Controller for microgrid powered interconnected greenhouses
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Solution Overview
Problem
Current greenhouse climate control systems face challenges in optimizing energy use due to lack of integrated frameworks for multi-input, multi-output information, neglecting global optimal operation, and failing to comprehensively model uncertainties and stochastic dynamics of weather and renewable power production.
Innovation Solution
A comprehensive energy management algorithm and controller system that integrates Grey-box resistance-capacitance modeling with model predictive control (MPC) to optimize environmental conditions in smart greenhouses, utilizing sensors for real-time data and renewable resource information to regulate temperature, CO2, lighting, and water levels, ensuring optimal crop growth conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional greenhouse control systems are used, then the system structure is simple, but energy optimization is insufficient due to lack of integrated frameworks
Solution Approach 1:
The control system is segmented into multiple functional modules: a climate model module that predicts future climate states, an optimization module that determines control actions, and an execution module that implements control. This modular segmentation allows complex energy optimization while maintaining manageable system structure.
Solution Approach 2:
The system performs preliminary climate prediction using the climate model to forecast future climate states before actual control actions are taken. This preliminary action enables proactive energy optimization by preparing control strategies in advance based on predicted conditions, rather than reacting to current states only.
2Productivity
If partial control methods are used, then the control implementation is simple, but global optimal operation is not achieved
Solution Approach 1:
The climate model serves multiple functions: it predicts future climate states, evaluates different control strategies, and provides the basis for optimization decisions. This multi-functionality enables global optimal operation without requiring separate specialized systems for each control aspect.
Solution Approach 2:
The system implements feedback by continuously comparing predicted climate states with desired target states, then adjusting control actions based on the deviation. This closed-loop feedback mechanism ensures global optimal operation by constantly refining control decisions based on actual system performance and predictions.
3Manufacturing precision
If comprehensive climate modeling is implemented, then crop growth optimization is improved, but computational requirements increase
Solution Approach 1:
The system changes parameters dynamically by adjusting prediction horizons, model complexity levels, and control update frequencies based on operational conditions. This allows the system to balance computational requirements with crop growth optimization needs, using more comprehensive modeling only when necessary.
Solution Approach 2:
The system applies partial modeling approaches by focusing computational resources on the most critical climate parameters and time periods that have the greatest impact on crop growth. Rather than modeling all parameters at full detail continuously, it applies comprehensive modeling selectively to achieve optimization with reduced computational burden.
Data Source
AI summary
A method for controlling an environmental system may include defining a reference signal of an environmental variable in a greenhouse. The method may also include receiving renewable resource information related to the greenhouse. The method may further include receiving dynamic weather information of an environment external to the greenhouse. In addition, the method may include determining an optimization control scheme based on the reference signal, renewable resource information, and dynamic weather information. Further, the method may include regulating environmental conditions in the greenhouse according to the optimization control scheme.