Multi-Objective Optimization of Bioelectricity Supply Chain With Life Cycle Assessment and Social Impact Analysis

Multi-Objective Optimization of Bioelectricity Supply Chain with Life Cycle Assessment and Social Impact Analysis

Maxim Slivinsky, Dajun Yue, Fengqi You*

Submitted to session 23C01 Sustainable Electricity: Generation and Storage

Abstract

There is a strong drive for exploration of bioelectricity production, which is rooted in the increasing costs and limited availability of fossil fuels as well as the desire for greater energy security.  Bioelectricity provides a promising alternative to fossil fuel power generation due to the compatibility with power generation infrastructure, reduction in greenhouse gas emissions, and associated job creation.  The existing power infrastructure, including power grid, steam turbines, etc., can be utilized in the production of bioelectricity given some equipment modifications at the plant level and in pre-processing facilities [1].  This results in a relatively low capital investment opportunity to produce electricity from biomass feedstocks.  This work approaches the supply-chain problem using three objectives: economic, environmental, and social.  The economic objective is minimization of total annualized costs, the environmental objective seeks to minimize the life cycle greenhouse gas emissions (GHG), and the economic objective is to maximize total accrued jobs. 

In this work a multi-objective mixed-integer linear programming problem (MILP) is developed to model the supply-chain optimization of bioelectricity production from feedstocks such as corn stover, forest residues, and switchgrass.  This MILP is solved using the ε-constraint method, where the Pareto-optimal solutions are determined by solving MILP sub-problems with varying values of ε [2].  A key constraint in the formulation is the minimum fraction of annual electricity produced from biomass for a given region.  The MILP is analyzed through different scenarios corresponding to this constraint parameter. Economic multipliers are taken from the Jobs and Economic Development Impact (JEDI) model to determine the social impact on the region [3].  The total accrued employment consists of direct employment (e.g. construction), indirect employment (jobs in upstream supply chain), and induced employment (jobs from money spent locally).  Since JEDI has data for only two boiler technologies, this formulation looks at circulating fluidized bed and stoker boilers.  The economic objective takes into account feedstock and utilities costs, transportation costs, construction costs, electricity demand by region, and processing limits.  The environmental objective uses life cycle analysis (LCA), considering gate-to-gate environmental impact analysis of the preprocessing facilities, transportation, and capacity expansions.  GWP is calculated using the 100 year timeframe per the Kyoto Protocol. 

This problem is evaluated through a county-level case study for Illinois, which consists of 102 counties.  County data is used to determine geographic biomass availability, existing power plant locations, and electricity demand.  The problem is solved to determine the optimal locations and capacities of preprocessing facilities and biomass technology expansions, as well as the network design and technologies used [4, 5].  The Pareto solutions to this MILP reveal the tradeoffs among the decision variables under economic, environmental, and social objectives.  The scenario analysis shows the economies of scale associated with replacing a fraction of power generation with bioelectricity.

References

[1]        Environmental Protection Agency [EPA], Combined Heat and Power Partnership (2007, September). “Biomass Combined Heat and Power Catalog of Technologies”, http://www.epa.gov/chp/documents/biomass_chp_catalog.pdf

[2]       B. H. Gebreslassie, Y. Yao, and F. You, "Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a Comparison between CVaR and downside risk," AIChE Journal, vol. 58, pp. 2155-2179, 2012.

[3]        National Renewable Energy Laboratory [NREL], Jobs and Economic Development Impact Models [JEDI] (2012, September), http://www.nrel.gov/analysis/jedi/

[4]        F. Q. You, L. Tao, D. J. Graziano, and S. W. Snyder, "Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input-output analysis," Aiche Journal, vol. 58, pp. 1157-1180, Apr 2012.

[5]        You, F., & Wang, B. (2011). Life Cycle Optimization of Biomass-to-Liquids Supply Chains with Distributed-Centralized Processing Networks. Industrial & Engineering Chemistry Research, 50, 10102–10127

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* Corresponding author. Email: you@northwestern.edu