Increasing Distributed Generation Hosting Capacity Based on a Sequential Optimization Approach Using an Improved Salp Swarm Algorithm

Abstract

In recent years, a pronounced transition to the exploitation of renewable energy sources has be observed worldwide, driven by current climate concerns and the scarcity of conventional fuels. However, this paradigm shift is accompanied by new challenges for existing power systems. Therefore, the hosting capacity must be exhaustively assessed in order to maximize the penetration of distributed generation while mitigating any adverse impact on the electrical grid in terms of voltage and the operational boundaries of the equipment. In this regard, multiple aspects must be addressed in order to maintain the proper functioning of the system following the new installations’ capacities. This paper introduces a sequential methodology designed to determine the maximum hosting capacity of a power system through the optimal allocation of both active and reactive power. To achieve this goal, an Improved Salp Swarm Algorithm is proposed, aiming to establish the appropriate operational planning of the power grid considering extensive distributed generation integration, while still ensuring a safe operation. The case study validates the relevance of the proposed model, demonstrating a successful enhancement of hosting capacity by 14.5% relative to standard models.

Optimal Sizing and Placement of Distributed Generation under N-1 Contingency Using Hybrid Crow Search–Particle Swarm Algorithm

“…Notably, distributed sources such as wind and solar play a significant role in the power system domain, characterized by their natural abundance, minimal environmental impact, cost-effectiveness, and eco-friendly nature. Nevertheless, this transition poses various challenges, encompassing issues related to generation intermittency and the complexities of grid integration [1,2]. As a result, the incorporation of distributed generators (DGs) with optimal allocation and sizing into TDSs is unavoidable not only to overcome the inadequacies of TDSs but also to improve the power systems' techno-economic performance [3][4][5][6].…”