Predictive control of the model to manage the efficiency of energy resources in smart buildings

Efficient management of energy resources is very important in smart buildings. In this work, model predictive control (MPC) is used to minimize the economic costs of buyers equipped with generation units, energy storage systems and electric vehicles. For this purpose, predictive control manages available energy resources by exploiting future information on energy prices, absorption and generation power profiles, and electric vehicle (EV) usage, such as departure and arrival times and predicted energy consumption. EV Batteries In particular, unlike the heuristic method, the MPC approach has been proven to be able to efficiently manage the available energy resources to ensure full recharging of the EV battery overnight.
Aiming at optimal performance in terms of economic cost minimization in time-varying price scenarios, reducing rms current stresses and recharging capability of EV batteries. Moreover, the proposed control is shown to be able to limit the maximum power absorption from the grid within the defined limits, which is a valuable feature in scenarios with widespread adoption of EVs in order to limit the stress on the electrical system. and the ability to recharge EV batteries. In particular, unlike the heuristic method, the MPC approach is proven to be able to efficiently manage the available energy resources to ensure full recharging of the EV battery overnight while always meeting all system constraints. Moreover, the proposed control is shown to be able to limit the maximum power absorption from the grid within the defined limits, which is a valuable feature in scenarios with widespread adoption of EVs in order to limit the stress on the electrical system. In addition, the proposed control is shown to be able to limit the maximum power absorption from the grid within the defined limits .