Smart grid is a promising power delivery infrastructure integrated with communication and information technologies. Its bi-directional communication and electricity flow enable both utilities and customers to monitor, predict, and manage energy usage. It also advances energy and environmental sustainability through the integration of vast distributed energy resources. Deploying such a green electric system has enormous and far-reaching economic and social benefits. Nevertheless, increased interconnection and integration also introduce cyber-vulnerabilities into the grid. Failure to address these problems will hinder the modernization of the existing power system. In order to build a reliable smart grid, an overview of relevant cyber security and privacy issues is presented. Based on current literatures, several potential research fields are discussed at the end of this paper.

With the advances of power electronic technology, direct-driven permanent magnet synchronous generators (PMSGs) have increasingly drawn the interest of wind turbine manufacturers. At the present time, a commercial PMSG wind turbine primarily makes use of a passive rectifier followed by an insulated gate bipolar transistor (IGBT) inverter. Although a PMSG wind turbine with two back-to-back voltage source IGBT converters is considered more efficient, it has not been widely adopted by the wind power industry. This paper investigates both the conventional and a novel vector control mechanism for a PMSG wind turbine that has two side-by-side voltage source pulsewidth modulation converters. The proposed approach is based on a direct-current vector control mechanism for control of both machine- and grid-side converters of a PMSG wind turbine. Then, an optimal control strategy is developed for integrated control of PMSG maximum power extraction, reactive power, and grid voltage support controls. A transient system simulation using SimPowerSystem is built to investigate the performance of the conventional and proposed control techniques for the PMSG wind turbine under steady and gusty wind conditions. This paper shows that when using the direct-current vector control structure, a PMSG system has excellent performance in various aspects.

This paper focuses on developing an interdisciplinary mechanism that combines machine learning, optimization, and data structure design to build a demand response and home energy management system that can meet the needs of real-life conditions. The loads of major home appliances are divided into three categories: 1) containing fixed loads; 2) regulate-able loads; and 3) deferrable loads, based on which a decoupled demand response mechanism is proposed for optimal energy management of the three categories of loads. A learning-based demand response strategy is developed for regulateable loads with a special focus on home heating, ventilation, and air conditioning (HVACs). This paper presents how a learning system should be designed to learn the energy consumption model of HVACs, how to integrate the learning mechanism with optimization techniques to generate optimal demand response policies, and how a data structure should be designed to store and capture current home appliance behaviors properly. This paper investigates how the integrative and learning-based home energy management system behaves in a demand response framework. Case studies are conducted through an integrative simulation approach that combines a home energy simulator and MATLAB together for demand response evaluation.

This paper uses data collected at Central and South West Services Fort Davis wind farm (USA) to develop a neural network based prediction of power produced by each turbine. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes-lower-than-expected wind power may be an early indicator of a need for maintenance. In this paper, characteristics of wind power generation are first evaluated in order to establish the relative importance for the neural network. A four input neural network is developed and its performance is shown to be superior to the single parameter traditional model approach.

This paper focuses on economical operation of a microgrid (MG) in real-time. A novel dynamic energy management system (EMS) is developed to incorporate efficient management of energy storage system (ESS) into MG real-time dispatch while considering power flow constraints and uncertainties in load, renewable generation and real-time electricity price. The developed dynamic energy management mechanism does not require long-term forecast and optimization or distribution knowledge of the uncertainty, but can still optimize the long-term operational costs of MGs. First, the real-time scheduling problem is modeled as a finite-horizon Markov decision process (MDP) over a day. Then, approximate dynamic programming (ADP) and deep recurrent neural network (RNN) learning are employed to derive a near optimal real-time scheduling policy. Last, using real power grid data from California Independent System Operator (CAISO), a detailed simulation study is carried out to validate the effectiveness of the proposed method.

A grid-forming inverter (GFI) is an important component for the operation of an islanded microgrid. Its purpose, similar to a conventional slack bus generator, is to build up a reference voltage for other distributed generating units in the microgrid. Usually, a nested-loop proportional plus integral (PI) control structure is employed to control a GFI in a dq reference frame. However, conventional PI-based nested-loop control method has a deteriorative performance under parameter variations. In this paper, a novel nested-loop control strategy is proposed for control of a GFI system containing an LC output filter and loads. The proposed method does not require a precise model for the inverter system and can better deal with uncertainties and LC filter resonance without using any passive or active damping mechanisms. It utilizes a sliding-mode control in the inner current loop and a mixed H ₂ /H _∞ optimal control in the outer voltage loop, which provides the advantages of constant switching frequency, low total harmonic distortion, robustness against parameters variations, and fast transient response. The simulation and hardware experiments presented in this paper demonstrate the proposed controller's improved transient and steadystate performance in various key criteria, over conventional PI-based nested-loop control strategy.