: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

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Nonlinear Time Series Analysis.

(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

ライブラリー Annual Energy Outlook 2000

Elementary Fixed Point Theorems * Theorem of Borsuk and Topological Transversality * Homology and Fixed Points * Leray-Schauder Degree and Fixed Point Index * The Lefschetz-Hopf Theory * Selected Topics * Index

Fixed Point Theory

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Stochastic Processes And Filtering Theory

Load modeling has significant impact on power system studies. This paper presents a review on load modeling and identification techniques. Load models can be classified into two broad categories: 1) static and 2) dynamic models, while there are two types of approaches to identify model parameters: 1) measurement-based and 2) component-based. Load modeling has received more attention in recent years because of the renewable integration, demand-side management, and smart metering devices. However, the commonly used load models are outdated, and cannot represent emerging loads. There is a need to systematically review existing load modeling techniques and suggest future research directions to meet the increasing interests from industry and academia. In this paper, we provide a thorough survey on the academic research progress and industry practices, and highlight existing issues and new trends in load modeling.

Load Modeling—A Review

In this paper, a day-ahead market-clearing model for smart distribution systems is proposed. Various types of distributed energy resources (DERs), such as distributed energy storage, distributed generators, microgrids, and load aggregators, can bid into the day-ahead distribution-level electricity market. Considering system Volt/VAR control, network reconfiguration, and interactions with the wholesale market, an optimization model is built to clear the day-ahead market, through which the distribution locational marginal pricing (DLMPs) for both active power and reactive power are determined. Through derivations of the Lagrangian function and sensitivity factors, DLMPs are decomposed to five components (i.e., marginal costs for active power, reactive power, congestion, voltage support, and loss), which provide price signals to motivate DERs to contribute to congestion management and voltage support. Finally, case studies demonstrate the effectiveness of the proposed method.

Distribution Locational Marginal Pricing (DLMP) for Congestion Management and Voltage Support

The power grid is rapidly transforming, and while recent grid innovations increased the utilization of advanced control methods, the next-generation grid demands technologies that enable the integration of distributed energy resources (DERs)—and consumers that both seamlessly buy and sell electricity. This paper develops an optimization model and blockchain-based architecture to manage the operation of crowdsourced energy systems (CESs), with peer-to-peer (P2P) energy trading transactions (ETTs). An operational model of CESs in distribution networks is presented considering various types of ETT and crowdsourcees. Then, a two-phase operation algorithm is presented: Phase I focuses on the day-ahead scheduling of generation and controllable DERs, whereas Phase II is developed for hour-ahead or real-time operation of distribution networks. The developed approach supports seamless P2P energy trading between individual prosumers and/or the utility. The presented operational model can also be used to operate islanded microgrids. The CES framework and the operation algorithm are then prototyped through an efficient blockchain implementation, namely, the IBM Hyperledger Fabric. This implementation allows the system operator to manage the network users to seamlessly trade energy. Case studies and prototype illustration are provided.

Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids

This paper presents a review of the literature on State Estimation (SE) in power systems While covering some works related to SE in transmission systems, the main focus of this paper is Distribution System State Estimation (DSSE) The paper discusses a few critical topics of DSSE, including mathematical problem formulation, application of pseudo-measurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security Both conventional and modern data-driven and probabilistic techniques have been reviewed This paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of DSSE

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

Wind speed forecasting is still a challenge due to the stochastic and highly varying characteristics of wind. In this paper, a graph deep learning model is proposed to learn the powerful spatio-temporal features from the wind speed and wind direction data in neighboring wind farms. The underlying wind farms are modeled by an undirected graph, where each node corresponds to a wind site. For each node, temporal features are extracted using a long short-term memory Network. A scalable graph convolutional deep learning architecture (GCDLA), motivated by the localized first-order approximation of spectral graph convolutions, leverages the extracted temporal features to forecast the wind-speed time series of the whole graph nodes. The proposed GCDLA captures spatial wind features as well as deep temporal features of the wind data at each wind site. To further improve the prediction accuracy and capture robust latent representations, the rough set theory is incorporated with the proposed graph deep network by introducing upper and lower bound parameter approximations in the model. Simulation results show the advantages of capturing deep spatial and temporal interval features in the proposed framework compared to the state-of-the-art deep learning models as well as shallow architectures in the recent literature.

Jianhui Wang

Papers

Load Modeling—A Review

Distribution Locational Marginal Pricing (DLMP) for Congestion Management and Voltage Support

Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting