Supervisory Model Predictive Control for Optimal Energy Management of Networked Smart Greenhouses Integrated Microgrid

摘要

This paper presents a novel high-level centralized control scheme for a smart network of greenhouses integrated microgrid (NGIM) forming a smart small power grid in the context of smart grids. The main purpose is to present an innovative control strategy-based coordinated model predictive control (MPC) that considers fluctuations of stochastic renewable sources as well as weather conditions. A comprehensive finite-horizon scheduling optimization model is formulated to optimally control the operation of the NGIM, which integrates both forecasts and newly updated information collected from the available sensors at the network level. The model can be implemented as a supervisory control and energy management system for the NGIM to manipulate the indoor climate and optimize the crop production. The cooperation is reached through a bidirectional communication infrastructure, where a master central controller is available at the network level and is in charge of coordinating and managing various control signals. An MPC-based algorithm is used for the future operation scheduling of all subsystems available in the NGIM. The MPC strategy is tested through a case study where the influences of climate data on the operation of the NGIM are analyzed via numerical results. Note to Practitioners-Under the smart grid paradigm, smart greenhouses can be taken as an alternative that can mitigate and face the development challenges of the agricultural sector. Smart greenhouses can be considered as active players that may play a key role in modernizing the agriculture by offering viable and new smart management solutions, advanced control strategies, and innovative decision-support tools, whose objective is to better support growers, investors, and professionals. Smart network of greenhouses integrated microgrid (NGIM) can play an increasing role in enhancing the sustainable energy supply in the agricultural sector. In addition, the incorporation of new information and communication technologies, advanced metering infrastructure, and optimal control strategies can support the agricultural sector to meet an increasing number of regulations on quality and environment. In this paper, a comprehensive scheduling optimization model-based MPC that considers fluctuations of stochastic renewable sources, as well as weather conditions, is formulated to optimally control the operation of the NGIM. We developed and validated an intelligent control and management system-based MPC algorithm for a NGIM that may be considered as a practical solution to mitigate and address the development challenges and support the transition to precision and sustainable agriculture as well as the modernization of the agriculture.