[24][23]Beltrán JC, Aristizábal AJ, López A, Castaneda M, Zapata S, Ivanova Y. Comparative analysis of deterministic and probabilistic methods for the integration of distributed generation in power systems. Energy Reports. 2020; 6:88-104.

[25][24]Zhu J. Optimization of power system operation. John Wiley & Sons; 2015.

[26][25]Aziz NI, Sulaiman SI, Musirin I, Shaari S. Assessment of evolutionary programming models for single-objective optimization. In international power engineering and optimization conference 2013 (pp. 304-8). IEEE.

[27][26]Mirjalili S. The ant lion optimizer. Advances in engineering software. 2015; 83:80-98.