# EV Charging Network Design Using MST Algorithms
> Learn how Kruskal's and Prim's Minimum Spanning Tree (MST) algorithms optimize EV charging station networks to minimize infrastructure costs.

Tags: ev-charging-network, minimum-spanning-tree, kruskals-algorithm, prims-algorithm, graph-theory, smart-campus, algorithm-analysis, python-implementation
## Slide 1: Introduction to EV Charging Network Design
- Objective: Design a minimum-cost layout for EV charging stations on a campus.
- Methodology: Modeling locations as nodes and cable costs as weights using Minimum Spanning Tree (MST) algorithms.

## Slide 3-5: Problem Statement and Theoretical Background
- Graph Model: 10 campus blocks (nodes) with 16 potential cable connections (edges).
- MST Concept: Connects all vertices with exactly n-1 edges, no cycles, and minimum total cost.
- Key Property: Uses the 'Cut Property' to guarantee the optimal solution by picking minimum cost edges.

## Slide 6-9: Algorithm Trace (Kruskal's & Prim's)
- Kruskal's Strategy: Edge-based approach using Disjoint Set Union (DSU) and sorting edges by weight.
- Prim's Strategy: Vertex-based approach growing from a starting node using a Min-Priority Queue.
- Comparison: Both algorithms identified the same optimal paths (e.g., G-H, C-I, F-G) for the network.

## Slide 10-11: Results and Code
- MST Result: Total optimal cable installation cost of ₹38,000.
- Algorithm Analysis: Kruskal's is noted as slightly more efficient for this sparse graph (10 nodes, 16 edges).
- Implementation: Python samples provided for both greedy algorithms.

## Slide 12-14: Applications and Future Scope
- Real-World Use Cases: Telecom fiber optics, smart grid design, and transportation networks.
- Future trends: Dynamic MST for real-time station additions and AI-powered routing optimization.
- Conclusion: Successfully bridged theoretical DAA concepts with practical infrastructure engineering.
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