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.

Carbon dioxide has increased drastically in the last decades due to energy production, exacerbating the global warming problem. To address this issue, researchers have focused on developing energy production technologies from renewable sources. From the renewable energy sources, solar has shown great promise chiefly due to its high availability. The conversion of solar energy into electricity (photovoltaics) requires specialized equipment such as solar cells, and a coordinated supply chain to be able to manufacture this technology in a sustainable way and at low cost. Therefore, this chapter proposes an approach based on mathematical programming for the optimal design of a solar photovoltaics manufacturing system considering diverse criteria linked to economic and environmental variables such as minimum sustainable price, transportation costs, and technical limits. In addition, the dependence of the minimum sustainable price over inflation, electricity price, and weighted average capital cost is analyzed, showing that a variation of minimum sustainable price could significantly change the manufacturing supply chain topology.

Solar photovoltaics (PV) manufacturing has experienced dramatic worldwide growth in recent years, enabling a reduction in module costs, and a higher adoption of these technologies. Continued sustainable price reductions, however, require strategies focused in further technological innovation, minimization of capital expenditures, and optimization of supply chain flows. We present a framework: Techno-economic Integrated Tool For Tariff And Transportation (TIT-4-TAT), that enables the study of these different strategies by coupling a techno-economic model with a tariff and transportation algorithm to optimize supply chain layouts for PV manufacturing under equally-weighted objectives. We demonstrate the use of this framework in a set of interacting countries (Mexico, China, USA, and Brazil) and two extreme tariff scenarios: no tariffs, and high tariff levels imposed. Results indicate that introducing tariffs between countries significantly increase the minimum sustainable price for solar PV manufacturing, alter the optimal manufacturing locations, and render a more expensive final solar PV module price which can hinder the adoption rates required to mitigate climate change. Recommendations for stakeholders on the optimization process, and techno-economic drivers are presented based on our results. This framework may be utilized by policymakers for the spatially-resolved planning of incentives, labor and manufacturing programs, and proper import tariff designs in the solar PV market.

We present a framework (TIT-4-TAT) that enables the study of manufacturing strategies by coupling a technoeconomic model with tariff and transportation algorithms to optimize supply chain layouts for PV manufacturing. We use this framework in a scenario where Mexico, China, USA, and Brazil interact under two tariffs scenarios. The optimal manufacturing locations due to tariff levels variations are highlighted through this approach.