The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

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Smart Grid — The New and Improved Power Grid: A Survey

Smart Grid - The New and Improved Power Grid:

Introduction to stochastic programming

This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol’s method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent. Mathematical modeling of complex systems often requires sensitivity analysis to determine how an output variable of interest is influenced by individual or subsets of input variables. A traditional local sensitivity analysis entails gradients or derivatives, often invoked in design optimization, describing changes in the model response due to the local variation of input. Depending on the model output, obtaining gradients or derivatives, if they exist, can be simple or difficult. In contrast, a global sensitivity analysis (GSA), increasingly becoming mainstream, characterizes how the global variation of input, due to its uncertainty, impacts the overall uncertain behavior of the model. In other words, GSA constitutes the study of how the output uncertainty from a mathematical model is divvied up, qualitatively or quantitatively, to distinct sources of input variation in the model [1].

Reliability Engineering and System Safety

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

https://sites.ualberta.ca/~ztian/index_files/Papers/Renewable%20Energy_2011.pdf

Condition based maintenance optimization for wind power generation systems under continuous monitoring

http://www.relialab.org/Upload/files/01_02_Multiple%20Failure%20Modes%20FMEA_EFA_2011.pdf

Multiple failure modes analysis and weighted risk priority number evaluation in fmea

Summary & Conclusions-This paper addresses system reliability optimization when component reliability estimates are treated as random variables with estimation uncertainty. System reliability optimization algorithms generally assume that component reliability values are known exactly, i.e., they are deterministic. In practice, that is rarely the case. For risk-averse system design, the estimation uncertainty, propagated from the component estimates, may result in unacceptable estimation uncertainty at the system-level. The system design problem is thus formulated with multiple objectives: (1) to maximize the system reliability estimate, and (2) to minimize its associated variance. This formulation of the reliability optimization is new, and the resulting solutions offer a unique perspective on system design. Once formulated in this manner, standard multiple objective concepts, including Pareto optimality, were used to determine solutions. Pareto optimality is an attractive alternative for this type of problem. It provides decision-makers the flexibility to choose the best-compromise solution. Pareto optimal solutions were found by solving a series of weighted objective problems with incrementally varied weights. Several sample systems are solved to demonstrate the approach presented in this paper. The first example is a hypothetical series-parallel system, and the second example is the fault tolerant distributed system architecture for a voice recognition system. The results indicate that significantly different designs are obtained when the formulation incorporates estimation uncertainty. If decision-makers are risk averse, and wish to consider estimation uncertainty, previously available methodologies are likely to be inadequate.

System optimization with component reliability estimation uncertainty: a multi-criteria approach

Maximizing system availability through joint decision on component redundancy and spares inventory

The production of wind energy often involves uncertainties due to the stochastic nature of wind speeds and the variation of the power curve. The latter occurs when a fleet of wind turbine generators (WTG) of the same type are deployed in the wind farm, yet each generator may produce different amounts of powers given the same wind speed. This paper aims to characterize the dynamics of the wind energy production and estimate the power uncertainty when a WTG operates between the cut-in wind speed and the rated wind speed. We propose a probabilistic model to characterize the dynamics of the output power assuming the power follows the normal distribution with a varying mean and a constant standard deviation. Based on that condition, the mean and the variance for the total wind energy production are derived by incorporating the stochastic wind speed and the power dynamics. Both simulations and numerical examples are provided to verify and illustrate the new model, respectively.

Tongdan Jin

Papers

Condition based maintenance optimization for wind power generation systems under continuous monitoring

Multiple failure modes analysis and weighted risk priority number evaluation in fmea

System optimization with component reliability estimation uncertainty: a multi-criteria approach

Maximizing system availability through joint decision on component redundancy and spares inventory

Uncertainty analysis for wind energy production with dynamic power curves