Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Overview of current development in electrical energy storage technologies and the application potential in power system operation

In solving the electrical power systems economic dispatch (ED) problem, the goal is to find the optimal allocation of output power among the various generators available to serve the system load. With the continuing search for alternatives to conventional energy sources, it is necessary to include wind energy conversion system (WECS) generators in the ED problem. This paper develops a model to include the WECS in the ED problem, and in addition to the classic economic dispatch factors, factors to account for both overestimation and underestimation of available wind power are included. With the stochastic wind speed characterization based on the Weibull probability density function, the optimization problem is numerically solved for a scenario involving two conventional and two wind-powered generators. Optimal solutions are presented for various values of the input parameters, and these solutions demonstrate that the allocation of system generation capacity may be influenced by multipliers related to the risk of overestimation and to the cost of underestimation of available wind power.

An Economic Dispatch Model Incorporating Wind Power

Energy is an essential ingredient of socio-economic development and economic growth. Renewable energy sources like wind energy is indigenous and can help in reducing the dependency on fossil fuels. Wind is the indirect form of solar energy and is always being replenished by the sun. Wind is caused by differential heating of the earth's surface by the sun. It has been estimated that roughly 10 million MW of energy are continuously available in the earth's wind. Wind energy provides a variable and environmental friendly option and national energy security at a time when decreasing global reserves of fossil fuels threatens the long-term sustainability of global economy. This paper reviews the wind resources assessment models, site selection models and aerodynamic models including wake effect. The different existing performance and reliability evaluation models, various problems related to wind turbine components (blade, gearbox, generator and transformer) and grid for wind energy system have been discussed. This paper also reviews different techniques and loads for design, control systems and economics of wind energy conversion system.

A review of wind energy technologies

Hybrid renewable energy systems (HRES) are becoming popular for remote area power generation applications due to advances in renewable energy technologies and subsequent rise in prices of petroleum products. Economic aspects of these technologies are sufficiently promising to include them in developing power generation capacity for developing countries. Research and development efforts in solar, wind, and other renewable energy technologies are required to continue for, improving their performance, establishing techniques for accurately predicting their output and reliably integrating them with other conventional generating sources. The paper describes methodologies to model HRES components, HRES designs and their evaluation. The trends in HRES design show that the hybrid PV/wind energy systems are becoming gaining popular. The issues related to penetration of these energy systems in the present distribution network are highlighted.

Modeling of hybrid renewable energy systems

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.

Using neural networks to estimate wind turbine power generation

This paper examines and compares regression and artificial neural network models used for the estimation of wind turbine power curves. First, characteristics of wind turbine power generation are investigated. Then, models for turbine power curve estimation using both regression and neural network methods are presented and compared. The parameter estimates for the regression model and training of the neural network are completed with the wind farm data, and the performances of the two models are studied. The regression model is shown to be function dependent, and the neural network model obtains its power curve estimation through learning. The neural network model is found to possess better performance than the regression model for turbine power curve estimation under complicated influence factors.

Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation

Microgrids, as individual controllable entities that can operate either islanded from or interconnected to main power grid, have emerged as a promising solution to improving energy efficiency and resilience to disturbance. When linked together in a self-organized manner, a cluster of microgrids can significantly enhance the reliability and power quality for critical load. With this insight, we study the self-organization and decentralized energy management of a microgrid cluster islanded from main grid after a disruptive event. In the self-organization stage, depending on the available generation resources, each microgrid decides on whether to connect to the cluster; and the microgrid energy management systems then “negotiate” on the optimal power exchange with each other in the cluster. Once the power exchange is determined, the generation and storage resources of each microgrid are managed to guarantee the energy reliability of critical loads and overall energy efficiency, through a scheduling procedure followed by a dispatch procedure. The effectiveness of the proposed method is revealed via case studies.

Reliability-constrained self-organization and energy management towards a resilient microgrid cluster

Microgrid combines with Distributed Energy Resources (DERs), energy storage, and controllable loads. It is connected to the maingrid through the point of common coupling (PCC). Economic dispatch using reduced gradient method is implemented for the optimization of energy in the microgrid using MATLAB. The optimization is obtained by minimizing the cost function of the system while meeting the load demand. The operation & maintenance cost, and investment costs are considered in the cost functions of the microsources. The minimum total cost of the system is obtained by comparing different scenarios of the microsources in the microgrid.

Economic dispatch optimization of microgrid in islanded mode

A variety of basic magnetic flux compression (MFC) generator geometries have been tested during the last three decades. Though size and operating regimes differ widely, it is apparent that the helical flux compression generator is the most promising concept with respect to current amplification and compactness. Though the geometry of the helical generator (dynamically expanding armature in the center of a current carrying helix) seems to be basic, it turns out that the understanding of all involved processes is rather difficult. This fact is apparent from the present lack of a computer model that is solely based on physical principles and manages without heuristic factors. A simple generator was designed to address flux and current losses of the helical generator. The generator's maximum current amplitude is given as a function of the seed current and the resulting "seed-current" spread is compared to the output of state-of-the-art computer models. Temporally resolved current and current time derivative signals are compared as well. The detailed generator geometry is introduced in order to facilitate future computer code bench marking or development. The impact of this research on the present understanding of magnetic flux losses in helical MFC generators is briefly discussed.

Michael Giesselmann

Papers

Using neural networks to estimate wind turbine power generation

Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation

Reliability-constrained self-organization and energy management towards a resilient microgrid cluster

Economic dispatch optimization of microgrid in islanded mode

Electrical behavior of a simple helical flux compression generator for code benchmarking