Optimal positioning of storage systems in microgrids based on complex networks centrality measures

Area of Science:

• Electrical Engineering
• Complex Networks Theory
• Power Systems Analysis

Background:

• High penetration of renewable energy sources (RES) introduces voltage fluctuations in power grids.
• Optimal placement of energy storage systems (ESS) is crucial for grid stability and quality of service.
• Traditional methods for ESS placement may not fully leverage network topology.

Purpose of the Study:

• To develop a criterion for optimal Energy Storage System (ESS) placement in power networks using complex network centrality metrics.
• To investigate the relationship between network centrality metrics and voltage fluctuations under high RES penetration.
• To validate the proposed criterion using prototypical IEEE networks.

Main Methods:

• Analysis of power grids as complex networks.
• Computation of centrality metrics (Eigenvector, Closeness, Pagerank, Betweenness) for network nodes.
• Correlation analysis between node centrality and voltage fluctuations caused by intermittent RES and loads.
• Testing on two IEEE network models.

Main Results:

• Network topological characteristics can identify optimal positions for ESS to mitigate voltage fluctuations.
• Eigenvector centrality demonstrated a statistically significant exponential correlation with voltage fluctuation reduction.
• The findings support the heuristic placement of ESS away from supply reactive nodes.

Conclusions:

• Complex network centrality metrics provide an effective criterion for optimal ESS placement.
• Eigenvector centrality is a key metric for guiding ESS deployment to enhance grid resilience.
• This approach offers computational and planning advantages for large-scale, resilient power networks, especially in microgrid scenarios with distributed energy sources.