A complementarity model for electric power transmission-distribution coordination under uncertainty

The growing penetration of stochastic renewable energy sources increases the need for operational flexibility to cope with imbalances. Existing proposals for flexibility procurement are envisioning markets where the transmission system operator (TSO) can access flexible resources located at the distribution system operator (DSO)-level and vice versa, but the coordination between these two entities is a matter of active research. We consider two trading floors, i.e., day-ahead and real-time markets, and propose a method for day-ahead coordination on how to share flexible resources, described as a complementarity model. The proposed coordination approach is to optimize prices and capacity limits at the physical interface of TSO and DSO, the so-called “coordination variables”. For given values of these variables, the DSO pre-qualifies the participation of DSO-level resources in the day-ahead market by capping their quantity bids. This way, the DSO ensures that the constraints of its system, modeled by a conic program, will be respected. Pursuing computational tractability, we decompose the model using a multi-cut Benders’ decomposition approach. It separates the conic modeling of real-time power flows under each scenario from the mixed-integer linear formulation of the day-ahead market-clearing problem. We quantify the potential benefit of the proposed coordination method in terms of improved social welfare. Using an ex-post out-of-sample simulation, the performance of the proposed coordination method is assessed against two benchmarks: (i) a fully uncoordinated scheme which obtains a lower bound for the expected social welfare, and (ii) an ideal benchmark which co-optimizes the TSO and DSO problems, providing an upper bound for the expected social welfare.