Distributed photovoltaic (PV) systems play an important role in supporting low-carbon and sustainable distribution network development; however, their high-penetration integration can cause nodal overvoltage, as midday PV generation often exceeds local demand and net reverse power flows accumulate along feeders. Existing studies mainly focus on technical mitigation, while entity-level responsibility quantification and its translation into differentiated mitigation tasks remain insufficiently explored. This paper proposes an overvoltage mitigation method integrating Shapley-value-based responsibility quantification with active–passive-responsibility-driven task allocation. Reverse power flow quotas are determined using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) by considering the differentiated impacts of PV entities on power flow distribution and nodal voltage response. The load-weighted voltage deviation index is adopted as the coalition cost function, and the Shapley value is used to quantify each entity’s overvoltage responsibility share. Active and passive responsibilities are characterized through the net reverse power ratio and reactive power–voltage sensitivity, respectively, establishing a mapping mechanism from responsibility shares to active power curtailment and reactive power regulation tasks. A multi-objective optimization model that jointly considers line losses, PV curtailment, fairness deviation, and task-allocation deviation is constructed and solved using the whale optimization algorithm. Case studies on the IEEE 33-bus system show that the proposed method reduces the maximum network voltage from 1.25 pu to 1.052 pu and eliminates all overvoltage violations. Compared with a cluster-based scheme, the 24-h cumulative line loss is reduced by 30.6%, and the fairness deviation is significantly lowered, thereby supporting the sustainable, economical, and equitable operation of distribution networks with high distributed PV penetration.
Yang et al. (Fri,) studied this question.