On the Coordination of Transmission-Distribution Grids: A Dynamic Feasible Region Method

Recently, the turnover of energy services between transmission-distribution grids has continued to increase, arousing widespread attention to the efficient coordination between transmission system operator (TSO) and distribution system operator (DSO). The existing literature has characterized the feasible region of distribution networks via equivalent projection methods, which is conducive to the participation of DSO in TSO scheduling. However, the redundant constraints of DSO and the processing of temporal-coupled constraints remain challenging in realistic cases. To achieve an effective interaction between TSO and DSO with least information, we develop a TSO-DSO coordination framework with temporal-coupled constraints embedded, i.e., the concept of dynamic feasible region (DFR). We theoretically derive the polyhedral form of a DFR, which can be characterized as a constraint set formed by the extreme points of the dual space of the DSO optimization program. A novel outer progressive approximation (OPA)-based algorithm is designed to effectively add feasibility cuts until convergence is achieved. To reduce the redundant constraints in the DSO operation model, the enhanced umbrella constraint identification (E-UCI) method is employed. Case studies based on i) a real-world distribution grid in China and IEEE 30-bus system, ii) a Caracas 141-bus distribution network and IEEE 118-bus transmission grid are adopted to validate the effectiveness and computational efficiency of our proposed method.

Automated summary

Recently, there has been increasing attention to the efficient coordination between transmission system operators (TSO) and distribution system operators (DSO) in the turnover of energy services between transmission-distribution grids. We propose a TSO-DSO coordination framework with temporal-coupled constraints embedded, called dynamic feasible region (DFR), to achieve effective interaction between TSO and DSO with minimal information. We derive the polyhedral form of DFR and design a novel outer progressive approximation (OPA)-based algorithm to effectively add feasibility cuts.