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Solar Thermal Power Plant Simulation

Conference Paper · October 2011

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Mohammad Abutayeh D. Yogi Goswami

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Title

Solar Thermal Power Plant Simulation

Session

Concentrated Solar for Power Generation and Chemical Processing II (T4A14)

Authors

1. Mohammad Abutayeh, Department of Chemical Engineering, University of South Florida,

Tampa, FL 33620, USA, abutayeh@mail.usf.edu

2. D. Yogi Goswami, Clean Energy Research Center, University of South Florida, Tampa, FL
33620, USA, goswami@eng.usf.edu

3. Elias K. Stefanakos, Clean Energy Research Center, University of South Florida, Tampa,
FL 33620, USA, stefanak@eng.usf.edu

Correspondence

Correspondence concerning this article should be addressed to Mohammad Abutayeh

Citation

M. Abutayeh, D. Y. Goswami, and E. K. Stefanakos, Solar Thermal Power Plant Simulation,

AIChE National Meeting, Minneapolis, MN, October 2011.

Abstract

A detailed model of a real solar thermal power plant has been built using a steady state power
plant simulation software. The plant includes numerous parabolic trough collectors tracking the
sun on a single axis. A heat transfer fluid flows in the focal line of the troughs collecting solar
heat which is transferred to high pressure water from the power block generating high pressure
steam that is sent to a steam turbine to generate power via an attached generator.

In addition, a spreadsheet has been formulated to work in tandem with the steady state power
plant simulation software to model the daily operation of the solar thermal power plant. The
spreadsheet is populated with one minute increment time–stamped operating data where
calculations are carried out to estimate the thermal energy contribution of the warm up and the
cool down transient operations. Solar radiation offsets are calculated based on those transient
heats and then incrementally added to the real solar radiation data to produce effective solar
radiation values.

Data columns of the effective solar radiation, ambient temperature, humidity, wind speed, time
of day, day of year, and other geographical and optical constants are incrementally passed
from the spreadsheet to the simulation program which executes and outputs results back into
the same spreadsheet. Simulation results matched well with plant data including data collected
during warm up and cool down transient operations.

Keywords

Alternative Energy/Fuels, Sustainability, solar modeling, solar power, solar thermal

Introduction

Solar power generation can be accomplished directly via photovoltaic cells (PV) or indirectly
via concentrating solar power systems (CSP). PV technology involves DC current generation
from sunlight using the photoelectric effect. Several solar panels composed of numerous PV
cells generate electricity due to emitted electrons from semiconductors absorbing
electromagnetic solar radiation. CSP technology involves AC current generation using
generators attached to turbines supplied with solar–generated steam. Several sun–tracking
mirrors focus sunbeams onto a small aperture producing immense heat that is used to
generate steam to drive the turbines of conventional Rankine cycle power plants.

At utility scale, CSP systems are currently more deployed than PV systems due to the high
cost, the low efficiency, and the small energy storage capability of PV systems. CSP systems
are also more appealing because they employ the familiar Rankine power cycle and can be
directly integrated into existing power plants. The most economical and commercially available
CSP technology is parabolic trough collector systems (PTC). PTC systems include numerous
parabolic trough mirrors tracking the sun on a single axis. A heat transfer fluid (HTF) flows in
the focal line of the troughs collecting solar heat that is transferred to high pressure water
generating high pressure steam. The solar–generated steam is then used to propel a steam
turbine connected to a generator producing electricity. A simplified schematic of a PTC
Rankine cycle power plant is shown in Figure 1.

Objective

Several computer programs have been developed over the years to model power plant
performance such as GateCycle™, HYSYS™, IPSEpro™, Thermoflex™, and others. These
software codes are geared towards modeling steady state operations and that is usually
considered sufficient for conventional power plants. Solar thermal power plants undergo
lengthy start–up and shut–down operations due to the sporadic nature of solar radiation;
therefore, valid modeling of their performance must address those transient operations.

The start–up operation involves drawing heat from the collected thermal energy in the early
morning hours to warm up the HTF and the metallic elements of its network, such as pipes and
vessels, which have cooled down overnight by losing their heat to the ambient. The shut–down
operation involves exploiting the thermal energy stored in the HTF and the metallic elements of
its network to further the production of steam, and consequently power, beyond sunset.

The purpose of this study is to accurately model the daily performance of a PTC solar thermal
power plant including start–up and shut–down operations. A spreadsheet has been built to
pass data to and receive data from a detailed model of an actual solar thermal power plant.
The model will perform steady state simulations of discrete data received from the spreadsheet
in one–minute increments. The spreadsheet data correspond to real records that have been
conditioned to account for the transient start–up and shut–down operations.

Schematics

A distributed control system (DCS) is constantly collecting and archiving plant data in mass
storage servers. PI ProcessBook™ [1] is the database program used in retrieving plant data so
it can be processed in a spreadsheet. Microsoft Excel™ [2] is the spreadsheet program used
in requesting and obtaining plant data so it can be processed in a modeling program.
IPSEpro™ [3] is the modeling program used in running sequential simulations based on data
supplied by the spreadsheet. A general data flow schematic is outlined in Figure 2.

The solar thermal power plant simulation process can be demystified by tracing the data
streams mapped out in Figure 2. Stream 1 represents the date of the plant operation to be
modeled. Stream 2 represents time–stamped data of direct normal insolation (DNI), ambient
temperature, humidity, wind speed, solar field (SF) availability, and produced power. Stream 2
also includes flow rates, temperatures, and pressures of the water and the HTF going into and
out of the heat exchanger train (HXT). Calculations are then performed in the spreadsheet to
compute direct incident insolation (DII) plus start–up and shut–down heats for each time
increment. Effective DII values representing the solar insolation that is essentially used to
generate power are then generated in the spreadsheet. Stream 3 represents discrete data sets
of date, time, DNI, effective DII, ambient temperature, humidity, wind speed, SF availability,
and temperatures, and pressures of the water going into the HXT. The solar thermal power
plant model is sequentially executed using the discrete data sets of Stream 3 producing results
that are forwarded to the spreadsheet as discrete data sets in Stream 4. Stream 4 represents
discrete data sets of heat collected by the SF and power produced. Stream 4 also includes
flow rates, temperatures, and pressures of water and HTF going into and out of the HXT.

Spreadsheet

The spreadsheet has been designed to retrieve time–stamped operating data in one minute
increments from plant servers upon providing a specific date. This adds up to 1440 data sets,
one set for every minute of the day. Each set includes ambient weather data such as solar
insolation, ambient temperature, humidity, wind speed, as well as other data to be compared to
model output later. In addition, the following constants are included in the spreadsheet:
Longitude, Latitude, Tilt, Orientation, N, L, FL, AW, RD, TZ, CMirror, ηOptical, MHTF, TOperation,
VMetal, ρMetal, and CpMetal.

The preceding data set records and constants represent a complete list of inputs that can be
forwarded to the model for execution; however, the warm up and the cool down transient
operations will not be reflected in that execution. Consequently, solar insolation records will
need to be adjusted to account for heat used in warming up the HTF and the metallic elements
of its network at the beginning of the day and to account for the thermal energy stored in the
HTF and the metallic elements of its network at the end of the day. This adjustment will
produce effective solar insolation records reflecting those transient operations, which will be
forwarded to the model for execution.
The following calculations are carried out for each data set in the spreadsheet to compute the
corresponding DII [4–6]

4 15 9.87 sin 81 7.53 cos 81 1.5 sin 81 (1)

, 0 /60 24
1, /60 0 1
364, /60 0 1 (2)
1, /60 24 365
1, /60 24 365
/60, 0 /60 24
24 /60, /60 0 (3)
/60 24, /60 24
24 1 (4)
sin 0.39795 cos 0.98563 /180 173 (5)
/180 15 12 (6)
sin sin sin cos cos cos (7)
sin cos sin / cos , cos tan / tan
(8)
2 sin cos sin / cos , cos tan / tan
, | | 1
(9)
tan | |
, | | 1
/

cos /2 (10)
1, 1
0, 0 (11)
, 0 1
cos 1 cos cos cos 1 cos (12)
cos 0.0300802842443682 · 0.0938882616103359 · (13)
1 tan (14)

· · · · (15)
· (16)

The following calculations are carried out for the entire data set in the spreadsheet to compute
the overall start–up and shut–down heat loads. Start–up heat is the thermal energy needed to
warm up the entire stock of HTF and the metallic elements of its network at the beginning of
the day to a specified operating temperature. Shut–down heat is the thermal energy exploited
by cooling down the entire stock of HTF and the metallic elements of its network at the end of
the day to a specified operating temperature.
 ⁄3.6 (17)
⁄3.6 (18)
⁄3.6 (19)
⁄3.6 (20)
(21)
(22)

Subscripts: Operation, Start, and End in the above equations refer to minimum operating
temperature, HTF temperature at the beginning of the day or sunrise, and HTF temperature at
the end of the day or sunset, respectively. An HTF temperature of 275 °C is a typical minimum
operating temperature. Therefore, the start–up operation at the beginning of the day involves
circulating the HTF in the SF while bypassing the HXT until the HTF is warmed up from TStart to
275 °C. Conversely, the shut–down operation at the end of the day involves circulating the
HTF through the HXT until the HTF is cooled down from TEnd to 275 °C.

The heat absorbed by the SF is calculated for each data set in the spreadsheet by

· · · (23)

Warm up DII offsets are calculated for each data set in the spreadsheet as follows

, ,
· · ·∆ (24)
0,

Cool down DII offsets are calculated for each data set in the spreadsheet as follows

,
, , · 1 hr
· · · · ·∆ ∆ (25)
0, · 1 hr

Integration limits: 0, ∞, and t in the above equations refer to first, last, and current time
increments, respectively. Superscript: Previous refers to the previous time increment and
accent ^ refers to an average value.

Finally, the effective DII is calculated for each data set in the spreadsheet as follows

(26)
The transient start–up and shut–down heats are now incorporated into these effective solar
insolation records. In other words, DIIEffective represents the power block (PB) supply source of
thermal energy used for power generation.

A screen shot of the beginning part of the spreadsheet is shown in Figure 3. The columns of
the spreadsheet extend horizontally to cover the whole day, one column for every minute. The
yellow region includes the input operating data to be forwarded to the model for execution. The
green region includes the output data resulting from model execution. The grey region includes
the above calculation to determine the effective DII for each data column, which is then written
in the yellow region to be forwarded to the model for execution.

Model

A detailed model of a real solar thermal power plant has been built using IPSEpro™ modeling
software. A simplified schematic of the plant is shown in Figure 4. The PB consists of a two–
stage steam turbine, a cooling tower driven condenser, and a high pressure feed water pump.
The SF comprises numerous parabolic trough collectors with a 51 hectare combined aperture
area tracking the sun on a single North–South axis in the East–West direction. Dowtherm™ A
HTF flows in the focal line of the troughs collecting solar heat that is then transferred to the
process loop via the HXT. The HXT is made up of an economizer, a steam generator, and a
super heater connected in series where the water and the HTF flow in a counter–current
pattern. High pressure water enters the economizer to be heated to near saturation then
evaporated to steam in the steam generator then turned into superheated steam in the super
heater before it is forwarded to the steam turbine. Hot HTF coming from the SF enters the
super heater on the shell side, then the steam generator on the tube side and the economizer
on the shell side giving up heat to the process loop to produce the needed high pressure
steam before it is pumped back to the SF.

The model would normally calculate DII using input DNI, time, date, and cleanliness records;
however, this approach overlooks the warm up and the cool down transient operations.
Consequently, the equation relating DII to DNI in the model is deactivated whereas the
DIIEffective calculated above is input to the model. This will render the DNI records forwarded to
the model useless or dummy values. The solution algorithm of the model is extensive due to
the large number of equations characterizing all the process equipment. In a nutshell, the HTF
mass flow required to attain a set point temperature out of or into the SF is calculated knowing
DIIEffective as well as PTC and piping characteristics, that is dimensions plus heat and pressure
loss coefficients. The water mass flow is calculated knowing ambient conditions as well as the
many characteristics of the process equipment: steam turbine, condenser, cooling tower, feed
water pump, and HXT.

Results

Metrological and operational data from a newly built PTC power plant was obtained for four
days with smooth uninterrupted operation. The plant does not include any thermal energy
storage (TES) system; therefore, it was on during sunlight hours plus sometime afterwards
during the cool down operation and it was off the rest of the day. HTF heat losses during the
night are not uniform as they depend on the location within the SF. HTF present in the header
pipes and vessels loses only a small amount of its heat due to good insulation, while HTF
present in the absorber tubes of the PTC assemblies loses a significant amount of its heat due
to exposure. The normal operation of the plant involves starting the HTF pump about an hour
before sunrise to homogenize the HTF temperature throughout the SF. PTC assemblies start
tracking the sun upon sunrise adding heat to the HTF which is circulating throughout the SF
but bypassing the HXT to bring up its temperature to the operating point. Once the HTF
reaches the operating temperature of 275 °C, it starts to run through the HXT to make steam.
The DCS aims to attain a 395 °C HTF temperature out of the SF by manipulating its residence
time in the absorber tubes running in the focal line of the PTC assemblies via regulating its
flow rate. This thermal energy flow into the HXT is what determines how much steam gets
made and therefore how much power is produced. This operation continues until sunset when
the thermal energy source heads off and the HTF begins to cool down. The plant keeps
running afterwards until the HTF cools down to 275 °C before it is shut.

Solar insolation records are shown in Figures 5−8. DNI represents normal insolation
encountered during daylight hours while DII represents the fraction of DNI that is incident on
the absorber tube. DII is obtained by multiplying DNI by several efficiency factors accounting
for mirror cleanliness, reflectivity, shadow effects, and incident angle as seen earlier. Effective
DII is a variation of DII representing the supply source of the thermal energy actually used for
power generation as seen earlier. Effective DII lags DII during start–up to account for warm up
heat, equals DII during normal operating period, and leads DII during shut–down to account for
cool down heat. This is consistent with the PTC power plant records where, on good sunny
days, start–up usually occurs about one hour after sunrise and production usually continues for
about two hours after sunset. Daylight savings time for 2011 started on March 13th, hence the
apparent one hour discrepancy between Figure 5 and Figures 6−8.

HTF temperature coming from the SF correlates with the amount of steam produced by the
HXT; therefore, it indirectly gauges electric power generation. HTF temperature records
coming from the SF and heading for the HXT are shown in Figures 9−12. Plant records outside
of the operating range do not represent true values for comparison with model predictions
since the HTF is not circulating; however, they demonstrate the small amount of HTF heat loss
from the insulated header pipes and vessels. On the other hand, the sudden drop of HTF
temperature upon starting HTF circulation is due to the much cooler HTF coming from the
exposed absorber tubes of the PTC assemblies in the SF. SS Model records correspond to
results obtained by running a model that does not employ the DIIEffective method outlined above;
therefore, it is dubbed steady state (SS) model. In contrast, QT Model records correspond to
results obtained by running a model that employs the DIIEffective method outlined above;
therefore, it is dubbed quasi transient (QT) model. QT Model records are clearly a lot closer to
plant data than SS Model records indicating a significant improvement to model simulations
gained by implementing the proposed DIIEffective method outlined above.

Gross electrical energy output is obtained by integrating the produced power over the entire
day. Table 1 demonstrates how close the model predictions of gross electrical energy
production are to the actual plant data for the four simulated days.

Conclusion

A novel technique has been developed to simulate the performance of solar thermal power
plants. The technique involves importing time–stamped operating data into a spreadsheet,
adjusting real solar insolation records to produce effective insolation records that reflect
transient start–up and shut–down operations, incrementally passing the adjusted records to a
SS model of the solar thermal power plant to be executed, and forwarding simulation results to
the spreadsheet. Simulation results obtained by applying this technique matched well with the
plant data, unlike simulation results obtained without solar insolation records adjustment,
including the data collected during the warm up and cool down transient operations.

Slight discrepancy is still present between the plant data and model results during the warm up
and cool down transient periods requiring further investigations. This discrepancy could be
attributed to a physical phenomenon not accounted for in the model or an inefficiency in plant
operation or process control.

Notation

A PTC aperture area, m²

Altitude altitude angle, radians
ASH annual solar hour, hour
AT altitude transverse, radians
Availability PTC loops operating, percent
AW PTC aperture width, m
Azimuth azimuth angle, radians
C PTC mirror cleanliness, percent
CD cool down
Cp heat capacity, kJ/kg–°C
CSP concentrating solar power
Day day of year, day
DCS distributed control system
Declination declination angle, radians
DII direct incident insolation, W/m²
DNI direct normal insolation, W/m²
FL PTC focal length, m
h enthalpy, kJ/kg
HA hour angle, radians
Hour hour of day, hour
HTF heat transfer fluid
HXT heat exchanger train
IA incident angle, radians
IAM incident angle modifier, percent
L PTC length, m
Latitude latitude angle, radians
Longitude longitude angle, degrees
M mass, kg
N PTC count
Orientation PTC orientation angle, radians
PB Power block
PTC parabolic trough collector
PV photovoltaic
q heat flow, W
Q heat, W−hour
QT quasi transient
RD PTC row distance, m
SA shadow argument
SD solar day, day
SF solar field
SH solar hour, hour
SS steady state
t time, hour
T temperature, °C
TC time correction term, minute
TES thermal energy storage
Tilt PTC tilt angle, radians
TZ time zone, hour
V volume, m³
WU warm up
Δt time increment, hour
η efficiency, percent
ρ density, kg/m³

Literature Cited

1. PI ProcessBook – Part of PI Software Development Kit. Version 3.2. OSIsoft LLC; San
Leandro, CA: 2010.

2. Excel – Part of Microsoft Office Professional Plus. Version 2007. Microsoft Corporation;
Redmond, WA: 2006.

3. IPSEpro – Integration Process Simulation Environment. Version 4.0. SimTech Simulation

Technology; Graz, Austria: 2003.

4. Sandia National Laboratories; Albuquerque, NM: 1995.

5. Cohen GE, Kearney DW, Kolb GJ. Final Report on the Operation and Maintenance
Improvement Program for Concentrating Solar Power Plants, SAND99-1290. Sandia National
Laboratories; Livermore, CA: 1999.

6. Rheinländer J, Bergmann S, Erbes MR. Technical and Economic Performance of Parabolic

Trough Solar Power Plants – A Computational Tool for Plant Feasibility Studies. 14th
SolarPACES International Symposium on Concentrated Solar Power and Chemical Energy
Technologies; Las Vegas, NV: 2008.

Table Legends

1. Gross electricity production in MWh

 Date Actual Output Model Prediction Difference
March 11, 2011 658 685 4%
March 18, 2011 653 679 4%
March 22, 2011 561 596 6%
March 25, 2011 553 570 3%

Figure Legends

1. PTC Rankine cycle power plant

2. Data flow schematic
3. Input–output spreadsheet
4. IPSEpro model schematic
5. Insolation on March 11, 2011
6. Insolation on March 18, 2011
7. Insolation on March 22, 2011
8. Insolation on March 25, 2011
9. HTF temperature on March 11, 2011
10. HTF temperature on March 18, 2011
11. HTF temperature on March 22, 2011
12. HTF temperature on March 25, 2011

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