# Accounting for Energy Cost When Designing Energy-Efficient Wireless Access Networks
## 3 Results and Discussion
### Results: Strategy 1, Simple Algorithms
#### 3.1.1. Buy in Advance: Storage Dimension and I Interval Maximum Duration Effects  
Considering the observations made above, it is interesting to perform simulations, varying the energy storage capacity, when _Buy in advance_ is used.  
The results are shown in Figure 10, varying the storage capacity from 30 kWh-180 kWh.  
Note that, as the energy storage capacity increases, money spent to buy energy from the grid decreases, since more energy can be bought in advance, when it has a lower price. When _Deactivate all microcell BSs_ is used, the slope of its decrease reduces at approximately 60 kWh. In fact, with an energy storage of 30 kWh, total cost is reduced by approximately 23%, while when it is 60 kWh, by approximately 28.86% (as shown in Table 2), and no additional reductions are obtained for storages greater than 80 kWh. In the scenario in which _Deactivate as much microcell BSs as needed_ is used, the total cost starts to stabilize at 50 kWh: with 30 kWh, the reduction is approximately 22.5%, while 27.65% with 60 kWh, as shown in Table 2, but no significant additional reductions are obtained for larger storage capacities. These interruptions in the decreasing trends are because the total cost is close to the minimum price needed to buy all the necessary energy. If we suppose to buy ideally all the necessary energy during the time interval at the minimum price, between EUR3 and EUR3.5 are necessary, according to the energy policies used. When no energy reducing action is used (_Undertake no action_), the total cost continues decreasing linearly, even with a smaller slope from 40 kWh on, since it is still far from the minimum total cost (approximately EUR11).  
Figure 10. _Buy in advance_: effects of storage dimension variation on the total outgo.  
Figure 9. Gross outgo (**a**) and gross income (**b**) obtained using energy- and cost-reducing actions of Strategy 1.  
Next, simulations are performed changing the duration of the interval \(I\). This parameter refers to the maximum number of hours (time intervals) for which energy can be bought in advance, if needed and if it is less expensive. Simulations are done using \(I\) interval duration values varying from 1-23 h (time intervals). The results are shown in Figure 11.  
For a duration of \(I=1\) h, the cost is very high since this scenario actually coincides with the one in which the reducing cost algorithm is not used. Increasing the duration of the interval \(I\), the algorithm behaves better since it is more likely to buy energy during the time slot that is the local minimum among hourly costs. As in the previous analysis, the stability is reached after \(I\) duration equal to 9 h, when _Deactivate all microcell BSs_ and _Deactivate as much microcell BSs as needed_ are used. Increasing its duration from 1-7 h, the cost is reduced by 19% and 18%, respectively. However, with I equal to 11 h, costs are further reduced by less than 5% only. This is explained by the storage saturation: the storage becomes full so that the purchase of energy for more future time intervals is not possible. With _Undertake no action_, saturation does not occur since the storage is more empty because of the larger power consumption in that scenario. Consequently, the total outgo continues decreasing even with great values for the duration of the interval \(I\).