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Optimal Design of PV Systems in Electrical Distribution Networks by Minimizing the Annual Equivalent Operative Costs

Brandon Cortés-Caicedo1, Federico Molina-Martin2, Luis Fernando Grisales-Noreña2,3

  • 1Facultad de Ingeniería, Instituto Tecnológico Metropolitano, Medellín 050036, Colombia.

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|February 15, 2022
PubMed
Summary

This study optimizes photovoltaic (PV) source placement and sizing to minimize annual operating costs in distribution networks over 20 years. The proposed method efficiently reduces costs by integrating PV systems optimally.

Keywords:
AC and DC distribution systemsannual operative costdiscrete-continuous vortex search algorithmlocation and sizing of PV systems

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Area of Science:

  • Electrical Engineering
  • Power Systems Engineering
  • Optimization Techniques

Background:

  • Rising energy costs necessitate efficient integration of renewable energy sources.
  • Optimal siting and sizing of photovoltaic (PV) sources are crucial for cost-effective distribution network operation.
  • Existing methods may not fully address the complexities of minimizing total annual operative costs including investment and maintenance.

Purpose of the Study:

  • To develop and validate an efficient methodology for minimizing the total annual operative cost of distribution networks over a 20-year planning period.
  • To determine the optimal siting and sizing of photovoltaic (PV) sources within distribution networks.
  • To compare the proposed optimization approach against established algorithms for PV integration.

Main Methods:

  • A mixed-integer nonlinear programming model was formulated to represent the cost minimization problem.
  • A master-slave methodology was employed, utilizing a discrete-continuous Vortex Search Algorithm (DCVSA) for optimal PV siting and sizing (master stage).
  • The Matricial Backward/Forward Power Flow Method was used in the slave stage to evaluate the fitness function for PV source configurations.

Main Results:

  • The proposed DCVSA-based master-slave methodology demonstrated efficiency in finding optimal locations and sizes for PV sources.
  • Numerical results on IEEE 33- and 69-node systems (AC and DC topologies) showed the effectiveness of the approach.
  • The method outperformed the discrete-continuous Chu and Beasley genetic algorithm in optimizing PV source placement.

Conclusions:

  • The developed master-slave optimization approach, using DCVSA, is effective for minimizing total annual operative costs in distribution networks with PV integration.
  • The methodology provides a robust solution for optimal PV siting and sizing, considering investment and maintenance costs.
  • The validated approach offers a valuable tool for power system planning and the integration of renewable energy sources.