Dynamic tariff-driven demand response and smart inverters for enhancing renewable and EV hosting capacity in active distribution networks

The large-scale integration of electric vehicles (EVs) and renewable-based distributed generation (RDG) improves distribution-system sustainability but introduces operational challenges when deployed without coordination. Accurate assessment and enhancement of distributed generation hosting capacity (DG-HC) and EV hosting capacity (EV-HC) are therefore essential. This paper proposes a hierarchical stochastic time-varying optimization framework to enhance feeder-level DG-HC and EV-HC under uncertainty using a bi-level dynamic tariff-based demand response scheme driven by a proposed availability index of renewable energy (AIRE), together with smart inverter Volt/VAR control. At the lower level, customer demand and EV charging profiles are adjusted in response to AIRE-based tariff signals, while at the upper level, the distribution system operator optimizes DG capacities, EV charging-station capacities, and smart inverter settings to maximize DG-HC and EV-HC while minimizing voltage deviation and active power loss. Uncertainties in load demand, renewable generation, and EV charging behavior are modeled using Monte Carlo simulation and Kantorovich distance method (KDM)-based scenario reduction. The resulting weighted-sum scalarized optimization problem is solved using the Pelican Optimization Algorithm (POA) and compared with several benchmark metaheuristic algorithms, including NSGA-II. Validation on the IEEE 33-bus system and a real 59-bus distribution network in Egypt demonstrates DG-HC improvements of up to 48.5% and 25.5%, respectively. Moreover, both networks can accommodate approximately three times their initial EV charging demand under the proposed framework, confirming the effectiveness of coordinated renewable-aware demand response and inverter-based voltage support for enhancing hosting capacity.