# Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

arXiv: https://arxiv.org/abs/1702.04699 · HTML: https://mlsanity.com/paper/1702.04699/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2017.

## Built on by (most cited first, 4 of 4)

- [Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning](https://mlsanity.com/paper/2106.06061/) (2021)
- [A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2403.01013/) (2024)
- [Co-designing Intelligent Control of Building HVACs and Microgrids](https://mlsanity.com/paper/2107.08378/) (2021)
- [Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources](https://mlsanity.com/paper/2606.24947/) (2026)

## Abstract (arXiv)

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.