A new generation of smart and connected stormwater and sewer systems is being enabled by emerging wireless technologies and data algorithms. Stormwater and combined sewer systems can be autonomously controlled (gates, valves, pumps) to allocate storage and adapt to changing inputs. As a result, there is an opportunity to begin viewing the collection system as an extension of the water resource recovery facility (WRRF), whereby flows in the collection system are dynamically controlled to benefit downstream treatment. The dynamic control of collection system storage will allow peak flows to be minimized and solid loads to the plant to be tuned in response to real-time WRRF states as they relate to treatment operation and performance. To that end, this paper presents a formulation of a real-time load-balancing algorithm to control distributed storage assets in the collection system, with objectives of improving flow and water quality dynamics at inflow to a treatment plant. We illustrate that this load-balancing approach can successfully attenuate wet-weather peaks and minimize dry-weather oscillations. The parameterization of the control algorithm is assessed in the context of competing objectives at the downstream WRRF and broader collection system (e.g. sediment loads, peak flows, flooding, and solids accumulation in the sewer system). By applying this control algorithm and analysis to an established case study, we identify a range of parameter values that provide most desirable performance across a number of system-wide objectives. Specifically, we discover a band of desirable performance, which not only improves inflow into the WRRF, but simultaneously reduces flooding and sedimentation in the collection system.