Distributed generation such as photovoltaic is more encouraged by government policies to exploit the renewable energy resource and for sustainable energy production. The integration of photovoltaic generation to the distribution network introduces large uncertainties, the inclusion of these uncertainties in load flow analysis used for planning, operation, and expansion of the power system networks is done by using the probabilistic load flow studies approach. With this, the risk identification and its mitigation associated with the power network can be easily performed. In this work, the modeling of photovoltaic generation based on historical data that is obtained by power system measurements is presented. The distribution of photovoltaic generation obtained is complex and does not match any standard distributions. A numerical method is used to generate the samples that are used for Monte Carlo simulation to include photovoltaic generation with its original characteristics and uncertainties in probabilistic analysis. The analysis is done by integrating a 1000 kW photovoltaic generation system in IEEE 13 Node Test Feeder, which is highly loaded and unbalanced. As a function of a photovoltaic generation uncertainty, statistical distribution of variables such as currents and voltage of each network elements are calculated. Using them, the network health and the power loss associated with the network are determined.

Modeling Photovoltaic Generation Uncertainties for Monte Carlo Method based Probabilistic Load Flow Analysis of Distribution Network

Harshavardhan Palahalli.;Maffezzoni P.;Gruosso G.
2020-01-01

Abstract

Distributed generation such as photovoltaic is more encouraged by government policies to exploit the renewable energy resource and for sustainable energy production. The integration of photovoltaic generation to the distribution network introduces large uncertainties, the inclusion of these uncertainties in load flow analysis used for planning, operation, and expansion of the power system networks is done by using the probabilistic load flow studies approach. With this, the risk identification and its mitigation associated with the power network can be easily performed. In this work, the modeling of photovoltaic generation based on historical data that is obtained by power system measurements is presented. The distribution of photovoltaic generation obtained is complex and does not match any standard distributions. A numerical method is used to generate the samples that are used for Monte Carlo simulation to include photovoltaic generation with its original characteristics and uncertainties in probabilistic analysis. The analysis is done by integrating a 1000 kW photovoltaic generation system in IEEE 13 Node Test Feeder, which is highly loaded and unbalanced. As a function of a photovoltaic generation uncertainty, statistical distribution of variables such as currents and voltage of each network elements are calculated. Using them, the network health and the power loss associated with the network are determined.
2020
UPEC 2020 - 2020 55th International Universities Power Engineering Conference, Proceedings
978-1-7281-1078-3
Monte Carlo methods
Numerical simulation
Photovoltaic systems
Probabilistic Load Flow
Probability distribution
Uncertainty analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1158197
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