Forecasting-Based Design and Optimization of Grid-Connected Solar Photovoltaic System Using MMC and ANFIS for Peak Load Shaving and Ancillary Support in Bahir Dar Distribution Substation

Abstract

Peak load shaving is a crucial strategy for enhancing grid reliability, efficiency, and sustainability by reducing maximum electricity demand. This study investigates the design and optimization of a 100-kW grid-connected Solar Photovoltaic (PV) system for peak load shaving and ancillary support at the Bahir Dar Distribution Substation (15 kV side). The system addresses the projected overloading of the substation within 22 months from 2023, as forecasted using an Artificial Neural Network (ANN) based on historical domestic customer data. Utilizing Bahir Dar’s abundant solar resources, the PV system replaces diesel gensets at the Ethiopian Electric Utility (EEU) datacenter, contributing to the national electrification goal by 2030. To enhance power quality and grid stability, the proposed system integrates an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based boost converter for Maximum Power Point Tracking (MPPT), increasing the DC-link voltage from 437.6V to 730.04V. A five-level Modular Multilevel Converter (MMC) with Voltage Oriented Control (VOC) is implemented. Achieving a significant reduction in Total Harmonic Distortion (THD) from 16.27% to 1.12%, ensuring compliance with international standards. Additionally, the economic feasibility analysis indicates that the PV system, consisting of 41 panels in series and 8 in parallel, requires a total installation cost of $39,013 and generates approximately 170,209.18 kWh annually. Compared to the existing diesel-based power system, which incurs an annual operating cost of 571,663.35 ETB, the PV system offers substantial cost savings with an estimated payback period of 27 months. Despite these advantages, challenges such as weather variability, transient response analysis, and system scalability remain. Future work will focus on real-world validation through hardware-in-the-loop (HIL) testing, grid disturbance simulations including Low Voltage Ride Through (LVRT) and Low Frequency Ride Through (LFRT), and field trials to assess large-scale deployment feasibility. The findings highlight the potential of grid-connected PV systems to enhance energy reliability, reduce fossil fuel dependence, and support Ethiopia’s transition to a cleaner and more sustainable power infrastructure.