A multi-objective approach for optimal placement of DG to enhance power distribution network performance using NSPSO

By: Call Number: AIT Thesis no.ET-13-17 Contributor(s): Material type: SeriesSeries: Asian Institute of Technology. Thesis ; no. ET-13-17Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2013Description: 36 leaves : ill. + 1 online resourceSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 2013 Summary: The study aims to find the best position and size of multi-type DG in distribution network base on three objective functions: maximize the increasable of load (loading factor), minimize the system real power loss and minimize the investment cost per year. IEEE 33-bus radial distribution system use to test by the NSPSO method to determine location and capacity of a specific number of DG in distribution system. The final optimal solution is compromised by fuzzy method. The optimization problems can separate to three cases by adding multi-type of DG. Case 1 is adding Photovoltaic (PV), Case 2 is adding Micro turbine (MT) and Case 3 is adding both PV and MT. The results of the study show Case 1 is the best plan with 49.61 percent of loading factor and 63.61 percent of system real power loss minimization compare to the base case. For economic planning, Case 3 is the best plan with 0.4238 million $/year of annualized investment cost.
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A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Energy

Thesis (M.Eng.) - Asian Institute of Technology, 2013

The study aims to find the best position and size of multi-type DG in distribution network base on three objective functions: maximize the increasable of load (loading factor), minimize the system real power loss and minimize the investment cost per year. IEEE 33-bus radial distribution system use to test by the NSPSO method to determine location and capacity of a specific number of DG in distribution system. The final optimal solution is compromised by fuzzy method. The optimization problems can separate to three cases by adding multi-type of DG. Case 1 is adding Photovoltaic (PV), Case 2 is adding Micro turbine (MT) and Case 3 is adding both PV and MT. The results of the study show Case 1 is the best plan with 49.61 percent of loading factor and 63.61 percent of system real power loss minimization compare to the base case. For economic planning, Case 3 is the best plan with 0.4238 million $/year of annualized investment cost.

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