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<p>Effective service restoration in modern Active Distribution Networks (ADNs) is challenged by the high penetration of volatile Distributed Energy Resources (DERs) and stringent thermal constraints. This paper proposes a novel multi-objective, security-constrained restoration framework that balances maximum load recovery with real power loss minimization and network thermal safety. Unlike conventional methods relying on linearized models, this study employs a full Alternating Current (AC) power flow formulation to ensure physical feasibility in stressed post-fault states. The optimization is driven by the Artificial Protozoa Optimizer (APO), a bio-inspired metaheuristic tailored for non-convex switching configurations. The framework was validated on modified IEEE 33-bus and IEEE 123-bus systems. For the IEEE 33-bus system, the APO identified a security-constrained optimal configuration that enhanced thermal operating margins while maintaining reliable load restoration. The obtained solution reduced total system losses by 42.1% and maintained all bus voltages within the permissible regulatory range of 0.95–1.05 p.u. Similarly, results for the IEEE 123-bus system demonstrate a 98.1% critical load recovery rate with a 48.2% reduction in active power losses. Comparative analysis reveals that the APO outperforms standard metaheuristics in convergence stability (standard deviation <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\approx 10^{-6}\)</EquationSource> </InlineEquation>) and computational speed (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(&lt;9\)</EquationSource> </InlineEquation> seconds), proving its suitability for real-time distribution management systems.</p>

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Multi-objective thermal-constrained service restoration in active distribution networks with distributed energy resources using artificial protozoa optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Jatin Soni,
  • Ashutosh Giri

摘要

Effective service restoration in modern Active Distribution Networks (ADNs) is challenged by the high penetration of volatile Distributed Energy Resources (DERs) and stringent thermal constraints. This paper proposes a novel multi-objective, security-constrained restoration framework that balances maximum load recovery with real power loss minimization and network thermal safety. Unlike conventional methods relying on linearized models, this study employs a full Alternating Current (AC) power flow formulation to ensure physical feasibility in stressed post-fault states. The optimization is driven by the Artificial Protozoa Optimizer (APO), a bio-inspired metaheuristic tailored for non-convex switching configurations. The framework was validated on modified IEEE 33-bus and IEEE 123-bus systems. For the IEEE 33-bus system, the APO identified a security-constrained optimal configuration that enhanced thermal operating margins while maintaining reliable load restoration. The obtained solution reduced total system losses by 42.1% and maintained all bus voltages within the permissible regulatory range of 0.95–1.05 p.u. Similarly, results for the IEEE 123-bus system demonstrate a 98.1% critical load recovery rate with a 48.2% reduction in active power losses. Comparative analysis reveals that the APO outperforms standard metaheuristics in convergence stability (standard deviation \(\approx 10^{-6}\) ) and computational speed ( \(<9\) seconds), proving its suitability for real-time distribution management systems.