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Particle Swarm Optimization for Risk-Aware Distributed Generation Planning in Distribution Networks

Introduction

Particle swarm optimization for risk-aware distributed generation planning in distribution networks. Optimize distributed generation planning in distribution networks using risk-aware Particle Swarm Optimization. Minimize power loss, voltage risk, reverse flow & maximize DG penetration.

Abstract

High penetration of distributed generation (DG) improves distribution network performance but may increase voltage rise and reverse power flow, threatening secure operation. This paper proposes a Particle Swarm Optimization (PSO)-based DG planning framework that simultaneously minimizes active power loss, mitigates voltage risk, suppresses reverse power flow, and maximizes DG penetration. A normalized multi-objective function integrates these criteria into a unified optimization model, while penalty functions enforce network operating constraints. The proposed method is validated on the 33-bus and 69-bus radial distribution systems and compared with GSA, MVMO-SH, and conventional PSO. For the 33-bus system, the proposed approach reduces active power loss by 36.47%, increases the minimum bus voltage from 0.913 p.u. to 0.964 p.u., and limits reverse power flow to 13.2 kW. For the 69-bus system, it achieves a 61.92% reduction in power loss, improves the minimum voltage from 0.909 p.u. to 0.972 p.u., and decreases reverse power flow to 18.9 kW while providing the highest DG penetration among the compared methods. The results demonstrate that explicitly considering voltage risk and reverse power flow enables more effective and reliable DG planning for active distribution networks. 

Review

This paper addresses the critical challenge of integrating high penetration distributed generation (DG) into distribution networks, which, while beneficial, can introduce operational issues such as voltage rise, reverse power flow, and overall network insecurity. The authors propose a timely and relevant framework for risk-aware DG planning, aiming to harness the advantages of DG while proactively mitigating associated risks. The core problem tackled is the simultaneous optimization of multiple, often conflicting, objectives to ensure both the economic and secure operation of active distribution networks. The proposed solution leverages a Particle Swarm Optimization (PSO)-based framework to achieve its multi-faceted objectives. A key innovation lies in the normalized multi-objective function, which systematically integrates the minimization of active power loss, mitigation of voltage risk, suppression of reverse power flow, and maximization of DG penetration into a unified optimization model. Furthermore, the methodology employs penalty functions to effectively enforce crucial network operating constraints, ensuring practical and feasible solutions. This explicit consideration of voltage risk and reverse power flow within the optimization algorithm represents a significant methodological contribution to robust DG planning. The efficacy of the proposed PSO-based approach is robustly validated on standard 33-bus and 69-bus radial distribution systems and compared against several established optimization algorithms, including GSA, MVMO-SH, and conventional PSO. The results consistently demonstrate superior performance, with impressive reductions in active power loss (36.47% for 33-bus, 61.92% for 69-bus), substantial improvements in minimum bus voltage (from ~0.91 p.u. to ~0.96-0.97 p.u.), and effective limitation of reverse power flow to minimal levels. Crucially, the method achieves these improvements while also providing the highest DG penetration, underscoring its ability to balance security with generation capacity. This paper provides valuable insights and a practical tool for more effective and reliable DG planning in the evolving landscape of active distribution networks.

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