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Test and optimization of a control algorithm for demand-oriented operation of CHP units using hardware-in-the-loop

Patrick Haase and Bernd Thomas

Applied Energy, 2021, vol. 294, issue C, No S0306261921004487

Abstract: This paper covers test and verification of a forecast-based Monte Carlo algorithm for an optimized, demand-oriented operation of combined heat and power (CHP) units using the hardware-in-the-loop approach. For this purpose, the optimization algorithm was implemented at a test bench at Reutlingen University for controlling a CHP unit in combination with a thermal energy storage, both in real hardware. In detail, the hardware-in-the-loop tests are intended to reveal the effects of demand forecasting accuracy, the impact of thermal energy storage capacity and the influence of load profiles on demand-oriented operation of CHP units. In addition, the paper focuses on the evaluation of the content of energy in the thermal energy storage under practical conditions. It is shown that a 5-layer model allows to determine the energy stored quite accurately, which is verified by experimental results. The hardware-in-the-loop tests disclose that demand forecasting accuracies, especially electricity demand forecasting, as well as load profiles strongly impact the potential for CHP electricity utilization on-site in demand-oriented mode. Moreover, it is shown that a larger effective capacity of the thermal energy storage positively affects demand-oriented operation. In the hardware-in-the-loop tests, the fraction of electricity generated by the CHP unit utilized on-site could thus be increased by a maximum of 27% compared to heat-led operation, which is still the most common modus operandi of small-scale CHP plants. Hence, the hardware-in-the-loop tests were adequate to prove the significant impact of the proposed algorithm for optimization of demand-oriented operation of CHP units.

Keywords: Optimization algorithm; Demand-oriented; Monte Carlo method; CHP unit; Hardware-in-the-Loop; Thermal energy storage (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2021.116974

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