PV Integration in LV Networks and Capacity Analysis

Abstract

The increasing integration of photovoltaic (PV) systems in low-voltage (LV) networks presents challenges in violation of permitted voltage changes in the LV network and conductor and transformer capacity, which are critical for maintaining grid reliability and operational efficiency. This paper analyzes PV integration, focusing on voltage control, conductor capacity, and the importance of day-ahead PV generation and consumption for proactive grid management. Using MATLAB, the LV network is modeled to assess voltage analysis and conductor capacity for PV capacities ranging from 3 kW to 8 kW per consumer. Predictions of day-ahead PV production are conducted using a feedforward neural network trained on meteorological data such as solar irradiance, temperature, and cloud cover. The predictive model enabled voltage drop simulations and capacity analysis under forecasted conditions. The results demonstrated that voltage levels remained within the permissible range (+5%, -10% of 400 V) for PV capacities up to 8 kW, ensuring operational reliability. The neural network-based predictions are closely aligned with modeled values, with minimal differences, validating the forecasting approach. Voltage variations increased with higher PV capacities, but conductor current levels consistently remained below thermal limits. Incremental PV capacity integration revealed the network's ability to support distributed generation effectively but with limitations at higher capacities. This research highlights the role of accurate forecasting and optimization in ensuring reliable renewable energy adoption.