A receding horizon data‐driven chance‐constrained approach for energy flexibility trading in multi‐microgrid distribution network

Abstract Unexpected imbalances between supply and demand during real‐time operation in the wake of high penetration of renewable energy resources necessitate system operators to improve flexibility of the system. Moreover, increasing growth in flexible distributed resources installation in distribution networks with multiple MicroGrids (MGs) such as fuel‐based distributed generators, storage units, and controllable loads makes distribution system operators be able to manage energy imbalances locally through exploiting potentials of aforementioned resources in flexibility enhancement. This paper proposes a receding horizon data‐driven chance‐constrained energy flexibility trading framework for active distribution networks with multiple MGs. Specifically, a distance‐based confidence set is constructed in a data‐driven manner to apply net load uncertainty into this modelling. According to this proposed data‐driven approach, the energy flexibility is modelled using flexibility envelope notion to demonstrate its dynamics in different scenarios in an imminent horizon. Furthermore, MGs' flexibility trading problem is modelled as an exact potential game that possesses a pure strategy Nash Equilibrium (NE). An iterative decentralized algorithm based on alternating direction method of multipliers is developed to achieve the NE strategy of MGs. Numerical results are also represented to show the performance efficiency of the proposed energy flexibility trading framework.