Optimal planning and partitioning of multiple distribution Micro-Grids based on reliability evaluation.

Area of Science:

• Electrical Engineering
• Power Systems Engineering
• Optimization Theory

Background:

• Micro-Grids (MGs) offer improved reliability, flexibility, and reduced emissions in distribution systems.
• Integrating multiple micro-grids (MMGs) presents complex planning and partitioning challenges.
• Balancing the objectives of MG owners and distribution system operators is crucial.

Purpose of the Study:

• To develop a single-level stochastic optimization framework for planning and partitioning distribution systems with MMGs.
• To minimize the total system cost, including investment, operation, losses, and reliability costs.
• To identify optimal MG investment sites considering voltage stability and stakeholder viewpoints.

Main Methods:

• A stochastic optimization framework incorporating voltage stability index for site identification.
• The Firefly Algorithm (FA) and probability-tree method to model uncertainties from renewable energy sources (PVs and WTs).
• Genetic Algorithm (GA) in MATLAB for solving the optimization model and network partitioning using Tie Switches (TS).

Main Results:

• Optimal MG investment locations were identified near the feeder's beginning.
• Improved load point reliability was achieved through strategic MG placement.
• Significant reductions in total active power losses and optimized energy management were demonstrated.

Conclusions:

• The proposed stochastic optimization framework effectively plans and partitions distribution systems with MMGs.
• Optimal MG placement near the feeder enhances overall system performance and economic viability.
• The model successfully balances economic and reliability objectives for diverse stakeholders.