A Multiscale Forecasting Methodology for Power Plant Fleet Management

In recent years the electric power industry has been challenged by a high level of uncertainty and volatility brought on by deregulation and globalization. A power producer must minimize the life cycle cost while meeting stringent safety and regulatory requirements and fulfilling customer demand for...

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Main Author: Chen, Hongmei
Format: Others
Language:en_US
Published: Georgia Institute of Technology 2005
Subjects:
Online Access:http://hdl.handle.net/1853/6849
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spelling ndltd-GATECH-oai-smartech.gatech.edu-1853-68492013-01-07T20:11:54ZA Multiscale Forecasting Methodology for Power Plant Fleet ManagementChen, HongmeiMaintenanceOperationPower plantMulti-scaleForecastingIn recent years the electric power industry has been challenged by a high level of uncertainty and volatility brought on by deregulation and globalization. A power producer must minimize the life cycle cost while meeting stringent safety and regulatory requirements and fulfilling customer demand for high reliability. Therefore, to achieve true system excellence, a more sophisticated system-level decision-making process with a more accurate forecasting support system to manage diverse and often widely dispersed generation units as a single, easily scaled and deployed fleet system in order to fully utilize the critical assets of a power producer has been created as a response. The process takes into account the time horizon for each of the major decision actions taken in a power plant and develops methods for information sharing between them. These decisions are highly interrelated and no optimal operation can be achieved without sharing information in the overall process. The process includes a forecasting system to provide information for planning for uncertainty. A new forecasting method is proposed, which utilizes a synergy of several modeling techniques properly combined at different time-scales of the forecasting objects. It can not only take advantages of the abundant historical data but also take into account the impact of pertinent driving forces from the external business environment to achieve more accurate forecasting results. Then block bootstrap is utilized to measure the bias in the estimate of the expected life cycle cost which will actually be needed to drive the business for a power plant in the long run. Finally, scenario analysis is used to provide a composite picture of future developments for decision making or strategic planning. The decision-making process is applied to a typical power producer chosen to represent challenging customer demand during high-demand periods. The process enhances system excellence by providing more accurate market information, evaluating the impact of external business environment, and considering cross-scale interactions between decision actions. Along with this process, system operation strategies, maintenance schedules, and capacity expansion plans that guide the operation of the power plant are optimally identified, and the total life cycle costs are estimated.Georgia Institute of Technology2005-07-28T17:52:28Z2005-07-28T17:52:28Z2005-02-14Dissertation3225584 bytesapplication/pdfhttp://hdl.handle.net/1853/6849en_US
collection NDLTD
language en_US
format Others
sources NDLTD
topic Maintenance
Operation
Power plant
Multi-scale
Forecasting
spellingShingle Maintenance
Operation
Power plant
Multi-scale
Forecasting
Chen, Hongmei
A Multiscale Forecasting Methodology for Power Plant Fleet Management
description In recent years the electric power industry has been challenged by a high level of uncertainty and volatility brought on by deregulation and globalization. A power producer must minimize the life cycle cost while meeting stringent safety and regulatory requirements and fulfilling customer demand for high reliability. Therefore, to achieve true system excellence, a more sophisticated system-level decision-making process with a more accurate forecasting support system to manage diverse and often widely dispersed generation units as a single, easily scaled and deployed fleet system in order to fully utilize the critical assets of a power producer has been created as a response. The process takes into account the time horizon for each of the major decision actions taken in a power plant and develops methods for information sharing between them. These decisions are highly interrelated and no optimal operation can be achieved without sharing information in the overall process. The process includes a forecasting system to provide information for planning for uncertainty. A new forecasting method is proposed, which utilizes a synergy of several modeling techniques properly combined at different time-scales of the forecasting objects. It can not only take advantages of the abundant historical data but also take into account the impact of pertinent driving forces from the external business environment to achieve more accurate forecasting results. Then block bootstrap is utilized to measure the bias in the estimate of the expected life cycle cost which will actually be needed to drive the business for a power plant in the long run. Finally, scenario analysis is used to provide a composite picture of future developments for decision making or strategic planning. The decision-making process is applied to a typical power producer chosen to represent challenging customer demand during high-demand periods. The process enhances system excellence by providing more accurate market information, evaluating the impact of external business environment, and considering cross-scale interactions between decision actions. Along with this process, system operation strategies, maintenance schedules, and capacity expansion plans that guide the operation of the power plant are optimally identified, and the total life cycle costs are estimated.
author Chen, Hongmei
author_facet Chen, Hongmei
author_sort Chen, Hongmei
title A Multiscale Forecasting Methodology for Power Plant Fleet Management
title_short A Multiscale Forecasting Methodology for Power Plant Fleet Management
title_full A Multiscale Forecasting Methodology for Power Plant Fleet Management
title_fullStr A Multiscale Forecasting Methodology for Power Plant Fleet Management
title_full_unstemmed A Multiscale Forecasting Methodology for Power Plant Fleet Management
title_sort multiscale forecasting methodology for power plant fleet management
publisher Georgia Institute of Technology
publishDate 2005
url http://hdl.handle.net/1853/6849
work_keys_str_mv AT chenhongmei amultiscaleforecastingmethodologyforpowerplantfleetmanagement
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