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August 16, 2016IEEE Transactions on Smart Grid362 citations

Energy Cooperation Optimization in Microgrids With Renewable Energy Integration

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KRKatayoun RahbarCCChin Choy ChaiRZRui Zhang

Key Points

  • This research aims to optimize energy management for cooperative microgrids with renewable energy integration.
  • Assumed perfect knowledge of renewable energy generation and load amounts to solve the offline energy management problem.
  • Developed online algorithms for real-time energy management of two cooperative microgrids.
  • Extended algorithms to accommodate more than two microgrids using a clustering approach.
  • The proposed online algorithms performed well under various scenarios with low complexity.
  • Demonstrated significant cost savings through effective energy cooperation among microgrids.

Abstract

Microgrids are key components of future smart grids, which integrate distributed renewable energy generators to efficiently serve the load locally. However, the intermittent nature of renewable energy generations hinders the reliable operation of microgrids. Besides the commonly adopted methods such as deploying energy storage system (ESS) and supplementary fuel generator to address the intermittency issue, energy cooperation among microgrids by enabling their energy exchange for sharing is an appealing new solution. In this paper, we consider the energy management problem for two cooperative microgrids each with individual renewable energy generator and ESS. First, by assuming that the microgrids' renewable energy generation/load amounts are perfectly known ahead of time, we solve the off-line energy management problem optimally. Based on the obtained solution, we study the impacts of microgrids' energy cooperation and their ESSs on the total energy cost. Next, inspired by the off-line optimization solution, we propose online algorithms for the real-time energy management of the two cooperative microgrids. It is shown via simulations that the proposed online algorithms perform well in practice, have low complexity, and are also valid under arbitrary realizations of renewable energy generations/loads. Finally, we present one method to extend our proposed online algorithms to the general case of more than two microgrids based on a clustering approach.

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Cite This Study

Rahbar et al. (2016) studied this question.

synapsesocial.com/papers/6a19d8ae60e90a7f5feabb53https://doi.org/10.1109/tsg.2016.2600863
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