Read wind speed, wind turbines, battery banks,
inverter specification
Annual power supply simulation
Diesel generator,
Load demand
Convergent
Select combination of component with the lowest cost
Y
Select combination of battery banks and Wind
turbines
Reliability evaluation
CO2 Emission calculation
System optimization
N
Fig. 3.6. System optimization
Table 3.5. Comparison with different configuration
Proposed method Diesel only
Number of wind turbines 20
-Number of battery banks 5
-Inverter (MW) 20
-ACC (million US$) 4.47 1.81
AOM (million US$) 1.26 0.54
AFC (million US$) 59.26 75.54
ARC (million US$) 0.50
-AEC (million US$) 4.78 6.12
ADC (million US$) 0.0021 2.36
Total annual cost 70.27 86.37
Table 3.6. Case of diesel only
Reliability (%) ACS (million US$) CO2 Emission (Ton/yr)
99.8514 86.37 203,570
these values are to be used to determine the value of ADC using the graph illustrated in Fig. 3.5. Meanwhile, the AEC can be calculated by multiplying the CO2 emission in Ton with emission cost factor. Table 3.5 shows the simulation results of optimal configuration with penetration of wind turbine is selected to be 25% of peak load demand. From Table 3.5, it can be seen that the proposed system consists of 20 units of WTs and 5 units of battery banks to guarantee the system work successfully. One unit of inverter with rated capacity is 20 MW required to deliver the excess energy from WTs and DGs or discharged power from battery banks. This capacity is based on the maximum depth of discharged and charged power from the battery banks. Meanwhile, the price of battery banks is accounted from price in US$/kWh multiplied by maximum capacity of the battery banks. Due to the life time of battery banks is not the same as project lifetime, so the battery banks need to be replaced throughout the project lifetime. Actually, the lifetime of battery banks depends on the operational behavior related to how depth the battery banks discharged. However, in this simulation, the lifetime of Battery banks is assumed to be 10 years regardless any technical limitation that influence the lifetime of battery
Table 3.7. Five units system capacity outage table
Statex Cap. In (MW) Cap. Out (MW) Probability px Cum. Probability
0 60 0 0.90392079 1.00000000
1 48 12 0.09223681 0.09607920
2 36 24 0.00376476 0.00384238
3 24 36 0.00007683 0.00007761
4 12 48 0.00000078 0.00000078
5 0 60 0.00000000 0.00000000
banks. From Table 3.5, it can be seen that the ACS for proposed method is 70.27 million US$. It is composed from ACC is 4.47, AOM is 1.26, AFC is 59.26, ARC is 0.5, AEC is 4.78 and ADC is 0.0021. Note all cost in million US$. From this result, it can be seen that the most expensive element is AFC of DGs. Therefore, that is the reason; the application of DGs for remote communities has to be minimized.
3.5.1 Case Study of DGs Only
It is assumed all load demand is supplied from DGs unit only. Meanwhile, the main result of operating DGs only is shown in Table 3.6. It could be seen here that the ACS of DGs only is 86.37 million US$, that is composed from ACC of DGs, AOM, AFC, ADC and AEC as shown in Table 3.5. AOM of DGs only is set to be 1.5% from capital cost of diesel generator. AFC of DGs is 75.54 million US$ and is categorized as the most expensive element in the ACS. Therefore, it must be reduced in order to minimize overall cost of system. The reliability level of this system with the value of FOR that is given by 0.02, gives the impact on the reliability is 99.8514% as shown in Table 3.6. In addition, the reliability level of DGs only will gives the impact to the ADC is 2.36 million US$. The phenomenon of probability of five units of DGs can be seen in Table 3.7. Meanwhile, the avoided cost of CO2 emission represented by AEC, the value is 6.12 million US$.
From this result, it can be summarized here that to generate the electricity using DGs only spends much higher cost to capture CO2 on the atmosphere instead of CO2 emission itself.
Table 3.8. Case of proposed configuration
Reliability (%) ACS (million US$) CO2 Emission (Ton/yr)
99.9876 70.27 159,404
Table 3.9. Impact of Number of WTs
NWT Reliability (%) ACS (million US$) CO2 Emission (Ton/yr)
5 99.9223 75.56 176,811.26
10 99.9485 71.48 165,624.91
15 99.9682 70.51 161,673.44
20 99.9876 70.27 159,404.38
25 99.9933 70.38 157,965.04
30 99.9938 70.64 156,936.55
3.5.2 Case Study of WTs/DGs/Battery Banks
In this case, the load demand is supplied from WTs, DGs and battery banks. The result of this configuration is shown in Table 3.8. The ACS value for this configuration is 70.27 million US$, this value is cheaper than operates DGs only. From Table 3.8, it can be seen that the percentage reliability of the proposed configuration is 99.9876% and has the impact to the ADC is about 0.0021 million US$. Meanwhile, the CO2 emission generated by DGs is 159.404 Ton/year and the AEC due to capturing CO2 is 4.78 million US$.
Fig. 3.7 depicts the monthly variation of EFORW throughout a year. Based on sliding window technique, FOR of WTs could be determined easily within specified range of time. In this case, the monthly moving window was utilized to determine the value of EFORW. The fluctuation value of EFORW depends on the fluctuation nature of wind speed. Therefore, every month within a year these values are always fluctuated. The fluctuation of generated power of WTs according to the value of EFORW is illustrated in Fig. 3.8.
According to Fig. 3.7 and Fig. 3.9, the pattern of EFORW is similar with the pattern of power generated by DGs. This phenomenon can be explained by observing Fig. Fig.
3.7 and Fig. 3.8; EFORW indicates the potential of WTs to generate electrical power each
0 2 4 6 8 10 12 0.4
0.45 0.5 0.55 0.6 0.65 0.7 0.75
EFORW
Time (Month)
Fig. 3.7. Effective forced outage rate of WTs
0 2 4 6 8 10 12
6 7 8 9 10 11 12 13
Energy (GWh)
Time (Month)
Fig. 3.8. Monthly energy from WTs
month, a low value of EFORW indicates that the variation of wind speed is quite well or probably near to the rated speed of WTs. Hence, WTs able to generate their power effectively to supply load demand and DGs just cover the portion of power that must be supplied by DGs itself. On the other hand, a high value of EFORW indicates that WTs are not reliable to supply load demand, it is occurred due to the variation of wind speed is not satisfy the limitation to generate electrical power from WTs effectively. If the value of EFORW is high, DGs have to generate their power to cover the entire insufficiency power.
Hence, from Fig. 3.9 it can be seen here that the energy of DGs also increases whenever the value of EFORW is high. Fig. 3.10 and Fig. 3.11 depicts the discharged and charged energy from battery banks every month. Energy from battery banks is required while the power from WTs cannot meet the penetration value. Otherwise, while energy from WTs or DGs is exceed the limit.
0 2 4 6 8 10 12 18.4
18.6 18.8 19 19.2 19.4 19.6
Energy (GWh)
Time (Month)
Fig. 3.9. Monthly energy from DGs
0 2 4 6 8 10 12
0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76 0.78 0.8
Energy (GWh)
Time (Month)
Fig. 3.10. Monthly discharged energy from the battery banks
3.5.2.1 Impact of Number of WTs
This section discusses the impact of incorporating of WTs into existing DGs from relia-bility level and annual cost point of view. Table 3.9. shows the impact of increasing the number of WTs with fixed number of battery banks. The reliability level of the study system increases due to adding several units of WTs. In addition, the annual cost of sys-tem also could be minimized. Increasing the generation capacity by adding or installing new units of DGs able to minimize probability of loss of load. Further, it will increase the reliability level of the system. However, adding of new units of DGs consumes more fuel and increase the annual cost of system. Otherwise, adding several WTs probably
0 2 4 6 8 10 12 0.8
0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 1.25
Energy (GWh)
Time (Month)
Fig. 3.11. Monthly charged energy to the battery banks
0 10 20 30
40 50
0 10 20 30 40 50 70 75 80 85 90 95
Number of Battery Banks Number of WTs
ACS (million US$)
Fig. 3.12. Impact number of WTs and battery banks to the ACS
increase the total capital cost of system, however it is zero in operation cost and further the utilization of WTs reduce the fuel consumption of DGs.
3.5.2.2 Impact of Number of WTs and Battery Banks
This section discusses the impact of incorporating the number of WTs and battery banks into existing DGs system. Increasing the number of these units will have the most signif-icant impact on the ACS as shown in Table 3.10. From the result shown in Table 3.5 it can be observed that the fuel cost for DGs is the most expensive element in the cost of system so that it needs to be minimized. The utilization of WTs and battery banks up to a certain number reduces the fuel consumption and effectively reduce the ACS as shown in Fig. 3.12. However, increasing the number of WTs and battery banks after reaching
0
10 20 30 40 50
0 10 20 30 40 50 0 0.02 0.04 0.06 0.08 0.1 0.12
Number of Battery Banks Number of WTs
LOLP (%)
Fig. 3.13. Impact of number of WTs and battery banks to the LOLP
0 10 20 40 30 50
0 10
20 30
40 50
4.5 5 5.5 6
Number of Battery Banks Number of WTs
AEC (million US$)
Fig. 3.14. Impact of number of WTs and battery banks to the AEC
the minimum point causes the value of ACS to increase. The reliability of the system indicated by LOLP as shown in Fig. 3.13 is also influenced by installation of the WTs and battery banks. The value of LOLP will decrease if the number of WTs and battery banks increases. However, increasing of such units could increase the ACS. AEC for DGs is also affected by the number of units installed as shown in Fig. 3.14. Incorporating WTs and battery banks reduces the emission and further reduce the AEC. In Fig. 3.13 and 3.14 adding several units of battery banks has a small impact to the value of LOLP and AEC. However, if observing Fig. 3.12 adding several battery banks after minimum point will have the value of ACS increases.
Table 3.10. Impact of number of WTs and battery banks
NWT NBAT Reliability ACS CO2 Emission
(%) (million US$) (Ton/year)
5 1 99.9204 74.79 177,329.97
10 2 99.9483 71.63 167,880.97
15 3 99.9676 70.64 163,252.78
20 4 99.9875 70.28 160,090.76
25 5 99.9933 70.38 157,965.04
30 6 99.9939 70.67 156,401.16