A Compatible Control Algorithm for Greenhouse Environment Control Based on MOCC Strategy

Conventional methods used for solving greenhouse environment multi-objective conflict control problems lay excessive emphasis on control performance and have inadequate consideration for both energy consumption and special requirements for plant growth. The resulting solution will cause higher energy cost. However, during the long period of work and practice, we find that it may be more reasonable to adopt interval or region control objectives instead of point control objectives. In this paper, we propose a modified compatible control algorithm, and employ Multi-Objective Compatible Control (MOCC) strategy and an extant greenhouse model to achieve greenhouse climate control based on feedback control architecture. A series of simulation experiments through various comparative studies are presented to validate the feasibility of the proposed algorithm. The results are encouraging and suggest the energy-saving application to real-world engineering problems in greenhouse production. It may be valuable and helpful to formulate environmental control strategies, and to achieve high control precision and low energy cost for real-world engineering application in greenhouse production. Moreover, the proposed approach has also potential to be useful for other practical control optimization problems with the features like the greenhouse environment control system

Hierarchical model predictive control of a venlo-type greenhouse

Greenhouse cultivation can increase crop yield and alleviate the food shortage caused by population growth and reduction of arable land. However, the greenhouse production process consumes lots of energy and water. The energy consumed mainly comes from the combustion of fossil fuels, which will produce lots of greenhouse gases. In addition, the operating efficiency of some greenhouses is low, resulting in energy and water waste and increasing production costs. Therefore, the greenhouse system needs to be optimized to improve the operating efficiency. In this thesis, different methods of greenhouse operation efficiency optimization to improve energy efficiency and water efficiency are studied. In Chapter 3, three strategies for greenhouse operation optimization are studied. Strategy 1 focuses on the optimization of the greenhouse heating system to save energy. The optimization of the heating system can effectively reduce energy consumption. However, people often pay more attention to reducing energy costs than reducing energy consumption in the production process to obtain more profits. Strategy 2 is to reduce the energy cost. It should be noted that Strategy 2 only considers the cost of heating and cooling, while the cost of ventilation and carbon dioxide (CO2) is not considered. Strategy 3 reduces the cost of greenhouse heating, cooling, ventilation and CO2 consumption. In addition, greenhouse environmental factors must be kept within the required ranges. In Chapter 3, a dynamic greenhouse climate model is proposed. In the modeling process, the influence of crop growth and the interaction between different variables are considered to improve model accuracy. The proposed optimization problems are solved by ‘fmincon’ function with sequential quadratic programming (SQP) algorithm in MATLAB. Compared with Strategy 1, Strategy 2 has higher energy consumption but lower energy cost. Because Strategy 2 can shift some loads from high electricity price period to low electricity price period. Moreover, among the three strategies proposed, Strategy 3 has the lowest cost. It should be pointed out that the strategies studied in Chapter 3 only consider the impact of the greenhouse climate, but ignore the irrigation, which is also important for greenhouse production. In Chapter 4, four optimization methods are proposed. These optimization methods consider climate control and irrigation control. Therefore, strategies proposed in this chapter can not only improve energy efficiency, but also increase water efficiency. Method 1 reduces the energy consumption. Method 2 reduces the water consumption. Method 3 reduces the CO2 consumption. Method 4 reduces the total cost of greenhouse heating, cooling, ventilation, irrigation and CO2 supply. In addition, greenhouse environmental factors and crop water demand need to be met. The dynamic model of greenhouse environmental factors presented in Chapter 3 is used for greenhouse climate control. A modified crop evapotranspiration model is proposed to predict crop water demand. Moreover, a sensitivity analysis method is introduced. The influence of prices and system constraints on optimization results is studied. The cost of Method 4 can be reduced compared with other methods. In addition, changes of prices and system constraints have a great impact on optimization results. In Chapters 3 and 4, open loop optimization strategies for a greenhouse system operation are studied. However, these strategies have low control accuracy under system disturbances. Therefore, it is necessary to adopt some control methods to improve the control accuracy. In Chapter 5, a hierarchical model predictive control method is presented. The upper layer generates the optimal reference trajectories by solving greenhouse operation optimization problems. The lower layer designs controllers to follow obtained reference trajectories. Two model predictive controllers (MPC) are designed. Two performance indicators, namely relative average deviation (RAD) and maximum relative deviation (MRD), are used to compare designed controllers. The simulation results show that the proposed MPC can deal with greenhouse system disturbances and the problem of model plant mismatch better than the open loop control method. In Chapter 6, the findings of this thesis are summarized. Moreover, some topics for future research are proposed.Thesis (PhD (Electrical Engineering))--University of Pretoria, 2021.Electrical, Electronic and Computer EngineeringPhD (Electrical Engineering)Unrestricte

Optimal greenhouse cultivation control: survey and perspectives

Abstract: A survey is presented of the literature on greenhouse climate control, positioning the various solutions and paradigms in the framework of optimal control. A separation of timescales allows the separation of the economic optimal control problem of greenhouse cultivation into an off-line problem at the tactical level, and an on-line problem at the operational level. This paradigm is used to classify the literature into three categories: focus on operational control, focus on the tactical level, and truly integrated control. Integrated optimal control warrants the best economical result, and provides a systematic way to design control systems for the innovative greenhouses of the future. Research issues and perspectives are listed as well

Open-loop optimal temperature control in greenhouses

Earlier research has revealed that considerable energy savings can be achieved by maintaining an average temperature in the greenhouse in stead of maintaining rigid pre-defined temperature `blue-prints¿. A model based optimal control approach has proven to be a suitable framework to tackle these kind of control problems and it has been shown that these algorithms can be implemented on-line. But, when on-line optimal temperature control is considered, interesting questions arise, some of which are still unresolved. The issue tackled in this paper concerns the relation between the resolution of the control strategy (sample time) and energy savings of the control strategy. One would expect that an accurate and frequent anticipation to changing outdoor climate conditions might result in reduced energy consumption. It was indicated in the literature that a sample-time of 0.25 h or 1 hour should be sufficient, but these choices were hardly motivated. In this research, the relation between the control resolution and energy savings was quantitatively investigated using a dynamic greenhouse climate model and measurements of Dutch outdoor climate conditions containing high-frequency components. The results indicate that for an open-loop optimal control problem concerning the realization of an average temperature during a fixed period of one day using a minimum amount of energy with full a-priori knowledge of the outdoor weather, a resolution of the heating profile between half an hour and a hour suffices to produce accurate results in terms of energy conservation. These results were not much affected by parameter variations (heat capacity of the air, the solar heating efficiency) or opening and closing of thermal screens

Optimal greenhouse design should take into account optimal climate management

The objective of this paper is to demonstrate that optimal greenhouse design must account for (and be combined to) optimal climate management. We prove this by showing that different strategies and set-points to control the greenhouse ventilators result in different ¿optimal sets¿ of design parameters. We determined these optimal sets for a passive greenhouse in Almería, Spain where tomatoes were grown. The greenhouse design parameters investigated in this research were: 1) the transmission of the cover for photosynthetically active radiation (PAR), 2) the transmission of near infrared (NIR) radiation and 3) the emission coefficient for longwave radiation of the cover. Six optimal sets of design parameters were determined by maximising the marginal revenues (crop yield minus costs of design parameters), under given climate conditions, and for different ventilation control strategies. Each ventilation control strategy had different set-points for the air temperature and carbon dioxide concen¬tration to control the greenhouse ventilators. To solve this optimization problem we used a dynamic crop-greenhouse model and an optimization algorithm. The model described the combined influence of the relevant design parameters, outdoor climate and ventilation control upon economic crop yield, through their effect on indoor climate. The yearly costs of the design parameters were empirically derived from prices, physical properties and lifespan of a number of greenhouse cover materials. Results showed that indeed for different strategies and set-points to control the green¬house ventilators different ¿optimal sets¿ of design parameters and marginal revenues were obtained. For example, the difference between the highest optimal NIR trans¬mission 1.00 and the lowest optimal NIR transmission 0.40 was 60%, while the highest marginal revenues 16.94 ¿m-2 differed 18,7% with the lowest marginal revenues of 13.77 ¿ m-2. Additionally, it was found that the cover design parameters were time dependent. In conclusion, only a combined optimal control and design approach that takes into account the best climate control strategy and the time dependency of the design parameters will ensure optimal design parameters and maximum marginal revenues

Efficiency Gains from "What"-Flexibility in Climate Policy: An Integrated CGE Assessment

We investigate the importance of ?what?-flexibility on top of ?where?- and ?when?-flexibility for alternative emission control schemes that prescribe long-term temperature targets and eventually impose additional constraints on the rate of temperature change. We find that ?what?-flexibility substantially reduces the compliance costs under alternative emission control schemes. When comparing policies that simply involve long-term temperature targets against more stringent strategies that include additional constraints on the rate of temperature increase, it turns out that the latter involve huge additional costs. These costs may be interpreted as additional insurance payments if damages should not only dependent on absolute temperature change but also on the rate of temperature change. --Climate policy,Integrated Assessment,What-flexibility

Decision support model for the selection of asphalt wearing courses in highly trafficked roads

The suitable choice of the materials forming the wearing course of highly trafficked roads is a delicate task because of their direct interaction with vehicles. Furthermore, modern roads must be planned according to sustainable development goals, which is complex because some of these might be in conflict. Under this premise, this paper develops a multi-criteria decision support model based on the analytic hierarchy process and the technique for order of preference by similarity to ideal solution to facilitate the selection of wearing courses in European countries. Variables were modelled using either fuzzy logic or Monte Carlo methods, depending on their nature. The views of a panel of experts on the problem were collected and processed using the generalized reduced gradient algorithm and a distance-based aggregation approach. The results showed a clear preponderance by stone mastic asphalt over the remaining alternatives in different scenarios evaluated through sensitivity analysis. The research leading to these results was framed in the European FP7 Project DURABROADS (No. 605404).The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 605404

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation