IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i19p6212-d645934.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/19/6212/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/19/6212/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abbas Rabiee & Ali Abdali & Seyed Masoud Mohseni-Bonab & Mohsen Hazrati, 2021. "Risk-Averse Scheduling of Combined Heat and Power-Based Microgrids in Presence of Uncertain Distributed Energy Resources," Sustainability, MDPI, vol. 13(13), pages 1-24, June.
    2. Arash Moradzadeh & Sahar Zakeri & Maryam Shoaran & Behnam Mohammadi-Ivatloo & Fazel Mohammadi, 2020. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    3. Maciej Slowik & Wieslaw Urban, 2022. "Machine Learning Short-Term Energy Consumption Forecasting for Microgrids in a Manufacturing Plant," Energies, MDPI, vol. 15(9), pages 1-16, May.
    4. Laurentiu-Mihai Ionescu & Nicu Bizon & Alin-Gheorghita Mazare & Nadia Belu, 2020. "Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy," Mathematics, MDPI, vol. 8(7), pages 1-26, July.
    5. Mehmood, Faiza & Ghani, Muhammad Usman & Ghafoor, Hina & Shahzadi, Rehab & Asim, Muhammad Nabeel & Mahmood, Waqar, 2022. "EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting," Applied Energy, Elsevier, vol. 324(C).
    6. Dana-Mihaela Petroșanu & Alexandru Pîrjan, 2020. "Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network," Sustainability, MDPI, vol. 13(1), pages 1-31, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6212-:d:645934. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.