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Two-stage stochastic programming formulation for optimal design and operation of multi-microgrid system using data-based modeling of renewable energy sources

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  • Han, Dongho
  • Lee, Jay H.

Abstract

Microgrid (MG) is intended to accommodate distributed renewable energy sources and manage the generated electricity using a dispatch and storage system to satisfy the demand of a local region over time. Main challenges associated with operating such a system stems primarily from the intermittent and uncertain nature of renewable energy sources causing the energy generation to be highly irregular and less predictable. Multi-microgrid (MMG) systems with flexibility to trade energy between individual MGs can alleviate the demand–supply mismatch problem, but its design and operation become more complicated. Previous studies on MMGs addressed only the case where all the MGs are fully connected to share electricity. However, the installation of electric cable connections over the full network can be costly and is often unjustified. To address the question of how to best achieve a tradeoff, this work proposes the use of a two-stage stochastic decision-making approach for designing and operating an MMG considering both the capital cost for installing the electric cables and operating cost. First, real weather data are used to develop stochastic models that describe the uncertain, intermittent nature of energy production through renewable sources over a day. Then, in the first stage, design-level decisions regarding the installation of the electric cable between individual pairs of the MGs are to be determined to achieve the best tradeoff for the modeled system and weather pattern. In the second stage, the optimal energy dispatch planning for each individual MG including the energy trading between the MGs is carried out based on a particular realized scenario of the renewable energy production.

Suggested Citation

  • Han, Dongho & Lee, Jay H., 2021. "Two-stage stochastic programming formulation for optimal design and operation of multi-microgrid system using data-based modeling of renewable energy sources," Applied Energy, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:appene:v:291:y:2021:i:c:s0306261921003299
    DOI: 10.1016/j.apenergy.2021.116830
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