05/08/2026
ENHANCED VS. STANDARD PRAIRIE DOG OPTIMIZATION FOR COORDINATED RECONFIGURATION, DG, AND CAPACITOR ALLOCATION: LOSS REDUCTION AND RELIABILITY ASSESSMENT ON THE IEEE 33-BUS SYSTEM
Abstract
Reliable and efficient operation of electrical distribution networks has become increasingly important due to the growing pe*******on of distributed energy resources and the need to enhance service continuity. This MATLAB-based optimization framework addresses the simultaneous distribution network reconfiguration (DNR) and coordinated allocation of distributed generation (DG) units and shunt capacitors to minimize active power loss while improving distribution system reliability. The proposed framework employs the Enhanced Prairie Dog Optimization Algorithm (EPDO), an improved metaheuristic inspired by the cooperative burrowing, foraging, communication, and predator-avoidance behaviors of prairie dog colonies. EPDO incorporates enhanced exploration and exploitation mechanisms to achieve faster convergence, improved solution diversity, and higher optimization accuracy for solving the resulting nonlinear mixed-integer optimization problem.
Rather than reporting EPDO performance in isolation, this study directly and systematically benchmarks EPDO against the conventional Prairie Dog Optimization (PDO) algorithm from which it is derived. Both algorithms solve the identical reconfiguration and DGโcapacitor allocation formulation under identical population size, iteration budget, and number of independent runs, so that any performance gain observed for EPDO can be attributed specifically to its algorithmic enhancements rather than to differences in problem setup.
A backwardโforward sweep (BFS) load flow algorithm is integrated into both the PDO and EPDO optimization loops to evaluate each candidate solution in terms of bus voltage profile, branch current distribution, and total active power loss. Distribution system reliability is assessed using six widely accepted reliability indices, namely System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Customer Average Interruption Duration Index (CAIDI), Average Service Availability Index (ASAI), Average Service Unavailability Index (ASUI), and Expected Energy Not Supplied (EENS). The proposed methodology is implemented in MATLAB and validated using the IEEE 33-bus radial distribution test system under eight practical operating scenarios, ranging from the uncompensated base case to simultaneous network reconfiguration with coordinated DG and capacitor allocation.
Keywords: Network reconfiguration; Distributed generation; Capacitor placement; Prairie Dog Optimization; Enhanced Prairie Dog Optimization; Algorithm benchmarking; Comparative optimization study; Reliability indices; Radial distribution system; IEEE 33-bus system; Backwardโforward sweep load flow.
Objective Function
The objective function below is used, without modification, as the fitness function evaluated by both the PDO and EPDO algorithms in this study, so that the two algorithms are compared on a strictly like-for-like basis. Following the loss-minimization formulation of Sedighizadeh et al. [1], the objective function adopted for the network reconfiguration and DGโcapacitor placement sub-problems is:
Min F=Min(P_(T,Loss)+ฮป_VรS_CV+ฮป_IรS_CI)
References
[1] M. Sedighizadeh, M. Dakhem, M. Sarvi, and H. Hosseini Kordkheili, "Optimal reconfiguration and capacitor placement for power loss reduction of distribution system using improved binary particle swarm optimization," Springer, 2014.
[2] G. Sasi Kumar, S. Sarat Kumar, and S. V. Jayaram Kumar, "DG Placement Using Loss Sensitivity Factor Method for Loss Reduction and Reliability Improvement in Distribution System," International Journal of Engineering and Technology (IJET), 2018.
[3] N. Gupta, A. Swarnkar, and K. R. Niazi, "Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms," Elsevier, 2013.
[4] A. E. Ezugwu, J. O. Agushaka, L. Abualigah, S. Mirjalili, and A. H. Gandomi, "Prairie Dog Optimization Algorithm," Neural Computing and Applications, 2022.
[5] A. E. Ezugwu et al., "Enhanced Prairie Dog Optimization with Levy Flight and Dynamic Opposition-Based Learning for Global Optimization and Engineering Design Problems," Neural Computing and Applications, 2024.
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