Photovoltaic Recycling Planning: Macro and Micro Perspectives

The usage of valuable resources and the generation of emissions during the life cycle of photovoltaic (PV) technologies dictate our proactive planning for a sound PV recycling infrastructure to ensure its sustainability. Some global PV manufacturers have begun recycling PV wastes generated from the PV manufacturing processes and the Endof-Life (EoL) PV modules. Therefore, it is vital to develop and institute economically feasible recycling technologies and infrastructure for the emerging PV industry in parallel with the rapid commercialization of these new technologies. Many issues must be accounted for in setting the optimal temporal and spatial system boundaries on the prospective PV recycling infrastructure since various stakeholders are involved in it. The issue of management covers diverse aspects, such as the collection, distribution, inventory, and reclaiming of materials. Therefore, a holistic consideration of the economic viability of the entire PV recycling network systems is necessary. We have developed mathematical models to analyze the profitability of recycling technologies and to guide tactical decisions for allocating optimal location of the PV take-back center. INTRODUCTION PV manufacturing has been growing over the past 10 years and further annual growth of 15% is expected until 2020 [1, 2]. A study on positioning a grand plan for United States solar power shows how vast PV arrays and other renewable energies can provides significant amount of electricity and total energy needs by 2050 [3]. Various new PV technologies have been introduced in the market and the existing technologies undergo significant further development. How all these developments will affect the fate of the PV wastes is uncertain. However, it is certain that the growing amount of PV productions wastes together with the significant amount of retiring PV modules installed over the several decades need to be disposed in somehow. In addition, the market price of some rare earth materials utilized in the manufacturing of the various PV technologies has been exponentially increased in past five years [4-5]. Therefore, it is necessary to set a proactive strategic planning for the treatment of the disposed PV wastes. One option would be the recycling PV wastes to reclaim those rare earth materials. However, economics of the prospective PV recycling has to be tested to initiate a sound recycling program. There are various issues involved in the economics of PV recycling in macro and micro level. In the macro level, strategies are needed for allocating the centralized/decentralized collection and recycling facilities in the optimal locations to minimize the total recycling system costs. This includes issues such as the optimal level of marginal capital costs to open up a PV take-back center (PVTBC), costs associated with the reverse logistics services for the collection of PV modules and transporting them to the recycling facilities. Account must be taken of the various stakeholders (e.g., dismantlers, recyclers, smelters) in the recycling infrastructure. In the micro level, optimized process planning is required to ensure the profitability of the PVTBC. Potential PVTBC will face with some challenging decisions on following issues; the material separation, revenue structures of current and future recycling processes with regard to the volatility of the market price of materials/components, cost associated with the processing, reverse logistics costs, and external social costs, such as landfill-tipping fees. Therefore, we have developed a generic mathematical modeling framework to evaluate the economic feasibility of the macro-level reverse logistics planning and the micro-level recycling process planning of the PV waste by considering the complex issues of the PV recycling planning listed above. A case study of the crystalline silicon PV waste recycling in Germany is presented to illustrate the applicability of the models. MODELING MACRO LEVEL LOGISTICS PLANNING MODEL (M1): The reverse logistics model is designed to allocate the optimized