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Optimal Power Management Strategy for Energy Storage with Stochastic Loads

Author

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  • Stefano Pietrosanti

    (School of Systems Engineering, University of Reading, Whiteknights, Reading RG6 6AY, UK)

  • William Holderbaum

    (School of Systems Engineering, University of Reading, Whiteknights, Reading RG6 6AY, UK)

  • Victor M. Becerra

    (School of Engineering, University of Portsmouth, Anglesea Road, Portsmouth PO1 3DJ, UK)

Abstract

In this paper, a power management strategy (PMS) has been developed for the control of energy storage in a system subjected to loads of random duration. The PMS minimises the costs associated with the energy consumption of specific systems powered by a primary energy source and equipped with energy storage, under the assumption that the statistical distribution of load durations is known. By including the variability of the load in the cost function, it was possible to define the optimality criteria for the power flow of the storage. Numerical calculations have been performed obtaining the control strategies associated with the global minimum in energy costs, for a wide range of initial conditions of the system. The results of the calculations have been tested on a MATLAB/Simulink model of a rubber tyre gantry (RTG) crane equipped with a flywheel energy storage system (FESS) and subjected to a test cycle, which corresponds to the real operation of a crane in the Port of Felixstowe. The results of the model show increased energy savings and reduced peak power demand with respect to existing control strategies, indicating considerable potential savings for port operators in terms of energy and maintenance costs.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:175-:d:65322
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    References listed on IDEAS

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    1. 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.
    2. Haben, Stephen & Ward, Jonathan & Vukadinovic Greetham, Danica & Singleton, Colin & Grindrod, Peter, 2014. "A new error measure for forecasts of household-level, high resolution electrical energy consumption," International Journal of Forecasting, Elsevier, vol. 30(2), pages 246-256.
    3. Magnus Hedlund & Johan Lundin & Juan De Santiago & Johan Abrahamsson & Hans Bernhoff, 2015. "Flywheel Energy Storage for Automotive Applications," Energies, MDPI, vol. 8(10), pages 1-28, September.
    4. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    5. Hao Liang & Weihua Zhuang, 2014. "Stochastic Modeling and Optimization in a Microgrid: A Survey," Energies, MDPI, vol. 7(4), pages 1-24, March.
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    Cited by:

    1. Ademulegun, Oluwasola O. & Keatley, Patrick & Agbonaye, Osaru & Moreno Jaramillo, Andres F. & Hewitt, Neil J., 2020. "Towards a sustainable electricity grid: Market and policy for demand-side storage and wind resources," Utilities Policy, Elsevier, vol. 67(C).
    2. Timur Yunusov & Maximilian J. Zangs & William Holderbaum, 2017. "Control of Energy Storage," Energies, MDPI, vol. 10(7), pages 1-5, July.
    3. Papaioannou, Vicky & Pietrosanti, Stefano & Holderbaum, William & Becerra, Victor M. & Mayer, Rayner, 2017. "Analysis of energy usage for RTG cranes," Energy, Elsevier, vol. 125(C), pages 337-344.
    4. 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.
    5. 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.
    6. 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.
    7. Iris, Çağatay & Lam, Jasmine Siu Lee, 2019. "A review of energy efficiency in ports: Operational strategies, technologies and energy management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 170-182.
    8. Oluwasola O. Ademulegun & Patrick Keatley & Motasem Bani Mustafa & Neil J. Hewitt, 2020. "Energy Storage on a Distribution Network for Self-Consumption of Wind Energy and Market Value," Energies, MDPI, vol. 13(11), pages 1-17, May.
    9. 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.

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