Enhancing Grid Resilience Through Renewable Energy Integration: A Hybrid Antlion-Social Spider Optimization Appraoch In Distribution Power System

Abstract

Objectives: This study addresses the need for reliable and efficient electricity generation in remote locations through the incorporation of renewable energy systems (RES), specifically hybrid wind and solar systems.

Contributions: The research aims to optimize the placement and sizing of photovoltaic panels (PVs) and wind turbines (WTs) within a distribution network to minimize losses and enhance reliability. It introduces the Hybrid Ant Lion-Social Spider Optimization (ALO-SSO) technique to analyze reliability indices and system costs effectively.

Methodology: The ALO-SSO technique is employed for optimizing the location and capacity of RES components based on technical and financial considerations. The optimization algorithm offers unique benefits including enhanced reliability, multi-objective optimization capability and superior compared to traditional methods.

Analysis: The study utilizes MATLAB/Simulink for implementation and simulation. The reliability of the hybrid RES system is assessed through metrics such as Expected Interruption Cost (ECOST) and Expected Energy Not Supplied (EENS).

Results: Implementation of the optimized RES configuration resulted in significant improvements, achieving a reduction in ECOST to 23.36 and EENS to 30.21MWh/yr. These outcomes underscore the enhanced reliability and cost-effectiveness of the developed methodology.

Findings: The study highlights that strategically locating and sizing PVs and WTs in distribution networks substantially enhance system reliability while minimizing operational costs.

Novelty: This research contributes by integrating advanced optimization techniques (ALO-SSO) for enhancing the flexibility of distribution networks against various disturbances and maximize reliability metrics. It provides a comprehensive framework for decision-making in deploying hybrid RES systems in remote areas, emphasizing both technical and financial viability.