Hierarchical Optimal Dispatch of Active Distribution Networks Considering Flexibility Auxiliary Service of Multi-community Integrated Energy Systems
<|reference_start|>Hierarchical Optimal Dispatch of Active Distribution Networks Considering Flexibility Auxiliary Service of Multi-community Integrated Energy Systems: Active distribution networks (ADNs) are the main platforms for carrying large-scale distributed renewable energy and flexible resources, and multi-community integrated energy systems (MCIESs) may become important flexible resource supplies in ADNs owing to their multi-energy synergistic and complementary advantages. To fully utilize the flexible regulation potential of MCIESs for ADNs, a novel hierarchical stochastic dispatch approach for ADNs that considers flexibility auxiliary services of MCIESs is proposed. In this approach, a flexibility auxiliary service pricing strategy that combines adjustment cost and flexibility margin is established by evaluating the operational flexibility of MCIESs. In addition, considering renewable uncertainty, an MCIES-ADN flexibility interaction mechanism based on insufficient flexibility risk is designed to optimize their operation strategies and reduce the uncertainty risk. In the solution phase, an analytical target cascading theory-based distributed solving method is developed to realize decoupling and parallel solving of multiple stakeholders. The simulation results for a PG&E 69-node system with three CIESs demonstrate that the proposed approach not only improves MCIES revenue but also enhances ADN flexibility to consume renewable energy, which provides a fundamental way for efficient application of regional mutual aid.<|reference_end|>
@article{wang2024hierarchical,
    title={Hierarchical Optimal Dispatch of Active Distribution Networks
  Considering Flexibility Auxiliary Service of Multi-community Integrated
  Energy Systems},
    author={Chunling Wang, Chunming Liu, Xiulin Zhou, Yang Li, Gaoyuan Zhang},
    journal={arXiv preprint arXiv:2409.04446},
    year={2024},
    doi={10.1109/TIA.2024.3462660},
    archivePrefix={arXiv},
    eprint={2409.04446},
    primaryClass={eess.SY cs.SY}
}