A multi-state model for the reliability assessment of a distributed generation system via universal generating function

International audienceThe current and future developments of electric power systems are pushing the boundaries of reliability assessment to consider distribution networks with renewable generators. Given the stochastic features of these elements, most modeling approaches rely on Monte Carlo simulation. The computational costs associated to the simulation approach force to treating mostly small-sized systems, i.e. with a limited number of lumped components of a given renewable technology (e.g. wind or solar, etc.) whose behavior is described by a binary state, working or failed. In this paper, we propose an analytical multi-state modeling approach for the reliability assessment of distributed generation (DG). The approach allows looking to a number of diverse energy generation technologies distributed on the system. Multiple states are used to describe the randomness in the generation units, due to the stochastic nature of the generation sources and of the mechanical degradation/failure behavior of the generation systems. The universal generating function (UGF) technique is used for the individual component multi-state modeling. A multiplication-type composition operator is introduced to combine the UGFs for the mechanical degradation and renewable generation source states into the UGF of the renewable generator power output. The overall multi-state DG system UGF is then constructed and classical reliability indices (e.g. loss of load expectation (LOLE), expected energy not supplied (EENS)) are computed from the DG system generation and load UGFs. An application of the model is shown on a DG system adapted from the IEEE 34 nodes distribution test feeder

An asset-based approach to social risk management : a conceptual framework

There is increasing concern about the vulnerability of poor and near-poor rural households, who have limited capabilities to manage risk and often resort to strategies that can lead to a vicious cycle of poverty. Household-related risk is ususally considered individual or private, but measures to manage risk are actually social or public in nature. Furthermore, various externality issues are associated with household-related risk, such as its links to economic development, poverty reduction, social cohesion, and environmental quality. Hence the need for a holistic approach to risk management, or"social risk management,"which encompasses a broad spectrum of private and public actions. An asset-based approach to social risk management is presented, which provides an integrated approach to considering household, community, and extra-community assets and risk-management strategies. The conceptual framework for social risk management focuses on rural Sub-Saharan Africa. The report concludes with several suggestions on moving from concepts to actions.Health Economics&Finance,Insurance&Risk Mitigation,Banking Law,Environmental Economics&Policies,Banks&Banking Reform

A Multi-State Power Model for Adequacy Assessment of Distributed Generation via Universal Generating Function

The current and future developments of electric power systems are pushing the boundaries of reliability assessment to consider distribution networks with renewable generators. Given the stochastic features of these elements, most modeling approaches rely on Monte Carlo simulation. The computational costs associated to the simulation approach force to treating mostly small-sized systems, i.e. with a limited number of lumped components of a given renewable technology (e.g. wind or solar, etc.) whose behavior is described by a binary state, working or failed. In this paper, we propose an analytical multi-state modeling approach for the reliability assessment of distributed generation (DG). The approach allows looking to a number of diverse energy generation technologies distributed on the system. Multiple states are used to describe the randomness in the generation units, due to the stochastic nature of the generation sources and of the mechanical degradation/failure behavior of the generation systems. The universal generating function (UGF) technique is used for the individual component multi-state modeling. A multiplication-type composition operator is introduced to combine the UGFs for the mechanical degradation and renewable generation source states into the UGF of the renewable generator power output. The overall multi-state DG system UGF is then constructed and classical reliability indices (e.g. loss of load expectation (LOLE), expected energy not supplied (EENS)) are computed from the DG system generation and load UGFs. An application of the model is shown on a DG system adapted from the IEEE 34 nodes distribution test feeder.Comment: Reliability Engineering & System Safety (2012) 1-2

Four Futures for Finance; A scenario study

This document presents four scenarios for the future of finance. The goal of our study is to imagine the future of finance and to identify challenges faced by policymakers in fighting systemic risk. It builds upon a tradition within the CPB to develop scenarios for policy analysis. We develop four scenarios for the future of finance. Our scenarios differ in two dimensions. First, to what extent soft information lies at the core of banks’ business. Second, to what extent scope economies exist between different banking activities. By combining these two dimensions, we obtain four scenarios: Isolated Islands, Big Banks, Competing Conglomerates, and Flat Finance. Market structure, market failures, and government failures vary between scenarios. These differences then translate into differences in the complexity of balance sheets, the ability to coordinate policy internationally, the information gap faced by regulators, the size of banks’ balance sheets, the tradability of banks’ assets, the level of interconnectedness, the potential for market discipline, and the threat of regulatory capture. As a result, each scenario calls for a different set of policies to combat systemic risk.

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated