Energy System Modelling

8/8/2019 Energy System Modelling

1/25

CASE STUDY

Solid Village is a small village in Sitio Tagbac Barangay San Jose Antipolo City,Philippines. This analysis investigates the options for providing electricity to the village

using renewable energy source such as wind, solar, or diesel power. The results show theimpact of different assumptions about the wind resource, fuel price, and required systemreliability.

OBJECTIVE

The goal of the study is to be able to determine the optimum configuration of Renewableenergy source such as wind and solar in term of net present cost (NPC) that will provideelectricity to a small village in Antipolo City.

PHYSICAL MODELLING

The system is composed of a primarily load with 300 KWh/day requirement. The villagehas a 23 KW peak and configured in such a manner, that power system will come from avariety of sources such as solar, wind and diesel power generator, see figure 1

Figure 1

Both Photovoltaic solar power and wind generator produces a Direct Current (DC) reasonwhy the author of his study placed a converter that will convert the Direct Current toAlternating Current (AC) and vise versa. The designer also placed an energy storagedevice that will store power in times wherein the generators produced surplus power anddischarge this power during peak hours. The study was configured and analyzes using theenergy optimization model for micropower system called HOMER.


2/25

RESOURCE DATA

SOLAR RESOURCE

The solar resource data used in the analysis is an actual imported file from the studymade by Tony Jimenez. His study provides relatively similar information needed in thismodel.

Figure 2 Global Horizontal Radiation

Figure 2 shows the Global Horizontal Radiation, where in it illustrates the

relationship of daily radiation (KWh/m 2 /d) with each month of the year. The graph alsoprovides the trends for clearness Index.

The clearness index is a dimensionless number between 0 and 1 indicating the fraction of the solar radiation striking the top of the atmosphere that makes it through the atmosphereto strike the Earth's surface.

Typical values of K t range from 0.25 (a very cloudy month) to 0.75 (a very sunnymonth).


3/25

The following equation defines the monthly average clearness index:

where:

H ave is the monthly average radiation on the horizontal surface of the earth[kWh/m 2 /day]

Ho,ave is the extraterrestrial horizontal radiation, meaning the radiation on ahorizontal surface at the top of the earth's atmosphere [kWh/m 2 /day]

Figure 3 Data Map of the Solar resource

A DMap (data map) is a type of graph showing one year of hourly data. With time of dayon one axis and day of the year on the other, each hour of the year is represented by a

rectangle which is colored according to the data value for that hour. The DMap allowsdaily and seasonal patterns to be seen much more easily than would be possible with asimple time series plot.

Scaled Annual average is 4.869 kWh/m 2 /day in this study.


4/25

Figure 4 Scaled data Monthly Averages

Figure 4 shows the relationship of the monthly average of KW/m 2 each monthproviding information on the maximum and minimum value.

Figure 5 Scaled data daily profile in each month

Figure 5 illustrate the scaled data daily profile showing that the highest KW/m 2 reading is between 10:00 in the morning to 2:00 in the afternoon.


5/25

WIND RESOURCE

The wind file was generated using this software, HOMER's wind data generator. Thedaily profile is based upon one day of measurements taken on site. Other enteredparameters are typical for the region. The wind file used in this analysis is based on

inadequate wind data. Better wind data should be obtained before proceeding further.

Figure 6 Wind Resource in monthly basis

Figure 7 Wind Speed Variation with height

Ground-level obstacles such as vegetation, buildings, and topographic features tend toslow the wind near the surface. Since the effect of these obstacles decreases with height

above ground, wind speeds tend to increase with height above ground. This variation of wind speed with height is called wind shear . Wind energy engineers typically modelwind shear using one of two mathematical models, the logarithmic profile or the powerlaw profile; in this study the author used the Logarithmic profile. Figure 8, 9, and 10illustrate the Wind resource profile in daily and monthly profile


6/25

Logarithmic profile

The logarithmic profile (or log law) assumes that the wind speed is proportional to thelogarithm of the height above ground. The following equation therefore gives the ratio of

the wind speed at hub height to the wind speed at anemometer height:

where:

zhub the hub height of the wind turbine [m]zanem the anemometer height [m]z0 the surface roughness length [m]v(zhub) wind speed at the hub height of the wind turbine [m/s]v(zanem) wind speed at anemometer height [m/s]ln(..) the natural logarithm

The surface roughness length is a parameter that characterizes the roughness of thesurrounding terrain. Below is a table of representative surface roughness lengths takenfrom Manwell, McGowan, and Rogers:

Table1. Surface roughness lengths Terrain Description z 0

Very smooth, ice or mud 0.00001 mCalm open sea 0.0002 mBlown sea 0.0005 mSnow surface 0.003 mLawn grass 0.008 mRough pasture 0.010 mFallow field 0.03 mCrops 0.05 m

Few trees 0.10 mMany trees, few buildings 0.25 mForest and woodlands 0.5 mSuburbs 1.5 mCity center, tall buildings 3.0 m


7/25

Figure 8 Wind speed per month in a year

Figure 9 Scaled data Monthly Averages of wind resources

Figure 10 Scaled data daily profile of wind resources

Scaled annual average is 3 m/s in this study


8/25

LOAD

PRIMARY LOAD

Primary load is electrical load that must be met immediately in order to avoid unmet load.

Unmet load is electrical load that the power system is unable to serve. It occurs when theelectrical demand exceeds the supply. For each system, HOMER calculates the totalunmet load that occurs over the year, as well as the unmet load fraction. By default,HOMER considers infeasible any system that experiences unmet load, but you canchange that by entering a non-zero value for the maximum annual capacity shortage. Thestudy uses HOMER's load data generator.

Figure 11 Graphical illustration based Table2 (HOMER Generated)

Figure 11 provide the Daily Profile of the primariy load based on the 24 hours loadshown in Table2. It also provides the data map (DMAP) of the load each month as wellas the seasonal profile for the whole year.

Table 2 Daily Profile of the primary load Hour Load (kW) Hour Load (kW)

00:00 - 01:00 3.712 12:00 - 13:00 12.99301:00 - 02:00 3.712 13:00 - 14:00 12.99302:00 - 03:00 3.712 14:00 - 15:00 14.84903:00 - 04:00 3.712 15:00 - 16:00 14.84904:00 - 05:00 3.712 16:00 - 17:00 14.84905:00 - 06:00 14.849 17:00 - 18:00 22.50606:00 - 07:00 14.849 18:00 - 19:00 22.50607:00 - 08:00 3.712 19:00 - 20:00 22.50608:00 - 09:00 3.712 20:00 - 21:00 22.50609:00 - 10:00 9.281 21:00 - 22:00 22.50610:00 - 11:00 9.281 22:00 - 23:00 14.84911:00 - 12:00 12.993 23:00 - 00:00 14.849

Table2 shows the daily profile shape is based on the data gathered from the investigation.


9/25

Typically, small village residential load profiles peak in the evening. It is important totry to get a good estimate of the peak load because this will affect the size of thegenerator and the inverter.

Table 3. Baseline and scaled data Baseline Scaled

Average (kWh/d) 300 300Average (kW) 12.5 12.5

Peak (kW) 22.5 22.5Load factor 0.555 0.555

The baseline data is the set of 8,760 values representing the average electric demand,expressed in kW, for each hour of the year. There are two ways to create baseline data:you can use HOMER to synthesize data, or you can import hourly data from a file. In thisstudy the author performed an hourly data importation (see table2).

Scaled Annual average is 300 kWh/day in this study.

Figure 12 Scaled data daily profile in each month

Figure12 illustrate the scaled data daily profile in each month showing that thehighest/peak KW demand is between 18:00 Hr to 20:00 Hr.


10/25

Figure 13 Scaled data Monthly Averages

Figure13 shows the relationship of the monthly average of KW each monthproviding information on the maximum and minimum value.


11/25

COMPONENTS

PV ARRAY

Table 4. Cost Table of PV

Size (kW) Capital ($) Replacement ($) O&M ($/yr)1.000 6900 6900 02.000 13800 13800 0

Figure 14 Cost Curve of the PV

In the cost table (table 4), enter the PV cost curve (figure 14), meaning the way the costvaries with size. Typically this requires only a single row since PV costs are oftenassumed to be linear with size. In the data shown above, the capital cost of PV panels isspecified at $6,900/kW and the replacement cost is specified at $6,900/kW. Theoperating and maintenance cost is specified as zero. Price and lifetime are based onfeasibility report made by other researchers.

HOMER models the PV array as a device that produces dc electricity in direct proportion

to the global solar radiation incident upon it, independent of its temperature and thevoltage to which it is exposed. HOMER calculates the power output of the PV arrayusing the equation

Where,f PV is the PV derating factor,YPV the rated capacity of the PVarray (kW),

IT the global solar radiation (beam plus diffuse) incident on the surface of the PVarray (kW/m2), andIS is 1 kW/m2, which is the standard amount of radiation used to rate the capacityof the PV array.


12/25

Performance data are commonly used defaults.Output current DCLifetime (years) 25

Derating Factor(%) 90Slope (degrees) 9Azimuth (degrees W of S) 0Ground reflectance (%) 20

WIND TURBINE GENERATOR

The power curves (Figure15) and table5 are system generated by HOMER as pergeneric 10KW Wind turbine.

Table5. Generic Information for Generic 10KW (G10)Wind Speed Power Output Wind Speed Power Output

(m/s) (kW) (m/s) (kW)0.00 0.000 13.00 9.8103.00 0.000 14.00 10.0004.00 0.190 15.00 10.0005.00 0.370 16.00 9.6306.00 0.930 17.00 8.8907.00 1.850 18.00 7.9608.00 3.330 19.00 7.2209.00 5.190 20.00 6.850

10.00 7.040 24.00 6.67012.00 9.440

Figure 15 Power Curve for Generic 10KW (G10) Wind Turbine

Figure 15 illustrate the Power Curve for Generic 10KW (G10) Wind Turbine. Thepower curve is the most important property of the wind turbine. It describes the amountof power the turbine produces versus the wind speed at hub height.


13/25

Table6. Cost Table for G10

Quantity Capital ($) Replacement ($) O&M ($/yr)1 27000 23000 3002 50000 43000 3503 73000 63000 4004 96000 83000 450

Figure 16 Cost Curve for Generic 10KW (G10)

Price data based on information in the feasibility report and manufacturer cost data forwind turbines of these sizes.

Note that the marginal cost of additional turbines is somewhat less than the cost of thefirst turbine. This reflects cost savings involved in shipping, installing and maintainingmultiple wind turbines. This highlights HOMER's ability to use arbitrary cost curves forthe components.

Lifetime (yrs) 15 hub height (m) 19.9526

Lifetime the number of years the turbine is expected to last before it requiresreplacement

Hub height the height above ground of the hub (the center of the rotor)

The wind turbine hub height is the height above ground at which the rotor sits. Hubheights typically range between 25m (for smaller wind turbines, 50 kW or less) and 100m

(for large, multi-megawatt wind turbines). Wind speeds tend to increase with heightabove ground, so if the hub turbine is not the same as the anemometer height, HOMERadjusts the wind speed data accordingly.


14/25

GENERATOR

Size (kW) Capital ($) Replacement ($) O&M ($/hr)8.000 6500 5500 0.200

10.000 8125 6875 0.25050.000 40625 34375 1.250

100.000 81250 68750 2.500150.000 162500 137500 5.000

Table 7. Cost Table for Generator

Figure 17 Cost Curve for Generator

Price and performance data are based on typical default values used by theanalyst. The initial cost is 20% higher than the replacement cost to account for ancillaryequipment such as controllers, fuel tanks, etc, that would not need to be replaced.

In the cost table (table 7), enter the generator cost curve (figure 17), meaning theway the cost varies with size. If you have a particular generator in mind, you can enter itssize and cost.

For this size of load, the lowest cost system typically includes a fossil fuelgenerator. A zero size is included so that HOMER will consider all-RE systems. Typicaldesign practice mandates that the diesel be sized to cover the largest anticipated load. Inthis case the peak hourly load is 23 kW. A 25 kW diesel is considered in order to ensurean adequate safety margin. Figure 18 shows the fuel curve of diesel while Figure 19shows the fuel properties.

Other PropertiesLifetime (operating hours) 15000Minimum Load Ratio (%) 0


15/25

Figure 18 Fuel Curve of diesel

Figure 19 Diesel Fuel Properties

Variable Description

Lower heating value the energy released per kg of fuel consumedDensity Density in kg/m3 (the density of water is 1000 kg/m3)Carbon content the mass-based carbon content of the fuelSulfur content the mass-based sulfur content of the fuel

BATTERIESTable 8. Cost Table for batteries

Quantity Capital ($) Replacement($)

O&M ($/yr)

1 1200 1100 50.002 2400 2200 100.00

Figure 20 Cost Curve for batteries


16/25

Battery information given in the feasibility report was sketchy and contradictory.Assumed the use of "marine" batteries, 20% added to initial cost to account for purchaseof wires, racks, etc, that do not need replacing when the batteries wear out.

Figure 21 Capacity Curve and Lifetime cure respectively for Surrette 6CS25P

The capacity of a battery is defined as the amount of energy that can be withdrawn fromit starting from a fully-charged state. But the capacity of a battery depends on the rate atwhich energy is withdrawn from it. The higher the discharge current, the lower thecapacity. One can create a capacity curve by measuring a battery's capacity at severaldifferent constant discharge currents.

In a lifetime test, the tester subjects the battery to repeated regular charge and dischargecycles. Each cycle, the battery is discharged down to a certain depth of discharge, thenfully charged again. The lifetime test determines how many such cycles the battery canwithstand before it needs replacement.

Manufacturers perform a series of these tests at different depths of discharge to create thebattery's lifetime curve. But HOMER also plots the lifetime throughput, which itcalculates for each point in the lifetime curve using the following equation:


17/25

Qlifetime,i = the lifetime throughput [kWh]

f i = the number of cycles to failure

di = the depth of discharge [%]

qmax = the maximum capacity of the battery [Ah]

Vnom = the nominal voltage of the battery [V]

HOMER plots these values as black diamonds on the lifetime curve (using the right-hand y-axis). Their values typically show only a weak dependence on the depth of discharge.HOMER's simulation logic makes the simplifying assumption that the lifetimethroughput is independent of the depth of discharge. The horizontal black line in the

lifetime curve shows the specified value of lifetime throughput. HOMER draws the lineonly across the allowable range of depth of discharge.

INVERTER

Table 9. Cost Table for InverterSize (kW) Capital ($) Replacement ($) O&M ($/yr)10.000 12500 12500 100

Figure 22 Cost curve of Inverter

The report listed a 10 kW inverter as costing PHP 500,000. This seems a little high.For other sized inverters assumed a cost of $1250/kW (PHP 50,000/kW). Performancedata inputs are default values used by the analysts.

Lifetime (years) 20Efficiency 90


18/25

ECONOMICS

The 8% real interest rate represents a typical commercial rate.

The 20 project lifetime is from the feasibility report. Typical project lifetimes are

20 - 30 years The $6000 system fixed capital costs is from the feasibility report. It represents

balance of system and distribution system costs that cannot be allocated to aspecific component.

CONSTRAINTS

Maximum annual capacity shortage (%) = 0

The maximum allowable value of the capacity shortage fraction, which is thetotal capacity shortage divided by the total annual electric load.

Minimum renewable fraction (%) = 30

The minimum allowable value of the annual renewable fraction.

SENSITIVITY VARIABLES

1. Solar resources scaled annual averages considered in this study are as follows:

1. 4.869 kWh/m2

/day2. 5 kWh/m 2 /day3. 6 kWh/m 2 /day

2. Wind resources scaled annual averages considered in this study are as follows:1. 3 m/s2. 4 m/s3. 5 m/s

3. Diesel price considered in the model are as follows:1. 0.2 $/L

2. 0.4 $/L3. 0.6 $/L4. 0.8 $/L5. 1.0 $/L


19/25

RESULT AND DISCUSSION

OPTIMIZATION

The variables considered in this optimatization process are as follows (the rest arediscussed in the previous section):

1. Global Solar equal to 6 KWh/m 2 /d2. Wind Speed equal to 5 m/s3. Diesel price of 0.8 $/L

Based on the optimization result the most optimum energy system configuration in termof net profit cost is shown in figure 23.

Figure 23 Optimization result

The system is composed of Wind/Dsl/Battery and has a total capital cost of $496,563.The Operating cost is said to be $28,761/year with the Total Net Present Cost (NPC) of only $803,580. The cost of Energy (COE) is $0.687 per kWh having a renewable fractionof 86%. The total diesel consumption is 12,583 liters and the diesel generator will operatefor 1,879 hours.

The cash flow summary is presented on figure 24 and table 10 shows the breakdown of the Net present cost tabular form. Based on the data, the Generic 10KW wind turbineconstitute 50% of the total NPC of the system, $ 349000, and is primarily due to capitalcost brought by installing the said renewable source of energy.

Figure 24 Cash flow of the optimum system configuration


20/25

The second factor that contributes to the total NPC of the system is the cost by installingand operating the Surrette 6CS25P Battery which is $155,174, while Diesel is $155,174which is third in the chart. The total Net Present Cost (NPC) of the energy system is$803,580.

Table 10Component Capital ($) Replacement($)

O&M ($) Fuel ($) Salvage ($) Total ($)

Generic10kW

349,000 95,518 10,675 0 -14,748 440,445

Diesel 20,313 17,049 12,536 107,455 -2,179 155,174Surrette6CS25P

90,000 45,772 40,030 0 -11,043 164,760

Converter 31,250 6,705 2,669 0 -3,422 37,201Other 6,000 0 0 0 0 6,000System 496,563 165,044 65,910 107,455 -31,392 803,580

The result of the model varies considerably on the sensitivity of the variables the authorof this study programmed. As mentioned earlier there are three (3) sensitivity variablesnamely solar resources scaled annual averages, wind resources scaled annual averagesand Diesel price influenced the choice for the optimum system configuration.


21/25

SENSITIVITY ANALYSIS

In this section, the different configuration is discussed based on the specified sensitivityvariable. Figure 25, illustrate the relationship of Wind speed in m/s (x-axis) with globalsolar in KWh/m 2 /d (y-axis) at diesel price of 1$/L.

Figure 25 Optimum system type at diesel price equal 1$/L

The Figure25 shows that at 1$/L price of diesel the optimum configuration is composedof PV/Dsl/Battery and Wind/Dsl/Battery configuration. At wind speed lower than 3.85m/s and diesel price of 1$/L the PV/Dsl/Battery configuration is the best choice in termof the Net present cost, while if the wind speed start to increased higher than 3.85 m/s theWind/Dsl/Battery configuration will be more suitable system type.

The net present cost is superimposed in the figure 25 showing that as the wind increasedand favors the configuration of the Wind/Dsl/Battery the Net profit cost also decreased.From a value of $ 966,716 it lowers to 828,949.

Figure 26 provide an example in the region that favor the PV/Dsl/Battery, you can seethat this configuration will have a $986,880 NPC value at wind speed of only 3.79m/sand Global solar of 5.49 kWh/m 2 /d. The least configuration considered with highest NPCbased on the specified resources values is the Wind/PV/Dsl with $1,363,363.


22/25

Figure 26 Interpolated Values of PV/Dsl Battery

Figure 27 provide an example in the region that favor the Wind/Dsl/Battery, you can seethat this configuration will have a $921,253 NPC value at wind speed of only 4.33m/sand Global solar of 5.62kWh/m 2 /d. The least configuration considered with highest NPCbased on the specified resources values is the Wind/PV/Dsl with $1,333,017.

Figure 27 Interpolated Values Wind/Dsl/Battery


23/25

3.0 3.5 4.0 4.5 5.0

5.0

5.2

5.4

5.6

5.8

6.0

G l o b a l

S o

l a r (

k W h / m / d )

Optimal System Type

Wind Speed (m /s)

943,401942,079

931,710

912,474912,474

912,474

803,580803,580

803,580

System Types

PV/Dsl

Wind/Dsl

Wind/Dsl/Battery

SuperimposedTotal NPC ($)

FixedDiesel Price = $0.8/L

Figure 28 Optimum system type at diesel price equal 0.8$/L

The Figure28 shows that at 0.8$/L price of diesel the optimum configuration is composedof three (3) configuration namely PV/Dsl, Wind/Dsl and Wind/Dsl/Battery configuration.At wind speed lower than 3.85 m/s and diesel price of 0.8$/L the PV/Dsl configuration isthe best choice in term of the Net present cost, while if the wind speed start to increasedbetween 3.85 and 4.15 m/s the Wind/Dsl configuration will be more suitable that that of PV/Dsl. If the wind speed increased higher that 4.15 m/s the optimum configurationsystem is the Wind/Dsl/Battery. The Net Profit Cost decreased from $942,079 to$803,580 relative to the increased in wind speed.

Figure 29 provide an example in the region that favor the PV/Dsl, you can see that thisconfiguration will have a $937,867 NPC value at wind speed of only 3.81m/s and Globalsolar of 5.41 kWh/m 2 /d. The least configuration considered with highest NPC based onthe specified resources values is the Wind/PV/Dsl with $1,313,203.

Figure 29 Interpolated Values of PV/Dsl


24/25

Figure 30 provide an example in the region that favor the Wind/Dsl, you can see that thisconfiguration will have a $908,455 NPC value at wind speed of only 4.05 m/s and Globalsolar of 5.66 kWh/m 2 /d. The least configuration considered with highest NPC based onthe specified resources values is the Wind/PV/Dsl with $1,301,125.

Figure 30 Interpolated Values of Wind/Dsl

Figure 31 provide an example in the region that favor the Wind/Dsl/Battery, you can seethat this configuration will have a $843,480 NPC value at wind speed of only 4.65 m/sand Global solar of 5.61 kWh/m 2 /d. The least configuration considered with highest NPCbased on the specified resources values is the Wind/PV/Dsl with $1,272,630.

Figure 31 Interpolated Values of Wind/Dsl/Battery


25/25

CONCLUSION

As the price of the diesel continue to escalate it is evident that both private and publicinterest on renewable energy source increase relatively. The result shows the impact of different inputs in the optimization process also have an impact to that of the calculationof the Net Present Cost of the energy system configuration. In this case, the variablesconsidered for sensitivity analysis are solar resources scaled annual average, Windresources scaled annual averages, and diesel price.

Therefore, the choice of system configuration relies on the accuracy of the data provided.Though this case study stated that Wind/Dsl/Battery energy configuration is the optimumenergy system because it has the least NPC among other system. The author of this studyrecommends that data should be re-stated/re-evaluated in a more accurate and extensivedata measurement in order to model and reflect the location true condition.

Documents

Energy System Modelling