Planning methodology for anaerobic digestion systems on animal production facilities under uncertainty. (1st March 2020) - Sharara, Mahmoud A.; Owusu-Twum, Maxwell Y.; Runge, Troy M.; Larson, Rebecca
Highlights: A planning methodology is presented to aid the planning of agricultural AD systems. A probabilistic approach was used to overcome uncertainty about project parameters. Herd size was key determinant to selecting location for AD projects. Geographic distribution and herd size were crucial to profitability of AD systems. Biomethane potential and electricity price are critical to AD feasibility. Abstract: Anaerobic digestion (AD) reduces GHG emission and facilitates renewable energy generation. The slow rate of adoption of this technology is often attributed to economic and technical considerations. Collaboration of two or more dairy farms into a centralized AD system can improve the process economics through economies of scale. However, uncertainties related to the process parameters and the scope/scale of the collaborative implementation impede its adoption. This study presents techno-economic optimization model as a design aid to determine ideal location, capacity, and participation level (cluster size) that maximize economic return on a cooperative digester. This study employs a probabilistic approach to overcome uncertainty regarding project parameters such as manure biomethane potential (BMP), project capital, and electricity sale price. Two case studies based on dairy production regions in Wisconsin were developed to test the model and demonstrate its capabilities. Herd sizes and spatial distribution in a given region were found to be critical factors in determining the viability of digestion projects in general, and collaborative digestion systems in particular. The number of simulation runs needed to capture the probability of profitable AD facility establishment was less than 1000 for both case studies assessed. Electricity sale price and biomethane potential of feedstock utilized were found to be the most restrictive to the feasibility of AD adoption. Changing the optimization objective function, to adopting maximization, favored the formation of collaborative AD facilities for both case studies evaluated.