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Simulation of model based predictive control applied to a solar assisted cold climate heat pump system.

Number: pap. 3610

Author(s) : CANDANEDO J. A., DEHKORDI V. R.

Summary

This paper presents a simulation study of model-based predictive control (MPC) applied to a heating system using two air-source cold-climate heat pumps. One of the heat pumps uses air preheated by a BIPV/T roof; the other heat pump uses outdoor air. The BIPV/T roof supplying heat to the heat pump has a peak electric output of about 52 kW (out of 104 kW for the entire roof) and covers an area of 320 m2. A 20-m3 water tank is used for storing thermal energy supplied by both heat pumps. The paper describes the modelling approaches followed for the building, the heat pumps, the BIPV/T roof and the TES tank. The performance of the system was studied in a Simulink environment. The MPC algorithm was developed in order to select the optimal sequence of operation states for both heat pumps under a time-of-use electricity pricing profile. The MPC algorithm calculates the expected heating load and solves the optimization problem with a genetic algorithm at 24 hour intervals. A comparison of the performance of the MPC algorithm with a benchmark, rule-based control strategy, indicates savings of about 8%.

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Pages: 10 p.

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Details

  • Original title: Simulation of model based predictive control applied to a solar assisted cold climate heat pump system.
  • Record ID : 30013802
  • Languages: English
  • Source: 2014 Purdue Conferences. 3rd International High Performance Buildings Conference at Purdue.
  • Publication date: 2014/07/14

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