A multi-layer scheduling framework for transmission network overload alleviation considering capabilities of active distribution networks

Abstract

As distributed generations and flexible loads are widely connected to distribution networks, traditional distribution networks become “active”, and the response of the active distribution network (ADN) to the transmission network is receiving increasing attention. In this paper, the congestion problem of transmission network is studied from a demand-side perspective, and a multi-layer scheduling framework considering capabilities of ADNs is developed to reduce unnecessary load shedding operations in transmission network. The proposed framework consists of three layers. In the first layer, ADN’s power flow is linearized by the second-order cone programming, based on which a reactive power optimization scheduling model is constructed to minimize network loss in ADNs. When the network loss reduction in ADN cannot meet the requirements for congestion mitigation, a bi-level self-cycle model based on analytical target cascading is constructed in the second layer, which contributes to dispatch distributed generators’ outputs and further curtail load from the ADN side. When the second layer fails to satisfy the required load curtailment, a demand response model considering user acceptance and rejection is proposed to obtain the optimal shedding strategy for interruptible loads, and ultimately alleviate the overload problem. A case study with varying degrees of overload in the transmission network is conducted to demonstrate the proposed framework.