A Model Predictive Control-Based Decision-Making Strategy for Residential Microgrids

Abstract

This work presents the development of a decision-making strategy for fulfilling the power and heat demands of small residential neighborhoods. The decision on the optimal operation of a microgrid is based on the model predictive control (MPC) rolling horizon. In the design of the residential microgrid, the new approach different technologies, such as photovoltaic (PV) arrays, micro-combined heat and power (micro-CHP) units, conventional boilers and heat and electricity storage tanks are considered. Moreover, electricity transfer between the microgrid components and the national grid are possible. The MPC problem is formulated as a mixed integer linear programming (MILP) model. The proposed novel approach is applied to two case studies: one without electricity storage, and one integrated microgrid with electricity storage. The results show the benefits of considering the integrated microgrid, as well as the advantage of including electricity storage.

Toward an intelligent community microgrid energy management system based on optimal control schemes

“…The revenue is earned from the excess sold to the UG from RE and battery per day. 33 Figures 15A,B shows the baseline optimal cost and gains with and without disturbances obtained by controllers; thus, it may be inferred that the installed MPC scheme can optimally minimize grid energy costs from 70.44 to 24.74$ day À1 in the first case, and from 70.44 to 18.24$ day À1 in the second case (with disturbances). It can also be observed from Figure 16A,B that the MPC model attains the maximum percentage of cost savings with and without disturbances.…”

“…Mechleri et al 33 create a decision-making technique for meeting small residential areas' electricity and heat demands. The MPC rolling horizon is used to determine the best operating.…”