Richard D. Christie

Transmission management in the deregulated environment

Three very different methods of accomplishing the same task-managing the operation of the transmission system in the deregulated power system operating environment-have been implemented as deregulated market structures have been created around the world. They are first, the optimal power flow (OPF) model found in various implementations in the United Kingdom, parts of the United States, and in Australia and New Zealand. Second, the point tariff, price area congestion control model used in the Nordpool market area in Norway and Sweden. Third, the US transaction-based model. All are pragmatic solutions implemented in advance of complete theoretical understanding. Each has strengths and flaws, and there are some surprising inter-relationships. Each maintains power system security but differs in its impact on the economics of the energy market. No clearly superior method has so far emerged. In the future, methods of combining decentralized market solutions with operational use of optimal power flow may provide better solutions to existing and emerging problems.

Load frequency control issues in power system operations after deregulation

Open transmission access is a legal requirement in the United States, but is not fully implemented. Discussion of deregulation has so far focused principally on the tariff structure for transmission access, but operating the power system in this new environment will present significant problems of an almost purely technical nature. Something as simple as frequency control becomes challenging when implemented in the competitive, distributed control environment that true third party wheeling creates. This paper seeks to identify likely deregulation scenarios, identify the technical issues associated with load frequency control, and identify technical solutions, such as standards and algorithms, needed for the operation of this key component of national infrastructure in the face of profound structural changes.

Methodology for Assessing the Resilience of Networked Infrastructure

In this paper, we outline a method to characterize the behavior of networked infrastructure for natural hazard events such as hurricanes and earthquakes. Our method includes resilience and interdependency measures. Because most urban infrastructure systems rely on electric power to function properly, we focus on the contribution of power delivery systems to post-event infrastructure recovery. We provide a brief example of our calculations using power delivery and telecommunications data collected post-landfall for Hurricane Katrina. The model is an important component of a scheme to develop design strategies for increased resilience of urban infrastructure for extreme natural hazard scenarios.

Distribution System Reliability Assessment Using Hierarchical Markov Modeling

Distribution system reliability assessment is concerned with power availability and power quality at each customers service entrance. This paper presents a new method, termed hierarchical Markov modeling (HMM), which can perform predictive distribution system reliability assessment. HMM is unique in that it decomposes the reliability model based on power system topology, integrated protection systems and individual protection devices. This structure, which easily accommodates the effects of backup protection, fault isolation and load restoration, is compared to simpler reliability models. HMM is then used to assess the reliability of an existing utility distribution system and to explore the reliability impact of several design improvement options.

Distribution system reliability assessment: momentary interruptions and storms

The goal of distribution system reliability assessment is to predict the availability of power at each customers service entrance. Existing methods predict the interruption frequency and duration each customer can expect, but omit two major contributing factors: momentary interruptions and storms. This paper presents methods to determine the impact of each phenomena. These methods are then used to assess the reliability of an existing utility distribution system and to explore the reliability impact of distribution automation.

Modeling and analysis of distribution reliability indices

Assessment of customer power supply reliability is an important part of distribution system operation and planning. Monte Carlo simulations can be used to find the statistical distribution of the reliability indices, along with their mean and standard deviation. The standard deviation of the reliability indices provides distribution engineers with information on the expected range of the annual values. However, the Monte Carlo simulation usually is a time-consuming computation. In this paper, an efficient Monte Carlo simulation method for distribution system reliability assessment is presented. Analysis of outage data from a practical distribution system is performed to determine the failure and repair models appropriate for use in the Monte Carlo simulation. The sensitivity of the reliability indices to the choice of model is presented. Finally, the impact of protection devices on the statistical distribution of System Average Interruption Frequency Index (SAIFI) for a practical distribution feeder is presented.

Distribution system reliability assessment due to lightning storms

Lightning is a significant cause of faults and outages in many electric power systems and is one of the major causes of poor system reliability. Predictive assessment of distribution reliability indices can be used to identify areas that have poor reliability so that appropriate changes in system design can be implemented. The assessment of distribution system performance under lightning conditions requires modeling of storm characteristics and system response. In this paper, a Monte Carlo simulation for evaluating distribution system reliability under lightning storm conditions is presented. The results from a practical distribution system show the importance of detailed modeling of storm characteristics and simulation of the system response in assessing distribution system reliability during lightning storms.

What future distribution engineers need to learn

It is getting increasingly clear that electric distribution systems are undergoing rapid changes due to deregulation, the penetration of distributed generation and power electronics technologies, and the adoption of efficient computation, communications, and control mechanisms. The primary goal of this paper is to recommend the development of a new two-course sequence to reflect the radical changes occurring or expected to happen in the future.

Envisioning power system data: concepts and a prototype system state representation

The use of graphics to understand power systems is considered. Presently, power systems are understood by collecting data about them, computing more data, and examining the data. The data are almost always in the form of numbers. The authors describe this process in more detail, point out that the use of numerical data are often inefficient, examine techniques presently used to improve understanding, find them inadequate, and suggest that proper graphical representations should create a breakthrough in understanding. The general question is examined of when and why graphics improve understanding, and when numbers are better. General representational techniques are discussed. Existing graphical representational techniques are examined, and the current trends in user interface development are evaluated to identify the benefits and drawbacks of different characteristics of these techniques. The characteristics of an ideal graphical representation are described. These insights are applied to develop a graphical representation for one power system operating state. >

Predicting vegetation-related failure rates for overhead distribution feeders

Faults on the electric power distribution system are responsible for a large portion of the interruptions that a customer will experience. To maintain a high level of system reliability, vegetation maintenance is often required. Analytical prediction of the effects of vegetation maintenance on distribution system reliability requires a model of the expected failure rate of line sections that includes the effects of vegetation. Vegetation-related failures are more likely to occur as the vegetation near the overhead power lines grows, increasing the line-section failure rate. Due to difficulties in using existing growth models, this paper proposes to use a direct model for failure-rate prediction based on factors that affect vegetation growth. Four models are considered: linear regression, exponential regression, linear multivariable regression, and an artificial neural network (ANN). The models are tested with historical vegetation growth parameter data and feeder failure rates. Results are compared and the features of each model are discussed.