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

Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System

Due to the inherent aleatory uncertainties in renewable generators, the reliability/adequacy assessments of distributed generation (DG) systems have been particularly focused on the probabilistic modeling of random behaviors, given sufficient informative data. However, another type of uncertainty (epistemic uncertainty) must be accounted for in the modeling, due to incomplete knowledge of the phenomena and imprecise evaluation of the related characteristic parameters. In circumstances of few informative data, this type of uncertainty calls for alternative methods of representation, propagation, analysis and interpretation. In this study, we make a first attempt to identify, model, and jointly propagate aleatory and epistemic uncertainties in the context of DG systems modeling for adequacy assessment. Probability and possibility distributions are used to model the aleatory and epistemic uncertainties, respectively. Evidence theory is used to incorporate the two uncertainties under a single framework. Based on the plausibility and belief functions of evidence theory, the hybrid propagation approach is introduced. A demonstration is given on a DG system adapted from the IEEE 34 nodes distribution test feeder. Compared to the pure probabilistic approach, it is shown that the hybrid propagation is capable of explicitly expressing the imprecision in the knowledge on the DG parameters into the final adequacy values assessed. It also effectively captures the growth of uncertainties with higher DG penetration levels

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

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Bringing power and progress to Africa in a financially and environmentally sustainable manner

EXECUTIVE SUMMARY: The future of electricity supply and delivery on the continent of Africa represents one of the thorniest challenges facing professionals in the global energy, economics, finance, environmental, and philanthropic communities. Roughly 600 million people in Africa lack any access to electricity. If this deficiency is not solved, extreme poverty for many Africans is virtually assured for the foreseeable future, as it is widely recognized that economic advancement cannot be achieved in the 21st Century without good electricity supply. Yet, if Africa were to electrify in the same manner pursued in developed economies around the world during the 20th Century, the planet’s global carbon budget would be vastly exceeded, greatly exacerbating the worldwide damages from climate change. Moreover, due to low purchasing power in most African economies and fiscal insolvency of most African utilities, it is unclear exactly how the necessary infrastructure investments can be deployed to bring ample quantities of power – especially zero-carbon power – to all Africans, both those who currently are unconnected to any grid as well as those who are now served by expensive, high-emitting, limited and unreliable electricity supply. With the current population of 1.3 billion people expected to double by 2050, the above-noted challenges associated with the African electricity sector may well get substantially worse than they already are – unless new approaches to infrastructure planning, development, finance and operation can be mobilized and propagated across the continent. This paper presents a summary of the present state and possible futures for the African electricity sector. A synthesis of an ever-growing body of research on electricity in Africa, this paper aims to provide the reader a thorough and balanced context as well as general conclusions and recommendations to better inform and guide decision-making and action. [TRUNCATED]This paper was developed as part of a broader initiative undertaken by the Institute for Sustainable Energy (ISE) at Boston University to explore the future of the global electricity industry. This ISE initiative – a collaboration with the Global Energy Interconnection and Development Cooperation Organization (GEIDCO) of China and the Center for Global Energy Policy within the School of International and Public Affairs at Columbia University – was generously enabled by a grant from Bloomberg Philanthropies. The authors gratefully acknowledge the support and contributions of the above funders and partners in this research

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems