Identifying Optimal Multi-state collaborations for reducing CO2 emissions by co-firing biomass in coal-burning power plants. (November 2016)
- Record Type:
- Journal Article
- Title:
- Identifying Optimal Multi-state collaborations for reducing CO2 emissions by co-firing biomass in coal-burning power plants. (November 2016)
- Main Title:
- Identifying Optimal Multi-state collaborations for reducing CO2 emissions by co-firing biomass in coal-burning power plants
- Authors:
- Dundar, Bayram
McGarvey, Ronald G.
Aguilar, Francisco X. - Abstract:
- Highlights: Identify min-cost approaches to reduce CO2 emissions via co-firing woody biomass. Extend model to identify min-cost multi-state partnerships for emission reduction. Apply model to 5 states, meets some (renewable energy) but not all (emission) goals. Abstract: The US Environmental Protection Agency (EPA) has recently proposed a rule that aims to reduce carbon emissions from US coal-fired power plants. The proposed "Clean Power Plan" specifies state-specific rate-based goals to achieve a total US carbon emission reduction of 32% below 2005 levels by 2030. An increase in the co-firing of woody biomass with coal to generate biopower is one of the potential approaches that electricity providers could take to comply with EPA's proposed rules. We develop a mixed integer linear programming (MILP) model to identify minimum-cost approaches for reducing CO2 emissions via co-firing biomass subject to spatially-explicit biomass availability constraints. An important feature of the EPA recommendations is an allowance for states to participate in multi-state compliance strategies. We extend the MILP model to optimize within a larger geographical framework that allows states to identify minimum-cost partnerships that meet aggregated emission reduction goals. We apply the MILP model to data for five Midwestern US states to determine the role that co-firing biomass could play in achieving their EPA-proposed emission reduction targets, and find that some states can meet theirHighlights: Identify min-cost approaches to reduce CO2 emissions via co-firing woody biomass. Extend model to identify min-cost multi-state partnerships for emission reduction. Apply model to 5 states, meets some (renewable energy) but not all (emission) goals. Abstract: The US Environmental Protection Agency (EPA) has recently proposed a rule that aims to reduce carbon emissions from US coal-fired power plants. The proposed "Clean Power Plan" specifies state-specific rate-based goals to achieve a total US carbon emission reduction of 32% below 2005 levels by 2030. An increase in the co-firing of woody biomass with coal to generate biopower is one of the potential approaches that electricity providers could take to comply with EPA's proposed rules. We develop a mixed integer linear programming (MILP) model to identify minimum-cost approaches for reducing CO2 emissions via co-firing biomass subject to spatially-explicit biomass availability constraints. An important feature of the EPA recommendations is an allowance for states to participate in multi-state compliance strategies. We extend the MILP model to optimize within a larger geographical framework that allows states to identify minimum-cost partnerships that meet aggregated emission reduction goals. We apply the MILP model to data for five Midwestern US states to determine the role that co-firing biomass could play in achieving their EPA-proposed emission reduction targets, and find that some states can meet their renewable energy generation targets through co-firing, although co-firing alone is not sufficient to achieve any state's emission reduction targets. … (more)
- Is Part Of:
- Computers & industrial engineering. Volume 101(2016)
- Journal:
- Computers & industrial engineering
- Issue:
- Volume 101(2016)
- Issue Display:
- Volume 101, Issue 2016 (2016)
- Year:
- 2016
- Volume:
- 101
- Issue:
- 2016
- Issue Sort Value:
- 2016-0101-2016-0000
- Page Start:
- 403
- Page End:
- 415
- Publication Date:
- 2016-11
- Subjects:
- Renewable energy -- Optimization
Engineering -- Data processing -- Periodicals
Industrial engineering -- Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03608352 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.cie.2016.09.028 ↗
- Languages:
- English
- ISSNs:
- 0360-8352
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.713000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 7554.xml