High-Performance Multi-Fuel AMTEC Power System

2000 ◽  
Author(s):  
Peter J. Loftus ◽  
Robert K. Sievers
Author(s):  
Catherine H. Crawford ◽  
Piotr Padkowski ◽  
Tomasz Baranski ◽  
Angela Czubak ◽  
Lukasz Raszka

Author(s):  
Paul A. Berman ◽  
Jeffrey A. Hynds

In the traditional pressurized fluid bed (PFB) power system, the PFB is located in the highest pressure portion of the power cycle, Figure 1. This results in the smallest volume flow through the PFB, but also requires the combustion products to flow through the entire expansion train. This is not expected to be a major problem when the PFB temperature is limited to 1600°F for effective sulfur capture and to avoid alkali vapors in the products of combustion. However, when topping combustion is added ahead of the turbine so as to reach state-of-the-art turbine inlet temperatures, a major risk for turbine corrosion and fouling develops.


Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4036 ◽  
Author(s):  
Kati Sidwall ◽  
Paul Forsyth

Real-time simulation and hardware-in-the-loop testing have increased in popularity as grid modernization has become more widespread. As the power system has undergone an evolution in the types of generator and load deployed on the system, the penetration and capabilities of automation and monitoring systems, and the structure of the energy market, a corresponding evolution has taken place in the way we model and test power system behavior and equipment. Consequently, emerging requirements for real-time simulators are very high when it comes to simulation fidelity, interfacing options, and ease of use. Ongoing advancements from a processing hardware, graphical user interface, and power system modelling perspective have enabled utilities, manufacturers, educational and research institutions, and consultants to apply real-time simulation to grid modernization projects. This paper summarizes various recent advancements from a particular simulator manufacturer, RTDS Technologies Inc. Many of these advancements have been enabled by growth in the high-performance processing space and the emerging availability of high-end processors for embedded designs. Others have been initiated or supported by developer participation in power industry working groups and study committees.


2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Ons Zarrad ◽  
Mohamed Ali Hajjaji ◽  
Aymen Jemaa ◽  
Mohamed Nejib Mansouri

In our day, solar energy and wind energy are becoming more and more used as renewable sources by various countries for different uses such as in an isolated home. These energies admit a unique limitation related to the characteristic of energy instability. For this, the objective of this manuscript is to command and synchronize the power flow of a hybrid system using two sources of energy (solar and wind). The first contribution of our work is the utilization of an artificial neural network controller to command, at fixed atmospheric conditions, the maximum power point. The second contribution is the optimization of the system respecting real-time constraints to increase a generating system performance. As a matter of fact, the proposed system and the controller are modeled using MATLAB/Simulink and a Xilinx System Generator is utilized for hardware implementation. The simulation results, compared with other works in the literature, present high performance, efficiency, and precision. The suggested system and its control strategy give the opportunity of optimizing the hybrid power system performance, which is utilized in rural pumping or other smart house applications.


2012 ◽  
Vol 2012 ◽  
pp. 1-19
Author(s):  
G. Ozdemir Dag ◽  
Mustafa Bagriyanik

The unscheduled power flow problem needs to be minimized or controlled as soon as possible in a deregulated power system since the transmission systems are mostly operated at their power-carrying limits or very close to it. The time spent for simulations to determine the current states of all the system and control variables of the interconnected power system is important. Taking necessary action in case of any failure of equipment or any other occurrence of an undesired situation could be critical. Using supercomputing facilities and parallel computing techniques together decreases the computation time greatly. In this study, a parallel implementation of a multiobjective optimization approach based on both genetic algorithms and fuzzy decision making to manage unscheduled flows is presented. Parallel computation techniques are applied using supercomputers (high-performance computers). The proposed method is applied to the IEEE 300 bus test system. Two different cases for some parameters of GA are considered to see the power of parallel computation technique. Then the simulation results are presented.


Author(s):  
Senthil Krishnamurthy ◽  
Raynitchka Tzoneva

<p>Multi-area Combined Economic Emission Dispatch (MACEED) problem is an optimization task in power system operation for allocating the amount of generation to the committed units within the system areas. Its objective is to minimize the fuel cost and the quantity of emissions subject to the power balance, generator limits, transmission line and tie-line constraints. The solutions of the MACEED problem in the conditions of deregulation are difficult, due to the model size, nonlinearities, and the big number of interconnections, and require intensive computations in real-time. High-Performance Computing (HPC) gives possibilities for the reduction of the problem complexity and the time for calculation by the use of parallel processing techniques for running advanced application programs efficiently, reliably and quickly. These applications are considered as very new in the power system control centers because there are not available optimization methods and software based on them that can solve the MACEED problem in parallel, paying attention to the existence of the power system areas and the tie-lines between them. A decomposition-coordinating method based on Lagrange’s function is developed in this paper. Investigations of the performance of the method are done using IEEE benchmark power system models.</p>


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