A Data-Driven Response Constraint-Based Reliability Assessment Method for Distribution Networks

To quantify the impact of IES access with interconnected energy flows on the reliability of distribution networks , a data-driven response constraint-based reliability assessment method is proposed for distribution networks and IES. Firstly, the impact of IES access on the reliability of distribution networks is analyzed, and a reliability assessment framework is proposed for distribution networks and IES. A data-driven optimization operation model for IES is developed, and a deep deterministic policy gradient (DDPG) algorithm is utilized to achieve the optimal economic dispatch of the IES. Subsequently, the trained DDPG model is linearized using the large M method and segmented linearization method to construct data-driven response constraints. Finally, with minimizing load loss in the distribution network as the objective, the data-driven response constraints are combined to establish the optimal load-slicing model of the distribution network with mixed-integer linear programming, and its validity is verified through IEEE RBTS-BUS6 system. Examples demonstrate that the proposed model can realize the reliability assessment for distribution networks under the condition of incomplete information, while IES provides reliability support for distribution networks through interconnected energy flow.