Abstract
This paper deals with the optimal placement of distributed generation (DG) units in distribution systems via an enhanced multi-objective particle swarm optimization (EMOPSO) algorithm. To pursue a better simulation of the reality and provide the designer with diverse alternative options, a multi-objective optimization model with technical and operational constraints is constructed to minimize the total power loss and the voltage fluctuation of the power system simultaneously. To enhance the convergence of MOPSO, special techniques including a dynamic inertia weight and acceleration coefficients have been integrated as well as a mutation operator. Besides, to promote the diversity of Pareto-optimal solutions, an improved non-dominated crowding distance sorting technique has been introduced and applied to the selection of particles for the next iteration. After verifying its effectiveness and competitiveness with a set of well-known benchmark functions, the EMOPSO algorithm is employed to achieve the optimal placement of DG units in the IEEE 33-bus system. Simulation results indicate that the EMOPSO algorithm enables the identification of a set of Pareto-optimal solutions with good tradeoff between power loss and voltage stability. Compared with other representative methods, the present results reveal the advantages of optimizing capacities and locations of DG units simultaneously, and exemplify the validity of the EMOPSO algorithm applied for optimally placing DG units.
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Project supported by the Science & Technology Innovation Team of Outstanding Youth of Hubei Provincial Universities (No. T201319) and the Scientific Research Foundation for Talents of China Three Gorges University (No. 0620130076)
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Cheng, S., Chen, My., Wai, Rj. et al. Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm. J. Zhejiang Univ. - Sci. C 15, 300–311 (2014). https://doi.org/10.1631/jzus.C1300250
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DOI: https://doi.org/10.1631/jzus.C1300250
Key words
- Distributed generation
- Multi-objective particle swarm optimization
- Optimal placement
- Voltage stability index
- Power loss