چكيده به لاتين
The use of alternative renewable energy such as biomass, solar and wind is increasing, due to the economic and environmental benefits. With the increasing trend of using distribution generation, It is essential optimizing sizing and sitting and study the effects of using these units in the power system, especially in distribution networks. The use of these energy resources provides potential benefits to the conventional distribution system. On the one hand, DG units will support voltage, reduce energy losses, improve system stability, improve the voltage profile, reduce power flow and delay the development of the network transmission with the injection of electrical power on near the consumer centers. On the other hand, This causes fluctuating power flow, which is one of the new challenges for conventional distribution systems. Therefore, if the site and size of distributed generation are not optimally determined, it will not only reduce losses, improve the voltage profile and improve system stability, but may increase system losses. Therefore, this should be done with care and in accordance with the different objective function.
In this study, several different types of time-varying voltage-dependent load models to determine the penetration level of photovoltaic units in a distribution network. Here, a new analytical expression based on the derivation of a multiobjective index that is formulated as a combination of three indices, is proposed to sizing and sitting a PV unit for reduction of active and reactive power losses and voltage deviations with considering time-varying load models and possible production. The effectiveness of the proposed approach was validated on 33-bus test distribution systems in Matlab.
In this study, five types of time-varying load models (including commercial, industrial, residential, hybrid and fixed load models) are considered. It is worth mentioning that hour static load model has been used for time-varying load modeling. In this study, The beta density function (PDF) was used to solar radiation modeling on every hour of the day using historic data collected over three years.
