Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

Techno-Economic Study by Teaching Learning-Based Optimization Algorithm for Optimal Placement of DG Units in Distribution Systems


Affiliations
1 Department of EEE, J N T University, Ananatapuramu, 515 002, Andhra Pradesh, India
2 Department of EEE, Lendi Institute of Engineering and Technology, Denkada, Jonnada 535 005, Andhra Pradesh, India
 

A significant improvement in system performance can be achieved by placing Distributed Generator (DG) units of the optimal size in optimum network of radial distribution locations. In order to maximize the economic and technological benefits, it is necessary to reduce yearly economic losses. These losses include expenditures associated with installation and operation of the buses as well as power loss and voltage difference between buses. In view of these multi-objective frameworks, the current problem is assessed and the best compromise solution also referred as the Pareto-optimal solution is provided. In the framework of the multi-objective optimization problem, specific equality as well as inequality constraints is investigated. It is shown in this study that a Multi-Objective Teaching-Learning Based Optimization (MOTLBO) algorithm has been proposed to solve the multi-objective problem. For the purpose of evaluating its performance, the proposed method is being deployed on IEEE-33 and IEEE-69 System of radial bus distribution. A comparison with other recent multi-objective algorithms such as OCDE, KHA and LSFSA is also included in this study. It has been revealed that the algorithm proposed can offer superior outcomes concerning power loss, annual economic loss mitigation and voltage profile enhancement.

Keywords

Distributed Generation Location, Distribution Radial System, Economic Loss Analysis, Loss Mitigation, Simultaneous DG Placement.
User
Notifications
Font Size

  • Ravindra K, Rao R S & Narasimham S V L, A novel method for optimal placement of distributed generation in distribution systems using HSDO, Int Trans Electr Energy Syst, 24 (2012) 547–561.

  • Rene P, Davor S & Vitomir K, Distributed generation allocation based on average daily load and power production curves, Int J Electr Power Energy Syst, 53 (2013) 612–622.

  • Singh B, Mukherjee V & Tiwari P, Genetic algorithm for impact assessment of optimally placed distributed generations with different load models from minimum total MVA intake viewpoint of main substation, Renew Sust Energ Rev, 57 (2016) 1611–1636.

  • Vatani M, Davood Solati A, Javad Sanjari M & Gevork B G, Multiple distributed generation units allocation in distribution network for loss reduction based on a combination of analytical and genetic algorithm methods, IET Gener Transm Distrib (2015).

  • Gandomkar M,Vakilian M & Ehsan M, A genetic–based tabu search algorithm for optimal DG allocation in distribution networks, Electr Power Compon Syst, 33 (2005) 1351–1362.

  • Moradi M H & Abedini M, A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems, Int J Electr Power Energy Syst, 34 (2012) 66−74.

  • Ouyang W, Haozhong C, Xiubin Z, Liangzhong Y & Masoud B, Distribution network planning considering distributed generation by genetic algorithm combined with graph theory, Electr Power Compon Syst, 38 (2010) 325–339.

  • Aman M M, Jasmon G B, Bakar A H A & Mokhlis H, A new approach for optimum DG placement and sizing based on voltage stability maximization and minimization of power losses, Energy Convers Manag, 70 (2013) 202–210.

  • Rasid M, Junichi M & Hirotaka T, Fossil fuel cost saving maximization: Optimal allocation and sizing of renewable-energy distributed generation units considering uncertainty via clonal differential evolution, applications, Therm Eng, 114 (2017) 1424–1432.

  • Nara K, Hayashi Y, Ikeda K & Ashizawa T, Application of tabu search to optimal placement of distributed generators IEEE Power Engineering Society Winter Meet (Columbus, OH, USA), 8 January 2001 - 01 February 2001.

  • Sultana S & Roy P K, Krill herd algorithm for optimal location of distributed generator in radial distribution system, Appl Soft Comput, 40 (2016) 391–404.

  • MohamedImra A & Kowsalya M, Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization, Swarm Evol Comput, 15 (2014) 58–65.

  • Farnaz S, Shadkam M & Zarei M, Optimal Distributed Generation allocation in distirbution systems employing ant colony to reduce losses, 43rd Int Univ Power Eng Conf, 2008, 1–5.

  • Hassan A A, Fahmy F H, Abd El-Shafy A N & Abu-elmagd Md A, Genetic single objective optimisation for sizing and allocation of renewable DG systems, Int J Sustain Energy, 36 (2017) 545–562.

  • Devabalaji K R & Ravi K, Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using bacterial foraging optimization algorithm, Ain Shams Eng J, 7 (2016) 959–971.

  • Chithra D S A, Lakshminarasimman L & Balamurugan R, Stud krill herd algorithm for multiple DG placement and sizing in a radial distribution system, Int J Eng Sci Technol, 20 (2017) 748–759.

  • Venkata Rao R & Patel V, An improved teaching-learningbased optimization algorithm for solving unconstrained optimization problems, Sci Iran, 20 (2013) 710–720.

  • Kumar S, Mandal K K & Chakraborty N, Optimal DG placement by multi-objective opposition based chaotic differential evolution for techno-economic analysis, Appl Soft Comput, 78 (2019) 70–83.

  • Kumar S, Mandal K K & Chakraborty N, A novel opposition-based tuned-chaotic differential evolution technique for techno-economic analysis by optimal placement of distributed generation, Optim Eng, 52 (2019) 303–324.

  • Satish K I & Kumar N P, A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems, Int J Electr Power Energy Syst, 45 (2013) 142–151.

  • Lewis A & Mirjalili S, The whale optimization algorithm, Adv Eng Softw, 95 (2016) 51–67.

  • Zhang C, Li J, Zhang Y J & Xu Z, Optimal location planning of renewable distributed generation units in distribution networks: An analytical approach, IEEE Trans Power Syst, 33 (2018) 2742–2753.

  • Warid W, Hizam H, Mariun N & Abdul−Wahab N I, A sensitivity based methodology for optimal placement of distributed generation in meshed power systems, Int J Simulation Syst Sci Technol, 17 (2017) 44.1–44.8.

  • Viral R & Khatod D K, Optimal planning of distributed generation systems in distribution system: A review, Renew Sust Energ Rev, 16 (2012) 5146–5165.

  • Okelola M O, Adebiyi O W, Salimon S A, Ayanlade S O & Amoo A L, Optimal sizing and placement of shunt capacitors on the distribution system using whale optimization algorithm, Niger J Technol Dev, 19(1) (2022) 39–47 doi: http://dx.doi.org/10.4314/njtd.v19i1.5

  • Jumaa FA, Neda O M & Mhawesh M A, Optimal distributed generation placement using artificial intelligence for improving active radial distribution system, Bull Electr Eng Inform, 10(5) (2021) 2345–2354.

  • Salkuti S R, Optimal location and sizing of shunt capacitors and distributed generation in power distribution systems, ECTI Trans Electr Eng Electron, 19(1) (2021) 34–42.

  • Nguyen T T, Enhanced sunflower optimization for placement distributed generation in distribution system Int J Electr Comput Eng, 11(1) (2021) 107–113.


Abstract Views: 42

PDF Views: 56




  • Techno-Economic Study by Teaching Learning-Based Optimization Algorithm for Optimal Placement of DG Units in Distribution Systems

Abstract Views: 42  |  PDF Views: 56

Authors

Hari Prasad C
Department of EEE, J N T University, Ananatapuramu, 515 002, Andhra Pradesh, India
K Subbaramaiah
Department of EEE, Lendi Institute of Engineering and Technology, Denkada, Jonnada 535 005, Andhra Pradesh, India
P Sujatha
Department of EEE, J N T University, Ananatapuramu, 515 002, Andhra Pradesh, India

Abstract


A significant improvement in system performance can be achieved by placing Distributed Generator (DG) units of the optimal size in optimum network of radial distribution locations. In order to maximize the economic and technological benefits, it is necessary to reduce yearly economic losses. These losses include expenditures associated with installation and operation of the buses as well as power loss and voltage difference between buses. In view of these multi-objective frameworks, the current problem is assessed and the best compromise solution also referred as the Pareto-optimal solution is provided. In the framework of the multi-objective optimization problem, specific equality as well as inequality constraints is investigated. It is shown in this study that a Multi-Objective Teaching-Learning Based Optimization (MOTLBO) algorithm has been proposed to solve the multi-objective problem. For the purpose of evaluating its performance, the proposed method is being deployed on IEEE-33 and IEEE-69 System of radial bus distribution. A comparison with other recent multi-objective algorithms such as OCDE, KHA and LSFSA is also included in this study. It has been revealed that the algorithm proposed can offer superior outcomes concerning power loss, annual economic loss mitigation and voltage profile enhancement.

Keywords


Distributed Generation Location, Distribution Radial System, Economic Loss Analysis, Loss Mitigation, Simultaneous DG Placement.

References