4.7 Article

Design of domestic photovoltaics manufacturing systems under global constraints and uncertainty

Journal

RENEWABLE ENERGY
Volume 148, Issue -, Pages 1174-1189

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2019.10.010

Keywords

Multi-objective optimization; Solar photovoltaics; Manufacturing photovoltaics system; Expected value; Worst case; Uncertainty

Funding

  1. Mexican government agency (ProMexico)
  2. Mexican government agency (SENER)
  3. Tecnologico de Monterrey through its BECI-ITESM fellowship
  4. Mexican National Council for Science and Technology (CONACyT), Secretaria de Energia - CONACyT trough the Binational Energy Laboratory grant

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As global political discourse is taking place where the need for a cleaner energy mix is constantly highlighted, manufacturing strategies are becoming more relevant. Thus, the photovoltaics system design is a crucial aspect related with the overall sustainability. In fact, various countries are considering the potential to locally manufacture different elements of the photovoltaics (PV) value chain and the strategies to incentivize a local manufacturing base. This paper develops a mathematical programming approach for the optimal design of a PV manufacturing value chain considering diverse criteria linked to economic and environmental performance such as minimum sustainable price, transportation capacity, among others, and considering uncertainty. In addition, the proposed methodology involves the dependence over time of supply chain variables and economic parameters such as inflation, electricity cost, and weighted average cost of capital, to determine the manufacturing system topology under uncertain conditions. Our results highlight the importance of planning models to develop markets policies related to supply chains, production level changes and imposed tariffs all while involving uncertainty in economic parameters, which is an improvement compared to planning models that use deterministic formulations. Finally, the proposed methodology and results can encourage decision-making considering probable variations in different parameters. (C) 2019 Elsevier Ltd. All rights reserved.

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