Optimal Placement of Renewable Energy Sources and Fuel Cell in Power System using Line Stability Index

The increasing penetration of renewable energy sources (RES) in modern power systems has raised concerns regarding voltage stability and system reliability. Proper placement and sizing of renewable energy sources and fuel cells are essential to enhance system performance while maintaining stability limits. This paper presents an optimal placement strategy for renewable energy sources and a fuel cell in a power system using the Line Stability Index (LSI) as a voltage stability indicator. Initially, load flow analysis is performed to identify weak transmission lines based on their LSI values. Buses associated with high LSI values are considered as candidate locations for the integration of RES and fuel cell units. An optimization problem is formulated with the objective of minimizing the maximum LSI, improving voltage profile, and reducing real power losses, subject to system operational constraints. A suitable optimization technique is employed to determine the optimal locations and capacities of the RES and fuel cell. Simulation studies conducted on a standard IEEE test system demonstrate that the proposed approach significantly enhances voltage stability, reduces power losses, and improves overall system performance. The results confirm the effectiveness of the Line Stability Index as a reliable tool for optimal resource placement in power systems with high renewable penetration. To overcome this limitation, a Fuel Cell—capable of continuously supplying both active and reactive power—is also installed at the same weak bus. A re-analysis shows that Fuel Cells provide greater improvements in voltage stability and L-index reduction compared to PV systems, particularly under varying load scenarios. To further assess system performance, a cost function evaluation is conducted to examine the economic implications of the integration. The algorithms tested include the Frilled Lizard Optimization (FLO), Osprey Optimization Algorithm (OOA), and Coati Optimization Algorithm (COA). Results indicate that FLO and OOA exhibit strong convergence and efficient search capabilities, making them highly effective for emission minimization in power system optimization. In conclusion, the research highlights the critical role of carefully planned renewable energy placement in improving grid stability while reducing environmental impacts. The combined methodology—spanning load flow analysis, Lindex evaluation, and optimization algorithm benchmarking - offers a comprehensive framework for future advantage.