Economic MPC with an Online Reference Trajectory for Battery Scheduling Considering Demand Charge Management

TL;DR

This paper proposes an economic model predictive control method with an online reference trajectory for battery scheduling in microgrids, effectively managing demand charges with shorter prediction horizons and outperforming traditional methods.

Contribution

It introduces a practical EMPC formulation with 24-48 hour horizons and an online reference trajectory, improving demand charge management in microgrids.

Findings

48-hour rolling horizon EMPC reduces annual costs by 2%.

The proposed method outperforms traditional EMPC benchmarks.

Incorporating BESS constraints ensures long-term energy level regulation.

Abstract

Monthly demand charges form a significant portion of the electric bill for microgrids with variable renewable energy generation. A battery energy storage system (BESS) is commonly used to manage these demand charges. Economic model predictive control (EMPC) with a reference trajectory can be used to dispatch the BESS to optimize the microgrid operating cost. Since demand charges are incurred monthly, EMPC requires a full-month reference trajectory for asymptotic stability guarantees that result in optimal operating costs. However, a full-month reference trajectory is unrealistic from a renewable generation forecast perspective. Therefore, to construct a practical EMPC with a reference trajectory, an EMPC formulation considering both non-coincident demand and on-peak demand charges is designed in this work for 24 to 48 h prediction horizons. The corresponding reference trajectory is computed at each EMPC step by solving an optimal control problem over 24 to 48 h reference (trajectory) horizon. Furthermore, BESS state of charge regulation constraints are incorporated to guarantee the BESS energy level in the long term. Multiple reference and prediction horizon lengths are compared for both shrinking and rolling horizons with real-world data. The proposed EMPC with 48 h rolling reference and prediction horizons outperforms the traditional EMPC benchmark with a 2% reduction in the annual cost, proving its economic benefits.