# Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

## TL;DR

This paper introduces a convex model predictive control method for distributed battery energy storage in microgrids, enabling fast, real-time optimal power flow management considering line losses, voltage, and converter constraints.

## Contribution

It presents a novel convex optimization-based control strategy that incorporates detailed battery efficiency variations and line constraints, improving real-time microgrid management.

## Key findings

- Achieves near non-convex optimization performance
- Reduces computation time by a factor of 1000
- Validates effectiveness through real-time simulations

## 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.