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Dynamic Programming for Optimal Energy Management of Hybrid Wind–PV–Diesel–Battery

Author

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  • Luu Ngoc An

    (Department of Power Systems, Faculty of Electrical Engineering, Danang University of Science and Technology—The University of Danang, Danang City 550000, Vietnam)

  • Tran Quoc Tuan

    (lternative Energies and Atomic Energy Commission (CEA), National Institute for Solar Energy (INES), 50 Avenue du Lac Léman, F-73375 Le Bourget-du-Lac, France)

Abstract

With the dramatic development of renewable energy resources all over the world, Vietnam has started to apply them along with the conventional resources to produce the electrical power in recent years. Visually, the aim of this action is to improve the economic as well as the environmental benefits. Therefore, a vast of hybrid systems that combine Wind turbine, Photovoltaic (PV), Diesel generator and battery have been considered with different configurations. According to this topic, there are lots of research trends in the literature. However, we aim to the optimal energy management of this hybrid system. In particular, in this paper, we propose an optimization method to deal with it. The interesting point of the proposed method is the usage of the information of sources, loads, and electricity market as an embedded forecast step to enhance the effectiveness of the actual operation via minimizing the operation cost by scheduling distributed energy resources (DER) while regarding emission reduction in the hybrid system is considered as the objective function. In this optimization problem, the constraints are determined by two terms, namely: the balance of power between the supply and the load demand, and also the limitations of each DER. Thus, to solve this problem, we make use of the dynamic programming (DP) to transform a system into a multi-stage decision procedure with respect to the state of charge (SOC), resulting in the minimum system cost (CS). In order to highlight the pros of the proposed method, we implement the comparison to a rule-based method in the same context. The simulation results are examined in order to evaluate the effectiveness of the developed methodology, which is a so-called global optimization.

Suggested Citation

  • Luu Ngoc An & Tran Quoc Tuan, 2018. "Dynamic Programming for Optimal Energy Management of Hybrid Wind–PV–Diesel–Battery," Energies, MDPI, vol. 11(11), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3039-:d:180729
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    References listed on IDEAS

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    1. Dalton, G.J. & Lockington, D.A. & Baldock, T.E., 2009. "Feasibility analysis of renewable energy supply options for a grid-connected large hotel," Renewable Energy, Elsevier, vol. 34(4), pages 955-964.
    2. Nelson, D.B. & Nehrir, M.H. & Wang, C., 2006. "Unit sizing and cost analysis of stand-alone hybrid wind/PV/fuel cell power generation systems," Renewable Energy, Elsevier, vol. 31(10), pages 1641-1656.
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    Citations

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    Cited by:

    1. Hossam A. Gabbar & Muhammad R. Abdussami & Md. Ibrahim Adham, 2020. "Micro Nuclear Reactors: Potential Replacements for Diesel Gensets within Micro Energy Grids," Energies, MDPI, vol. 13(19), pages 1-38, October.
    2. Elena Sosnina & Andrey Dar’enkov & Andrey Kurkin & Ivan Lipuzhin & Andrey Mamonov, 2022. "Review of Efficiency Improvement Technologies of Wind Diesel Hybrid Systems for Decreasing Fuel Consumption," Energies, MDPI, vol. 16(1), pages 1-38, December.
    3. Alexander N. Kozlov & Nikita V. Tomin & Denis N. Sidorov & Electo E. S. Lora & Victor G. Kurbatsky, 2020. "Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques," Energies, MDPI, vol. 13(10), pages 1-20, May.
    4. Pier Giuseppe Anselma, 2021. "Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort," Energies, MDPI, vol. 14(10), pages 1-28, May.
    5. Pedro Bento & Hugo Nunes & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2019. "Daily Operation Optimization of a Hybrid Energy System Considering a Short-Term Electricity Price Forecast Scheme," Energies, MDPI, vol. 12(5), pages 1-25, March.
    6. Seyfettin Vadi & Sanjeevikumar Padmanaban & Ramazan Bayindir & Frede Blaabjerg & Lucian Mihet-Popa, 2019. "A Review on Optimization and Control Methods Used to Provide Transient Stability in Microgrids," Energies, MDPI, vol. 12(18), pages 1-20, September.
    7. Rovick Tarife & Yosuke Nakanishi & Yining Chen & Yicheng Zhou & Noel Estoperez & Anacita Tahud, 2022. "Optimization of Hybrid Renewable Energy Microgrid for Rural Agricultural Area in Southern Philippines," Energies, MDPI, vol. 15(6), pages 1-29, March.

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