Optimal distributed generation allocation in radial distribution networks using a modified seagull optimization algorithm with elite reserve strategy

•Two mSOA-based algorithms are proposed for single- and multi-objective DG allocation.•Proposed algorithms effectively reduce system power loss and voltage deviation.•Novel MLPE strategy enables robust multi-level evaluation for DG allocation schemes.•Proposed techniques support sustainable, reliabl...

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Bibliographic Details
Published in:Energy conversion and management. X Vol. 28; p. 101228
Main Authors: Qian, Jie, Wei, Min, Wang, Ping, Wang, Fei, Dai, Jianbo
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.10.2025
Elsevier
Subjects:
Distributed generation allocation
Energy management
Multi-level performance evaluation
Renewable energy
Seagull optimization algorithm
Seagull optimization algorithm
Multi-level performance evaluation
Renewable energy
Distributed generation allocation
Energy management
ISSN:2590-1745, 2590-1745
Online Access:Get full text
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Summary:•Two mSOA-based algorithms are proposed for single- and multi-objective DG allocation.•Proposed algorithms effectively reduce system power loss and voltage deviation.•Novel MLPE strategy enables robust multi-level evaluation for DG allocation schemes.•Proposed techniques support sustainable, reliable and flexible power system planning. Reconstructing radial distribution networks (RDNs) by integrating distributed generation (DG) is essential for enhancing the sustainability, reliability, and flexibility of power supply, thereby improving renewable energy utilization. This paper investigates the optimal distributed generation allocation (ODGA) problem, which involves both discrete (DG location) and continuous (DG capacity and power factor) decision variables and exhibits high non-convexity and computational complexity. To address these challenges, a single-objective modified seagull optimization algorithm (mSOA-SO) is proposed, incorporating an elite reserve strategy with access location guidance (ALG) and effective re-migration (ERM) into the base algorithm. Experimental results demonstrate that mSOA-SO achieves active power loss reductions of 93.96 %, 98.11 %, and 63.18 % on the IEEE 33-, 69-, and 119-node RDNs, respectively, by optimally integrating DGs with controllable power factors. To extend the method for practical multi-objective ODGA scenarios, this paper further develops a multi-objective modified seagull optimization algorithm (mSOA-MO) through the integration of an innovative multi-level performance evaluation (MLPE) strategy. Notably, mSOA-MO identifies superior DG schemes for both dual- and triple-objective ODGA problems, effectively reducing voltage deviation, active and reactive power loss in RDNs. Thus, the proposed mSOA-SO and mSOA-MO effectively identify advantageous DG allocation schemes, serving as robust techniques for the secure integration of renewable energy into RDNs. This study emphasizes the crucial role of intelligent algorithms in energy management and enhancing the environmental benefits of energy supply.
ISSN:2590-1745
2590-1745
DOI:10.1016/j.ecmx.2025.101228