Simultaneous distributed generation and electric vehicles hosting capacity enhancement through a synergetic hierarchical bi-level optimization approach based on demand response and Volt/VAR control.

In the context of sustainable development, electric vehicles (EVs) and renewable-based distributed generation (RDG) integration into the distribution networks (DNs) introduce various merits. They involve lowering harmful emissions, employing various types of energy sources, and encouraging the dependence of renewable energy. However, the two most challenging issues posing a grave danger to the technical operation of the DN would be the inappropriate integration of RDGs and EVs. Consequently, in order to guarantee safe operation, distribution system operator (DSO) is responsible for precisely identifying two crucial terms, distributed generation hosting capacity (DG-HC) and electric vehicles hosting capacity (EV-HC). Despite an increase in the amount of research on HC approaches, there is still a remarkable research gap in the discussion of models that effectively combine demand response (DR), smart inverters (SI) Volt/VAR control, DG-HC, and EV-HC targets simultaneously. This study offers a hierarchical bi-level optimization HC framework depends on both dynamic tariff-based DR and SI Volt/VAR control. In the lower layer, Participating customers' load curves and EV aggregators' charging demands are optimally adjusted, on a forecast basis, based on the proposed dynamic tariff. Nonetheless, the suggested multi-objective function-which includes DG-HC, EV-HC maximization, and loss minimization-is optimized by the DSO at the upper layer based on all of these optimal load curves. To further support the proposed objective function, DR is mixed with the optimal Volt/VAR controlling offered by SIs. In addition, the role of grid-connected EVs (GCEVs) on DG-HC increase is revealed with both uncoordinated and coordinated charging schemes. To check the proposed approach robustness, three types of loads are considered. Through comparison with three other optimization approaches, the effectiveness of the equilibrium optimizer (EO) is demonstrated when it is employed to solve the proposed optimization scheme. The suggested planning approach is applied on both the IEEE 33-bus test system, and a real DN with 59 buses in Cairo, Egypt. Several significant conclusions are validated by the obtained results. First, the DG-HC assessment differs significantly depending on whether the EV charging demand is considered or not. Considering the EV integration in IEEE 33-bus, the mean value of the optimal DG-HC increased by more than 133% during the day. Secondly, the implementation of the proposed dynamic-pricing DR program in the IEEE 33-bus DN significantly improved both the DG-HC and EV-HC, named as the combined DG-EV-HC, with improvements of around 34% and 27% for DG-HC and EV-HC respectively. Finally, in IEEE 33-bus, the combined DG-EV hosting capacity was improved by approximately 49.2% regarding DG-HC and 61.2% regarding EV-HC, using the proposed synergistic DR-Volt/VAR control enhancement technique.