Optimal Location and Sizing of Distributed Generators Based on Renewable Energy Sources Using Modified Moth Flame Optimization Technique

• Published in: IEEE access Vol. 8; pp. 109625 - 109638
• Main Authors: Elattar, Ehab E., Elsayed, Salah K.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Alternative energy sources; bus location index; coefficient single objective function; Distributed generation; distributed generators; Energy distribution; Energy resources; Generators; Indexes; Linear programming; Modified moth flame algorithm; Multiple objective analysis; Operating costs; Optimization; Optimization techniques; Power system stability; Reactive power; Renewable energy sources; Renewable resources; Sizing
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3001758

Due to the great impact of the penetration and locations of distributed generators (DG) on the performance of the distribution system, this paper proposes a modified moth flame optimization (MMFO) algorithm. Two modifications are proposed in MMFO to enhance the exploration and exploitation balance and overcome the shortcomings of the original MFO. The proposed MMFO is used to find the optimal location and sizing of DG units based on renewable energy sources in the distribution system. The main objective function is to minimize the total operating cost of the distribution system by considering the minimization of the total active power loss, voltage deviation of load buses, the DG units cost, and emission. This multi-objective function is converted to a coefficient single objective function with achieving different constraints. Also, the bus location index is employed to introduce the sorting list of locations to accomplish the narrow candidate buses list. Based on the candidate buses, the proposed MMFO is used to get the optimal location and sizing of DG units. The proposed MMFO algorithm has been applied to the IEEE 69-bus test distribution system and the results are compared with other published algorithms to prove its effectiveness and superiority.