Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125100, China.
Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125100, China; Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa.
Sci Total Environ. 2022 Nov 20;848:157756. doi: 10.1016/j.scitotenv.2022.157756. Epub 2022 Aug 1.
Poverty, food insecurity and climate change are global issues facing humanity, threatening social, economic and environmental sustainability. Greenhouse cultivation provides a potential solution to these challenges. However, some greenhouses operate inefficiently and need to be optimized for more economical and cleaner crop production. In this paper, an economic model predictive control (EMPC) method for a greenhouse is proposed. The goal is to manage the energy-water‑carbon-food nexus for cleaner production and sustainable development. First, an optimization model that minimizes the greenhouse's operating costs, including costs associated with greenhouse heating/cooling, ventilation, irrigation, carbon dioxide (CO) supply and carbon emissions taking into account both the CO equivalent (CO-eq) emissions caused by electrical energy consumption and the negative emissions caused by crop photosynthesis, is developed and solved. Then, a sensitivity analysis is carried out to study the impact of electricity price, supplied CO price and social cost of carbon (SCC) on the optimization results. Finally, a model predictive control (MPC) controller is designed to track the optimal temperature, relative humidity, CO concentration and incoming radiation power in presence of system disturbances. Simulation results show that the proposed approach increases the operating costs by R186 (R denotes the South African currency, Rand) but reduces the total cost by R827 and the carbon emissions by 1.16 tons when compared with a baseline method that minimizes operating costs only. The total cost is more sensitive to changes in SCC than that in electricity price and supplied CO price. The MPC controller has good tracking performance under different levels of system disturbances. Greenhouse environmental factors are kept within specified ranges suitable for crop growth, which increases crop yields. This study can provide effective guidance for growers' decision-making to achieve sustainable development goals.
贫困、粮食不安全和气候变化是人类面临的全球性问题，威胁着社会、经济和环境的可持续性。温室种植为应对这些挑战提供了一个潜在的解决方案。然而，一些温室的运行效率低下，需要进行优化，以实现更经济、更清洁的作物生产。本文提出了一种温室的经济模型预测控制（EMPC）方法。该方法的目标是管理能源-水-碳-食物关系，以实现更清洁的生产和可持续发展。首先，开发并求解了一个最小化温室运行成本的优化模型，该成本包括与温室加热/冷却、通风、灌溉、二氧化碳（CO）供应以及考虑到电能消耗引起的 CO 当量（CO-eq）排放和作物光合作用产生的负排放相关的成本。然后，进行了敏感性分析，以研究电价、供应 CO 价格和社会碳成本（SCC）对优化结果的影响。最后，设计了一个模型预测控制器（MPC），以在存在系统干扰的情况下跟踪最优温度、相对湿度、CO 浓度和入射辐射功率。仿真结果表明，与仅最小化运行成本的基准方法相比，所提出的方法将运行成本增加了 R186（R 表示南非货币，兰特），但降低了总成本 R827 和碳排放量 1.16 吨。总成本对 SCC 的变化比对电价和供应 CO 价格的变化更为敏感。MPC 控制器在不同水平的系统干扰下具有良好的跟踪性能。温室环境因素保持在适合作物生长的规定范围内，从而提高了作物产量。本研究可以为种植者的决策提供有效的指导，以实现可持续发展目标。
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