An improved MOPSO technique based optimal location and size of DGs in distribution power grids considering true multi-objectives
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Abstract
Distribution grids have received a lot of attention lately because of the increase in load demand. Promoting the widespread use of distributed generation (DG) is a strategy to meet the growing demand for energy. To achieve the best technical, economic, commercial, and regulatory objectives, DG planning must consider the optimal location and size of DGs. In this research, multi-objective particle swarm optimization (MOPSO) is improved and proposed to find the optimal location and size of DGs subject to equality and inequality constraints during power flow analysis using the Newton-Raphson method. It is a multi-objective optimization problem where properly installing DGs should simultaneously reduce total active power loss, total annual economic loss, and voltage profile improvement. The Pareto set represents the optimal solutions to multi-objective optimization problems. The clustering-based Pareto front reduction process is applied to reduce the size of the Pareto set by grouping similar solutions together, which can provide a diverse and compact subset of the Pareto set. Furthermore, fuzzy set theory is employed to select the best compromised solution from the Pareto optimal set based on decision preferences. The proposed algorithm is validated on IEEE 33 and IEEE 69 bus radial distribution systems to ensure its effectiveness and robustness by comparing its performance with other nature‐inspired heuristic‐based algorithms in terms of the aforementioned objectives. Detailed comparisons show that the proposed technique can determine the locations and sizes of DG units more effectively than other methods. The study is performed by MATLAB M-Files.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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