Phd Defense by Junchen Yan

Event Details
  • Date/Time:
    • Friday December 13, 2019
      12:00 pm - 2:00 pm
  • Location: Capstone Building, Room 338C, 828 West Peachtree St
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Summary Sentence: Parametric Life Cycle Assessment of Combined Cooling, Heating, and Power Integrated with Renewable Energy and Energy Storage

Full Summary: No summary paragraph submitted.

School of Civil and Environmental Engineering


Ph.D. Thesis Defense Announcement


Parametric Life Cycle Assessment of Combined Cooling, Heating,

and Power Integrated with Renewable Energy and Energy Storage




Junchen Yan



Dr. John C. Crittenden (CEE)


Committee Members:

Dr. John E. Taylor (CEE), Dr. Marilyn A. Brown (PUBP),

Dr. Santiago Carlos Grijalva (ECE), Dr. Valerie Thomas

(ISyE), Dr. Yongsheng Chen (CEE)


Date and Time: Friday, December 13th, 2019 at 12:00 noon


Location Capstone Building, Room 338C, 828 West Peachtree St


Complete Announcement, with abstract, is attached.


Buildings use about 40% of global energy supply, mainly from natural gas and electric grids powered by fossil
fuel-based centralized power plants. This study examines a more sustainable energy generation system --- the
distributed combined cooling, heating, and power integrated with renewable energy and energy storage system
(CCHP-RE-ESS). A parametric hybrid life cycle assessment framework approach is used to evaluate the
environmental, economic, and social impacts of the proposed distributed energy generation system. The rationale for
a parametric LCA approach is that it extends conventional LCA, which is cases-specific and shows how impacts
change with different input factors such as ambient temperature, climate, and operation strategies. The impact results
integrate with a multi-objective optimization method, Pareto front, to find the optimal environmental and economic
impact trade-offs for different building energy demand scenarios. The parametric framework includes six
commercially available trigeneration technologies: two for prime movers (microturbine and fuel cells), two for
renewables (solar power and small wind turbine), and two for energy storage (lithium-ion battery and compressed air
energy storage). The model is able to find the best combination of technologies and their corresponding sizes for
different building demand profiles. After billions of simulations, the Microturbine-Solar PVs-Lithium ion Battery and
Fuel Cells-Solar PVs-Lithium-ion Battery are two optimal distributed energy solutions. The simulation impact result
shows that the system can primarily reduce the environmental impact as compared to the conventional energy system.
However, the life cycle cost of CCHP-RE-ESS is higher than the traditional energy generation, especially for fuel
cell-based system.
Finally, the model evaluates the social cost and the current U.S. clean energy policy incentives impacts on the
distributed CCHP-RE-ESS system. The model uses the Air Pollution Emission Experiments and Policy model to
evaluate the marginal damages emissions on a dollar per ton basis. Results show that the social cost of conventional
energy is significantly higher than the distributed energy generation. Based on the simulation result, it is estimated
that the installation of the distributed CCHP-RE-ESS can help avoid more than 50 billion dollars of social cost per
year for commercial buildings in U.S. Besides, the model study the cost-saving potential of current U.S. clean energy
policy incentives, including federal tax credit, low-interest loan, and Modified Accelerated Cost Recovery System
(MACRS). The tax credit and MACRS can primarily reduce the cost of distributed energy by average 50%, while
low-interest loan increases the cost by average 30%. In some scenarios, the after-policy life cycle cost of distributed
energy generation is competitive compared to conventional power, but for most situations, the life cycle cost is still
higher as compared to conventional power. Future work includes: (1) integrate more trigeneration technologies (e.g.,
thermal storage), (2) adopting hybrid operation strategy that switching between following electrical load and
following the thermal load, and (3) customized building design for more realistic building energy demand profile.

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Phd Defense
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Nov 27, 2019 - 11:56am
  • Last Updated: Nov 27, 2019 - 11:56am