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In relation to this article, we declare that there is no conflict of interest.
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Received March 20, 2009
Accepted May 18, 2009
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Optimization of district heating systems based on the demand forecast in the capital region

Department of Chemical Engineering, Hanyang University, Seoul 133-791, Korea 1Department of Chemical Engineering, Seoul National University of Technology, Seoul 135-743, Korea 2Infortrol, Inc., Yangchun-gu, Seoul 158-735, Korea
Korean Journal of Chemical Engineering, November 2009, 26(6), 1484-1496(13), 10.1007/s11814-009-0282-8
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Abstract

A district heating system (DHS) consists of energy suppliers and consumers, heat generation and storage facilities and power transmission lines in the region. DHS has taken charge of an increasingly important role as the energy cost increases recently. In this work, a model for operational optimization of the DHS in the metropolitan area is presented by incorporating forecast for demand from customers. In the model, production and demand of heat in the region of Suseo near Seoul, Korea, are taken into account as well as forecast for demand using the artificial neural network. The optimization problem is formulated as a mixed integer linear programming (MILP) problem where the objective is to minimize the overall operating cost of DHS. The solution gives the optimal amount of network transmission and supply cost. The optimization system coupled with forecast capability can be effectively used as design and longterm operation guidelines for regional energy policies.

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