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Development of a simulation and dynamic optimization tool for thermal solar plant
ENSGTI - Université de Pau et des Pays de l'Adour (UPPA), PAU
Laboratoire de Thermique Energétique et Procédés (LaTEP), PAU
Energy Chemical Engineering 
Full Description:

Context

In a general framework of ecologic and energetic improvement of industrial processes, newHeat company aims to develop, fund, build and exploit thermal solar generator devoted to provide heat to industrial plants (such as agro-food industry for example). In a first step, newHeat wants to set up a research and development program in order to improve the eco-efficiency of these solar energy production systems. For this purpose, in Martillac (33), a test bench will be implemented, which will contain several solar panels configurations. Within this context, a PhD-thesis is proposed to support this project.

 

Thesis subject

This thesis aims to develop a methodology for dynamic optimization of a thermal solar plant. The
software tool will be designed in a modular way (enabling simulation of different solar panels configurations) as well as in an integrated way (proposing features from simulation to optimization and control).

 

The main steps are as following:

1 – Literature study

This step includes a review of the most recent studies dealing with thermal solar plants and their
modelling on one hand, and numerical methods of simulation and optimization on the other hand.

2 – Modular modelling of the system and validation with test bench data’s

Steady state modelling and dynamic modelling (start-up and stop stages, transient behaviour of the system submitted to external perturbations…) – Models implemented in this section will have to take into account operating parameters (temperatures, pressures, mass flow rates and heat fluxes) and sizing parameters (exchange areas, number of panels…) of the different modules of the solar plant (exchangers, panels…).

Such models, which are typically developed in Chemical Engineering / Energetics, can ensure a quite good accuracy while remaining reliable models when used at the system scale.

The proposed models are then solved with suitable numerical methods (algebraic or algebro-differential systems).

This modelling step will be validated by fitting modelled data with experimental data provided by the experimental campaign performed on the bench test.

3 – Simulation and sensitivity analysis

First, simulations will be performed using previous models in order to carry out a sensitivity analysis of the different parameters: the influence of sizing and operating parameters on the system performances is thus obtained. 

4 – Steady state optimization

Following the sensitivity analysis, a numerical optimization (mathematical programming) is achieved to obtain the values of sizing and operating parameters, which optimize (minimize or maximize) an objective function provided by the user. This latter can concern the COP (Coefficient Of Performance) or an economic criterion… This optimization is subject to a set of constraints. Steady state is assumed.

This step could provide the optimal injection point of heat into the industrial process.

5 – Dynamic optimization

Once the optimal operating point with the optimal sizing parameters is achieved, external perturbations impacting the system have to be taken into account. The aim is then to determine optimal temporal profiles of operating parameters (orientation of trackers, flows…) that will ensure to meet the optimal operating point set.

Many scenarios could be studied, such as overheat or storage management. Modelled results could be compared to experimental data.

 

Keywords

Thermal solar, optimization, dynamic system, energetic integration, energetic efficiency of processes.