Energy System Design to Maximize Net Energy Production Considering Uncertainty in Scale-up: A Case Study in Artificial Photosynthesis

Abstract

Increasing energy demands coupled with decreasing resources and increased concerns about the long-term impacts of energy production are driving the development and full-scale implementation of new energy production systems. In general, several considerations must be addressed to move technology from the prototype phase to complete deployment, while ensuring adequate return on investment. These considerations include: availability of capable manufacturing processes, appropriate material selection, and supply chain resiliency. For new energy production systems, it is also important to evaluate net energy production and life cycle impacts. There is significant uncertainty associated with each of these considerations. This paper proposes a framework for maximizing net energy production in full-scale systems considering these uncertainties. The utility of the framework is demonstrated through a case study focused on an analysis of prototyping and scale-up of artificial photosynthesis as a solar-fuel generator.

A systematic approach to assess the environmental impact of emerging technologies: A case study for the GHG footprint of CIGS solar photovoltaic laminate

“…Many different methods for uncertainty quantification have been developed which can also be considered for prospective LCA. Some examples from the literature include Likert's scale of (dis)similarities (Walczak, Hutchins, & Dornfeld, 2014), Monte Carlo simulations (Mann, de Wild‐Scholten, Fthenakis, van Sark, & Sinke, 2013; Parisi, Maranghi, & Basosi, 2014; Ravikumar, Seager, Cucurachi, Prado, & Mutel, 2018), and sensitivity analysis (Mann et al., 2013; Raugei, Bargigli, & Ulgiati, 2007). Of these, Monte Carlo simulations could be particularly useful in uncertainty analyses of the applied size scaling factor and environmental experience rate in the size scaling and industrial learning steps of the proposed approach.…”

Life cycle assessment of emerging technologies: A review

“…Walczak et al. () considered the technology readiness levels and the potential uncertainty and described a 6‐step procedure to scale up in LCA studies. Typically, different scales of technology can be described as lab scale, pilot or engineering scale, and full scale operation (i.e., commercial or industrial scale).…”

“…Moreover, funding agencies (e.g., the U.S. Department of Energy [DOE]) are asking for LCA along with technoeconomic analysis (TEA) for newly proposed projects, even for research at very early stages of technology development. For example, the Joint Center for Artificial Photosynthesis was required to perform a TEA–LCA study of a potential utility scale solar‐to‐hydrogen‐gas system (Walczak, Hutchins, & Dornfeld, ). Both the DOE MEGA‐BIO (DOE, ) and Integrated Biorefinery Optimization 2017 (DOE, ) programs require calculation and reporting of life cycle greenhouse gas (GHG) emissions and minimum fuel selling price of the proposed system operating at commercial scale; and the DOE Targeted Algal Biofuels and Bioproducts program requires GHG and energy return on investment for proposed technology (DOE, ).…”

Life cycle assessment of emerging technologies: Evaluation techniques at different stages of market and technical maturity

“…Incorporation of other methods into an LCA can also be helpful in this quadrant. Models to scale‐up emerging technologies have been proposed in the literature (e.g., Piccinno, Hischier, Seeger, & Som, ; Simon et al., ) as well as several case studies (e.g., Caduff, Huijbregts, Koehler, Althaus, & Hellweg, ; Piccinno et al., ; Shibasaki, Fischer, & Barthel, ; Walczak, Hutchins, & Dornfeld, ). Thermodynamic modeling to estimate the distance from the technologies’ respective physical efficiency limits (and thus potential for improvement), use of learning or experience curves to project product improvement, or development of causal scenarios of potential process improvements can also be helpful.…”