IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i10p2596-d360633.html
   My bibliography  Save this article

A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads

Author

Listed:
  • Feras Alasali

    (Department of Electrical Engineering, Hashemite University, Zarqa 13113, Jordan)

  • Stephen Haben

    (Mathematical Institute, University of Oxford, Andrew Wiles Building, Oxford OX2 6GG, UK)

  • Husam Foudeh

    (Department of Electrical Engineering, Mutah University, Karak 61710, Jordan)

  • William Holderbaum

    (Mechanical Engineering and Design, Aston Institute of Materials Research, Aston University, Birmingham B4 7ET, UK)

Abstract

This paper aims to present the significance of predicting stochastic loads to improve the performance of a low voltage (LV) network with an energy storage system (ESS) by employing several optimal energy controllers. Considering the highly stochastic behaviour that rubber tyre gantry (RTG) cranes demand, this study develops and compares optimal energy controllers based on a model predictive controller (MPC) with a rolling point forecast model and a stochastic model predictive controller (SMPC) based on a stochastic prediction demand model as potentially suitable approaches to minimise the impact of the demand uncertainty. The proposed MPC and SMPC control models are compared to an optimal energy controller with perfect and fixed load forecast profiles and a standard set-point controller. The results show that the optimal controllers, which utilise a load forecast, improve peak reduction and cost savings of the storage device compared to the traditional control algorithm. Further improvements are presented for the receding horizon controllers, MPC and SMPC, which better handle the volatility of the crane demand. Furthermore, a computational cost analysis for optimal controllers is presented to evaluate the complexity for a practical implementation of the predictive optimal control systems.

Suggested Citation

  • Feras Alasali & Stephen Haben & Husam Foudeh & William Holderbaum, 2020. "A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads," Energies, MDPI, vol. 13(10), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2596-:d:360633
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/10/2596/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/10/2596/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Monica Alonso & Hortensia Amaris & Jean Gardy Germain & Juan Manuel Galan, 2014. "Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms," Energies, MDPI, vol. 7(4), pages 1-27, April.
    2. Feras Alasali & Antonio Luque & Rayner Mayer & William Holderbaum, 2019. "A Comparative Study of Energy Storage Systems and Active Front Ends for Networks of Two Electrified RTG Cranes," Energies, MDPI, vol. 12(9), pages 1-14, May.
    3. Matthew Rowe & Timur Yunusov & Stephen Haben & William Holderbaum & Ben Potter, 2014. "The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction," Energies, MDPI, vol. 7(6), pages 1-24, May.
    4. Holjevac, Ninoslav & Capuder, Tomislav & Zhang, Ning & Kuzle, Igor & Kang, Chongqing, 2017. "Corrective receding horizon scheduling of flexible distributed multi-energy microgrids," Applied Energy, Elsevier, vol. 207(C), pages 176-194.
    5. Sani Hassan, Abubakar & Cipcigan, Liana & Jenkins, Nick, 2017. "Optimal battery storage operation for PV systems with tariff incentives," Applied Energy, Elsevier, vol. 203(C), pages 422-441.
    6. Xie, Shanshan & He, Hongwen & Peng, Jiankun, 2017. "An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 196(C), pages 279-288.
    7. Douglas Halamay & Michael Antonishen & Kelcey Lajoie & Arne Bostrom & Ted K. A. Brekken, 2014. "Improving Wind Farm Dispatchability Using Model Predictive Control for Optimal Operation of Grid-Scale Energy Storage," Energies, MDPI, vol. 7(9), pages 1-16, September.
    8. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
    9. Zhenya Ji & Xueliang Huang & Changfu Xu & Houtao Sun, 2016. "Accelerated Model Predictive Control for Electric Vehicle Integrated Microgrid Energy Management: A Hybrid Robust and Stochastic Approach," Energies, MDPI, vol. 9(11), pages 1-18, November.
    10. Seaseung Oh & Suyong Chae & Jason Neely & Jongbok Baek & Marvin Cook, 2017. "Efficient Model Predictive Control Strategies for Resource Management in an Islanded Microgrid," Energies, MDPI, vol. 10(7), pages 1-16, July.
    11. Feras Alasali & Stephen Haben & Victor Becerra & William Holderbaum, 2017. "Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-18, October.
    12. Stefano Pietrosanti & William Holderbaum & Victor M. Becerra, 2016. "Optimal Power Management Strategy for Energy Storage with Stochastic Loads," Energies, MDPI, vol. 9(3), pages 1-17, March.
    13. Wang, Xiaonan & Palazoglu, Ahmet & El-Farra, Nael H., 2015. "Operational optimization and demand response of hybrid renewable energy systems," Applied Energy, Elsevier, vol. 143(C), pages 324-335.
    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. Feras Alasali & Mohammad Salameh & Ali Semrin & Khaled Nusair & Naser El-Naily & William Holderbaum, 2022. "Optimal Controllers and Configurations of 100% PV and Energy Storage Systems for a Microgrid: The Case Study of a Small Town in Jordan," Sustainability, MDPI, vol. 14(13), pages 1-20, July.
    2. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.
    3. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).

    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. Feras Alasali & Stephen Haben & Victor Becerra & William Holderbaum, 2017. "Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-18, October.
    2. Dawei Chen & Wangqiang Niu & Wei Gu & Nigel Schofield, 2019. "Game-Based Energy Management Method for Hybrid RTG Cranes," Energies, MDPI, vol. 12(18), pages 1-23, September.
    3. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    4. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    5. Azuatalam, Donald & Paridari, Kaveh & Ma, Yiju & Förstl, Markus & Chapman, Archie C. & Verbič, Gregor, 2019. "Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 555-570.
    6. Tang, Ruoli & Wu, Zhou & Li, Xin, 2018. "Optimal operation of photovoltaic/battery/diesel/cold-ironing hybrid energy system for maritime application," Energy, Elsevier, vol. 162(C), pages 697-714.
    7. Sani Hassan, Abubakar & Cipcigan, Liana & Jenkins, Nick, 2018. "Impact of optimised distributed energy resources on local grid constraints," Energy, Elsevier, vol. 142(C), pages 878-895.
    8. Peter Horan & Mark B. Luther & Hong Xian Li, 2021. "Guidance on Implementing Renewable Energy Systems in Australian Homes," Energies, MDPI, vol. 14(9), pages 1-24, May.
    9. Su, Huai & Zhang, Jinjun & Zio, Enrico & Yang, Nan & Li, Xueyi & Zhang, Zongjie, 2018. "An integrated systemic method for supply reliability assessment of natural gas pipeline networks," Applied Energy, Elsevier, vol. 209(C), pages 489-501.
    10. Zhang, Hailong & Peng, Jiankun & Dong, Hanxuan & Tan, Huachun & Ding, Fan, 2023. "Hierarchical reinforcement learning based energy management strategy of plug-in hybrid electric vehicle for ecological car-following process," Applied Energy, Elsevier, vol. 333(C).
    11. Anthony Roy & François Auger & Jean-Christophe Olivier & Emmanuel Schaeffer & Bruno Auvity, 2020. "Design, Sizing, and Energy Management of Microgrids in Harbor Areas: A Review," Energies, MDPI, vol. 13(20), pages 1-24, October.
    12. Feras Alasali & Antonio Luque & Rayner Mayer & William Holderbaum, 2019. "A Comparative Study of Energy Storage Systems and Active Front Ends for Networks of Two Electrified RTG Cranes," Energies, MDPI, vol. 12(9), pages 1-14, May.
    13. Xiaogang Guo & Zhejing Bao & Zhijie Li & Wenjun Yan, 2018. "Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid," Energies, MDPI, vol. 11(1), pages 1-17, January.
    14. Trovão, João P. & Silva, Mário A. & Antunes, Carlos Henggeler & Dubois, Maxime R., 2017. "Stability enhancement of the motor drive DC input voltage of an electric vehicle using on-board hybrid energy storage systems," Applied Energy, Elsevier, vol. 205(C), pages 244-259.
    15. Hegde, Bharatkumar & Ahmed, Qadeer & Rizzoni, Giorgio, 2020. "Velocity and energy trajectory prediction of electrified powertrain for look ahead control," Applied Energy, Elsevier, vol. 279(C).
    16. Zhu, Jianhua & Peng, Yan & Gong, Zhuping & Sun, Yanming & Lai, Chaoan & Wang, Qing & Zhu, Xiaojun & Gan, Zhongxue, 2019. "Dynamic analysis of SNG and PNG supply: The stability and robustness view #," Energy, Elsevier, vol. 185(C), pages 717-729.
    17. Avilés A., Camilo & Oliva H., Sebastian & Watts, David, 2019. "Single-dwelling and community renewable microgrids: Optimal sizing and energy management for new business models," Applied Energy, Elsevier, vol. 254(C).
    18. Matija Kostelac & Lin Herenčić & Tomislav Capuder, 2022. "Planning and Operational Aspects of Individual and Clustered Multi-Energy Microgrid Options," Energies, MDPI, vol. 15(4), pages 1-17, February.
    19. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Kendall, Alissa & Træholt, Chresten, 2018. "Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty," Applied Energy, Elsevier, vol. 230(C), pages 836-844.
    20. Romero-Quete, David & Garcia, Javier Rosero, 2019. "An affine arithmetic-model predictive control approach for optimal economic dispatch of combined heat and power microgrids," Applied Energy, Elsevier, vol. 242(C), pages 1436-1447.

    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:13:y:2020:i:10:p:2596-:d:360633. 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.