OPTIMAL INTEGRATION OF RENEWABLE ENERGY RESOURCES USING INTELLIGENT TECHNIQUES

Abstract

The renewable energy capacity of India has risen from 17 GW in 2010 to 190 GW till 2025, wherein the growth rate of solar energy has been 24.8% annually. While the fast growth is imperative to meet the targets of non-fossil fuels to reach 500 GW by 2030, there are many issues such as intermittency, voltage regulation, frequency and congestion of transmission lines. The existing operating principles cannot address these issues; hence, a smarter control method is required. In this research a genetic algorithm based optimal ANFIS controller is proposed to perform maximum power point tracking for solar PV system connected with boost converter. The genetic algorithm is used to optimize the membership function parameters and rule weights of ANFIS to improve its adaptability in changing environments. The GA-optimized ANFIS controller showed improved performance with a tracking efficiency of 99.12%, settling time of 0.041 seconds and an absolute error of 2.91 for normal irradiance condition. For varying irradiance conditions, relative error stayed at 4.12% and oscillations around MPP were reduced to 41.7%. The controller exhibited good flexibility with partial shading conditions, ramp/step changes in irradiance and variation in temperature conditions as well. The research study has been able to bridge the gap between the MPPT optimization at the converter level and the renewable integration at the system level. The increase in the output power of 8.6%, along with reduced tracking error, has made an important contribution towards the renewable energy integration objectives of India.