Browsing by Subject "Smart grid"

Now showing 1 - 3 of 3
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    Dynamic learning and resource management under uncertainties for smart grid and cognitive radio networks
    (2014-05) Yahyasoltani, Nasin
    The importance of timely applications and decisions in dynamic environments, has led to the integration of intelligent networks to increase efficiency and end-user satisfaction in various application domains including telecommunication and power grid networks. Contemporary intelligent networks require advanced statistical signal processing and optimization tools to learn, infer and control their operation. This integration poses new challenges and has witnessed the emergence of novel resource management and learning techniques to cope with dynamics. In addition, in order to have implementable resource management algorithms, it is crucial to model the underlying sources of uncertainty in the optimization framework. This thesis develops algorithms for resource allocation under channel uncertainty in cognitive radio (CR) communication networks and contributes to demand coordination under uncertainty in power networks.Demand coordination through real-time pricing is addressed first by capitalizing on the uncertainty involved in the consumption behavior of consumers. Prerequisite to the demand coordination task is learning the uncertainty present in power consumption data. The dependency of consumers' consumption behavior on the announced prices and their neighbors' behavior, is modeled through graphical models. In particular, the electric vehicle (EV) consumers are considered and the adopted model also captures dynamics of EV consumers' time-varying charging decisions. Leveraging the online convex optimization (OCO) framework, an online algorithm for tracking the model is devised. With minimal assumptions on the structure of the temporal dynamics, and while accounting for the possibly adversarial consumption behavior of consumers, the proposed online algorithm provides performance guarantees. The probability distributions obtained through the tracking algorithm are then deployed as input to stochastic economic profit maximization for real-time price setting.Learning in the presence of missing data is a pervasive problem in statistical data analysis. Next, attention is turned to tracking the dynamic charging behavior of EV consumers, when at each time slot some of the consumers' consumption decisions are possibly missing. The problem amounts to online classification with missing labels. An online algorithm is proposed to wed real-time estimation of the missing data with learning of complete data in the OCO framework.As regards CR networks, this thesis introduces novel resource allocation algorithms for orthogonal frequency-division multiple access (OFDMA) CR under channel uncertainty where the unique approaches can be fitted to a class of large-scale robust mixed-integer problems. Due to the lack of cooperation of the licensed system, CRs must resort to less efficient channel estimation techniques thus incurring an inevitable channel estimation error. It is shown that CR interference constraints under channel uncertainty can be cast as chance constraints. On the other hand, instead of just modeling the user rates by logarithmic functions of transmit-powers, justified under ideal Gaussian coding, practical finite-alphabet constellations are adopted which leads to an optimization objective of a weighted sum of mutual information. When multiple users are present, due to the combinatorial search for optimal subcarrier assignment, the problem is non-convex and hard to solve, as the optimization variables are coupled across all subcarriers. To circumvent the resulting computational hurdle, tight and conservative approximations of the chance constraint are introduced to break the coupling and enforce separability per subcarrier. The separableproblem across subcarriers opens the door to the dual decomposition approach, which leads to a near-optimal and computationally efficient solution.
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    Dynamic power flow control for a smart micro-grid by a power electronic transformer.
    (2011-05) Shah, Jalpa Kaushil
    A novel strategy, for control of the power flow for a smart micro-grid is proposed. The utility grid power is dynamically controlled by a Power Electronic Transformer (PET). A 60 Hz, step-down transformer is generally used at the point of common coupling (PCC), to connect the micro-grid to the power system grid. Substitution of the conventional 60Hz transformer, by a PET, results in enhanced micro-grid power management system, during grid-connected operation. The smart micro-grid is a set of controllable loads and distributed energy resources (DER); both renewable and non-renewable; that supply demand of a group of customers. The proposed dynamic power limiter (also referred to as PET) is a high-frequency, isolated power-converter system, comprised of a highfrequency step-down transformer and three-phase to single-phase matrix converters. The matrix converters are modulated with a novel pulse width modulation (PWM) strategy for a bi-directional power flow control. The output of the matrix converter generates a high frequency (few kHz) pulsating single phase AC at the primary and secondary of the transformer, which are phase shifted for active power control. The PET also allows voltage regulation by control of reactive power. The entire system; represented as two, three-phase AC systems with an intermediate high-frequency transformer; is simulated using Matlab/Simulink. The equivalent system has utility grid at the input side and a micro-grid on the output side. The micro-grid is modeled as an interconnected system consisting of set of DERs and smart loads. The simulation analyzes the change in micro grid’s power generation and consumption in response to the change in its local grid frequency, upon limiting the utility grid power. The PET hence restores the system frequency by adjusting supply and demand at the PCC. The micro-grid can now participate in frequency regulation for the main grid. The simulation results are obtained to verify the operation and claims of the dynamic power limiter as stated below: 1. Restricted active power flow to the micro-grid, at a desired value determined by the main utility grid. 2. Utilization of the change in local grid frequency, to dynamically control the active power generation or consumption within the micro-grid. 3. Decentralized control of the DERs as well as the controllable loads, which operate synchronously, to supply the demand within the micro-grid. 4. Bi-directional active-power flow capability at the PCC. 5. Voltage regulation by control of reactive power. 6. Contribution of the micro-grid components in frequency regulation of the main grid. 7. Smooth transition from islanding to grid-connected mode of the micro-grid, without the need of grid synchronization. 8. Extra degree of freedom due to the presence of active-power controller in a possible deregulation and market strategy within the micro-grid.
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    Resource management in wireless networks and the smart power grid.
    (2012-06) Gatsis, Nikolaos
    Optimal resource management is a crucial task in a plethora of scientific fields, including wireless communication and electric power networks, where it ensures efficient operation and user satisfaction. The pressing need to modernize the aging power grid has culminated to a vision encouraging interaction of the end users with the grid through demand response, which amounts to electricity end users adapting their power consumption in response to pricing schemes varying over time (e.g., every hour or day). By the same token, delivering data, voice, and video seamlessly over wireless networks with the quality-of-service demanded by today's multimedia applications requires optimal link-adaptive allocation of the available resources, e.g., power, to the different network nodes and layers. This thesis develops algorithms for (a) scheduling of demand response in the smart power grid, and (b) cross-layer wireless network design. First, demand response is considered in a multiple-residence setup. The utility company adopts a cost function representing the cost of providing energy to end users. Each residential end user has a base load, two types of adjustable loads, and possibly a storage device. The first load type must consume a specified amount of energy over the scheduling horizon, but the consumption can be adjusted across different slots. Charging a plug-in hybrid electric vehicle is an example. The second type does not entail a total energy requirement, but operation away from a user-specified level results in user dissatisfaction. The research issue amounts to minimizing the electricity provider cost plus the total user dissatisfaction, subject to the individual constraints of the loads. The problem can be solved by a distributed subgradient method. The utility company and the end users exchange information through the Advanced Metering Infrastructure (AMI)---a two-way communication network---in order to converge to the optimal amount of electricity production and the optimal power consumption schedule. The algorithm finds near-optimal schedules even when AMI messages are lost, which can happen in the presence of malfunctions or noise in the communications network. The algorithm amounts to a subgradient iteration with outdated Lagrange multipliers, for which convergence results of wide scope are established. Next, attention is turned to an energy consumption scheduling problem for a single residential end user, but with an added complexity. Each adjustable load is interruptible in the sense that the load can be either operated (resulting in nonzero power consump- tion), or not operated (resulting in zero power consumption). The task amounts to minimizing the cost of electricity plus user dissatisfaction, subject to individual load consumption constraints. The resulting problem is nonconvex, but it is shown to have zero duality gap if a continuous-time horizon is considered. This opens up the possibility of using Lagrangian dual algorithms without loss of optimality in order to come up with efficient demand response scheduling schemes. As regards wireless networking, the challenge is to jointly optimize application-level rates, routes, link capacities, power consumption, and power allocation across frequency tones, neighboring terminals, and fading states. The physical layer is interference-limited, whereby network terminals treat interference as noise. Provably convergent algorithms yield (near-)optimal end-to-end rates, multicommodity flows, link capacities, and average powers. These design variables are obtained offline, and are subsequently used for control during network operation. Moreover, physical layer power allocation algorithms that are seamlessly integrated into layered architectures are developed using successive convex approximations.