Model Predictive Control Based Energy Management System of Clustered Micro grids

Abstract

As the energy sector undergoes a major transition towards renewable energy-based systems, new challenges arise due to the intermittent, stochastic, and distributed nature of power production and consumption. In response to these challenges, clustered microgrids have emerged as an innovative approach to generate and utilize renewable energy. This thesis investigates a model predictive control (MPC) based receding horizon method for the energy management system (EMS) in clustered microgrid (MG) operations. By sharing power with each other, microgrid clusters improve overall system performance and efficiency. The frequently intermittent nature of renewable energy sources can cause problems with nonlinear or unpredictable energy management. EMS can balance supply and demand while considering all MG constraints in a cost-effective, safe, and reliable way, ensuring the stability and dependability of MG clusters. The operation of shared distributed energy resources (DER) and MGs is synchronized to maximize the flexibility of available sources and achieve a shared objective, such as reducing energy exchange with the distribution grid and total energy costs. Each MG is equipped with an MPC-based energy management system. The proposed Summation algorithm is distributed and guarantees that constraints are satisfied, power is exchanged between MGs through a converter, and shared resources are used fairly. The framework also guarantees cost savings for every MG. The performance of the proposed approach is evaluated through simulations using the MATLAB environment on a case study of clustered MGs. The results highlight the effectiveness and efficiency of the proposed policy.