Nationwide Energy Supply Chain Analysis for Transportation Fuels
- Type: Conference Presentation
- Conference Type:
AIChE Annual Meeting
- Presentation Date:
October 16, 2011
- Skill Level:
Hybrid energy processes are an attractive avenue towards a more sustainable energy future due to their flexibility in converting multiple types of inputs into a consistent range of products. For the transportation sector, Fischer-Tropsch synfuels can be generated from a diverse of feedstock inputs to replace petroleum-based products . Using the novel hybrid coal, biomass, and natural gas to liquids (CBGTL) process, the United States can utilize domestically available resources to produce gasoline, diesel, and kerosene to fulfill the fuel demands for the country [2-6]. Further, the incorporation of different types of biomass as feedstocks helps mitigate greenhouse gas emissions and further the efforts for renewable energy production . Such hybrid processes, however, require the cooperation between multiple economic domains to achieve efficient resource allocation. Thus, an integrated and systematic approach that evaluates the interconnection between key stakeholders is crucial to attain a cost-competitive energy supply chain.
We developed an optimization framework to solve a large-scale, nationwide energy supply chain problem that takes into account the varying degrees of resource availabilities and the demand profile for the United States . The supply chain begins at the feedstock source locations, ends at the demand locations, consists of optimized CBGTL facilities using the process synthesis with simultaneous heat, power, and water integration method . These facilities are the 108 optimal plant topologies analyzed in  with distinct combinations of six coal feedstock types, three biomass feedstock types, one natural gas composition, three plant capacities, and two carbon management options (i.e., with or without carbon sequestration). A superset of candidate facility locations across the country is derived from a filtered list of United States counties in the lower 48 states based on the availability and transportation cost of feedstocks to each county.
The CBGTL facilities and the candidate locations, along with information on the nationwide configuration for the feedstock, fuel demands, and modes of transportation obtained from published government-based databases , serve as parameter inputs to the formulated large-scale mixed-integer linear optimization (MILP) model that represents discrete and continuous decisions in the energy supply chain. The model is solved using CPLEX to obtain the optimal supply chain topology with the minimum overall cost of fuel production for the entire network, the facility selections and locations, and the interconnections between feedstock sources, facility locations, and demand locations.
Ten case studies are presented to illustrate the performance of the optimal supply chain network under three major scenarios, namely the supply chain network with i) unrestricted topology, where the selections of the facility locations are not restricted to any geographical constraints, ii) imposed degrees of plant distribution, where a minimum number of selected facilities in the network is introduced, and iii) restricted topology, where the selection of the plant locations are restricted to be in proximity to current energy infrastructures. Facets of the model solution provide a quantitative basis to evaluate and examine trade-offs in investment decisions under these different scenarios.
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