Marietta, OH, United States
Marietta, OH, United States

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Chen S.,University of Maine, United States | Onwuachumba A.,University of Maine, United States | Musavi M.,University of Maine, United States | Lerley P.,Power System Engineering Inc.
Clemson University Power Systems Conference, PSC 2016 | Year: 2016

In order to assess the reliability of power systems, System Planners must conduct both steady-state and stability simulations. The transient stability simulation of the reliability studies usually involve extensive variations and thus generate large amounts of data. Conventional analysis methods include visual examination of the simulation data plots to classify the severity of disturbances, objectively decide to apply the logical relationships between criteria. This paper, presents a quantification method for power system performance to achieve a more efficient stability analysis assessment. By using a criteria function matrix and a logic matrix, the proposed method evaluates the performance of each contingency and dispatch numerically and gives the overall stability assessment result. Damping and voltage sag criteria functions are applied for identification of the worst fault. This method will free engineers from tedious, time consuming and error-susceptible offline visual analysis and yield significantly more objective and quantified results. © 2016 IEEE.

Triplett J.,Power System Engineering Inc. | Rinell S.,Otsego Electrical Cooperative Inc. | Foote J.,Otsego Electrical Cooperative Inc.
Papers Presented at the Annual Conference - Rural Electric Power Conference | Year: 2010

Distribution system losses are a reality due to the physics associated with various system components that make up any power system. Techniques for analyzing losses are not new but have primarily focused on evaluating system losses during certain peak demand periods due to limitations on available data. Such "traditional" analyses estimate energy losses using industry accepted approaches that rely heavily on assumptions and that focus only on peak and average demands on major system components. Another potential shortcoming of a traditional loss analysis is the level of system detail evaluated. A gross analysis in terms of system components such as substation power transformers, distribution lines, distribution transformers, secondary conductors, etc. is typical. The disadvantage is that the relative contribution of various system components to the overall system losses may not be defined to the level required to truly evaluate mitigation techniques, especially when time periods other than peak demand times are being evaluated. Given the limitations of traditional loss evaluations, this paper will explore enhanced loss evaluation techniques utilizing interval load data collected from a deployed Advanced Metering Infrastructure (AMI) system and detailed system data from an available Geographic Information System (GIS). The test case system used to present this approach was a distribution cooperative with these systems presently in place. © 2010 IEEE.

Ivanov C.,Power System Engineering Inc. | Getachew L.,Power System Engineering Inc. | Fenrick S.A.,Power System Engineering Inc. | Vittetoe B.,ICF Consulting
Utilities Policy | Year: 2013

We examine the demand impact of a smart meter pilot conducted by Connexus Energy from 2008 until 2010. We focus on the amount of peak time energy use reduction, either through forgone usage or load shifting to off-peak times, as a result of enabling technologies in the form of in-home displays and smart thermostats. The in-home display allows the treatment group members to voluntarily alter their power use during "red alert" (critical peak) days. The smart thermostats also installed for the treatment group enable the utility to reduce AC usage of that group during red alert days by remotely turning up the temperature setting by 3 degrees Fahrenheit (°F) (i.e., a form of direct load control). We use hourly fixed effects models to examine peak time energy use changes in the summer of 2010. We find that treatment group members reduced their peak time energy use relative to the control group, which had no in-home displays or smart thermostats. Treatment group members who had the enabling technologies used, on average, 0.47 less kW, or 15% less energy, during peak hours on an average red alert day. © 2012 Elsevier Ltd.

Triplett J.M.,Power System Engineering Inc. | Kufel S.A.,Power System Engineering Inc.
Papers Presented at the Annual Conference - Rural Electric Power Conference | Year: 2012

Conservation Voltage Reduction (CVR) has been a hot topic in the industry for some time, particularly during the last few years. A number of methods and strategies have been employed on utility distribution systems to effectively lower the voltage with the objectives of decreasing coincident peak demand and/or energy over time. © 2012 IEEE.

Fenrick S.A.,Power System Engineering Inc. | Getachew L.,Power System Engineering Inc. | Ivanov C.,Power System Engineering Inc. | Smith J.,Power System Engineering Inc.
Energy Journal | Year: 2014

In this paper, we provide demand impact estimates of a critical peak pricing (CPP) program tested in the summer of 2011. We develop econometric models that examine demand responses of participants in "opt-in," "opt-out," and "tech only" CPP programs. Opt-out customers received bill protection while tech only customers received in-home displays alerting them of critical peak times, but they were not placed on the CPP rate. Our results indicate that opt-in customers reduced critical peak period demand the most while opt-out customers' appear to attenuate their reduction because of bill protection. Additionally, we refine our findings using participant survey responses. In general, we find participants in test groups whose environmental or "green" attitude is high had the strongest demand response. © 2014 by the IAEE. All rights reserved.

Chambers T.,Power System Engineering Inc.
Papers Presented at the Annual Conference - Rural Electric Power Conference | Year: 2016

An approach for arc flash hazard analysis in the presence of distributed resources based on the use of TCC curve equations is proposed in this paper. Determining the available arc flash incident energy and appropriate protective clothing to wear while working on an energized distribution feeder can be a time consuming task. Utilities typically refer to a single, worst-case incident energy per feeder over its entire length to simplify clothing requirements, it is therefore necessary to determine the maximum incident energy present on all feeders that may be worked on energized. The interplay between arcing current and over current protection characteristics makes determining the maximum incident energy and the location of its occurrence a lengthy problem to solve. This is further complicated to a significant degree in the presence of distributed resources. This paper introduces a proposed method that removes the need for an iterative approach in calculating incident energy at several locations on a feeder in a systematic attempt to determine the maximum energy, and introduces the concept of plotting incident energy as a function of system impedance (and so feeder length). This is achieved via an interface to the IEEE 1584 Arc Flash Hazard Calculator correlating discreet impedance increments and available fault current to the time component of TCC curve equations, and plotting the associated arc flash incident energy. © 2016 IEEE.

Kufel S.A.,Power System Engineering Inc.
Papers Presented at the Annual Conference - Rural Electric Power Conference | Year: 2015

The number of applications for Distributed Generation (DG) interconnections received by utility system operators is increasing every year. As the various DG technologies mature and the cost per kilowatt of generation goes down, utilities may receive more applications for generation interconnections large enough to require detailed system impact analysis before approval. Many utilities are already performing their own system impact studies, or are planning to begin doing so in the near future. A number of computer-based analysis programs are available that can model generation resources, with varying degrees of complexity. Utilities that are already performing their own studies need to be completely familiar with all of the capabilities of the particular tool or tools they use, and should have a reliable procedure in place for performing their analyses. This paper will examine the process of performing a DG system impact study with a computer-based system modeling tool. Topics of discussion will include information and data needed for accurate modeling, using various types of generator models, correctly adding generation to existing models, types of analysis that should be performed, common errors in modeling and analysis, and general best practices for completing system impact studies. Practical experience gained from performing many DG studies for utilities using several widely-available modeling tools will be shared. © 2015 IEEE.

Jeffrey M. Triplett P.E.,Power System Engineering Inc.
Papers Presented at the Annual Conference - Rural Electric Power Conference | Year: 2014

Distributed Generation (DG) interconnections with utility systems are becoming increasingly common. After a DG application has been processed, all needed studies completed and any required interconnection facilities constructed to accommodate the safe and reliable parallel operation of the DG facility, testing and commissioning is required. This paper will describe typical inspection, testing and commissioning procedures for a range of DG installation sizes and types. Practical experience gained from real-world installations and best practices for utilities will be shared in the form of a sample check list and procedure for smaller inverter-based systems. © 2014 IEEE.

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