ELLS-NSBA three-stage framework for optimal DG/SC placement and EV integration in distribution networks

Area of Science:

• Electrical Engineering
• Power Systems Analysis
• Optimization Algorithms

Background:

• Growing integration of distributed generation (DG), shunt capacitors (SC), and electric vehicles (EVs) presents challenges for distribution network management.
• Existing metaheuristic methods often result in large search spaces and lack post-optimization structural resilience assessment.
• Optimal siting is crucial for balancing operational efficiency and network structural resilience.

Purpose of the Study:

• To propose a novel three-stage planning framework for optimal siting of DG, SC, and EVs in distribution networks.
• To reduce the combinatorial search space and incorporate structural resilience assessment into the optimization process.
• To enhance the efficiency and resilience of power distribution systems.

Main Methods:

• Stage 1: Introduction of the Enhanced Laplacian Loss Sensitivity (ELLS) index to shortlist candidate buses, reducing search space significantly (45-265×).
• Stage 2: Application of five metaheuristics, including a hybrid Grasshopper-Grey Wolf Optimizer (GOA-GWO), with ELLS-biased initialization and a six-objective fitness function.
• Stage 3: Evaluation of placement structural quality using the Network Structural Bus Analysis (NSBA) framework with topology-aware metrics (EDDI, VSC, CBC).

Main Results:

• ELLS guidance improved mean fitness by 6-14% and reduced variance by 33-51% across tested systems.
• The Hybrid GOA-GWO achieved the best composite fitness and significant loss reduction (78.2% on the 33-bus system).
• Operational optimality does not guarantee structural resilience; NSBA revealed performance variations among placements with competitive fitness.

Conclusions:

• The proposed three-stage framework effectively addresses the limitations of existing methods in siting distributed resources.
• ELLS index and NSBA framework provide significant improvements in search space reduction and resilience assessment.
• The Hybrid GOA-GWO demonstrates superior performance in optimizing both efficiency and resilience for distribution networks.