Emerging sustainable solutions in smart grids such as Distributed Energy Resources(DERs), Vertical Farms (VFs), and Electric Vehicles (EV s) enhances the resilience of the Active Distribution Networks (ADNs). We propose an operational framework to integrate emerging technologies into ADN under solar and demand uncertainty to reduce the operation cost, carbon emissions, and power quality issues of the smart communities. We primarily focus on enhancing the reliability and efficiency of power systems through advanced optimization techniques. A key contribution of this work is the development of a second-order cone programming (SOCP) relaxation approach that addresses cycle constraints in the optimal xxi power flow (OPF) problem. We demonstrate how this method improves the tightness of solutions and reduces computation burdens compared to traditional convex relaxation methods. Additionally, we introduced a strategic bidding framework in electricity markets based on the convexified AC market clearing problem (MCP) for market participants to optimize their bids under various market conditions. Our results demonstrate that the proposed framework renders 52.3% more profit for the market participants. The increase in utilization of solar generation units and EV s in past decades, especially in California, lead to power quality and uncertainty issues in the grid. We proposed the model of Smart Inverters (SI s) to leverage the potential of DERs to offer grid services through reactive power compensation. The results show that solar dispatchability increases by 12% and voltage violation is mitigated when the proposed volt/VAR model is utilized for IEEE 33-bus system. Furthermore, we propose the model of various systems in VFs as an emerging sustainable demand responsive asset in ADNs to mitigate the carbon emissions of the smart communities and increase the resiliency of ADNs against uncertainties in demand and solar generation. To quantify the uncertainties more accurate based on the real data and decrease the conservatism of the robust optimization (RO) method, we propose the data-driven mean robust optimization (MRO) approach for the vertical farming expansion planning problem in ADNs. It is shown that the proposed MRO method can mitigate the carbon emissions of the smart city by up to 10% and decrease the total operation and planning cost of the system compared to utilizing the more conservative RO method.