Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

Abstract

This paper proposes a new convex model predictive control strategy for dynamic optimal power flow between battery energy storage systems distributed in an AC microgrid. The proposed control strategy uses a new problem formulation, based on a linear d-q reference frame voltage-current model and linearised power flow approximations. This allows the optimal power flows to be solved as a convex optimisation problem, for which fast and robust solvers exist. The proposed method does not assume real and reactive power flows are decoupled, allowing line losses, voltage constraints and converter current constraints to be addressed. In addition, non-linear variations in the charge and discharge efficiencies of lithium ion batteries are analysed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical feeder, with distributed battery energy storage systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on non-convex optimisation, while reducing the required computation time by a factor of 1000, making it suitable for a real-time model predictive control implementation.

A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems

“…A stochastic approach was implemented to forecast weather and load uncertainties. OGS and MPC working simultaneously can reduce computational load, as achieved in [85]. Matthiss et al [93] used a MPC to maintain high levels of self-consumption, reduce the peak feed-in power to improve grid compatibility, and to minimize energy costs.…”

“…Simulations were made on a real-time digital simulator (RTDS) which used nonlinear battery models and switching converter models. The convex MPC provides an environment for generating reasonable approximated values based on the microgrid operating state, allowing reduction of the computation time by a factor of 1000 [85]. Including additional constraints to the MPC to keep the battery voltage below the upper threshold voltage level provided by the manufacturer leads to the MPC decreasing the charging current [86].…”

“…A stochastic approach was implemented to forecast weather and load uncertainties. OGS and MPC working simultaneously can reduce computational load, as achieved in [85].…”

Distributed energy storage system‐based control strategy for hybrid DC/AC microgrids in grid‐connected mode

“…To provide a stable operation for the converters used in microgrid, many control strategies have been considered in recent literatures . In Baharizadeh et al, a new decentralized control method has been proposed for a hybrid microgrid to regulate the common bus voltage magnitude, improve the power sharing, and provide a stable operation for the interlinking converter.…”

“…To provide a stable operation for the converters used in microgrid, many control strategies have been considered in recent literatures. [14][15][16][17] In Baharizadeh et al, 18 a new decentralized control method has been proposed for a hybrid microgrid to regulate the common bus voltage magnitude, improve the power sharing, and provide a stable operation for the interlinking converter. By the use of sliding mode hysteresis controller, a dc microgrid has been modeled and analyzed in Rashad et al 19 to provide high robustness, fast dynamic response, and good stability for large load variations.…”