# Accounting for Energy Cost When Designing Energy-Efficient Wireless Access Networks
## 4 Conclusions  
During the last few years, data traffic demand has increased greatly, as has the energy price. Wireless access networks have expanded, as well, in terms of size and provided capability and, consequently, in terms of power consumption. For this reason, the cost to buy the amount of power consumed by the base stations of those networks has become very high, impacting the communication cost. The goal of the paper is to design strategies and algorithms to minimize the total outgo for the purchase of the needed electricity for the supply of the base stations of a wireless access network. The network considered is an energy-efficient network, composed of a group of macrocell and/or microcell base stations, powered by a solar panel system, energy batteries and the traditional power grid. Exploiting the variability of the energy prices (real-time pricing is considered) reduces the total cost. When energy is bought in advance, if it is less expensive, the cost reduction is between 4.83% and 37.36%, when a 100-kWp PV panel system is used. The value of the cost reduction depends on the considered energy reducing strategies, the settings of the energy storage system and the parameter I. Recommended values for the interval duration I, during which energy can be bought in advance, are between 9 and 23 h. Regarding the storage capacity, the best value is around 60 kWh. The necessary energy cost is also reduced, when microcell base stations are switched off in case the energy price is greater than a given threshold. The value of the reduction depends on the setting of this threshold. Nevertheless the network performance worsens. For this reason, a compromise between the reduction of cost and the quality of service is needed. Therefore, the recommended value of the threshold is the 70 th percentile. When the network is equipped with a 100-kWp PV panel system and a 50-kWh energy battery, an approximately 43% cost drop is achieved. The user coverage is reduced by less than 2.5%, but it remains larger than 94%, which is still acceptable as operators aim for 90% user coverage at least. In these cases, the obtained benefits are in terms of energy cost reduction, but also in terms of the reduction of greenhouse gas emissions since the access network responds as expected to the demand and response program; therefore, it contributes to minimizing the peak power demand. Next, the energy sale is introduced. With this strategy, the obtained results are not very promising since the sold energy has to be bought back: no more than a 3% cost reduction is obtained, selling energy at half of its market price. Finally, the usage of a wireless access network composed of only microcell base stations is proposed. Since its power consumption is reduced, also the total necessary energy cost decreases. When a 50-kWh energy battery and a 100-kWp PV panel system are used, up to a 31% reduction of cost is achieved, without negative effects on the user coverage. However, the new configurations require a huge Capex expenditure due to the high number of base stations needed to provide a good quality of service. Performances can be further improved with the turning off of some base stations, in the case of renewable energy shortage. Nevertheless, this impacts the quality of service. Therefore, a compromise between the reduction of cost and the network performance is necessary. The suggested value for the time window is 4 h. When a 50-kWh energy storage and a 100-kWp PV panel system are used, an energy cost reduction between 34.28% and 45.88% is provided, with a user coverage drop between 0.33% and 4.22%, depending on the chosen configuration and the minimum ensureduser coverage. This network parameter remains larger than 92.92%. As future work, different energy pricing methods and different renewable energy resources will be considered. Furthermore, it could be interesting to improve the sale of energy: when the price is higher, injection should be performed.