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

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

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%

A Novel Energy Model for Renewable Energy-Enabled Cellular Networks Providing Ancillary Services to the Smart Grid

In this paper, we consider cellular networks powered by the smart grid (SG) and by local renewable energy (RE) sources. While this configuration promises energy savings, usage of cleaner energy, and cost reduction, it has some intrinsic complexity due to the interaction between the network operators and the SG. Motivated by the significant advancement in the SG, we consider the case where cellular networks provide the SG with ancillary services by replying to the grid's explicit requests to increase or decrease their grid consumption. We propose a new approach for configuring and operating base stations (BSs) to provide ancillary services. Based on real data, we model the energy state of a BS as a Markov chain taking into account the proposed energy management policy, randomness of SG requests, and RE generation. We use the model to evaluate the performance of the system, and to decide proper settings of its parameters in order to minimize the energy operational cost. The performance of our proposal is then compared against those of other approaches. Results show that important cost savings, with negligible degradation in quality of service, are possible when RE generation, SG patterns, and storage sizes are properly taken into account

Processing ANN Traffic Predictions for RAN Energy Efficiency

The field of networking, like many others, is experiencing a peak of interest in the use of Machine Learning (ML) algorithms. In this paper, we focus on the application of ML tools to resource management in a portion of a Radio Access Network (RAN) and, in particular, to Base Station (BS) activation and deactivation, aiming at reducing energy consumption while providing enough capacity to satisfy the variable traffic demand generated by end users. In order to properly decide on BS (de)activation, traffic predictions are needed, and Artificial Neural Networks (ANN) are used for this purpose. Since critical BS (de)activation decisions are not taken in proximity of minima and maxima of the traffic patterns, high accuracy in the traffic estimation is not required at those times, but only close to the times when a decision is taken. This calls for careful processing of the ANN traffic predictions to increase the probability of correct decision. Numerical performance results in terms of energy saving and traffic lost due to incorrect BS deactivations are obtained by simulating algorithms for traffic predictions processing, using real traffic as input. Results suggest that good performance trade-offs can be achieved even in presence of non-negligible traffic prediction errors, if these forecasts are properly processed

Energy-Efficient Fault-Tolerant Scheduling Algorithm for Real-Time Tasks in Cloud-Based 5G Networks

© 2013 IEEE. Green computing has become a hot issue for both academia and industry. The fifth-generation (5G) mobile networks put forward a high request for energy efficiency and low latency. The cloud radio access network provides efficient resource use, high performance, and high availability for 5G systems. However, hardware and software faults of cloud systems may lead to failure in providing real-time services. Developing fault tolerance technique can efficiently enhance the reliability and availability of real-time cloud services. The core idea of fault-tolerant scheduling algorithm is introducing redundancy to ensure that the tasks can be finished in the case of permanent or transient system failure. Nevertheless, the redundancy incurs extra overhead for cloud systems, which results in considerable energy consumption. In this paper, we focus on the problem of how to reduce the energy consumption when providing fault tolerance. We first propose a novel primary-backup-based fault-tolerant scheduling architecture for real-time tasks in the cloud environment. Based on the architecture, we present an energy-efficient fault-tolerant scheduling algorithm for real-time tasks (EFTR). EFTR adopts a proactive strategy to increase the system processing capacity and employs a rearrangement mechanism to improve the resource utilization. Simulation experiments are conducted on the CloudSim platform to evaluate the feasibility and effectiveness of EFTR. Compared with the existing fault-tolerant scheduling algorithms, EFTR shows excellent performance in energy conservation and task schedulability

Machine Learning Algorithms for Provisioning Cloud/Edge Applications

Mención Internacional en el título de doctorReinforcement Learning (RL), in which an agent is trained to make the most favourable decisions in the long run, is an established technique in artificial intelligence. Its popularity has increased in the recent past, largely due to the development of deep neural networks spawning deep reinforcement learning algorithms such as Deep Q-Learning. The latter have been used to solve previously insurmountable problems, such as playing the famed game of “Go” that previous algorithms could not. Many such problems suffer the curse of dimensionality, in which the sheer number of possible states is so overwhelming that it is impractical to explore every possible option. While these recent techniques have been successful, they may not be strictly necessary or practical for some applications such as cloud provisioning. In these situations, the action space is not as vast and workload data required to train such systems is not as widely shared, as it is considered commercialy sensitive by the Application Service Provider (ASP). Given that provisioning decisions evolve over time in sympathy to incident workloads, they fit into the sequential decision process problem that legacy RL was designed to solve. However because of the high correlation of time series data, states are not independent of each other and the legacy Markov Decision Processes (MDPs) have to be cleverly adapted to create robust provisioning algorithms. As the first contribution of this thesis, we exploit the knowledge of both the application and configuration to create an adaptive provisioning system leveraging stationary Markov distributions. We then develop algorithms that, with neither application nor configuration knowledge, solve the underlying Markov Decision Process (MDP) to create provisioning systems. Our Q-Learning algorithms factor in the correlation between states and the consequent transitions between them to create provisioning systems that do not only adapt to workloads, but can also exploit similarities between them, thereby reducing the retraining overhead. Our algorithms also exhibit convergence in fewer learning steps given that we restructure the state and action spaces to avoid the curse of dimensionality without the need for the function approximation approach taken by deep Q-Learning systems. A crucial use-case of future networks will be the support of low-latency applications involving highly mobile users. With these in mind, the European Telecommunications Standards Institute (ETSI) has proposed the Multi-access Edge Computing (MEC) architecture, in which computing capabilities can be located close to the network edge, where the data is generated. Provisioning for such applications therefore entails migrating them to the most suitable location on the network edge as the users move. In this thesis, we also tackle this type of provisioning by considering vehicle platooning or Cooperative Adaptive Cruise Control (CACC) on the edge. We show that our Q-Learning algorithm can be adapted to minimize the number of migrations required to effectively run such an application on MEC hosts, which may also be subject to traffic from other competing applications.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Antonio Fernández Anta.- Secretario: Diego Perino.- Vocal: Ilenia Tinnirell

Virtual Power Plant for Smart Grid Ready Buildings and Customers

This report contains a summary of results from the ForskEL project: Virtual Power Plant for Smart Grid Ready Buildings and Customers

ACUTA Journal of Telecommunications in Higher Education

In This Issue Making Dollars and Sense Out of Cloud Computing Surfing the Wave of Cloud Computing VolP Meets the Cloud A Quick Look at Cloud Computing in Higher Education,2012 Cloud Computing: ls the Forecast Bright or Overcast? Cloud E-Mail Momentum Swells Institutional Excellence Award lndividual Awards President\u27s Message From the Executive Director Q&A with the CI

Investigation into the impact of wind power generation on demand side management (DSM) practices

The construction of a number of wind farms in South Africa will lay the foundation for the country to embrace the generation of greener energy into the National Grid. Despite the benefits derived from introducing wind power generation into the grid, this source encompasses adverse effects which need to be managed. These adverse effects include the intermittency and lack of predictability of wind. In power systems with a high penetration of wind energy, these effects can severely affect the power system’s security and reliability in the event of significant rapid ramp rates. Recently, many utilities around the world have been exploring the use of Demand Side Management (DSM) and Demand Response (DR) initiatives and programmes to support and manage the intermittency of wind power generation. This report outlines the programmes and benefits of DSM/DR and provides a critical analysis of the challenges facing South Africa with implementing these initiatives. Introducing these programmes necessitates the employment of a number of Smart Grid technologies including Advanced Metering Infrastructure (AMI), next generation telecommunications technologies, smart meters, enterprise system integration and dynamic pricing. These tools and techniques are discussed and their challenges described within the context of South Africa’s current state of the power system. The current practices for DSM/DR in South Africa have been evaluated in this report. Despite, the success of many DSM/DR initiatives in the commercial, industrial and agricultural sectors, it is found that much work is still required in the residential sectors as the current DSM initiatives are not adequate for managing wind power generation. A detailed analysis and recommendations for South Africa’s DR program is then presented based on industry best practices and experiences from other utilities who are currently exploring DSM/DR in the residential sector using Smart Grid technologies