Fiscal- and Space-Constrained Energy Optimization Model for Hybrid Grid-Tied Solar Nanogrids

Abstract

Due to rising fossil fuel costs, electricity tariffs are also increasing. This is motivating users to install nanogrid systems to reduce their electricity bills using solar power. However, the two main constraints for a solar system installation are the initial financial investment cost and the availability of space for the installation of solar panels. Achieving greater electricity savings requires more panels and a larger energy storage system (ESS). However, a larger ESS also increases the electricity bill and reduces the available solar power due to higher charging power requirements. The increase in solar power leads to the need for more space for solar panel installation. Therefore, achieving the maximum electricity savings for a consumer unit requires an optimized number of solar panels and ESS size within the available financial budget and the available physical space. Thus, this study presents a fiscal- and space-constrained mixed-integer linear programming-based nanogrid system model (FS-MILP) designed to compute the optimal number of solar panels and ESS requirements, and the daily electricity unit consumption and savings. The proposed model is also validated through an OMNET++-based simulation using real-time solar irradiance and residential load values of one year for the city of Karachi, Pakistan. The investigation results show that a maximum of 1050 electricity units can be saved and exported to the main power grid within the maximum financial budget of PKR 1,000,000/-.