A data-driven robust optimization model for integrated network design solar photovoltaic to micro grid

In recent years, energy experts have paid considerable attention to renewable resource energy. The most widely renewable energy resource, in the world is solar energy. Therefore, optimizing the sustainable supply chain of solar energy production like photovoltaic helps policy-makers in this field. This paper proposes a two-phase method to elaborate a sustainable supply network design model for the solar photovoltaic supply chain. The first phase selects the suitable land for setting up solar photovoltaic power plants by the robust best-worst method, and the second phase develops the photovoltaic supply network design. Additionally, this work deals with the most significant concerns for policy-makers and investors, which are the uncertainty of radiation and demand parameters by a data-driven robust optimization model. As a significant result, it can be obtained with actualized data which the proposed uncertainty approach versus classical closed uncertainty convex sets has less conservatism. This advantage can impose less cost on the system due to its robustness. For validation of the proposed model, an in-depth case study is conducted. Its results show that solar power plants in Yazd, South Khorasan and Kerman are located as strategic decisions, and material flow between suppliers and wafer and ingot plants are determined as operational decisions. Also, on the tactical level, the number of vehicles required to transfer the flow of goods in the supply chain layers based on different time intervals and various transportation modes has been optimized. (C) 2022 Elsevier Ltd. All rights reserved.