A data-driven robust optimization model for integrated network design solar photovoltaic to micro grid. (September 2022)

• Record Type: Journal Article
• Authors: Gilani, Hani; Sahebi, Hadi; Pishvaee, Mir Saman
• Is Part Of: Sustainable energy, grids and networks. Volume 31(2022)
• Publication Date: 2022-09
• Subjects: Renewable energy supply chain -- Sustainability -- Solar photovoltaic -- Data-driven robust optimization; Renewable energy sources -- Periodicals; Smart power grids -- Periodicals; Electric power systems -- Periodicals; 333.794
• DOI: 10.1016/j.segan.2022.100714
• Languages: English
• ISSNs: 2352-4677

Abstract

Abstract: 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. Highlights: Dealing with the intrinsic uncertainty nature of the photovoltaic supply chain to micro grid using a Data-Driven robust optimization approach. Using a robust best–worst method for selecting potential points of solar plant that has low need to exact decision maker's opinion than other MCDM approaches. Comparing the solution of the proposed model in two cases for showing performance of the DDRO approach, these modes are: (1) Box close convex uncertainty set (the highest level of conservatism) (2) Deterministic approach with nominal data Developing a sustainable photovoltaic supply chain for DC electricity grids and pay attention to the environmental effects analyzes by Simapro. Implementing a case study in order to validate the applicability of the proposed mathematical model.