Optimization of district heating systems based on the demand forecast in the capital region

Tae Chang Park; Ui Sik Kim; Lae-Hyun Kim; Weon Ho Kim; Yeong Koo Yeo

Korean Journal of Chemical Engineering, Vol.26, No.6, 1484-1496, November, 2009

DOI10.1007/s11814-009-0282-8 Export Citation

Optimization of district heating systems based on the demand forecast in the capital region

Tae Chang Park, Ui Sik Kim, Lae-Hyun Kim¹, Weon Ho Kim², and Yeong Koo Yeo^†

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

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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.

Keywords:District Heating System;Optimal Operation;Demand Forecast;District Network;Mixed Integer Linear Programming

[References]

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[Related Articles]

[1] Ibrahim M, Saifure R, IEEE Trans. Power Syst., 4 (1989)

[2] Lru K, Subbarayan S, Shoults RR, Manry MT, Kwan C, Iewis FL, Naccarino J, IEEE Trans. Power Syst., 11 (1996)

[3] Alex D, Timothy C, IEEE Trans. Power Syst., 5, 1535 (1990)

[4] Jie B, Short-term load forecasting based on neural network and moving average, Iowa State University (2002)

[5] Dejan I, FME Transactions, 34, 165 (2006)

[6] Yalcinoz T, Eminoglu U, Energy Conv. Manag., 46(9-10), 1393 (2005)

[7] Al-Shareef AJ, Mohamed EA, Al-Judaibi E, One hour ahead load forecasting using artificial neural network for the western area of saudi arabia, International Journal of Electrical Systems Science and Engineering (2006)

[8] Fan JY, McDonald JD, IEEE Trans. Power Syst., 9, 988 (1994)

[9] Dotzauer E, Appl. Energy, 73(3-4), 277 (2002)

[10] Jovic F, Rajkovic V, Jagnjic Z, Vuksanovic D, Information Technology Interfaces, 1, 325 (2001)

[11] Comakli K, Yuksel B, Comakli O, Appl. Thermal Eng., 24, 1009 (2004)

[12] Sandou G, Font S, Tebbani S, Hiret A, Mondon C, Decision and Control, 44, 7372 (2005)

[13] Soderman J, Pettersson F, Appl. Thermal Eng., 26, 1400 (2005)

[14] Soderman J, Appl. Thermal Eng., 27, 2665 (2007)

[15] Weber C, Heckl I, Friedler F, Marechal F, Favrat D, Network synthesis for a district energy system: A step towards sustainability, 16th European Symposium on Computer Aided Process Engineering and 9th International Symposium on Process Systems Engineering, 1869-1874 (2006)