UC Berkeley

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.