An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Application of computational physics within Northrop

An overview of Northrop programs in computational physics is presented. These programs depend on access to today's supercomputers, such as the Numerical Aerodynamical Simulator (NAS), and future growth on the continuing evolution of computational engines. Descriptions here are concentrated on the following areas: computational fluid dynamics (CFD), computational electromagnetics (CEM), computer architectures, and expert systems. Current efforts and future directions in these areas are presented. The impact of advances in the CFD area is described, and parallels are drawn to analagous developments in CEM. The relationship between advances in these areas and the development of advances (parallel) architectures and expert systems is also presented

Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature

Integrated Generation Management for Maximizing Renewable Resource Utilization

Two proposed methods to reduce the effective intermittency and improve the efficiency of wind power generation in the grid are spatial smoothing of wind generation and utilization of short term electrical storage to deal with lulls in production. In this thesis, based on a concept called integrated generation management (IGM), we explore the impact of spatial smoothing and the use of emerging plug-in hybrid electric vehicles (PHEVs) as a potential storage resource to the smart-grid. IGM combines nuclear, slow load-following coal, fast load-following natural gas, and renewable wind generation with an optimal control method to maximize the renewable generation and minimize the fossil generation. With the increasing penetration of PHEVs, the power grid is seeing new opportunities to make itself smarter than ever by utilizing those relatively large batteries. Based on current projections of PHEV market penetration and various wind generation scenarios, we demonstrate the potential for efficient wind integration at levels of approaching 30% of the aver- age electrical load with utilization efficiency exceeding 65%. At lower levels of integration (e.g. 15%), efficiencies are possible exceeding 85%

Microgrids & District Energy: Pathways To Sustainable Urban Development

A microgrid is an energy system specifically designed to meet some of the energy needs of a group of buildings, a campus, or an entire community. It can include local facilities that generate electricity, heating, and/or cooling; store energy; distribute the energy generated; and manage energy consumption intelligently and in real time. Microgrids enable economies of scale that facilitate local production of energy in ways that can advance cost reduction, sustainability, economic development, and resilience goals. As they often involve multiple stakeholders, and may encompass numerous distinct property boundaries, municipal involvement is often a key factor for successful implementation. This report provides an introduction to microgrid concepts, identifies the benefits and most common road blocks to implementation, and discusses proactive steps municipalities can take to advance economically viable and environmentally superior microgrids. It also offers advocacy suggestions for municipal leaders and officials to pursue at the state and regional level. The contents are targeted to municipal government staff but anyone looking for introductory material on microgrids should find it useful

Control design toolbox for large scale variable speed pitch regulated wind turbines

The trend towards large multi-MW wind turbineshas given new impetus to the development of wind turbine controllers.Additional objectives are being placed on the controllermaking the specification of the control system more complex. A new toolbox, which assists with most of the control design cycle,has been developed. Its purpose is to assist and guide the control system designer through the design cycle, thereby enabling faster design. With the choice of control strategy unrestricted,the toolbox is sufficiently flexible to support the design processfor the aforementioned more complex specifications

Stabilization of grid frequency through dynamic demand control

Frequency stability in electricity networks is essential to the maintenance of supply quality and security. This paper investigates whether a degree of built-in frequency stability could be provided by incorporating dynamic demand control into certain consumer appliances. Such devices would monitor system frequency (a universally available indicator of supply-demand imbalance) and switch the appliance on or off accordingly, striking a compromise between the needs of the appliance and the grid. A simplified computer model of a power grid was created incorporating aggregate generator inertia, governor action and load-frequency dependence plus refrigerators with dynamic demand controllers. Simulation modelling studies were carried out to investigate the system's response to a sudden loss of generation, and to fluctuating wind power. The studies indicated a significant delay in frequency-fall and a reduced dependence on rapidly deployable backup generation