A Robust, resilience and risk-aware solar energy farm location by Bi-Level programming approach

¹ Department Industrial Engineering, Yazd University, Yazd, Iran
² Behineh Gostar Sanaye Arman, Tehran, Iran
³ School of Computing, Clemson University, Clemson, SC, USA
⁴ Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
⁵ Department of Industrial Engineering and Management Systems, AmirKabir University of Technology (Tehran Polytechnic), Tehran, Iran
⁶ Department of Statistics and Analytics, Bank Mellat, Tehran, Iran
⁷ Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
⁸ Department of Business and Management, University of Science and Culture, Tehran, Iran

^* Corresponding author: reza.lotfi.ieng@gmail.com

Received: 4 January 2024
Accepted: 3 May 2024

Abstract

In challenging circumstances such as war, governments are shifting their focus towards Solar Energy (SE) as a Renewable Energy option through PhotoVoltaic panels due to the rising costs associated with fossil fuel extraction and exploration. This model recommends a SE Location (SEL) that prioritizes Robustness, Resilience, and Risk awareness which is called 3RSEL. As a result, a Bi-Level Programming (BLP) is proposed to solve this problem for the first time. A heuristic approach is defined for a BLP mathematical model. This research generates a lower and upper bound to solve the model quickly. The results show that Yazd and Kerman are the optimal location for SEL. The main problem is compared to a situation where risk and robustness are not considered. It can be observed that the supplier’s profit and energy production are lower than without risk and robustness, with a gap of −4.4%. The variability of the conservatism coefficient, discount rate, confidence level of Conditional Value at Risk, and problem scale are considered. Increasing the conservatism coefficient decreases the supplier’s profit function and energy output. Alternatively, increasing the discount rate decreases the supplier’s profit function without affecting the energy output. Conversely, boosting the confidence level does not alter suppliers’ profit function but results in declining energy output. Finally, as stated, it can be observed that the computation time increases with an increase in the scale of the problem.