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A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges

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  • van Staden, Adam Jacobus
  • Zhang, Jiangfeng
  • Xia, Xiaohua

Abstract

This paper defines and simulates a closed-loop optimal control strategy for load shifting in a plant that is charged for electricity on both time-of-use (TOU) and maximum demand (MD). A model predictive control approach is used to implement the closed-loop optimal control model, and the optimization problem is solved with integer programming. The simulated control model yields near optimal switching times which reduce the TOU and MD costs. The results show a saving of 5.8% for the overall plant, and the largest portion of the saving is due to a reduction in MD. The effect of disturbances, model uncertainty and plant failure is also simulated to demonstrate the benefits of a model predictive control model.

Suggested Citation

  • van Staden, Adam Jacobus & Zhang, Jiangfeng & Xia, Xiaohua, 2011. "A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges," Applied Energy, Elsevier, vol. 88(12), pages 4785-4794.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:12:p:4785-4794
    DOI: 10.1016/j.apenergy.2011.06.054
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    References listed on IDEAS

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    1. Yu, Yingdong & Liu, Jiahong & Wang, Hao & Liu, Miao, 2011. "Assess the potential of solar irrigation systems for sustaining pasture lands in arid regions – A case study in Northwestern China," Applied Energy, Elsevier, vol. 88(9), pages 3176-3182.
    2. Berg, Sandford V. & Savvides, Andreas, 1983. "The theory of maximum kW demand charges for electricity," Energy Economics, Elsevier, vol. 5(4), pages 258-266, October.
    3. Ashok, S., 2006. "Peak-load management in steel plants," Applied Energy, Elsevier, vol. 83(5), pages 413-424, May.
    4. Ould-Amrouche, S. & Rekioua, D. & Hamidat, A., 2010. "Modelling photovoltaic water pumping systems and evaluation of their CO2 emissions mitigation potential," Applied Energy, Elsevier, vol. 87(11), pages 3451-3459, November.
    5. Zhang, Shirong & Xia, Xiaohua, 2010. "Optimal control of operation efficiency of belt conveyor systems," Applied Energy, Elsevier, vol. 87(6), pages 1929-1937, June.
    6. Middelberg, Arno & Zhang, Jiangfeng & Xia, Xiaohua, 2009. "An optimal control model for load shifting - With application in the energy management of a colliery," Applied Energy, Elsevier, vol. 86(7-8), pages 1266-1273, July.
    7. Sallem, Souhir & Chaabene, Maher & Kamoun, M.B.A., 2009. "Energy management algorithm for an optimum control of a photovoltaic water pumping system," Applied Energy, Elsevier, vol. 86(12), pages 2671-2680, December.
    8. Alawaji, Saleh & Smiai, Mohammed Salah & Rafique, Shah & Stafford, Byron, 1995. "PV-powered water pumping and desalination plant for remote areas in Saudi Arabia," Applied Energy, Elsevier, vol. 52(2-3), pages 283-289.
    9. Ashok, S. & Banerjee, R., 2000. "Load-management applications for the industrial sector," Applied Energy, Elsevier, vol. 66(2), pages 105-111, June.
    Full references (including those not matched with items on IDEAS)

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