A novel two-stage TSO–DSO coordination approach for managing congestion and voltages.

This paper proposes a novel approach to TSO–DSO coordination in day-ahead operation planning for the procurement of ancillary services (AS) such as congestion management and voltage control. The approach overcomes two major issues hindering practical applicability of existing TSO–DSO coordination mechanisms, namely the computational complexity due to the large number of times the TSO and DSO problems are resolved as well as the inability to map analytically the cost and amount of flexibility at DSO level into TSO problem. To this end, a two-fold novelty of our approach consists of the decomposition of the overall AS problem into two sequential stages, which seek the procurement of: (i) "active power related" AS (i.e., for congestion management) and then (ii) the "reactive power related" AS (i.e., for voltage support), respectively as well as a fast approximation of the cost of aggregated active and reactive power flexibilities of active distribution systems (ADSs). Unlike existing mechanisms, the proposed approach considers real-world challenging aspects such as the N-1 security at TSO level and operation uncertainties at both TSO and DSO levels. Accordingly, the approach relies on tailored versions of stochastic multi-period AC security-constrained optimal power flow (S-MP-SCOPF) at TSO level and stochastic multi-period AC optimal power flow (S-MP-OPF) at DSO level, which are formulated as non-linear programming (NLP) problems. The importance and performance of the proposed approach are illustrated on a power system connecting a 60-bus transmission system (under 33 N-1 contingencies) with five 34-bus ADSs. • This paper proposes a two-stage approach to TSO–DSO coordination in day-ahead operation. • The approach procures optimally ancillary services for managing congestion and voltages. • A new mapping cost vs amount of aggregated flexibility in distribution grid is proposed. • The paper considers real-world challenges such as operation uncertainties and the N-1 security. • The approach relies on tailored versions of stochastic multi-period AC optimal power flow. [ABSTRACT FROM AUTHOR]