BibTeX:

@article{IJIRSTV3I6086,
     title={A Unified Framework for Participation of Responsive End-User Devices of Smart Grid with IMOPSO},
     author={P. Amarnath and Prof. T. Gowri Manohar},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={6},
     pages={203--210},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I6086.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

The paper presents a unified control framework which allows the responsive end-user devices (REDs) such as inverter-based photovoltaic systems (PVs), plug-in hybrid electric vehicles (PHEVs), and domestic controllable loads at residential level to effectively participate in the voltage and frequency control of the smart grid. The presented control framework basically relies on extracting information from active and reactive power sensitivities at different buses. In this framework, for voltage control, two support groups namely the active support group and there active support group are dedicated to each transm–ission bus. However, for frequency control, only one active support group is defined for the entire system. The REDs used for voltage and frequency control are classified based on their controllability degree. The idea of selecting the most effective buses is also presented to minimize the burden of communication commands. Following the detection of voltage or frequency violation in the system, the targeted buses are identified and receive corrective control signals to accordingly change their active and/or reactive powers. To minimize the manipulated active and reactive powers, the whole process is formulated as improved multi­objective problem solved by the particle swarm optimization. The control procedure involves a series of commands for which the incident command system is used as a secure communication structure. In this paper an improved multi-objective particle swarm optimization algorithm (IMOPSO) is designed to efficiently solve multi-objective discrete optimization problems .In the IMOPSO, a novel similarity-based selecting scheme isused to selection of the global best solution and individual best solution for each particle, and an external set truncation strategy is used to maintain the diversity in the Pareto optimal solutions. Additionally, a local search subroutine is applied on every particle to improve the search efficiency of optimization.The IMOPSO is compared with two multi-objective particleswarm optimization algorithms proposed in the literature on several test problems, and experimental results show that the IMOPSO has good performance in multi-objective discrete optimization.

Active support group, incident command system (ICS), Improved multiobjective optimization, reactive support group, responsive end-user device (REDs), smart grid