Weather forecast-based optimization of integrated energy systems.

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In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, ... continued below

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Zavala, V. M.; Constantinescu, E. M.; Krause, T. & Anitescu, M. March 1, 2009.

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Description

In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.

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  • Report No.: ANL/MCS-TM-305
  • Grant Number: DE-AC02-06CH11357
  • DOI: 10.2172/951585 | External Link
  • Office of Scientific & Technical Information Report Number: 951585
  • Archival Resource Key: ark:/67531/metadc925846

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  • March 1, 2009

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Feb. 16, 2017, 4:34 p.m.

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Zavala, V. M.; Constantinescu, E. M.; Krause, T. & Anitescu, M. Weather forecast-based optimization of integrated energy systems., report, March 1, 2009; United States. (digital.library.unt.edu/ark:/67531/metadc925846/: accessed April 26, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.