A Novel Unscented Transformation-Based Framework for Distribution Network Expansion Planning Considering Smart EV Parking Lots

摘要

Public parking lots equipped with electric vehicle (EV) charging facilities place huge power demands on distribution networks. These huge demands, if not carefully considered at the planning stage, can create several operational problems. To address this issue, this paper proposes a novel distribution network expansion planning framework, which gives full consideration to the charging power demands of large EV parking lots. This framework provides several alternatives for construction/reinforcement of feeders and substations, while taking all the necessary constraints into account. Furthermore, the unscented transformation (UT) method is employed to model the uncertainties of load demands and EV parking lot demands. The ability of the UT method to accurately model correlated uncertain parameters makes it highly applicable in the context of distribution network expansion planning, where considerable correlated uncertainties exist. The proposed UT-based framework is formulated as a mixed-integer linear programming (MILP) problem, which can be solved using off-the-shelf mathematical programming solvers that guarantee convergence to the global optimal solution. A 24-node distribution system is used to verify the effectiveness of the proposed methodology.