Nicholas Baker
Mentors: Curtis Robbins (Desert Research Institute) & Alan Gertler (Desert Research Institute)
Table 2 – Annual GHG Emissions Matrix
This research project focuses on creating a climate
impact assessment methodology to evaluate the
effectiveness of deploying renewable energy
technologies to offset greenhouse gas (GHG)
emissions. The methodology has been applied to a
test case and refined for implementation with other
types of renewable energy generation technologies to
determine equivalent GHG emissions of each. The
test scenario will be a thermoelectric generator (TEG)
that converts heat provided by an air-based solar
collector into useful electricity.
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BIO – Biomass
PV – Photovoltaic (traditional solar power)
CSP – Concentrated Solar Power (solar
tower)
GEO – Geothermal
HYDRO – Hydropower
NUC – Nuclear power
LFO – Liquid Fuel Oil
LPG – Liquid Propane Gas
NG – Natural Gas
HP – Heat Pump
WC – Wood Chips
SC – Solar Collector
GHGEF – Greenhouse Gas Emission Factor
The 54 m2 solar collector (Fig. 1) is located at the
Desert Research Institute (DRI) campus in Reno, NV.
Performance data has been collected to perform an
energy balance on the system and used to simulate
annual performance. GHG emissions offset by this
form of energy generation are then estimated from
the results of the simulations. The combination of a
dynamic computer-based energy model and
empirical data has allowed for determining the
feasibility & climate change impacts of using TEGs
with a solar air collector for off-grid power that can be
extended to other renewable energy systems.
The methodology’s framework required four steps:
1. Determine GHG emissions of both conventional
& renewable heating and power generation
technologies with a normalized value.
2. Define building characteristics including heating
& electrical loads, orientation, and heat loss
rate.
3. Build & test proposed thermoelectric generator
(TEG) technology to determine GHG emissions
offset by its integration with existing building
systems.
4. Generate model based on empirical data to
simulate ideal annual system performance.
GHG Emission Factor [g-CO2eq/kWhe]
Nomenclature
Fig. 1 – Air-based solar collector located at
DRI’s Reno campus.
Fig. 2 – Power generation GHG emission
factors by type - # of sources.
GHG Emission Factor [g-CO2eq/kWhth]
The first step was implemented by conducting a
literature search. Over 800 reports found in an
online database outlining GHG emissions factors
(GHGEFs, g-CO2equivalent/kWh) for power
generation technologies [1]. Significantly less
sources were found for heating & cooling
technologies. Figures 3 & 4 show high, low, and
normalized values for each type of technology.
Fig. 3 – Heating/Cooing generation GHG
emission factors by type - # of sources.
The second step included defining the building’s
characteristics
(heat
loss
rate
Pspecific,
location/orientation,
and
indoor
set
point
temperature). These values were based off of
measurements whenever possible. It was assumed
that the building would be occupied 360 days/year,
and have a minimal heating & electrical loads.
Table 1 outlines the assumptions made when
determining the building’s GHG emission factor
matrix.
The GHGEF allowed for a matrix-style decision chart to examine annual impacts of various
system configurations (Table 2). These values were found by multiplying the GHGEF by the
annual heating & electrical loads. For the third step of the climate-assessment methodology,
a weighted decision matrix was generated based off information provided by the TEG
manufacturers (Table 3). Cost efficiency [$/We] was the highest weighted value to ensure the
most power output for the least cost. Based off of known temperatures in the solar collector, a
Used Power Density [UPD] and Maximum Power Density [MPD] were defined based on
known & expected temperatures in the solar collector. A final score was assigned to each
TEG, and the highest scoring modules were purchased.
Seasonal performance of both the
air & water heating functions of the
solar collector are given in Fig. 7 &
8. These values are generally
characterized in system efficiency
vs direct solar radiation, and have
been determined from three days
of data collection in each
operational mode (direct air
heating in winter and water
heating only in summer).
Fig. 7 – Solar Collector
efficiency vs Solar Radiation.
Fig. 8 – Heat Exchanger efficiency vs
Solar Radiation.
Fig. 9 – TEG Output Power density vs Temperature
difference, Solar Collector.
Table 3 –TEG Purchase Decision Matrix
The workshop on which the DRI solar collector
is built has the capacity to test various forms of
heat generation. An 18.6 m2 section of floor
allows for thermal storage in the form of heat in
winter & evaporated-cooled air in the summer.
This thermal storage allowed for two TEG
configurations; the first with the heat supplied by
the air stream of the solar collector and removed
by chilled water (“Solar Collector”), the second
with the heat supplied by water heated by the
collector and removed by the floor’s cooled
thermal storage (“Air Floor”). Figure 4 shows air
temperatures at the location of the TEG system
over a typical day. Figure 5 shows results from
summer testing of the cooled floor area
(Columns 4-12) vs. an uncooled control area
(Columns 1-3) .
Once the configurations had been determined
and the materials had been purchased, the
TEG heat exchanger assembly was built.
Figure 6 shows an exploded view of the TEG,
with the air flow directed through the fins
located on the top of the assembly and the
water flow directed through the hollow
aluminum block on the bottom of the assembly.
Figure 7 shows the completed assembly prior to
installation & testing.
Fig. 10 – TEG Output Power density vs Temperature
difference, Air Floor.
Fig. 4 – Typical daily Solar Collector
temperatures.
Power densities for both the solar
collector & air floor configurations are
shown as a function of temperature
difference across the TEGs in Fig. 9 &
10.Manufacturer-specified performance
of each has been used for comparison.
Based on temperatures & flow rates of
each configuration, an idealized system
performance model was generated in the
EES software package. The EES values
represent how the system could be
expected to perform under ideal cases.
This also shows a need for improved
TEG efficiency for future applications.
During testing, it was observed that direct conduction
through the thermoelectric modules themselves
substantially diminished the temperature difference
across them, leading to lower power outputs once the
system had reached steady state. Fig. 11 & 12 show
both the hot and cold side temperatures vs time for
the solar collector & air floor configurations.
Fig. 11 – Hot and Cold TEG surface
temperatures, Solar Collector.
Fig. 5 – Typical daily Air Floor temperatures.
Fig. 12 – Hot and Cold TEG surface
temperatures, Air Floor.
Due to the inordinately high values
for GHGEFs, low confidence can
be placed in these results.
To determine system scaling available and GHGs
offset traditional coal-generated electricity, the
available area for large-scale installations was
multiplied by the maximum power density for each
configuration. Table 4 shows the GHGEF for the TEG
from empirical data. Future work could include
validation with further testing.
Table 4 –TEG Scaled Performance & GHG emission factors
Table 1 – Assumptions used in GHG matrix
calculations
SolidWorks Student Edition.
For Academic Use Only.
Fig. 6 – TEG assembly schematic
exploded view.
Fig. 7 – Instrumented TEG assembly.
[1] Database courtesy: Lifecycle Harmonization Project, http://en.openei.org/apps/LCA/. Accessed
6/20/2012.
[2] Engineering Equation Solver f-chart Software, McGraw-Hill.
Further references available upon request.
Many thanks are in order for the many people who made this research possible: Curtis Robbins, for
always making sure I gave my best; Alan Gertler, for his editorial and advisory expertise; Christopher
Glover for help getting my experiment set up and writing the EES model; and of course my wife,
Kristen Baker, for her support & for letting me stay up way past my bedtime to get things done on
time.