Prioritization of transmission and distribution system operator collaboration for improved flexibility provision in energy markets

The effective use of the flexibility sources of transmission and distribution networks necessitates smooth coordination between transmission and distribution system operators (TSO–DSO) as well as proper interfacing between the energy and flexibility markets. In this paper, mathematical centralized/decentralized optimization frameworks of flexibility market structures is proposed for four market mechanisms of a transmission-level centralized market, a local distribution- and centralized transmission-level market, a TSO priority market, and a TSO–DSO price equilibrium market. Further, prioritization mechanisms are developed by allocating ramp flexibilities in multi-interval day-ahead market clearing procedures for the wholesale (transmission level) and local markets (distribution level) to reduce the prediction error and improve the performance of the real-time flexibility market, which is a single-interval optimization market. Accordingly, the concurrent provision of the energy and flexibility requirements of the transmission and distribution networks is explored in a joint energy and flexibility day-ahead market. As a base case, the centralized market model does not prioritize TSO–DSO flexibility. The second market model gives the DSO priority in terms of providing flexibility. The DSO starts by using the resources of the distribution network. It then makes the upstream market an offer for the flexibility capacities of the remaining unused resources. In the third proposed market, the TSO receives priority in providing flexibility. The optimization frameworks proposed for the third market mechanisms are formulated as mixed-integer linear programming models (MILP). In addition, to ensure a higher resilience, a lower complexity of algorithms, and the possibility to better adapt to the efficient flexibility allocation in the TSO/DSO collaboration, an approach of the decentralized alternating direction method of multipliers (ADMM) in the decentralized optimization framework is performed to clear the proposed wholesale market while taking into account the local markets at the distribution level. Finally, to evaluate the proposed multi-level centralized/decentralized optimization frameworks, simulation results are performed on a modified IEEE 30-bus/118-bus transmission network with three/five IEEE 33-node distribution networks connected. In the case studies, various market clearing and prioritization mechanisms are implemented and evaluated through numerical simulations.