We represent 245 dairy CAFOs as suppliers of manure, and a 246 th supplier representing collective electricity generation in the State of Wisconsin (competing with MP systems). We assume that CAFOs offer manure for free; the electricity supplier bid is set to various wholesale market prices in order to explore the impact of price variation. These suppliers are collectively indexed in the set i ∈ G each with a bid parameter α g i ∈ R + , capacity parameter g i ∈ R + , and allocation g i ∈ R + . The 120 MP systems available are represented as a set of technology providers m ∈ M with bid processing cost α ξ m ∈ R + , processing capacity parameter ξ m ∈ R + , and allocation ξ m ∈ R + . We include a single consumer that requests electricity from the MP systems. This represents the wholesale electricity market and is captured as a single hub location. The capacity of this hub is consistent with annual electricity consumption rates in Wisconsin and is indexed as j ∈ D with bid parameter α d j ∈ R + , capacity parameter d j ∈ R + , and allocation variable d j ∈ R + . Waste and electricity transport are both indexed in the set l ∈ L (which captures the supply chain disposition of farm locations) with bids α f l ∈ R + , capacities f l ∈ R + , and allocation variables f l ∈ R + . Using these parameters and variables, the optimization problem is formulated as in .