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An Energy Management System for the Control of Battery Storage in a Grid-Connected Microgrid Using Mixed Integer Linear Programming

Author

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  • Marvin Barivure Sigalo

    (Penryn Campus, College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter TR10 9FE, UK)

  • Ajit C. Pillai

    (Penryn Campus, College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter TR10 9FE, UK)

  • Saptarshi Das

    (Penryn Campus, College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter TR10 9FE, UK)

  • Mohammad Abusara

    (Penryn Campus, College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter TR10 9FE, UK)

Abstract

This paper proposes an energy management system (EMS) for battery storage systems in grid-connected microgrids. The battery charging/discharging power is determined such that the overall energy consumption cost is minimized, considering the variation in grid tariff, renewable power generation and load demand. The system is modeled as an economic load dispatch optimization problem over a 24 h horizon and solved using mixed integer linear programming (MILP). This formulation, therefore, requires knowledge of the expected renewable energy power production and load demand over the next 24 h. To achieve this, a long short-term memory (LSTM) network is proposed. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the EMS that benefits from using actual generation and demand data on the day. At each hour, the LSTM predicts generation and load data for the next 24 h, the dispatch problem is then solved and the battery charging or discharging command for only the first hour is applied in real-time. Real data are then used to update the LSTM input, and the process is repeated. Simulation results show that the proposed real-time strategy outperforms the offline optimization strategy, reducing the operating cost by 3.3%.

Suggested Citation

  • Marvin Barivure Sigalo & Ajit C. Pillai & Saptarshi Das & Mohammad Abusara, 2021. "An Energy Management System for the Control of Battery Storage in a Grid-Connected Microgrid Using Mixed Integer Linear Programming," Energies, MDPI, vol. 14(19), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6212-:d:645934
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    References listed on IDEAS

    as
    1. Fatma Yaprakdal & M. Berkay Yılmaz & Mustafa Baysal & Amjad Anvari-Moghaddam, 2020. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
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    Cited by:

    1. Can Ding & Yiyuan Zhou & Qingchang Ding & Kaiming Li, 2022. "Integrated Carbon-Capture-Based Low-Carbon Economic Dispatch of Power Systems Based on EEMD-LSTM-SVR Wind Power Forecasting," Energies, MDPI, vol. 15(5), pages 1-27, February.
    2. Saif Jamal & Jagadeesh Pasupuleti & Nur Azzammudin Rahmat & Nadia M. L. Tan, 2022. "Energy Management System for Grid-Connected Nanogrid during COVID-19," Energies, MDPI, vol. 15(20), pages 1-20, October.
    3. Marvin B. Sigalo & Saptarshi Das & Ajit C. Pillai & Mohammad Abusara, 2023. "Real-Time Economic Dispatch of CHP Systems with Battery Energy Storage for Behind-the-Meter Applications," Energies, MDPI, vol. 16(3), pages 1-20, January.
    4. Cristian Hoyos-Velandia & Lina Ramirez-Hurtado & Jaime Quintero-Restrepo & Ricardo Moreno-Chuquen & Francisco Gonzalez-Longatt, 2022. "Cost Functions for Generation Dispatching in Microgrids for Non-Interconnected Zones in Colombia," Energies, MDPI, vol. 15(7), pages 1-14, March.
    5. Asmita Ajay Rathod & Balaji Subramanian, 2022. "Scrutiny of Hybrid Renewable Energy Systems for Control, Power Management, Optimization and Sizing: Challenges and Future Possibilities," Sustainability, MDPI, vol. 14(24), pages 1-35, December.
    6. Mulleriyawage, U.G.K. & Wang, P. & Rui, T. & Zhang, K. & Hu, C. & Shen, W.X., 2023. "Prosumer-centric demand side management for minimizing electricity bills in a DC residential PV-battery system: An Australian household case study," Renewable Energy, Elsevier, vol. 205(C), pages 800-812.
    7. Angel L. Cedeño & Reinier López Ahuar & José Rojas & Gonzalo Carvajal & César Silva & Juan C. Agüero, 2022. "Model Predictive Control for Photovoltaic Plants with Non-Ideal Energy Storage Using Mixed Integer Linear Programming," Energies, MDPI, vol. 15(17), pages 1-21, September.
    8. Aritra Ghosh, 2022. "Recent Advances in Renewable Energy and Clean Energy," Energies, MDPI, vol. 15(9), pages 1-2, April.

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