Model predictive control for optimal energy management of connected cluster of microgrids with net zero energy multi-greenhouses.

This paper intends to present a cooperative control framework for a connected cluster of microgrids with multi-smart greenhouses creating a smart local electric grid in the framework of smart grids. Each microgrid comprises renewable generators, pumps, advanced communication and metering infrastructure, water reservoir, energy storage device, and a set of greenhouses where each one includes heating, ventilation and air conditioning (HVAC), CO 2 injector, artificial lighting, sensors, local pump, and fans. The key objective is to formulate a coordinated optimization framework embedded in a model predictive control (MPC) scheme to optimally control the operation of the clustered microgrids and manage the power flows exchange ensuring a high quality of service. The microgrids are connected permitting the power exchanges to enhance the utilization of local renewable generations. Furthermore, the cluster is connected to the main grid through a power link permitting power exchange in excess/shortage case. The cooperation is achieved throughout a bidirectional communication infrastructure, where a centralized controller is responsible of managing the different control signals. A comprehensive scheduling optimization algorithm is developed and implemented to effectively control the clustered microgrids operation considering the operational constraints, where the purpose is to enhance energy efficiency, and manipulating effectively the microclimate variables defining the optimal environment for crops development in all greenhouses. An MPC-based energy management framework is implemented and applied to a case study to demonstrate its performance and effectiveness through extensive numerical simulations. • We present a control framework for a cluster of microgrids with multi-greenhouses. • We formulate a coordinated optimization framework embedded in a MPC scheme. • The purpose is to optimally control the operation of the clustered microgrids. • An MPC-based algorithm is developed to control the microclimate in each greenhouse. • The algorithm is applied to a case study to demonstrate its performance. [ABSTRACT FROM AUTHOR]