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008			220908t20212021my a|||fr|||| 000 0 eng d
020			_aTHE000910 (Local) _qhardback
040			_aUMP _beng _cUMP _erda
090			_aKK.H34 2021 r Thesis
100	1		_aNorhafidzah Mohd Saad, _eauthor.
245	1	0	_aOptimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH / _cNorhafidzah Mohd Saad
264		1	_aPahang : _bUMP, _c2021
264		4	_c© 2021
300			_axix, 196 pages : _bIllustration ; _c30 cm. + _e1 CD-ROM
336			_atext _2rdacontent
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aCollege of Engineering
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2021
504			_aIncludes bibliographical reference
520	3		_aIn recent years, photovoltaic distributed generation (PVDG) has seen rapid growth due to its benefits in supporting the power system network, enhancing the transmission and distribution of power, and minimizing power congestion. PVDGs are connected directly to the load and produce power locally for the users, thus help to relieve the entire grid by reducing the demand especially during the peak load. Due to the random nature of the weather and occurrences of uncertainty, the planning and optimization of PVDG in the power system network with predicted uncertainty in photovoltaic generations and load variations are of crucial importance to minimize power losses. Thus, this research aims to develop a new optimization framework based on Monte Carlo embedded hybrid variant mean – variance mapping optimization (MVMO-SH) for the planning of PVDGs by considering these uncertainties. In this work, the probabilistic method in managing the risk of solar irradiance uncertainty with load variability is prepared. Uncertainty management is focused on the Malaysian tropical climate. Using meteorological data for one reference year, the Monte-Carlo simulation is performed in the Beta probability density function (PDF) to model continuous random variables of solar irradiances. For the load modelling studies, the Monte Carlo simulation is performed in Gaussian PDF to develop a probability model of various types of loads. The urban residential, commercial and industrial load profiles for one reference year are used for the load modelling. The probabilistic values of PV generation and load models are employed as the input data to the load flow analysis for the radial distribution network. The load flow patterns will significantly have affected when uncertain PV generation – load models are considered into the power flow algorithm. A new method of probabilistic backward – forward sweep power flow (BFSPF) based on Monte Carlo – PDF is developed as the fitness evaluation for the PVDG planning. A hybrid population – based stochastic optimization method named MVMO-SH algorithm is proposed to optimize PVDG locations and sizes in the grid system network. The objective function is to minimize the active power loss (APL) index. The proposed algorithm is applied to the standard radial test system to examine the usefulness and effectiveness of the proposed method. The impacts of PVDG on the power system network have been examined. As the results of the study, the uncertainty model of solar irradiance in Monte Carlo – Beta PDF has shown an almost similar pattern with less than 15% deviation as compared to the model from SEDA. The reductions in the power system’s total power losses have been shown with appropriate planning of PVDG in the power system network considering uncertainty in PV generation and load variations based on the Malaysian Tropical climate. When probabilistic BFSPF is optimized by MVMO-SH embedded Monte Carlo – PDF under uncertainties, the results show a better APL index compared to utilizing PSO and GA. The results also revealed that the uncertainties had the greatest influence on the optimal planning of PVDG in the power system network.
610	2	0	_aCollege of Engineering
650		0	_aUniversities and colleges
650		0	_aThesis
942			_2lcc _cTHESIS