(703c) Multi-Objective Optimization of Bioelectricity Supply Chain With Life Cycle Assessment and Social Impact Analysis

Authors: 
Slivinsky, M., Northwestern University
Yue, D., Northwestern University
You, F., Cornell University



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




* Corresponding author. Email: you@northwestern.edu