Final report on the evaluation of RRM/CRRM algorithms

Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin

Measuring And Improving Internet Video Quality Of Experience

Streaming multimedia content over the IP-network is poised to be the dominant Internet traffic for the coming decade, predicted to account for more than 91% of all consumer traffic in the coming years. Streaming multimedia content ranges from Internet television (IPTV), video on demand (VoD), peer-to-peer streaming, and 3D television over IP to name a few. Widespread acceptance, growth, and subscriber retention are contingent upon network providers assuring superior Quality of Experience (QoE) on top of todays Internet. This work presents the first empirical understanding of Internet’s video-QoE capabilities, and tools and protocols to efficiently infer and improve them. To infer video-QoE at arbitrary nodes in the Internet, we design and implement MintMOS: a lightweight, real-time, noreference framework for capturing perceptual quality. We demonstrate that MintMOS’s projections closely match with subjective surveys in accessing perceptual quality. We use MintMOS to characterize Internet video-QoE both at the link level and end-to-end path level. As an input to our study, we use extensive measurements from a large number of Internet paths obtained from various measurement overlays deployed using PlanetLab. Link level degradations of intra– and inter–ISP Internet links are studied to create an empirical understanding of their shortcomings and ways to overcome them. Our studies show that intra–ISP links are often poorly engineered compared to peering links, and that iii degradations are induced due to transient network load imbalance within an ISP. Initial results also indicate that overlay networks could be a promising way to avoid such ISPs in times of degradations. A large number of end-to-end Internet paths are probed and we measure delay, jitter, and loss rates. The measurement data is analyzed offline to identify ways to enable a source to select alternate paths in an overlay network to improve video-QoE, without the need for background monitoring or apriori knowledge of path characteristics. We establish that for any unstructured overlay of N nodes, it is sufficient to reroute key frames using a random subset of k nodes in the overlay, where k is bounded by O(lnN). We analyze various properties of such random subsets to derive simple, scalable, and an efficient path selection strategy that results in a k-fold increase in path options for any source-destination pair; options that consistently outperform Internet path selection. Finally, we design a prototype called source initiated frame restoration (SIFR) that employs random subsets to derive alternate paths and demonstrate its effectiveness in improving Internet video-QoE

Microgrid availability during natural disasters

textA common issue with the power grid during natural disasters is low availability. Many critical applications that are required during and after natural disasters, for rescue and logistical operations require highly available power supplies. Microgrids with distributed generation resources along with the grid provide promising solutions in order to improve the availability of power supply during natural disasters. However, distributed generators (DGs) such as diesel gensets depend on lifelines such as transportation networks whose behavior during disasters affects the genset fuel delivery systems and as a result affect the availability. Renewable sources depend on natural phenomena that have both deterministic as well as stochastic aspects to their behavior, which usually results in high variability in the output. Therefore DGs require energy storage in order to make them dispatchable sources. The microgrids availability depends on the availability characteristics of its distributed generators and energy storage and their dependent infrastructure, the distribution architecture and the power electronic interfaces. This dissertation presents models to evaluate the availability of power supply from the various distributed energy resources of a microgrid during natural disasters. The stochastic behavior of the distributed generators, storage and interfaces are modeled using Markov processes and the effect of the distribution network on availability is also considered. The presented models supported by empirical data can be hence used for microgrid planning.Electrical and Computer Engineerin

Predictive Energy Management of Islanded Microgrids with Photovoltaics and Energy Storage

Islanded microgrids powered primarily by photovoltaic (PV) arrays present a challenging control problem due to the intermittent production and the relatively close scale between the sources and the loads. Energy storage in such microgrids plays an important role in balancing supply with demand, and in extending operation during periods when the PV supply is not available or insufficient. The efficient operation of such microgrids requires effective management of all resources. A predictive energy management strategy can potentially avoid or effectively mitigate upcoming outages. This thesis presents an energy management system (EMS) for such microgrids. The EMS uses a predictive approach to set operational schedules in order to (a) prolong the supply to critical system loads and (2) minimize the chances and duration of system-wide outages, specifically through pre-emptive load shedding. Online weather forecast data has been combined with the PV system model to assess potential energy production over a 48 hour period. These predictions, along with load forecasts and a model of the energy storage system, are used to predict the state-of-charge of the storage devices and characterize potential power shortages. Pre-emptive load shedding is subsequently planned and executed to avert outages or minimize the duration of unavoidable outages. A bounding technique has also been proposed to account for uncertainties in estimates of the stored energy. The EMS has been implemented using an event-driven framework with network communication. The approach has been validated through simulations and experiments using recorded real-world solar irradiance data. The results show that the outage durations have been reduced by a factor of 87% to 100% for an example operating scenario, selected to demonstrate the features of the scheme. The impact of uncertainties in the prediction models has also been investigated, specifically for the PV system rating and the battery capacity. A technique has been developed to compensate for such uncertainties by analyzing the data streams from the source and storage units. The technique is applied to the developed EMS strategy, where it is able to shorten the total outage duration by a factor of 12% over a 42-day scenario exhibiting a variety of irradiance conditions

Using probability density functions to analyze the effect of external threats on the reliability of a South African power grid

Includes bibliographical references.The implications of reliability based decisions are a vital component of the control and management of power systems. Network planners strive to achieve an optimum level of investments and reliability. Network operators on the other hand aim at mitigating the costs associated with low levels of reliability. Effective decision making requires the management of uncertainties in the process applied. Thus, the modelling of reliability inputs, methodology applied in assessing network reliability and the interpretation of the reliability outputs should be carefully considered in reliability analyses. This thesis applies probability density functions, as opposed to deterministic averages, to model component failures. The probabilistic models are derived from historical failure data that is usually confined to finite ranges. Thus, the Beta distribution which has the unique characteristic of being able to be rescaled to a different finite range is selected. The thesis presents a new reliability evaluation technique that is based on the sequential Monte Carlo simulation. The technique applies a time-dependent probabilistic modelling approach to network reliability parameters. The approach uses the Beta probability density functions to model stochastic network parameters while taking into account seasonal and time-of- day influences. While the modelling approach can be applied to different aspects such as intermittent power supply and system loading, it is applied in this thesis to model the failure and repair rates of network components. Unlike the conventional sequential Monte Carlo methods, the new technique does not require the derivation of an inverse translation function for the probability distribution applied. The conventional Monte Carlo technique simulates the up and down component states when building their chronological cycles. The new technique applied here focuses instead on simulating the down states of component chronological cycles. The simulation determines the number of down states, when they will occur and how long they will last before developing the chronological cycle. Tests performed on a published network show that focussing on the down states significantly improves the computation times of a sequential Monte Carlo simulation. Also, the reliability results of the new sequential Monte Carlo technique are more dependent on the input failure models than on the number of simulation runs or the stopping criterion applied to a simulation and in this respect gives results different from present standard approaches. The thesis also applies the new approach on a real bulk power network. The bulk network is part of the South African power grid. Thus, the network threats considered and the corresponding failure data collected are typical of the real South African conditions. The thesis shows that probability density functions are superior to deterministic average values when modelling reliability parameters. Probability density functions reflect the variability in reliability parameters through their dispersion and skewness. The time-dependent probabilistic approach is applied in both planning and operational reliability analyses. The component failure models developed show that variability in network parameters is different for planning and operational reliability analyses. The thesis shows how the modelling approach is used to translate long-term failure models into operational (short-term) failure models. DigSilent and MATLAB software packages are used to perform network stability and reliability simulations in this thesis. The reliability simulation results of the time-dependent probabilistic approach show that the perception on a network's reliability is significantly impacted on when probability distribution functions that account for the full range of parameter values are applied as inputs. The results also show that the application of the probabilistic models to network components must be considered in the context of either network planning or operation. Furthermore, the risk-based approach applied to the interpretation of reliability indices significantly influences the perception on the network's reliability performance. The risk-based approach allows the uncertainty allowed in a network planning or operation decision to be quantified

Jamming Attack Resiliency and Performance Analysis of Cognitive Radio Communication Networks

Cognitive radio technology emerges as a promising solution for overcoming shortage and inefficient use of spectrum resources. In cognitive radio networks, secondary users, which are users equipped with cognitive radios, can opportunistically access spectrum assigned to primary users, the spectrum license holders. Although it improves spectrum utilization efficiency, this opportunistic spectrum access incurs undesired delays that can degrade the quality of service (QoS) of delay-sensitive applications substantially. It is therefore important to understand, model, and characterize these delays, as well as their dependency on primary user behaviors. Moreover, the lack of access priority leads to significant performance degradation when the network is under jamming attacks. It turns out that addressing jamming attacks while maintaining a desired QoS is very challenging. In this thesis, we characterize the properties of the random process that describes the availability of the opportunistic resources, and analytically model and analyze cognitive network average delays. Furthermore, we propose and study new techniques that mitigate jamming attacks in mobile cognitive radio networks. More specifically, this thesis consists of the following three complimentary frameworks:Bechir Hamdaoui1. Stochastic Resource Availability Modeling and Delay Analysis. In this framework, we define and characterize the properties of the random process that describes the availability of the opportunistic network resources. We apply the mean residual service time concept to derive an analytical solution for the cognitive network queueing delay. We model the service mechanism, and determine the manner in which it depends on spectrum availability. We show that the delay becomes unbounded if spectrum dynamics are not carefully considered in network design.2. Mitigating Jamming through Pseudorandom Time Hopping. In this framework, we propose and evaluate jamming countermeasure approaches for mobile cognitive users. We propose two time-based techniques which, unlike other existing frequency-based techniques, do not assume accessibility to multiple channels and hence do not rely on spectrum handoff to countermeasure jamming. In these two techniques, we allocate data over time based on cryptographic and estimation methods. We derive analytical expressions of the jamming, switching and error probabilities. Our findings show that our proposed technique outperforms other existing frequency-based techniques.3. Optimally Controlled Time-Hopping Anti-Jamming Technique. In this framework, we propose a jamming and environment aware resource allocation method for mobile cognitive users. We propose to mitigate jamming based on an optimal allocation of data over time. In addition, we optimally control network mobility to meet a desired QoS. Our findings show that our proposed technique achieves better QoS than those achieved by existing cryptographic methods while not compromising jamming resiliency.Keywords: Performance Analysis, Delay Analysis, Cognitive Radio Networks, Jamming Attack Resilienc