scholarly journals Hybrid Nonlinear MPC of a Solar Cooling Plant

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2723 ◽  
Author(s):  
Eduardo F. Camacho ◽  
Antonio J. Gallego ◽  
Juan M. Escaño ◽  
Adolfo J. Sánchez
Keyword(s):  
Control Strategy ◽  
Air Conditioning ◽  
Operation Mode ◽  
Outlet Temperature ◽  
Continuous Variables ◽  
Solar Cooling ◽  
Operation Conditions ◽  
Predictive Controller ◽  
Solar Field ◽  
Layer Control

Solar energy for cooling systems has been widely used to fulfill the growing air conditioning demand. The advantage of this approach is based on the fact that the need of air conditioning is usually well correlated to solar radiation. These kinds of plants can work in different operation modes resulting on a hybrid system. The control approaches designed for this kind of plant have usually a twofold goal: (a) regulating the outlet temperature of the solar collector field and (b) choosing the operation mode. Since the operation mode is defined by a set of valve positions (discrete variables), the overall control problem is a nonlinear optimization problem which involves discrete and continuous variables. This problems are difficult to solve within the normal sampling times for control purposes (around 20–30 s). In this paper, a two layer control strategy is proposed. The first layer is a nonlinear model predictive controller for regulating the outlet temperature of the solar field. The second layer is a fuzzy algorithm which selects the adequate operation mode for the plant taken into account the operation conditions. The control strategy is tested on a model of the plant showing a proper performance.

scholarly journals Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 81
Author(s):  
Rongjiang Ma ◽  
Shen Yang ◽  
Xianlin Wang ◽  
Xi-Cheng Wang ◽  
Ming Shan ◽  
...  
Keyword(s):  
Data Mining ◽  
Energy Consumption ◽  
Energy Saving ◽  
Air Conditioning ◽  
Storage System ◽  
Operation Mode ◽  
Systematic Method ◽  
Potential Analysis ◽  
Energy Saving Potential ◽  
Air Conditioning Systems

Air-conditioning systems contribute the most to energy consumption among building equipment. Hence, energy saving for air-conditioning systems would be the essence of reducing building energy consumption. The conventional energy-saving diagnosis method through observation, test, and identification (OTI) has several drawbacks such as time consumption and narrow focus. To overcome these problems, this study proposed a systematic method for energy-saving diagnosis in air-conditioning systems based on data mining. The method mainly includes seven steps: (1) data collection, (2) data preprocessing, (3) recognition of variable-speed equipment, (4) recognition of system operation mode, (5) regression analysis of energy consumption data, (6) constraints analysis of system running, and (7) energy-saving potential analysis. A case study with a complicated air-conditioning system coupled with an ice storage system demonstrated the effectiveness of the proposed method. Compared with the traditional OTI method, the data-mining-based method can provide a more comprehensive analysis of energy-saving potential with less time cost, although it strongly relies on data quality in all steps and lacks flexibility for diagnosing specific equipment for energy-saving potential analysis. The results can deepen the understanding of the operating data characteristics of air-conditioning systems.

A multi-objective operation strategy optimization for ice storage systems based on decentralized control structure

2020 ◽  
Vol 42 (1) ◽  
pp. 62-81
Author(s):  
Yanhuan Ren ◽  
Junqi Yu ◽  
Anjun Zhao ◽  
Wenqiang Jing ◽  
Tong Ran ◽  
...  
Keyword(s):  
Energy Saving ◽  
Decentralized Control ◽  
Optimization Algorithm ◽  
Control Strategy ◽  
Load Distribution ◽  
Air Conditioning ◽  
Control Structure ◽  
Ice Storage ◽  
Swarm Optimization ◽  
Multi Objective

Improving the operational efficiency of chillers and science-based planning the cooling load distribution between the chillers and ice tank are core issues to achieve low-cost and energy-saving operations of ice storage air-conditioning systems. In view of the problems existing in centralized control architecture applied in heating, ventilation, and air conditioning, a distributed multi-objective particle swarm optimization improved by differential evolution algorithm based on a decentralized control structure was proposed. The energy consumption, operating cost, and energy loss were taken as the objectives to solve the chiller’s hourly partial load ratio and the cooling ratio of ice tank. A large-scale shopping mall in Xi’an was used as a case study. The results show that the proposed algorithm was efficient and provided significantly higher energy-savings than the traditional control strategy and particle swarm optimization algorithm, which has the advantages of good convergence, high stability, strong robustness, and high accuracy. Practical application: The end equipment of the electromechanical system is the basic component through the building operation. Based on this characteristic, taken electromechanical equipment as the computing unit, this paper proposes a distributed multi-objective optimization control strategy. In order to fully explore the economic and energy-saving effect of ice storage system, the optimization algorithm solves the chillers operation status and the load distribution. The improved optimization algorithm ensures the diversity of particles, gains fast optimization speed and higher accuracy, and also provides a better economic and energy-saving operation strategy for ice storage air-conditioning projects.

scholarly journals Aiming Strategy on a Prototype-Scale Solar Receiver: Coupling of Tabu Search, Ray-Tracing and Thermal Models

2021 ◽  
Vol 13 (7) ◽  
pp. 3920
Author(s):  
Benjamin Grange ◽  
Gilles Flamant
Keyword(s):  
Tabu Search ◽  
Ray Tracing ◽  
Wall Temperature ◽  
Gradual Increase ◽  
Particle Temperature ◽  
Outlet Temperature ◽  
Operation Conditions ◽  
Start Up ◽  
Fluidized Particle ◽  
Solar Receiver

An aiming point strategy applied to a prototype-scale power tower is analyzed in this paper to define the operation conditions and to preserve the lifetime of the solar receiver developed in the framework of the Next-commercial solar power (CSP) H2020 project. This innovative solar receiver involves the fluidized particle-in-tube concept. The aiming solution is compared to the case without the aiming strategy. Due to the complex tubular geometry of the receiver, results of the Tabu search for the aiming point strategy are combined with a ray-tracing software, and these results are then coupled with a simplified thermal model of the receiver to evaluate its performance. Daily and hourly aiming strategies are compared, and different objective normalized flux distributions are applied to quantify their influence on the receiver wall temperature distribution, thermal efficiency and particle outlet temperature. A gradual increase in the solar incident power on the receiver is analyzed in order to keep a uniform outlet particle temperature during the start-up. Results show that a tradeoff must be respected between wall temperature and particle outlet temperature.

scholarly journals Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3731
Author(s):  
Simon Kamerling ◽  
Valéry Vuillerme ◽  
Sylvain Rodat
Keyword(s):  
Mass Flow Rate ◽  
Mass Flow ◽  
Flow Rate ◽  
Solar Thermal ◽  
Outlet Temperature ◽  
Field Mass ◽  
Heat Demand ◽  
Process Heat ◽  
Solar Field ◽  
Control Trajectory

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.

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