Considering the exponential increase in mobile traffic, requiring denser cellular access networks, the use of renewable energy (RE) to power base stations (BSs) may contribute to reduction of the huge operational cost faced by mobile network operators (MNOs) due to power supply. Furthermore, the smart grid (SG) paradigm is deeply changing the energy market, envisioning an active interaction between grid and customers. Hence, MNOs can combine renewable-powered BSs with properly designed energy management strategies to improve the interaction with the SG, with the twofold objective of reducing the energy bill and providing ancillary services. We propose a stochastic model to investigate a renewable-powered mobile network dynamically adapting its energy consumption to accomplish SG requests, and receiving rewards in return, by exploiting two techniques. First, resource on demand (RoD) can dynamically switch off unneeded BSs. Second, an energy management policy takes efficient decisions about using/harvesting locally produced RE, depending on SG requests. The proposed techniques highly increase the probability of responding to ancillary service demands, up to 90%, also depending on the RE system size, with RoD raising, by up to 20%, the probability of accomplishing SG requests. Consequently, cost saving can become equal to the total cost or even greater, providing revenues to the MNO.

While designed for being energy efficient, the deployment of 5G networks will further increase Radio Access Networks (RANs) energy consumption with the twofold effect to raise sustainability issues and increase operational costs for Mobile Network Operators (MNOs). However, the energy waste occurring during low traffic periods can be mitigated through Advanced Sleep Modes (ASMs) that make the BSs enter into progressively deeper and less consuming sleep modes. Deep sleep modes, unfortunately, have longer reactivation times, and may jeopardize service quality. In this paper, focusing on 5G latency requirements in low traffic periods, we propose a framework to dynamically adapt the ASM configuration settings to the actual traffic load so as to meet a desired constraint on the average BS reactivation delay.

The success of cloud computing services has led to big computing infrastructures that are complex to manage and very costly to operate. In particular, power supply dominates the operational costs of big infrastructures, and several solutions have to be put in place to alleviate these operational costs and make the whole infrastructure more sustainable. In this paper, we investigate the case of a complex infrastructure composed of data centers (DCs) located in different geographical areas in which renewable energy generators are installed, co-located with the data centers, to reduce the amount of energy that must be purchased by the power grid. Since renewable energy generators are intermittent, the load management strategies of the infrastructure have to be adapted to the intermittent nature of the sources. In particular, we consider $\mathrm{EcoMultiCloud}$ , a load management strategy already proposed in the literature for multi-objective load management strategies, and we adapt it to the presence of renewable energy sources. Hence, cost reduction is achieved in the load allocation process, when virtual machines (VMs) are assigned to a data center of the considered infrastructure, by considering both energy cost variations and the presence of renewable energy production. Performance is analyzed for a specific infrastructure composed of four data centers. Results show that, despite being intermittent and highly variable, renewable energy can be effectively exploited in geographical data centers when a smart load allocation strategy is implemented. In addition, the results confirm that $\mathrm{EcoMultiCloud}$ is very flexible and is suited to the considered scenario.

In this paper we focus on the design of the power system for off-grid cellular base stations powered by a photovoltaic (PV) solar panel and a battery. Several papers already tackled this problem, with different approaches, modeling either the day-to-day behavior, or the hourly dynamics. In addition, the meteorological characteristics were modeled using a variable number of levels. Different approaches produced different results, hardly comparable. In this paper, we discuss the choice of parameter quantization for time, weather, and energy storage, with the objective of deriving guidelines for the development of accurate and credible models that can support the power system design. Our investigation shows that quantization has an important impact on the mathematical model outputs. Hence, quantization must be carefully taken into account, to achieve a correct dimensioning of the power system.