Energy cost optimization in microgrids using model predictive control and mixed integer linear programming

© 2019 IEEE. This paper presents a model predictive control (MPC) approach based on the mixed integer linear programming (MILP) to develop an optimal power management strategy (PMS) for minimizing the electricity bill of commercial buildings in a domestic on-grid system. The optimal PMS is first formulated as a MILP-MPC with time-varying constraints. The constraints are then linearized at each sampling time so that a receding horizon principle can be used to determine the control input applied to the plant and update the model. The time-varying efficiency of power electronic converters is evaluated for each time interval and assumed to be persistent for the prediction time horizon. The numerical results show that the proposed MILP-MPC strategy with variable efficiency is effective in utilizing photovoltaic power generation to save the cost on electricity for buildings

Minimization of building energy cost by optimally managing PV and battery energy storage systems

© 2017 IEEE. The combination of residential photovoltaic (PV) panels and battery energy storage system (BESS) is a promising solution in a building's microgrid. This paper presents an optimal energy management system (EMS) to minimize the electricity bill of residential buildings. The objective is to achieve peak shaving and electrical energy cost minimization for the owner, making use of a dynamic energy pricing model. Due to variability of PV generation and limitations of BESS, the optimization method should account for the utilisation of PV power generation and the conversion losses of power electronics converters. To deal with non-linearities involved, a particle swarm optimization (PSO) algorithm has been developed for minimization of the cost function. Simulation results show that the PSO-based EMS can achieve, as compared to standard schedule for a 24 hour period, a significant saving of daily electricity cost, which prompts for a suitable on-line control strategy