Optimal Placement and Sizing of Multiple Distributed Generation using Combined Differential Evaluation – HPSO Method
V. Veera Nagireddy1, D. V. Ashok Kumar2, K. Venkata Reddy3
1V. Veera Nagireddy, Research Scholar, Department of EEE, JNTUK, Kakinada, (A.P), India.
2D. V. Ashok Kumar, Professor, Department of EEE, SDIT, Nandyal, Kurnool (DT), (A.P), India.
3K. Venkata Reddy, Asst. Prof, Department of EEE, JNTUK, Kakinada, (A.P), India.
Manuscript received on September 20, 2014. | Revised Manuscript received on October 09, 2014. | Manuscript published on October 30, 2014. | PP: 57-62 | Volume-4 Issue-1, October 2014. | Retrieval Number: A3453104114/2013©BEIESP
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© 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: Integration of renewable energy based Distributed Generation (DG) units in modern days of conventional radial distribution systems provides potential benefits in terms service continuity and makes more reliable. The power injections from renewable sources are located close to the load centers which provide system voltage support, reduction in system losses and performance improvement. This paper presents an enhanced approach for DG placement in radial distribution feeders to reduce the real power loss and to improve the voltage profile. The DG placement approach involves the identification of location for DG placement and the size of the DG to be installed at the identified location. The location of the nodes where the DG should be placed is decided by a hybrid Particle Swam Optimization and Differential Evaluation method. A case study with an IEEE 34 bus distribution feeder is presented. A comparison is made between the proposed HPSO approach and the classical Particle Swarm Optimization (PSO). The proposed hybrid Differential Evaluation Particle Swarm Optimization (DEPSO) method is proven to give better results in terms of loss reduction and better voltage profile.
Keywords: Particle swarm optimization, PSO, Differential evolution, DEPSO, Distributed generation, Voltage profile improvement, loss reduction, Load flows.