Chapter 1 Introduction
5.3 Numerical Results
Table 5.1 reports on-site and off-site renewable energy sources at each location with the corresponding average capacity factor (CF), defined as the ratio of the actual power output to the maximum rated capacity.
Source Location Avg CF(%)
Onsite
Wind California 27
Wind Illinois 32
Wind Texas 33.6
Solar California 22.8
Solar Texas 23.46
Offsite
Wind Arizona 30
Wind Colorado 43
Wind Iowa 34
Solar Arizona 27.74 Solar Colorado 24.61
Table 5.1: Capacity factor for various green energy sources
We used the models from NREL [79] and [59] for solar and wind energy generation, respectively. Based on the meteorological data from NREL [31], we calculated the total power generated. At each site, we considered 20 wind turbines of capacity1.5MW, each and 10,000 solar panels of120W, each. The cost of generating wind and solar power is obtained by taking the installation cost of 1630$/kW and 3100$/kW, and life time of 20 yrs and 25 yrs, respectively [14]. We took quarterly average of client demand and renewable energy generated for every hour of the day.
The brown electricity price at different locations is taken from US energy information administration website [21].
5.3 Numerical Results
5.3.1 TCO Comparison
To show the TCO reduction with our model, we designed a baseline model (termed CED-B) that minimizes the TCO when the data centers are powered only with brown energy keeping the other constraints same. For a full-site failure scenario, Fig. 5.2 shows the percentage gain in the TCO using GACED model compared to the CED-B model. Even after failure, the gain is 2% with the green energy usage of 20%. The gain reduces with increase in green energy usage because, our model increases the amount of (expensive) green energy purchased to satisfy the constraint.
On the other hand, CED-B model has no cost from green energy usage. With the GACED model, greening can be achieved with very little or no extra cost unless we target high renewable energy usage.
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0 20 40 60 80
Gain (%)
Minimum bound on Green Usage(%)
Figure 5.2: Impact of varying green energy usage on the TCO of GACED model
5.3.2 Impact of Failure Percentage
In this experiment we studied the impact of varying the failure percentage on the TCO while green usage bound is satisfied. Fig. 5.3 compares the TCO for the two models while forcing 40% green energy usage. We see that the TCO is almost similar for both the models due to the fact that, the GACED model optimally uses cheaper renewable energy to reduce the TCO. Fig. 5.4 illustrates the offsite wind energy usage in the GACED model and its corresponding price at the Texas data center.
0.5 0.6 0.7 0.8 0.9 1 1.1
0 20 40 60 80 100
Normalized TCO
Percentage of failed servers in a data center
GACED CED-B
Figure 5.3: Impact of varying failure per- centage on the TCO of GACED model
0 10 20 30
0 10 20 30 40 50 60 70 80 90 100 0 2 4 6
Price in cent/kWh Power used (MW)
Time (in hours)
Brown price Wind used Wind price
Figure 5.4: Illustration of wind energy usage
When the wind energy is cheaper, GACED uses more of it to maintain the same TCO (as with CED-B), albeit with reduced carbon footprint. Due to intelligent usage of green energy, GACED meets the target renewable energy usage of 40% at all times. We conclude that the GACED model can lead to greener data center deployment with no or little additional cost (though green energy procurement is costlier).
5.3.3 Impact of Demand
To understand the impact of increase in demand on TCO, we evaluate the models for 6 data centers with varying demand. The demand is varied as a multiple of baseline demand. The multiplicative factor is varied between 1 and 5. Fig. 5.5 shows the impact of increase in demand on the TCO and green energy procurement decision.
We set green energy usage and failure percentage to 40% and 20%, respectively. As demand increases, the TCO for both the models increases due to obvious reasons.
However, TCO with GACED model increases with the demand due to the green energy usage constraint. We note that, even with five-fold increase in the demand (with a cost of almost 30 cents/kW for wind energy at Texas), it is possible to meet the 40% green energy usage constraint with a meagre 4% increase in the TCO.
5.3 Numerical Results
0 0.2 0.4 0.6 0.8 1
1 2 3 4 5
Normalized TCO
Demand (X the actual demand)
GACED CED-B
Figure 5.5: Impact of demand variation on the TCO of GACED model
5.3.4 Impact of Latency
In this experiment we studied the impact of relaxing the latency bound on the TCO. Fig. 5.6 shows the impact of relaxing latency requirement on the TCO.Dfmax is set to twice Dmax. We notice that both the models reduce the TCO for relaxed latency bound, since there is more choice in selecting the data center to serve the requests. However, GACED model lowers the TCO by considering locations powered by cheaper green energy.
0.92 0.94 0.96 0.98 1
40 50 60 70 80 90 100
Normalized TCO
Delay (ms)
GACED CED-B
Figure 5.6: Impact of latency relaxation on the TCO of GACED model
5.3.5 Sensitivity Analysis
We quantitatively evaluate the impact of uncertainty in the renewable energy availability on the performance of GACED model. For the case of complete data center failure and 40 % green energy usage requirement, the capacity factor was varied between −40% and 40%. Fig. 5.7 shows the percentage gain in the TCO with GACED model (compared to the CED-B model). Since, the cost of renewable energy decreases with increasing capacity factor [14], the GACED model has lower TCO compared to the CED-B model (by about 4%). This is because it efficiently exploits cheaper green energy.
We also conducted another experiment here to demonstrate that, if the fore- casted green energy availability is inaccurate, we can work with 20% green energy usage requirement, where GACED model has only 3% higher TCO. To do this, we vary the capacity factor by −40% and the result is shown in Fig. 5.8.
The cost of generating renewable energy tends to reduce with time due to the technological improvements in the equipment efficiency and the increasing deployment. Harnessing green energy depends upon the cost and efficiency of technology, which is constantly improving thereby reducing the cost. In order to understand the impact of long term prediction of green energy cost on both models,
-12 -9 -6 -3 0 3 6
-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40
Gain (%)
Change in capacity factor(%)
Figure 5.7: Gain in TCO for GACED and CED-B models varying CF