A flexible urban load density-dependent framework for low-carbon distribution expansion planning in the presence of hybrid hydrogen/battery/wind/solar energy systems

Popularization of renewable energy sources (RESs), driven by the goal of carbon footprint mitigation in urban areas, invites unprecedented uncertainties into power distribution networks (PDNs). The uncertainties stemming from intrinsic intermittency of the RESs force network planners to meet flexibility requirements. For handling this challenge, multiple energy storage systems have recently emerged as a pivotal component across the PDNs. In this sense, the authors of this current study present here a new urban-load density-dependent framework for multi-period distribution expansion planning (DEP) considering hybrid hydrogen/battery/wind/solar energy systems for both flexibility enhancement and transition toward low-carbon PDNs. The proposed framework, from a new perspective, aims to divide the PDNs into multiple zones, according to load density of different urban areas under two simultaneous incommensurable objective functions: (i) minimization of investment and operation and maintenance costs; and, (ii) maximization of the supply-demand-related flexibility (SDF) improvement metric minus network-related flexibility (NTF) degradation metric. As the resultant optimization problem formulation has a challenging non-convex mixed-integer nonlinear structure, a fuzzy-based symphony orchestra search algorithm (F-SOSA) was employed to determine the final optimal solution. The effectiveness of the newly developed framework was verified through simulation results on standard 54-node and realistic 95-node distribution test networks. The results illustrate that the integration of hydrogen/battery energy systems brought about an increase of 9.52% and a decrease of 14.96% for the SDF and NTF, respectively, in comparison to their absence. One can further stat that applying these multiple energy systems is associated with a reduction of 20.64% of the total investment cost.