Governing Distributed Generators and FACTS in Restructured Power system for System Adequacy using Genetic Algorithm
Mahiban Lindsay1, A.K.Parvathy2
1Mahiban Lindsay, Department of Electrical and Electronics Engineering, Hindustan Institute of Technology and Science, Chennai, (Tamil Nadu), India.
2A.K.Parvathy, Department of Electrical and Electronics Engineering, Hindustan Institute of Technology and Science, Chennai, (Tamil Nadu), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 961-965 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2621037619/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents a novel technique for optimizing distribution generators in the restructured power systems and estimate the system adequacy and security through various power system reliability indices. The main objective of this paper is to identify the right location to place reserve generators along with the FACTS devices strategically in the deregulated power system. The weaker portion in the restructured power system network will be identified and the distributed generators and the FACTS devices will be placed in the weaker portion in the network to improve the stability and minimize the losses. The optimal location to comprise the DGs can be done by the composite optimal load flow analysis. The control modes of the FACTS devices are also optimized to achieve the loss reduction in restructured network. The problem defined as a multi-objective power system optimization problem and solution is presented through Genetic Algorithm. The Proposed method is applied to 14 bus system and the simulation results are verified using optimal power flow solution in the Electrical Transient Analysis Programming Tool. The sensitivity of DGs on corresponding locations with different groupings were compared to achieve the optimum values. Results reveal that the proposed method yields better results which can apply in the deregulated power system. The system Adequacy and security also verified in the deregulated power system with the inclusion of DGs and FACTS.
Keywords: Distributed Generation (DG), Genetic Algorithm (GA),EENS.
Scope of the Article: Distributed Mobile Applications Utilizing IoT