Three-Stage Optimization-Based Framework for Grid-Connected Sizing of a PV Power Plant System

The grid-connected sizing of a photovoltaic (PV) power plant system aims to determine the equipment and components necessary to operate and integrate PV panels into an on-grid system with compatibility, efficiency, affordability, and reliability. In this context, this article proposes a new three-stage optimization-based framework for the grid-connected sizing of a PV power plant. In Stage 1, the $k$-means clustering algorithm is utilized to determine the placement and dimensions of the inverters, as well as the number of strings and the arrangement of PV modules in strings. During Stage 2, the DC wiring is sized to facilitate the connection between the PV strings and the inverters. Finally, in Stage 3, the AC wiring is sized to connect the inverters to the power substation. Stages 2 and 3 are formulated as mixed-integer linear programming problems to minimize investment costs and reduce AC power losses. The framework is implemented using different tools and libraries, including Quantum Geographic Information System for geospatial panel placement, Python libraries such as scikit-learn for clustering, and Pyomo for modeling the optimization problem of Stages 2 and 3 with the Gurobi solver. A real-world case study of a PV power plant located in Brazil, which covers an area of 13 000 m$^{2}$ and a nominal power of 1.2 MW, is used to validate the proposed framework. The results demonstrate the robustness, feasibility, and scalability of the proposed methodology.