• Energy Research

AbstractAs solar energy is a variable power source, solar power plants are facing transients that are not experienced in conventional power plants such as nuclear or fossil ones. It is thus of primary importance to be able to simulate the dynamic behavior of the solar plants for their design and operation. The regulation modes have to be decided and the operation strategy has to be optimized. Using concentrated solar energy enables to convert solar power into heat before running thermodynamic cycles. Thermal inertia of the systems along with possible heat thermal storages help to smooth solar variations provided that these systems can be managed dynamically. Two solar power plants (with oil or water/steam as heat transfer fluid) are simulated with Dymola using Modelica code. The solar power plant using oil as heat transfer fluid is already running and preliminary results are compared with simulated data. Concerning the solar steam power plant, the model is run to investigate the regulation scheme of the plant that will be commissioned at the end of 2013. For both plant a DNI perturbation is tested and results are discussed concerning the system response and possible improvements.

This paper deals with the 2018 experimental campaign of the ALSOLEN 450 prototype. The ALSOLEN 450 prototype is a CSP plant with direct steam generation coupled with innovative thermal storage. In the first part of the paper, the prototype is described with an emphasis to the storage whose design is of particular interest. Then, some of the results obtain during the 2018 test campaign are discussed. As a starting point, it should be noted that the main outcome of the former experimental campaign (2017) has been the demonstration of the capacity of the prototype to produce superheated steam up to [115b ; 450°C]. During this new campaign, the prototype was successfully operated during 23 days out of 29. The main outcome of this campaign is the demonstration of the capability of the prototype to generate superheated steam at a given and control quality from a Linear Fresnel Reflectors (LFR) solar field with Direct Steam Generation (DSG) and to store this steam into an innovative direct multistage steam storage.

Concentrating Solar Power (CSP) plants generate renewable electricity using the conversion of solar direct normal irradiation into thermal energy, then into mechanical work and electricity through the use of a thermodynamic cycle. Among the several available technologies, Direct Steam Generation (DSG), in which steam is generated directly in the absorber tubes of the solar field, and then directly fed to the turbine or thermal storage, holds interesting advantages. However, the steam generation system shows a difficult dynamic behavior which constitutes a challenge for the control system design. It is mainly due to the conjunction of the natural transient condition of solar irradiation and the presence of two-phase flow in the absorber tubes. This paper reviews the control methods of the DSG systems used in linefocus CSP. The control systems are either proposed in literature, or actually applied in currently running plants or prototypes, although an extensive description is difficult to obtain in the case of the latter. The control systems are classified according to which DSG operation mode they refer to.

The present paper deals with the modelling and control of a solar reactor designed to produce syngas, by exploiting concentrated solar power. A model of the reactor based on the thermodynamic equilibrium is developed. Two model-based predictive control strategies are proposed: the first strategy (MPC strategy 1) aims to maintain the reactor's temperature at its nominal value whereas the second strategy (MPC strategy 2) aims to maintain the reactor's temperature at its nominal value, while maximizing the use of solar energy. Finally, these strategies are compared to a reference strategy, which is based on a combination of a rule-based controller and an adaptive PID controller with optimized gains. The robustness of the MPC controller to forecast errors is also studied by testing different DNI forecasting models. Parts of this paper were published as journal articleKarout, Y.; Curcio, A.; Eynard, J.; Thil, S.; Rodat, S.; Abanades, S.; Vuillerme, V.; Grieu, S. Model-Based Predictive Control of a Solar Hybrid Thermochemical Reactor for High-Temperature Steam Gasification of Biomass. Clean Technol. 2023, 5, 329-351. https://doi.org/10.3390/cleantechnol5010018

Using solar power for industrial process heat is an increasing trend to fight against climate change thanks to renewable heat. Process heat demand and solar flux can both present intermittency issues in industrial systems, therefore solar systems with storage introduce a degree of freedom on which optimization, on a mathematical basis, can be performed. As the efficiency of solar thermal receivers varies as a function of temperature and solar flux, it seems natural to consider an optimization on the operating temperature of the solar field. In this paper, a Mixed Integer Linear Programming (MILP) algorithm is developed to optimize the operating temperature in a system consisting of a concentrated solar thermal field with storage, hybridized with a boiler. The MILP algorithm optimizes the control trajectory on a time horizon of 48 h in order to minimize boiler use. Objective function corresponds to the boiler use, for completion of the heat from the solar field, whereas the linear constraints are a simplified representation of the system. The solar field mass flow rate is the optimization variable which is directly linked to the outlet temperature of the solar field. The control trajectory consists of the solar field mass flow rate and outlet temperature, along with the auxiliary mass flow rate going directly to the boiler. The control trajectory is then injected in a 0D model of the plant which performs more detailed calculations. For the purpose of the study, a Linear Fresnel system is investigated, with generic heat demand curves and constant temperature demand. The value of the developed algorithm is compared with two other control approaches: one operating at the nominal solar field output temperature, and the other one operating at the actual demand mass flow rate. Finally, a case study and a sensitivity analysis are presented. The MILP’s control shows to be more performant, up to a relative increase of the annual solar fraction of 4% at 350 °C process temperature. Novelty of this work resides in the MILP optimization of temperature levels presenting high non-linearities, applied to a solar thermal system with storage for process heat applications.

This study tackles the theoretical controllability of a hybrid solar-autothermal biomass gasifier, subject to dynamic variations of the solar power input, for round-the-clock operation. An industrial-scaled spouted bed reactor is considered, which can ensure the continuous conversion of 2 to 3 t/h of woody biomass particles. Insufficient solar power is dynamically counterbalanced by in situ oxy-combustion, to maintain the reaction temperature at 1200 K and the total H2 + CO flowrate production at 1000 NL/s. A Model Predictive Control (MPC) algorithm is thus implemented, and the feasibility of hybridized operation is demonstrated on a second-per-second basis. Daily and yearly performance results are achieved to discuss the relevance of several model assumptions and design choices, and a sensitivity analysis is proposed. In the region of Targasonne (French Pyrenees), hybridized gasification enables reducing biomass and O2 consumptions by 6.2 % and 19.5 %, respectively, as compared with autothermal gasification for the same gas flowrate production. The yearly solar heat share reaches 22 %, while a 7.2 % dumping of the solar heat available is necessary to avoid over-heating. Within this scope, higher H2 + CO production rates can only be achieved at the cost of lower solar heat shares but lower dumping rates, thus better utilization of the available solar resource. The feasibility of dynamic control of a solar-autothermal biomass gasifier was successfully demonstrated for the determination of annual process performance with reasonable computational costs, paving the way to stable and controllable solar gasification process operation.

Solar thermochemical fuel production technologies, such as biomass gasification, are confronted to the intermittency of solar irradiance. The development of dynamic simulation tools is thus required to design around-the-clock control strategies. An innovative model was developed here, based on unsteady mass and energy conservation equations, considering gas-phase thermodynamic equilibrium and heterogeneous char oxidation kinetics. The accumulation of char and gas species production rates were therefore tracked throughout operation, giving insight into the reactor dynamics with optimized computational cost. The model was validated via a comparison with experimental results, regarding both thermal and chemical reactor performances. Simulations reliably predicted the evolution of reactor temperatures and syngas production rates, under both solar-only and hybridized (solar-autothermal) operation. Parametric studies regarding the impact of reactants injection rates on steady-state performances were finally proposed. Steam addition (0.22 to 0.60 g/min) increased the syngas H$_ 2$:CO molar ratio significantly (1.13 to 1.47). Biomass addition (1 to 3 g/min) boosted the solar-to-fuel efficiency (0.22 to 0.47), but altered the reactor temperature. Finally, oxygen addition kept the reactor running despite fluctuations of solar power, while decreasing the total H$_2$ +CO production and cold-gas efficiency linearly. A constant H$_2$ +CO production (2.17 NL/min) could however be achieved by feeding additional biomass and oxygen during hybridization, thus limiting the cold-gas efficiency decrease and improving the reactor energy efficiency (0.29 to 0.40). Such a dynamic reactor model can be further applied to hybridized gasification process optimization and dynamic control under real fluctuating solar irradiation conditions. International audience