Model based predictive control for a solar-thermal system

Ferhatbegovic, Tarik; Zucker, Gerhard; Pálenský, Peter

doi:10.1109/afrcon.2011.6071992

Cited by 18 publications

(13 citation statements)

References 15 publications

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“…Its output temperature can be controlled by adjusting the water flow inside the solar field by manipulating the B1 pump speed. Pursuant to [33], the employment of a model-based predictive control algorithm for the energy-efficient temperature control of a solar-thermal system consists of a solar collector and heat exchanger; the model predictive control strategy showed good performance with regards to disturbance rejections.…”

Section: Related Workmentioning

confidence: 99%

Smart Control System to Optimize Time of Use in a Solar-Assisted Air-Conditioning by Ejector for Residential Sector

et al. 2018

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Abstract:The present work provides a series of theoretical improvements of a control strategy in order to optimize the time of use of solar air-conditioning by an ejector distributed in multiple solar collectors of vacuum tubes for the residential sector, which will allow us to reduce carbon-dioxide emissions, costs and electrical energy consumption. In a solar ejector cooling system, the instability of the solar source of energy causes an operational conflict between the solar thermal system and ejector cooling cycle. A fuzzy control structure for the supervisory ejector cycle and multi-collector control system is developed: the first control is applied to control the mass flow of the generator and the evaporator for different cooling capacities (3, 3.5, 4, 4.5 and 5 kW) and set a temperature reference according to the operating conditions; the second is applied to keep a constant temperature power source that feeds the low-grade ejector cooling cycle using R134aas refrigerant. For the present work, the temperature of the generator oscillates between 65 • C and 90 • C, a condenser temperature of 30 • C and an evaporator temperature of 10 • C. For the purpose of optimization, there are different levels of performance for time of use: the Mode 0 (economic) gives a performance of 17.55 h, Mode 5 (maximum cooling power) 14.86 h and variable mode (variable mode of capacities) 16.25 h, on average. Simulations are done in MATLAB-Simulink applying fuzzy logic for a mathematical model of the thermal balance. They are compared with two different types of solar radiation: real radiation and disturbed radiation.

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Section: Related Workmentioning

confidence: 99%

Smart Control System to Optimize Time of Use in a Solar-Assisted Air-Conditioning by Ejector for Residential Sector

et al. 2018

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“…A variety of computational models have been developed to evaluate and optimize the design and performance of solar thermal systems [6][7][8][9][10][11][12]. Those models have been implemented in many engineering software tools such as TRNSYS [13], EnergyPlus [14], and Modelica [15].…”

Section: Introductionmentioning

confidence: 99%

Transient Uncertainty Analysis in Solar Thermal System Modeling

Cho

Smith

Luck

et al. 2017

J. Uncertain. Anal. Appl.

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Complex, dynamic, computational models are routinely used to evaluate and optimize the design and performance of solar thermal systems. As models become more complex, performing uncertainty analysis on such models can be quite challenging and computationally expensive. This paper presents an effective approach to quantify uncertainties associated with transient simulation results from a dynamic solar thermal energy system model with uncertain parameters. The proposed method utilizes the concept of impulse response and convolution process to estimate the sensitivities to time-varying external inputs. Using this method, the number of simulations required to propagate uncertainties through dynamic models can be significantly reduced. An example is presented throughout the paper to demonstrate the procedure of the proposed uncertainty analysis approach.

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“…Load scheduling has been applied to many load types, such as thermal loads, residential appliances, power industry, and EV charging [2]. In [3], a case study was implemented to overcome the wind power variability through EV charging. An example for residential appliance scheduling was shown in [4].…”

Section: Introductionmentioning

confidence: 99%

Model predictive load scheduling using solar power forecasting

Habib

Kleissl

Callafon

2016

2016 American Control Conference (ACC)

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In this paper a model is developed to solve the on/off scheduling of (non-linear) dynamic electric loads based on predictions of the power delivery of a (standalone) solar power source. Knowledge of variations in the solar power output is used to optimally select the timing and the combinations of a set of given electric loads, where each load has a desired dynamic power profile. The optimization exploits the desired power profiles of the electric loads in terms of dynamic power ramp up/down and minimum time on/off of each load to track a finite number of load switching combinations over a moving finite prediction horizon. Subsequently, a userspecified optimization function with possible power constraints is evaluated over the finite number of combinations to allow for real-time computation of the optimal timing and switching of loads. A case study for scheduling electric on/off loads with switching dynamics and solar forecast data at UC San Diego is carried out.

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Model based predictive control for a solar-thermal system

Cited by 18 publications

References 15 publications

Smart Control System to Optimize Time of Use in a Solar-Assisted Air-Conditioning by Ejector for Residential Sector

Smart Control System to Optimize Time of Use in a Solar-Assisted Air-Conditioning by Ejector for Residential Sector

Transient Uncertainty Analysis in Solar Thermal System Modeling

Model predictive load scheduling using solar power forecasting

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