Research Papers

Optimal Placement and Sizing of Photovoltaic Based Distributed Generation Considering Costs of Operation Planning of Monocrystalline and Thin-Film Technologies

[+] Author and Article Information
Aida Fazliana Abdul Kadir, Loo Soon Lii, Elia Erwani Hassan

Department of Industrial Power Engineering,
Faculty of Electrical Engineering,
Universiti Teknikal Malaysia Melaka,
Melaka 76100, Malaysia

Tamer Khatib

Department of Energy Engineering
and Environment,
An-Najah National University,
Nablus 97300, Palestine

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received May 18, 2018; final manuscript received August 1, 2018; published online September 14, 2018. Assoc. Editor: Nieves Vela.

J. Sol. Energy Eng 141(1), 011017 (Sep 14, 2018) (8 pages) Paper No: SOL-18-1222; doi: 10.1115/1.4041105 History: Received May 18, 2018; Revised August 01, 2018

Distributed generation (DG) technology has been growing rapidly in industries as this technology can increase the overall efficiency to the power systems. Improper placement and sizing can lead to power losses and interrupt the voltage profile of distribution systems. Studies have been done to solve the DG placement and sizing problem considering several factors, and one of the common factor is minimizing the power losses. However, it is not adequate by only considering the power losses, whereas, the costs of the generation, investment, maintenance, and losses of the distribution system must be taken in consideration. In this research, DG chosen to study is photovoltaic (PV) type which is monocrystalline and thin-film. Costs of operation planning with respect to the power losses is considered which include the costs of investment, maintenance, power loss, and generation that are determined for optimal placement and sizing of DG. The proposed method improved gravitational search algorithm (IGSA) is used in the matlab environment to find the optimal placement and sizing of DG and is tested with the IEEE 34-bus system. The performance of IGSA is then compared with gravitational search algorithm (GSA) and particle swarm optimization (PSO) to find out which algorithm gives the best fitness value and convergence rate. The purpose of this research is to identify the operation planning cost based on the optimization results and improves the optimal placement and sizing of DG in future, to provide maximum economical, technical, environmental benefits, and increase the overall efficiency to the power system.

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Grahic Jump Location
Fig. 1

Concept of PSO in search space [16]

Grahic Jump Location
Fig. 2

Particle swarm optimization flowchart [15]

Grahic Jump Location
Fig. 3

Concept of GSA in search space

Grahic Jump Location
Fig. 4

Gravitational search algorithm flowchart [18]

Grahic Jump Location
Fig. 5

Improved gravitational search algorithm flowchart [6]

Grahic Jump Location
Fig. 6

Single line diagram of IEEE 34-bus system [23]

Grahic Jump Location
Fig. 7

FiT Rates for solar PV1

Grahic Jump Location
Fig. 8

Convergence characteristics of PSO, GSA, and IGSA for one DG in 34-bus system

Grahic Jump Location
Fig. 9

Power losses before and after installation of DG in 34-bus system

Grahic Jump Location
Fig. 10

Correlation between CP and DG size in 34-bus system



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