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Placement of PV Units Considering Uncertainties of Generation and Load in Distribution Systems
Anshu Parashar1, Anand Kumar Pandey2, Ritesh Kumar Rai3

1Anshu Parashar*, EEE, JIMS Engineering Management Technical Campus, Greater Noida, (U.P.), India.
2Anand Kumar Pandey, EEE, JSS Academy of technical education, Noida, (U.P.), India.
3Ritesh Rai*, EEE, JIMS Engineering Management Technical Campus, Greater Noida, (U.P.), India. 

Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 102-106 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.B4107079220 | DOI: 10.35940/ijrte.B4107.099320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In conventional power system the transmission and distribution (T&D) losses is a major concern. Renewable energy resources placed at load centers can reduce the T&D losses. For power system planners and researchers it is essential to find the optimal size and position of renewable energy resources to be place in distribution networks. Renewable energy source such as solar energy is abundantly present in the environment. With the help of solar photovoltaic (SPV) system solar energy can be converted to electrical energy. Placement of SPV in distribution system is an interesting area for researchers and planners, the random placement of SPV in distribution system leads to more power losses and poor voltage profile. In this article mathematical modelling of time varying nature of SPV and variable load has been explained and particle swarm optimization (PSO) method is proposed to find the best size and location of the SPV system. This method is tested on IEEE 33 bus system. For the validation of result existing technique based on analytical expression is selected. It is found that PSO gives better result in compare to analytical method. 
Keywords: Solar photovoltaic system, Multi-objective index, Time varying solar irradiance, Power system optimization, Particle swarm optimization.