Operational Bottleneck Identification Based Energy Storage Investment Requirement Analysis for Renewable Energy Integration

Abstract

Operational bottlenecks are commonly observed in power systems and lead to severe system security issues, which may be caused by the fluctuating and uncertain nature of renewable energy. This paper presents an approach to define, identify and eliminate such bottlenecks in the scope of system balance for renewable energy integrated bulk power systems, so as to quantify the requirement of energy storage. A mixed-integer linear programming (MILP) formulation for system operational bottleneck identification is proposed given renewable generation profile, in order to obtain operational restriction indices to assess the adequacy of power adjustment margin and power ramp rate. Cosine similarity based density-based spatial clustering of applications with noise (DBSCAN) method is used to cluster a large number of scenarios by operational restriction indices, then scenarios with bottlenecks are attributed to corresponding clusters. Finally, various bottleneck elimination options, including energy storage with different technologies, are compared for each cluster. Case studies of an eight-bus test system and a practical Chinese power system are presented to verify the proposed approach, the numerical results indicate energy storage is the most effective option to eliminate bottlenecks identified in power downward adjustment margin and ramp rate dominated clusters aforementioned.