Top > JACIII > Operation Planning of District Heating and Cooling Plants Con ...

single-jc.php

JACIII Vol.13 No.3 pp. 185-192
doi: 10.20965/jaciii.2009.p0185
(2009)

Paper:

Views over last 60 days: 702

Operation Planning of District Heating and Cooling Plants Considering Contract Violation Penalties

Kosuke Kato*, Masatoshi Sakawa*, Keiichi Ishimaru**,
and Satoshi Ushiro**

*Graduate School of Engineering, Hiroshima University 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan

**Urban Facilities Division, Shinryo Corporation 3-7-1 Nishishinjuku, Shinjuku-ku, Tokyo 163-1021, Japan

Received:
November 22, 2008
Accepted:
February 9, 2009
Published:
May 20, 2009
Creative Commons License
Keywords:
district heating and cooling system, operation planning, nonlinear integer programming, contract violation penalty
Abstract
Urban district heating and cooling (DHC) systems operate large freezers, heat exchangers, and boilers to stably and economically supply hot and cold water, steam, etc., based on customer demand. We formulate an operation-planning problem as a nonlinear integer programming problem for an actual DHC plant. To reflect actual decision making appropriately, we incorporate contract-violation penalties into the running cost consisting of fuel and arrangements expenses. We, then, solve operation-planning problems with and without penalties, demonstrating the effectiveness of taking penalties into consideration.
Cite this article as:
K. Kato, M. Sakawa, K. Ishimaru, , and S. Ushiro, “Operation Planning of District Heating and Cooling Plants Considering Contract Violation Penalties,” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.3, pp. 185-192, 2009.
Data files:
References
  1. [1] K. Ito and R. Yokoyama, “Optimal Planning of Co-Generation Systems,” Sangyo Tosho, 1990 (in Japanese).
  2. [2] K. Kato, M. Sakawa, K. Ishimaru, S. Ushiro, and T. Shibano, “Heat load prediction through recurrent neural network in district heating and cooling systems,” Proc. of 2008 IEEE Int. Conf. on Systems, Man and Cybernetics (SMC 2008), pp. 1401-1406, 2008.
  3. [3] M. Sakawa, K. Kato, and S. Ushiro, “Cooling load prediction in a district heating and cooling system through simplified robust filter and multi-layered neural network,” Applied Artificial Intelligence, Int. Journal, Vol.15, No.7, pp. 633-643, 2001.
  4. [4] R. Yokoyama and K. Ito, “A revised decomposition method for MILP problems and its application to operational planning of thermal storage systems,” Journal of Energy Resources Technology, Vol.118, pp. 277-284, 1996.
  5. [5] D. Henning, S. Amiri, and K. Holmgren, “Modelling and optimisation of electricity, steam and district heating production for a local Swedish utility,” European Journal of Operational Research, Vol.175, No.2, pp. 1224-1247, 2006.
  6. [6] M. Sakawa, K. Kato, and S. Ushiro, “Operation-planning of district heating and cooling plants through genetic algorithms for nonlinear 0-1 programming,” Computers & Mathematics with Applications, Vol.42, No.10-11, pp. 1365-1378, 2001.
  7. [7] M. Sakawa, K. Kato, S. Ushiro, and M. Inaoka, “Operation-planning of district heating and cooling plants using genetic algorithms for mixed integer programming,” Applied Soft Computing, Vol.1, No.2, pp. 139-150, 2001.
  8. [8] S. Hanafi and A. Freville, “An efficient tabu search approach for the 0-1 multidimensional knapsack problem,” European Journal of Operational Research, Vol.106, No.2-3, pp. 659-675, 1998.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Sep. 21, 2023