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Multidispatch for Microgrid including Renewable Energy and Electric Vehicles with Robust Optimization Algorithm

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  • Ruifeng Shi

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
    China Institute of Energy and Transportation Integrated Development, Beijing 102206, China)

  • Penghui Zhang

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Jie Zhang

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Li Niu

    (School of Information Resource Management, Renmin University of China, Beijing 100872, China)

  • Xiaoting Han

    (School of Economics and Management, Beihang University, Beijing 100083, China)

Abstract

With the deterioration of the environment and the depletion of fossil fuel energy, renewable energy has attracted worldwide attention because of its continuous availability from nature. Despite this continuous availability, the uncertainty of intermittent power is a problem for grid dispatching. This paper reports on a study of the scheduling and optimization of microgrid systems for photovoltaic (PV) power and electric vehicles (EVs). We propose a mathematical model to address the uncertainty of PV output and EV charging behavior, and model scheduling optimization that minimizes the economic and environmental cost of a microgrid system. A semi-infinite dual optimization model is then used to deal with the uncertain variables, which can be solved with a robust optimization algorithm. A numerical case study shows that the security and stability of the solution obtained by robust optimization outperformed that of stochastic optimization.

Suggested Citation

  • Ruifeng Shi & Penghui Zhang & Jie Zhang & Li Niu & Xiaoting Han, 2020. "Multidispatch for Microgrid including Renewable Energy and Electric Vehicles with Robust Optimization Algorithm," Energies, MDPI, vol. 13(11), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2813-:d:366117
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    References listed on IDEAS

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    Cited by:

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    3. Héricles Eduardo Oliveira Farias & Camilo Alberto Sepulveda Rangel & Leonardo Weber Stringini & Luciane Neves Canha & Daniel Pegoraro Bertineti & Wagner da Silva Brignol & Zeno Iensen Nadal, 2021. "Combined Framework with Heuristic Programming and Rule-Based Strategies for Scheduling and Real Time Operation in Electric Vehicle Charging Stations," Energies, MDPI, vol. 14(5), pages 1-27, March.
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