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

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 fullmonth 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 hour rolling reference and prediction horizons outperforms the traditional EMPC benchmark with a 2% reduction in the annual cost, proving its economic benefits.

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Publication Details

Journal
IEEE Transactions on Smart Grid
Published
2025-08-18
DOI
https://doi.org/10.1109/tsg.2025.3600047
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Cristian Cortes-Aguirre, Adil Khurram, Jan P. Kleissl, Avik Ghosh et al.
IEEE Transactions on Smart Grid
Smart Grid Energy Management
article

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

Cristian Cortes-Aguirre, Adil Khurram, Jan P. Kleissl, Avik Ghosh, Yi‐An Chen
article en

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 fullmonth 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 hour rolling reference and prediction horizons outperforms the traditional EMPC benchmark with a 2% reduction in the annual cost, proving its economic benefits.

IEEE Transactions on Smart GridVol. 16(6)
University of San Diego (US), University of California San Diego (US)
California Energy Commission, Comisión Nacional de Investigación Científica y Tecnológica
Openalex Percentile: Top 81%
Smart Grid Energy Management
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Economic MPC With an Online Reference Trajectory for Battery Scheduling Considering Demand Charge Management — Cristian Cortes-Aguirre, Adil Khurram, et al. · IEEE Transactions on Smart Grid (2025) | TGRS Research Map | TGRS