Abstract:
The integration of renewable energy sources into modern power systems has introduced significant challenges in energy storage optimization within smart grids. Efficient energy storage management is critical for ensuring grid stability, reducing operational costs, and enhancing sustainability. This paper explores an advanced approach to optimizing energy storage in smart grids using graph theory. The smart grid is modeled as a graph G(V,E), where V represents nodes such as energy sources, storage units, and consumers, and E denotes the set of edges representing power flow and communication links. We formulate the optimization problem as minimizing the total energy loss: min S(i,j)?E wijfij where w_ij is the weight (loss factor) on edge (i,j) and f_ij is the energy flow. Advanced graph-theoretic algorithms such as minimum spanning trees, shortest path, and network flow optimization are employed to enhance energy routing and storage utilization. Additionally, graph partitioning and clustering techniques are applied for decentralized energy management and demand response strategies. Simulation results demonstrate that graph-based optimization significantly improves energy storage efficiency, reduces peak loads tress, and supports real-time decision-making in smart grids. This research provides a scalable and robust framework for future smart grid architectures, paving the way for intelligent, resilient, and energy- efficient power systems.
Page(s):
176-176
DOI:
DOI not available
Published:
Journal: 4th International Conference of Sciences “Revamped Scientific Outlook of 21st Century, 2025” , November 12,2025, Volume: 1, Issue: 1, Year: 2025
Keywords:
Smart grid
,
Energy storage
,
network optimization
,
Load balancing
,
Graph Theory