Computational modeling demonstrates enhanced flexibility in active distribution networks coupled with microgrids, indicating improved power system stability under renewable energy loads.
With the continuous increase in renewable energy penetration, the traditional distribution network is gradually evolving into the active distribution network. Facing increasingly severe regulation pressure, relying solely on resource allocation from a single side of the distribution network can no longer provide adequate flexibility support. As an effective carrier integrating sources and loads, collaborative mutual assistance between the microgrid and the distribution network has become an inevitable trend to tap the flexibility potential of multiple entities. However, the current insufficient coordination of multi-type flexibility resources and the lack of deep interaction between the distribution network and the microgrid limit system flexibility, affecting the secure operation of the power grid. Therefore, this paper proposes an active distribution network–microgrid collaborative planning method for flexibility enhancement. Firstly, a collaborative optimal allocation model of nodal and grid-level flexibility resources for the active distribution network is established. The upper tier minimizes the annualized comprehensive cost, while the lower tier minimizes the annual operational cost and optimizes flexibility indices, comprehensively considering constraints like equipment investment, system security, and flexibility supply–demand balance. Secondly, the coupling relationship between the distribution network and the microgrid is established through tie-lines to construct the active distribution network–microgrid collaborative planning model. Finally, an accelerated and robust analytical target cascading solution strategy is proposed. By constructing a balancing coefficient, it eliminates the algorithm’s sensitivity to initial penalty weights, effectively improving the stability and efficiency of the model solution. Case study analysis verifies the effectiveness of the proposed method.
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Ren et al. (2026) studied this question.
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