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As an important part of smart grid construction, the distribution network (DN) optimization problem has always attracted great attention, especially under the background that large-scale penetration of distributed generators (DGs) and electric vehicles (EVs) into building cluster poses both opportunities and challenges to the energy management. This research presents a hierarchical optimization strategy, for improving the safe and economical operation of DN considering the DGs and EVs integration. In Stage 1, the MPPT control model of DGs is designed to obtain the best energy conversion efficiency. In Stage 2, load models of EVs and battery energy storage system (BESS) under coordinate charging/discharging stimulated by a time-of-use incentive mechanism are established respectively, to achieve a load curve with a minimized peak-to-valley difference (PVD). In Stage 3, aiming for the best compromise between the active power loss and node voltage excursion, daily optimal scheduling of the static Var compensator (SVC) capacitors is dynamically worked out according to the varying power demand, as the solution for the defined multi-objective optimization problem. For enhancing the convergence speed, an advanced genetic algorithm with elite preservation strategy is employed. The proposed hierarchical strategy is demonstrated on an IEEE 33-node DN test case, and the simulation results show that first, the MPPT control ensures the maximum power outputs of DGs; next, power supply pressure could be relieved by the load shifting effects of the coordinated vehicle-to-grid (V2G) service and BESS configuration, reflected in the decreased load peak from 4,370.1 to 3,424.99 kW, and the optimized PVD from 1763.8 to 703.8 kW; meanwhile,  via  applicable power planning of the SVC components, optimized power loss and voltage quality can both be achieved, proving the feasibility of the optimization strategy, which promotes the economic and reliable operation of the DN system.

A Hierarchical Strategy for Multi-Objective Optimization of Distribution Network Considering DGs and V2G-Enabled EVs Integration