ThermoCycle: A Modelica library for the simulation of thermodynamic systems

Quoilin, Sylvain; Desideri, Adriano; Wronski, Jorrit; Bell, Ian H.; Lemort, Vincent

doi:10.3384/ecp14096683

Cited by 47 publications

(43 citation statements)

References 18 publications

(18 reference statements)

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“…In order to assess the performance of the different developed control strategies, a dynamic model of the ORC system presented in Figure 1 has been developed in the Modelica language using existent components from the ThermoCycle library [27]. Dymola is selected as the simulation environment software.…”

Section: The Orc Unit Modelica Modelmentioning

confidence: 99%

“…The thermo-physical properties of the working fluid (SES36), the cooling fluid (glycol-water) and the thermal oil (therminol66) are computed coupling Modelica with the open-source fluid properties' library CoolProp [30] through the use of the ExternalMedia package [27]. For a more detailed description of the modeling approach, the interested reader can refer to [25].…”

Section: The Orc Unit Modelica Modelmentioning

confidence: 99%

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Real-Time Optimization of Organic Rankine Cycle Systems by Extremum-Seeking Control

et al. 2016

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Abstract:In this paper, the optimal operation of a stationary sub-critical 11 kW el organic Rankine cycle (ORC) unit for waste heat recovery (WHR) applications is investigated, both in terms of energy production and safety conditions. Simulation results of a validated dynamic model of the ORC power unit are used to derive a correlation for the evaporating temperature, which maximizes the power generation for a range of operating conditions. This idea is further extended using a perturbation-based extremum seeking (ES) algorithm to identify online the optimal evaporating temperature. Regarding safety conditions, we propose the use of the extended prediction self-adaptive control (EPSAC) approach to constrained model predictive control (MPC). Since it uses input/output models for prediction, it avoids the need for state estimators, making it a suitable tool for industrial applications. The performance of the proposed control strategy is compared to PID-like schemes. Results show that EPSAC-MPC is a more effective control strategy, as it allows a safer and more efficient operation of the ORC unit, as it can handle constraints in a natural way, operating close to the boundary conditions where power generation is maximized.

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Section: The Orc Unit Modelica Modelmentioning

confidence: 99%

Section: The Orc Unit Modelica Modelmentioning

confidence: 99%

Real-Time Optimization of Organic Rankine Cycle Systems by Extremum-Seeking Control

et al. 2016

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“…The modelling of the system is performed with the Dymola software and the Coolprop and Thermocycle libraries [7]. The global model and sub-components are extensively presented in [8].…”

Section: Modellingmentioning

confidence: 99%

Economic assessment of electric energy storage for load shifting in positive energy building

Dumont

Carmo

Georges

et al. 2016

Int J Energy Environ Eng

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Net zero energy buildings and positive energy buildings are gaining more and more interest. This paper evaluates the impact of the integration of a battery in a positive energy building used to increase its self-consumption of electricity. Parametric studies are carried out by varying the building envelope characteristics, the power supply system, the climate, the lighting and appliances profiles, the roof tilt angle, the battery size and the electricity tariffs, leading to 3200 cases. The analysis is performed on an annual basis in terms of self-consumption and self-production rate and payback period. It is shown that the battery size leading to the minimum payback period within the input range is comprised between 2.6 and 4.5 kWh. The lowest payback periods, (*7 years), are reached with a well-insulated building envelope, a high lightning and appliance consumption, a low feed-in tariff and a 3.7 kWh battery. Finally, simple correlations (based on the feed-in tariff, the annual electrical consumption and production) to predict the optimal size of battery and the lowest payback period are proposed.

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“…1) has been developed in the Modelica language using existent components from the ThermoCycle library (Quoilin et al [2014] The identification has been performed using a multisine excitation signal and the prediction error method (pem) (Ljung [2007]). The sampling time T s = 5 s has been chosen according to the fastest dynamics of the system.…”

Section: The Organic Rankine Cycle Systemmentioning

confidence: 99%

Increasing the efficiency of Organic Rankine Cycle Technology by means of Multivariable Predictive Control

Hernández

Desideri

Ionescu

et al. 2014

IFAC Proceedings Volumes

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The Organic Rankine Cycle (ORC) technology has become very popular, as it is extremely suitable for waste heat recovery from low-grade heat sources. As the ORC system is a strongly coupled nonlinear multiple-input multiple-output (MIMO) process, conventional control strategies (e.g. PID) may not achieve satisfactory results. In this contribution our focus is on the accurate regulation of the superheating, in order to increase the efficiency of the cycle and to avoid the formation of liquid droplets that could damage the expander. To this end, a multivariable Model Predictive Control (MPC) strategy is proposed, its performance is compared to the one of PI controllers for the case of variable waste-heat source profiles.

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ThermoCycle: A Modelica library for the simulation of thermodynamic systems

Cited by 47 publications

References 18 publications

Real-Time Optimization of Organic Rankine Cycle Systems by Extremum-Seeking Control

Real-Time Optimization of Organic Rankine Cycle Systems by Extremum-Seeking Control

Economic assessment of electric energy storage for load shifting in positive energy building

Increasing the efficiency of Organic Rankine Cycle Technology by means of Multivariable Predictive Control

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