The adequacy of the present practice in dynamic aggregated modelling of wind farm systems

Large offshore wind farms are usually composed of several hundred individual wind turbines, each turbine having its own complex set of dynamics. The analysis of the dynamic interaction between wind turbine generators (WTG), interconnecting ac cables, and voltage source converter (VSC) based High Voltage DC (HVDC) system is difficult because of the complexity and the scale of the entire system. The detailed modelling and modal analysis of a representative wind farm system reveal the presence of several critical resonant modes within the system. Several of these modes have frequencies close to harmonics of the power system frequency with poor damping. From a computational perspective the aggregation of the physical model is necessary in order to reduce the degree of complexity to a practical level. This paper focuses on the present practices of the aggregation of the WTGs and the collection system, and their influence on the damping and frequency characteristics of the critical oscillatory modes. The effect of aggregation on the critical modes are discussed using modal analysis and dynamic simulation. The adequacy of aggregation method is discussed

Characterising aggregations with existence dependency.

Abstract: The concept of aggregation is considered as one of the basic principles in object-oriented analysis. There is however no standard definition of this concept and each object-oriented analysis method has its own definition of aggregation. The aim of this paper is not to discuss the different types of aggregation that exist. However, having assessed the complexity of the concept, we will illustrate how a basic set of formal concepts is sufficient to define of the structural and behavioral aspects of different existing flavours of aggregation. If a development method wants to offer a rich concept such as aggregation, it can define the semantics of the desired flavour of the aggregation using these core formal concepts. Analysts then have the choice to use the aggregation defined by the method or to fall back on the core concepts if a different flavour of aggregation is needed to model the situation at hand.Principles; Model;

The Computational Complexity of Generating Random Fractals

In this paper we examine a number of models that generate random fractals. The models are studied using the tools of computational complexity theory from the perspective of parallel computation. Diffusion limited aggregation and several widely used algorithms for equilibrating the Ising model are shown to be highly sequential; it is unlikely they can be simulated efficiently in parallel. This is in contrast to Mandelbrot percolation that can be simulated in constant parallel time. Our research helps shed light on the intrinsic complexity of these models relative to each other and to different growth processes that have been recently studied using complexity theory. In addition, the results may serve as a guide to simulation physics.Comment: 28 pages, LATEX, 8 Postscript figures available from [email protected]

A Graph Model for Opportunistic Network Coding

Recent advancements in graph-based analysis and solutions of instantly decodable network coding (IDNC) trigger the interest to extend them to more complicated opportunistic network coding (ONC) scenarios, with limited increase in complexity. In this paper, we design a simple IDNC-like graph model for a specific subclass of ONC, by introducing a more generalized definition of its vertices and the notion of vertex aggregation in order to represent the storage of non-instantly-decodable packets in ONC. Based on this representation, we determine the set of pairwise vertex adjacency conditions that can populate this graph with edges so as to guarantee decodability or aggregation for the vertices of each clique in this graph. We then develop the algorithmic procedures that can be applied on the designed graph model to optimize any performance metric for this ONC subclass. A case study on reducing the completion time shows that the proposed framework improves on the performance of IDNC and gets very close to the optimal performance

Reconciling model and information uncertainty in development appraisal

This paper investigates the effect of choices of model structure and scale in development viability appraisal. The paper addresses two questions concerning the application of development appraisal techniques to viability modelling within the UK planning system. The first relates to the extent to which, given intrinsic input uncertainty, the choice of model structure significantly affects model outputs. The second concerns the extent to which, given intrinsic input uncertainty, the level of model complexity significantly affects model outputs. Monte Carlo simulation procedures are applied to a hypothetical development scheme in order to measure the effects of model aggregation and structure on model output variance. It is concluded that, given the particular scheme modelled and unavoidably subjective assumptions of input variance, simple and simplistic models may produce similar outputs to more robust and disaggregated models

A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment

Research has shown that aggregation of independent expert judgments significantly improves the quality of forecasts as compared to individual expert forecasts. This “wisdom of crowds” (WOC) has sparked substantial interest. However, previous studies on strengths and weaknesses of aggregation algorithms have been restricted by limited empirical data and analytical complexity. Based on a comprehensive analysis of existing knowledge on WOC and aggregation algorithms, this paper describes the design and implementation of a static stochastic simulation model to emulate WOC scenarios with a wide range of parameters. The model has been thoroughly evaluated: the assumptions are validated against propositions derived from literature, and the model has a computational representation. The applicability of the model is demonstrated by investigating aggregation algorithm behavior on a detailed level, by assessing aggregation algorithm performance, and by exploring previously undiscovered suppositions on WOC. The simulation model helps expand the understanding of WOC, where previous research was restricted. Additionally, it gives directions for developing aggregation algorithms and contributes to a general understanding of the WOC phenomenon