Example Carbon Production Reduction Schemes Appendix A. With the deregulation of the power industry having occurred in many coun-tries across the world, the industry has been experiencing many changes lead-ing to increasing complexity, interconnectivity, and uncertainties. Demandfor electricity has also increased signicantly in many countries, whichresulted in increasingly stressed power systems. The insucient investmentin the infrastructure for reliable electricity supply had been regarded as akey factor leading to several major blackouts in North America and Europein More recently, the initiative toward development of the smart gridagain introduced many additional new challenges and uncertainties to thepower industry.
In this chapter, a general overview will be given startingfrom deregulation, covering electricity markets, present uncertainties, loadmodeling, situational awareness, and control issues. The electricity industry has been undergoing a signicant transformationover the past decade.
Deregulation of the industry is one of the most impor-tant milestones. The industry had been moving from a regulated monopolystructure to a deregulated market structure in many countries including theUS, UK, Scandinavian countries, Australia, New Zealand, and some SouthAmerican countries.
Deregulation of the power industry is also in the processrecently in some Asian countries as well.
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The main motivations of deregula-tion are to:. Together with deregulation, there are two major objectives for establishingelectricity markets. They are 1 to ensure a secure operation and 2 tofacilitate an economical operation Shahidehpour et al. Post on Dec views. Category: Documents 5 download. All rights are reserved, whether the whole or part of the material is concerned, specically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microlm or in any other way, and storage in data banks.
Never used!. Seller Inventory P Seller Inventory Book Description Hardcover. Facing new challenges in the field, this up-to-date reference discusses emerging methods in power system analysis. The new techniques described include data mining, grid computing, probab. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Book Description Springer.
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Seller Inventory x The new techniques described include data mining, grid comp. KG, Germany, It is a far more complex problem than a simple voltage collapse based on the information available so far.
As clearly indicated in many literatures about this event, the reasons for such large scale blackouts are extremely complex, and have yet to be fully understood. It can be seen that the information involved to properly assess the security of a power system is increasingly complex with open access and deregulation. New techniques are needed to handle such problems. Cascading failure is a main form of system failure leading to blackouts.
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To face the impending challenges from operation and planning with respect to cascading failure avoidance, power system reliability analysis needs new evaluation tools. So far, the widely recognized contingency analytical method of large interconnection power systems is the N-1 criterion CIGRE, Since catastrophic disruptions are normally caused by cascading failures of electrical components, the importance of studying the inherent mechanism of cascading outages is attracting more and more attention.
So far, many models have been documented on simulating cascading failures.
In the article by Dobson et al. At start, the system components will be allocated a virtual load randomly. Then the model will be initiated by adding a disturbance load to all the components. A component will be tripped when its load exceeds the maximum limit, and other unfailed components will receive a constant load from this failure.
This cascading procedure will terminate when there are no component failures within a cascading scenario. This model can fully explore all the possibilities of cascading cases of the system. This cascading model is further improved by incorporating branching process approximation in the article by Dobson et al. However, both of them did not address the joint interactions among system components during cascading scenarios. In the article by Chen et al.
International Journal of Emerging Electric Power Systems
Chen et al. However, both methods above do not consider failures of other network components, such as generators and loads. In the article by Stubna and Fowler, , to describe the statistics of robust complex systems under uncertain conditions, highly optimised tolerance HOT model is introduced in simulating blackout phenomena in power systems.
Besides these proposed models, the investigation of critical transitions of a system according to the system loading conditions during cascading procedure is also studied Carreras et al. In the article by Carreras et al. This work shows that the cascading collapse of systems may be caused by the power system global nonlinear dynamics instead of weather or other external triggering disturbances. This evidence provides a global philosophy for understanding the catastrophic failures in power systems.
Due to the complexity inherit in power grids the study of system topology is another interesting approach. In the article by Lu et al. Paper Xu and Wang, employs scale-free coupled map lattices CML models to investigate the cascading phenomena. The result indicates that the increase in the homogeneity of the network will be helpful to enhance the system stability. However, since topology analyses normally require networks to be homogeneous and non-weighted, it might need approximations when dealing with power grid issues.
The model addresses the trips of loads, generators, and protection control groups PCG. In every cascading scenario, the value of load node voltages, generator node voltages as well as circuit overloads will be investigated sequentially, and the next cascading fault will be determined from the result.
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The model is very complex for application Makarov and Hardiman, The research and development in this area continue with various techniques Liu et al. It is an important step in the process of knowledge discovery in databases Olaru and Wehenkel, It has been used in a number of areas for power system analysis where large amount data are involved such as forecasting and contingency assessment. With increasing complexity in modern power systems, the corresponding system data are exponentially increasing.
Many companies store such data but are not yet able to fully utilize them. The major shortcomings of Lack of statistical information from NN outputs is also a major concern which limits its application.
Data mining based real time security assessment approaches are able to provide statistically reliable results and have been widely practiced in many complex systems such as telecommunications system and internet security areas. For applications such as a power system online DSA, it is critical to have assessment results within a very short time in order for the system operator to take corresponding control actions to prevent series system security problems.
Data mining based approaches, with their mathematically and statistically reliable characteristics open up a realistic solution for on-line DSA type tasks. They outperform the traditional AI based approach in many aspects. Second, a variety of data cleaning techniques have been incorporated into data mining algorithms, hence enabling data mining algorithms with strong noisy input tolerance capabilities. As a result, these techniques are able to handle large-scale data sets.
The statistical robustness means that if the system is assessed to have a security problem, it will experience such a problem with a given probability of occurrence if no actions are taken.
The operator needs to be sure that a costly remedial action such as load shedding is necessary before that action takes place. Data mining normally involves four types of tasks The factors which are relevant to the contingencies e.
Power System Analysis - Emerging Issues for Utilities - Final
Regression is used to model the data series with the least error. Association rule learning is used to discover relationships between variables in a data base Han, More detailed discussion on data mining will be given in Chapter 3 of this book. In the past few years, grid computing technology has been catching up and is receiving much attention from power engineers and researchers Ali et al. Grid computing technology is an infrastructure, which can provide high performance computing and a communication mechanism for providing services in these areas of the power system. In parallel processing, the tasks can be divided into a number of subtasks of equal size to all systems.
For this purpose, all machines need to be dedicated and should be homogeneous, i. Consequently, there should be a mechanism for processing the distributed and multi-owner data repositories Cannataro and Talia, In addition, the parallel processing techniques involve tightly coupling of the machines Chen et al.