Optimal placement and sizing of distributed generation considering FACTS devices and load uncertainty using hybrid sine-cosine algorithm and particle swarm optimization (HSCA-PSO)

Using Distributed Generation (DG) in electrical distribution networks brings many advantages and thus, optimal placement and sizing of these units become important. Most of the researches in this field neglect the effect of transmission system on distribution section. These researches also ignore the effect of Flexible Alternating Current Transmission Systems (FACTS). This thesis proposes a new method for optimal placement and sizing of distributed generation considering FACTS devices. In this method, Thevenin’s equivalent model is used to model transmission section include FACTS devices and the results are compare to integrated transmission-distribution network and slack bus models. Thyristor Controlled Series Capacitor (TCSC) and Static VAR Compensator are used for this goal. As an innovation, uncertainty of electrical load is considered using scenario tree method. Optimisation problem is defined as minimizing a combinatory objective function which includes power loss, voltage deviation and voltage stability indexes. As another innovation, optimization problem is solved using hybrid sine-cosine algorithm and particle swarm optimization (HSCA-PSO) method. Simulation is carried out on IEEE 9 and 16 bus transmission-distribution test system in MATLAB software. Simulation results show that using FACTS in transmission section and DG in distribution section both leads to objective function reduction and therefore improves network performance in the terms of losses, voltage deviation and voltage stability indexes. Results indicate that Thevenin’s equivalent model has better performance in the comparison to slack bus model in modeling transmission section and FACTS devices. Results also illustrate that uncertainties increase the installed capacity of DG units and ignoring uncertainty can leads to wrong solutions.