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

Abstract

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.