Dimensioning Renewable Energy Systems to Power Mobile Networks

To face the huge increase in the mobile traffic demand, denser cellular access networks are extensively deployed by mobile operators, entailing high cost for energy supply. Hence, renewable energy (RE) sources are often adopted to power base stations (BSs), in order to make them more self-sufficient and reduce the energy bill. Nevertheless, sizing an RE generation system is a critical task, and the dimensioning methods available in the literature are based on simulation or optimization approaches, hence resulting time consuming or computationally complex. This paper proposes and validates a simple still effective analytical method that, based on the location dependent mean value and variance of RE production, allows to find feasible combinations of photovoltaic (PV) panel and battery sizes, suitable to power a BS and decrease the storage depletion probability below a target threshold. Furthermore, the application of this method highlights the role of RE production variance. Higher values of the variance require larger PV panels, almost doubled with respect to locations with low variance. However, only locations with higher variance benefit from increasing the battery size and relaxing the constraint on energy self-sufficiency, with the scope of reducing the required PV panel capacity and the capital expenditures

Green Mobile Networks: from self-sustainability to enhanced interaction with the Smart Grid

Nowadays, the staggering increase of the mobile traffic is leading to the deployment of denser and denser cellular access networks, hence Mobile Operators are facing huge operational cost due to power supply. Therefore, several research efforts are devoted to make mobile networks more energy efficient, with the twofold objective of reducing costs and improving sustainability. To this aim, Resource on Demand (RoD) strategies are often implemented in Mobile Networks to reduce the energy consumption, by dynamically adapting the available radio resources to the varying user demand. In addition, renewable energy sources are widely adopted to power base stations (BSs), making the mobile network more independent from the electric grid. At the same time, the Smart Grid (SG) paradigm is deeply changing the energy market, envisioning an active interaction between the grid and its customers. Demand Response (DR) policies are extensively deployed by the utility operator, with the purpose of coping with the mismatches between electricity demand and supply. The SG operator may enforce its users to shift their demand from high peak to low peak periods, by providing monetary incentives, in order to leverage the energy demand profiles. In this scenario, Mobile Operators can play a central role, since they can significantly contribute to DR objectives by dynamically modulating their demand in accordance with the SG requests, thus obtaining important electricity cost reductions. The contribution of this thesis consists in investigating various critical issues raised by the introduction of photovoltaic (PV) panels to power the BSs and to enhance the interaction with the Smart Grid, with the main objectives of making the mobile access network more independent from the grid and reducing the energy bill. When PV panels are employed to power mobile networks, simple and reliable Renewable Energy (RE) production models are needed to facilitate the system design and dimensioning, also in view of the intermittent nature of solar energy production. A simple stochastic model is hence proposed, where RE production is represented by a shape function multiplied by a random variable, characterized by a location dependent mean value and a variance. Our model results representative of RE production in locations with low intra-day weather variability. Simulations reveal also the relevance of RE production variability: for fixed mean production, higher values of the variance imply a reduced BS self-sufficiency, and larger PV panels are hence required. Moreover, properly designed models are required to accurately represent the complex operation of a mobile access network powered by renewable energy sources and equipped with some storage to harvest energy for future usage, where electric loads vary with the traffic demand, and some interaction with the Smart Grid can be envisioned. In this work various stochastic models based on discrete time Markov chains are designed, each featuring different characteristics, which depend on the various aspects of the system operation they aim to examine. We also analyze the effects of quantization of the parameters defined in these models, i.e. time, weather, and energy storage, when they are applied for power system dimensioning. Proper settings allowing to build an accurate model are derived for time granularity, discretization of the weather conditions, and energy storage quantization. Clearly, the introduction of RE to power mobile networks entails a proper system dimensioning, in order to balance the solar energy intermittent production, the traffic demand variability and the need for service continuity. This study investigates via simulation the RE system dimensioning in a mobile access network, trading off energy self-sufficiency targets and cost and feasibility constraints. In addition, to overcome the computational complexity and long computational time of simulation or optimization methods typically used to dimension the system, a simple analytical formula is derived, based on a Markovian model, for properly sizing a renewable system in a green mobile network, based on the local RE production average profile and variability, in order to guarantee the satisfaction of a target maximum value of the storage depletion probability. Furthermore, in a green mobile network scenario, Mobile Operators are encouraged to deploy strategies allowing to further increase the energy efficiency and reduce costs. This study aims at analyzing the impact of RoD strategies on energy saving and cost reduction in green mobile networks. Up to almost 40% of energy can be saved when RoD is applied under proper configuration settings, with a higher impact observed in traffic scenarios in which there is a better match between communication service demand and RE production. While a feasible PV panel and storage dimensioning can be achieved only with high costs and large powering systems, by slightly relaxing the constraint on self-sustainability it is possible to significantly reduce the size of the required PV panels, up to more than 40%, along with a reduction in the corresponding capital and operational expenditures. Finally, the introduction of RE in mobile networks contributes to give mobile operators the opportunity of becoming prominent stakeholders in the Smart Grid environment. In relation to the integration of the green network in a DR framework, this study proposes different energy management policies aiming at enhancing the interaction of the mobile network with the SG, both in terms of energy bill reduction and increased capability of providing ancillary services. Besides combining the possible presence of a local RE system with the application of RoD strategies, the proposed energy management strategies envision the implementation of WiFi offloading (WO) techniques in order to better react to the SG requests. Indeed, some of the mobile traffic can be migrated to neighbor Access Points (APs), in order to accomplish the requests of decreasing the consumption from the grid. The scenario is investigated either through a Markovian model or via simulation. Our results show that these energy management policies are highly effective in reducing the operational cost by up to more than 100% under proper setting of operational parameters, even providing positive revenues. In addition, WO alone results more effective than RoD in enhancing the capability to provide ancillary services even in absence of RE, raising the probability of accomplishing requests of increasing the grid consumption up to almost 75% in our scenario, twice the value obtained under RoD. Our results confirm that a good (in terms of energy bill reduction) energy management strategy does not operate by reducing the total grid consumption, but by timely increasing or decreasing the grid consumption when required by the SG. This work shows that the introduction of RE sources is an effective and feasible solution to power mobile networks, and it opens the way to new interesting scenarios, where Mobile Network Operators can profitably interact with the Smart Grid to obtain mutual benefits, although this definitely requires the integration of suitable energy management strategies into the communication infrastructure management

RESOURCE DIMENSIONING AND MANAGEMENT FOR SOLAR POWERED CELLULAR BASE STATIONS

Ph.DDOCTOR OF PHILOSOPH

From self-sustainable Green Mobile Networks to enhanced interaction with the Smart Grid

Due to the staggering increase of mobile traffic, Mobile Network Operators (MNOs) are facing considerable operational cost due to power supply. Renewable Energy (RE) sources to power Base Stations (BSs) represent a promising solution to lower the energy bill, but their intermittent nature may affect the service continuity and the system self-sufficiency. Furthermore, in the new energy market dominated by the Smart Grid, new potentialities arise for MNOs in a Demand Response (DR) framework, since they can dynamically modulate the mobile network energy demand in accordance with SG requests, thus obtaining significant rewards. This work proposes various stochastic models to reliably and accurately characterize the RE production and the operation of a green mobile network, also analyzing the impact of parameter quantization on the model performance. The RE system dimensioning is investigated, trading off cost saving and feasibility constraints, and evaluating the impact of Resource on Demand (RoD) strategies, that allow to achieve more than 40% cost reduction. Finally, by exploiting RoD and WiFi offloading techniques, various energy management policies are designed to enhance the interaction of a green mobile network with the SG in a DR framework, leading to fully erase the energy bill and even gain positive revenues

Caching at the edge in high energy-efficient wireless access networks

In the next generation of Radio Access Networks (RANs), Multi-access Edge Computing (MEC) is considered a promising solution to reduce the latency and the traffic load of backhaul links. It consists of the placement of servers, which provide computing platforms and storage, directly at each Base Station (BS) of these networks. In this paper, the caching feature of this paradigm is considered in a portion of a RAN, powered by a renewable energy generator system, energy batteries and the power grid. The performance of the caching in the RAN is analysed for different traffic characteristics, as well as for different capacity of the caches and different spread of it. Finally, we verify that the usage of a strategy that aims at reducing the energy consumption does not impact the benefits provided by the mobile edge caching

Household users cooperation to reduce cost in green mobile networks

The staggering mobile traffic growth is leading to a huge increase of operational costs for Mobile Operators (MOs) due to power supply. In a Smart Grid (SG) scenario, where Demand Response (DR) strategies are widely adopted to better balance the Demand-Supply mismatch, new opportunities arise for MOs, that can receive some monetary rewards for accomplishing the SG requests of periodically increasing or decreasing their energy consumption. This study considers a mobile network that exploits Renewable Energy (RE) to power the BSs and Resource on Demand (RoD) strategies to dynamically adapt the number of active radio resources to the varying traffic demand, in order to better react to the SG requests. On top of this, the purpose of this work is investigating the effects of the cooperation between Household Customers (HCs) engaged in the DR program and the mobile network. Based on a predefined agreement, HCs cooperate with the MO in order to increase its capability to accomplish the SG requests, receiving in return some benefits when stipulating the Internet provisioning contract with the MO. HCs can contribute to achieving the MO goals by means of two techniques. On the one hand, a fraction of the electric loads that are postponed by the HCs when the SG asks for a reduction of the energy consumption can be shifted on behalf of the mobile network, that will receive the corresponding monetary rewards (HC Trade - HCT). On the other hand, HCs can accept to handle some additional mobile traffic, that is moved to their own WiFi Access Points from the BSs, in order to reduce the energy load of the mobile network (WiFi Offloading - WO).Our results show that, although HCT alone provides limited saving in the energy bill due to the poor attitude of HCs to postpone their electric loads, up to 18% of cost saving can be achieved under full HCs cooperation when HCT is combined with WO. The effects of HCs cooperation can be further enhanced by installing larger sized RE generators, allowing to significantly reduce the energy bill up to more than 90%

Planning Solar in Energy-managed Cellular Networks

There has been a lot of interest recently on the energy efficiency and environmental impact of wireless networks. Given that the base stations are the network elements that use most of this energy, much research has dealt with ways to reduce the energy used by the base stations by turning them off during periods of low load. In addition to this, installing a solar harvesting sys- tem composed of solar panels, batteries, charge con- trollers and inverters is another way to further reduce the network environmental impact and some research has been dealing with this for individual base stations. In this paper, we show that both techniques are tightly coupled. We propose a mathematical model that captures the synergy between solar installation over a network and the dynamic operation of energy-managed base stations. We study the interactions between the two methods for networks of hundreds of base stations and show that the order in which each method is intro- duced into the system does make a difference in terms of cost and performance. We also show that installing solar is not always the best solution even when the unit cost of the solar energy is smaller than the grid cost. We conclude that planning the solar installation and energy management of the base stations have to be done jointly

Low Cost and Reliable Wireless Sensor Networks for Environmental Monitoring

This thesis utilizes wireless sensor network systems to learn of changes in wireless network performance and environment, establishing power efficient systems that are low cost and are able to perform large scale monitoring. The proposed system was built at the University of Maine’s Wireless Sensor Networks (WiSe-Net) laboratory in collaboration with University of New Hampshire and University of Vermont researchers. The system was configured to perform soil moisture measurement with provision to include other sensor types at later stages in collaboration with Alabama A & M University. In the research associated with this thesis, a general relay energy assisted scenario is considered, where a transmitter is powered by an energy source through both direct and relay links. An energy efficient scheduling method is proposed for the system model to determine whether to transmit data or stay silent based on the stored energy level and channel state. An analytical expression has been derived to approximate outage probability of the system in terms of energy and data thresholds. In addition, we propose a model for evaluating the outage probability of a solar powered base station, equipped with a selected photo voltaic panel size and battery configuration. The energy harvesting environment location has been selected as the state of Maine, during a variety of weather conditions, considering base station loading during different days of the week. Simulation results shows the required photo-voltaic panel size and number of batteries for specific tolerable outage probability of the system. The fundamental contribution of this work is in development of hardware and software based on new methodologies to optimize network longevity using AI/ML. One of the most important metrics to define longevity and reliability is the outage probability of a network. We have derived equations for the outage probability, based upon power configuration panel size, battery capacity and the environmental factors, meteorological and diurnal. This will impact the observed cost function which is outage probability. The system models proposed in this thesis result in much more energy efficient systems with less outage probabilities compared to the current systems