Achieving Optimal PV Allocation in Distribution Networks Using a Modified Reptile Search Algorithm

Abstract

As a result of advancements in technology and population growth, there has been a significant rise in global electrical demand. Consequently, the integration of renewable sources such as photovoltaic (PV) systems into distribution systems has gained popularity as an effective solution to meet the increasing load requirements. This research paper introduces an optimized approach for allocating PV systems at various penetration levels, utilizing a powerful optimization algorithm known as the modified Reptile Search Algorithm (MRSA). MRSA is an enhanced version of the Reptile Search Algorithm (RSA) that addresses issues related to local optima stagnation and premature convergence by incorporating the disperse ambush strategy and proportional selection method. To assess the efficacy of the proposed optimizer, a comprehensive set of comparative experiments was conducted using the CEC’2020 test suite. The experimental results consistently demonstrate that the proposed technique outperforms alternative methods in terms of both convergence speed and accuracy. Additionally, the MRSA algorithm was employed to determine the optimal allocation of PV systems, with the total power loss serving as a single objective function while considering equality and inequality constraints. The IEEE 33-bus RDS was employed as the test system. The obtained results provide evidence that incorporating multiple PV systems yields superior outcomes compared to a single PV system at various penetration levels within the RDS. Furthermore, integrating PV systems at higher penetration levels yields better results than incorporating them at lower penetration levels.

Keywords

• Distribution network
• renewable energy
• PV
• penetration
• metaheuristic
• optimization