MULTIOBJECTIVE PREDICTABILITY-BASED OPTIMAL PLACEMENT AND PARAMETERS SETTING OF UPFC IN WIND POWER INCLUDED POWER SYSTEMS

    Power systems encounter more and more uncertainties due to the probabilistic nature of renewable generations, load fluctuations, and probable failure of system parts. Power system uncertainties bring in various operational challenges and lead to increase of risk in operational decisions due to the unpredictable nature of the system state. Under probabilistic situations, the predictability of power system is associated with its certainty and/or uncertainty, directly. The more the power system is certain, the more the power system is predictable. Increasing power system predictability can result in better decision making by the power system operator. This Matlab design proposes a multi-objective framework for optimal placement and parameters setting of a unified power flow controller (UPFC) considering system predictability. The well-known multi-objective nondominated sorting genetic algorithm is implemented to handle various objective functions such as active power losses and predictability of system in the presence of operational constraints and uncertainties. The point estimate method is used for modeling probabilistic nature of the wind power. Using the proposed method, statistical information of voltage magnitude and apparent power of converters of UPFCs can be obtained, which are especially useful in making decision on the sizing of UPFCs. Comprehensive discussions are provided using the simulations on the IEEE 57-bus test system. Also, to validate the obtained results, a multi-objective particle swarm optimization algorithm is implemented and the results of two algorithms i.e NSGA-II and MOPSO are compared with each other.
Reference Paper: Multiobjective Predictability-Based Optimal Placement and Parameters Setting of UPFC in Wind Power Included Power Systems
Author’s Name: Sadjad Galvani, Mehrdad Tarafdar Hagh , Mohammad Bagher Bannae Sharifian,and Behnam Mohammadi-Ivatloo
Source: IEEE
Year :2019

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