Optimal power dispatch in networks of high-dimensional models of synchronous machines

This paper investigates the problem of optimal frequency regulation of multi-machine power networks where each synchronous machine is described by a sixth order model. By analyzing the physical energy stored in the network and the generators, a port-Hamiltonian representation of the multi-machine system is obtained. Moreover, it is shown that the open-loop system is passive with respect to its steady states which implies that passive controllers can be used to control the multi-machine network. As a special case, a distributed consensus based controller is designed that regulates the frequency and minimizes a global quadratic generation cost in the presence of a constant unknown demand. In addition, the proposed controller allows freedom in choosing any desired connected undirected weighted communication graph.Comment: 7 pages, submitted to Conference on Decision and Control 201

Wind Power Integration Control Technology for Sustainable, Stable and Smart Trend: A Review

The key to achieve sustainable development of wind power is integration absorptive, involving the generation, transmission, distribution, operation, scheduling plurality of electric production processes. The paper based on the analyses of the situation of wind power development and grid integration requirements for wind power, summarized wind power integration technologies' development, characteristics, applicability and trends from five aspects, grid mode, control technology, transmission technology, scheduling, and forecasting techniques. And friendly integration, intelligent control, reliable transmission, and accurate prediction would be the major trends of wind power integration, these five aspects interactive and mutually reinforcing would realize common development both grid and wind power, both economic and ecological

DEEP LEARNING BASED POWER SYSTEM STABILITY ASSESSMENT FOR REDUCED WECC SYSTEM

Power system stability is the ability of power system, for a giving initial operating condition, to reach a new operation condition with most of the system variables bounded in normal range after subjecting to a short or long disturbance. Traditional power system stability mainly uses time-domain simulation which is very time consuming and only appropriate for offline assessment. Nowadays, with increasing penetration of inverter based renewable, large-scale distributed energy storage integration and operation uncertainty brought by weather and electricity market, system dynamic and operating condition is more dramatic, and traditional power system stability assessment based on scheduling may not be able to cover all the real-time dispatch scenarios, also online assessment and self-awareness for modern power system becomes more and more important and urgent for power system dynamic security. With the development of fast computation resources and more available online dataset, machine learning techniques have been developed and applied to many areas recently and could potentially applied to power system application. In this dissertation, a deep learning-based power system stability assessment is proposed. Its accurate and fast assessment for power system dynamic security is useful in many places, including day-ahead scheduling, real-time operation, and long-term planning. The simplified Western Electricity Coordinating Council (WECC) 240-bus system with renewable penetration up to 49.2% is used as the study system. The dataset generation, model training and error analysis are demonstrated, and the results show that the proposed deep learning-based method can accurately and fast predict the power system stability. Compared with traditional time simulation method, its near millisecond prediction makes the online assessment and self-awareness possible in future power system application

An energy-based analysis of reduced-order models of (networked) synchronous machines

Stability of power networks is an increasingly important topic because of the high penetration of renewable distributed generation units. This requires the development of advanced techniques for the analysis and controller design of power networks. Although there are widely accepted reduced-order models to describe the power network dynamics, they are commonly presented without details about the reduction procedure. The present article aims to provide a modular model derivation of multi-machine power networks. Starting from first-principle fundamental physics, we present detailed dynamical models of synchronous machines and clearly state the underlying assumptions which lead to some of the standard reduced-order multi-machine models. In addition, the energy functions for these models are derived, which allows to represent the multi-machine systems as port-Hamiltonian systems. Moreover, the systems are proven to be shifted passive, which permits for a power-preserving interconnection with other passive components. [GRAPHICS]

Particle swarm optimization of air-cored axial flux permanent magnet generator for small-scale wind power systems

Axial flux permanent magnet synchronous machines with aircored configuration is particular suitable for small scale wind power system due to their advantages of low synchronous reactance, cogging torque free, high efficiency and high power factor. However, due to the number of machine parameters, with some tightly `coupled' with each other, optimisation of the design could become extremely challenging by conventional analytical means. Here, the particle swarm optimization method is used in the design of an axial flux permanent magnet generator for small-scale wind power system. Five inter-dependent design parameters are adjusted simultaneously to achieve an optimal solution for the application. Three-dimensional finite element analysis is employed to evaluate the electromagnetic performance for the optimization. The results show the proposed optimization method is efficient and with fast convergence

Optimizing Nonlinear Dynamics in Energy System Planning and Control

Understanding the physical dynamics underlying energy systems is essential in achieving stable operations, and reasoning about restoration and expansion planning. The mathematics governing energy system dynamics are often described by high-order differential equations. Optimizing over these equations can be a computationally challenging exercise. To overcome these challenges, early studies focused on reduced / linearized models failing to capture system dynamics accurately. This thesis considers generalizing and improving existing optimization methods in energy systems to accurately represent these dynamics. We revisit three applications in power transmission and gas pipeline systems. Our first application focuses on power system restoration planning. We examine transient effects in power restoration and generalize the Restoration Ordering Problem formulation with standing phase angle and voltage difference constraints to enhance transient stability. Our new proposal can reduce rotor swings of synchronous generators by over 50\% and have negligible impacts on the blackout size, which is optimized holistically. Our second application focuses on transmission line switching in power system operations. We propose an automatic routine actively considering transient stability during optimization. Our main contribution is a nonlinear optimization model using trapezoidal discretization over the 2-axis generator model with an automatic voltage regulator (AVR). We show that congestion can lead to rotor instability, and variables controlling set-points of automatic voltage regulators are critical to ensure oscillation stability. Our results were validated against PowerWorld simulations and exhibit an average error in the order of 0.001 degrees for rotor angles. Our third contribution focuses on natural gas compressor optimization in natural gas pipeline systems. We consider the Dynamic Optimal Gas Flow problem, which generalizes the Optimal Gas Flow Problem to capture natural gas dynamics in a pipeline network. Our main contribution is a computationally efficient method to minimize gas compression costs under dynamic conditions where deliveries to customers are described by time-dependent mass flows. The scheme yields solutions that are feasible for the continuous problem and practical from an operational standpoint. Scalability of the scheme is demonstrated using realistic benchmark data