Optimizing solar farm interconnection networks using graph theory and metaheuristic algorithms with economic and...

Area of Science:

• Electrical Engineering
• Computer Science
• Operations Research

Background:

• Rising global energy demand necessitates efficient integration of renewable sources like solar farms.
• Interconnecting distributed solar farms to power grids presents challenges in optimizing transmission losses and infrastructure costs.

Purpose of the Study:

• To introduce a novel graph-theoretic framework for designing optimal interconnection networks for distributed solar farms.
• To compare the efficiency of Prim's algorithm against Particle Swarm Optimization (PSO) for network design.
• To develop a graph-based reliability metric for assessing network robustness.

Main Methods:

• Utilizing Prim's algorithm to construct a minimum spanning tree for network design.
• Benchmarking the deterministic approach against Particle Swarm Optimization (PSO).
• Developing and applying a novel graph-based reliability metric to evaluate network robustness.

Main Results:

• Prim's algorithm demonstrated superior performance over PSO in minimizing power losses and capital investment.
• The graph-based approach achieved higher topological reliability compared to PSO.
• PSO offered better load balancing, but the graph-based method is more effective for loss-sensitive and cost-driven designs.

Conclusions:

• The proposed graph-theoretic framework provides a computationally efficient and economically viable solution for optimal solar farm integration.
• The methodology is scalable and accommodates practical constraints, laying groundwork for AI/ML integration in dynamic optimization.