Robust Capacity Planning of a Grid-Connected Photovoltaic System Under Climate Change and Economic Instabilities

Fossil fuel combustion to produce power is responsible for a substantial amount of greenhouse gases and is essential to climate change and global warming. Therefore, the leading industrial countries are urged to invest in renewable energy and reduce reliance on traditional fossil fuel supplies. Photovoltaic (PV) solar systems are a promising renewable energy technology that has developed and expanded rapidly in recent years. However, capacity planning for the PV system is a strategic decision-making process that lasts about 20 years and is affected by climate and economic changes during this period. For this purpose, this research proposes a robust capacity planning approach by formulating a multiobjective mixed-integer linear programming (MO-MILP) optimization model. Climate and economic changes such as global irradiation, ambient temperature, and inflation rate are represented by a set of possible scenarios with a probability of occurrence. MO optimization helps study the trade-offs among economic, technical, and environmental objectives. A real-world case of capacitating a grid-connected PV system to supply power to the residential area at King Fahd University of Petroleum and Minerals is provided to demonstrate the practicability of the optimization model. In addition, a sensitivity analysis is conducted to get more technical and managerial insights into the planning under more scenarios. As a result, it is recommended to use 1252 PV arrays at an annual cost of M$ 1.18 and M$ 0.127 per year to satisfy the shortage and reduce CO₂ emissions by 96.31%.