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Demand-side flexibility is an important tool for enhancing the interaction of renewable energy resources and reducing the need for grid upgrades. To employ this flexibility as a market product, it is necessary to aggregate and coordinate by coordinating responsive loads. In this regard, designing effective load coordination mechanisms that consider the preferences of aggregators, end-users, and network operators is critical for the successful implementation of demand response (DR) programs. This paper proposes an incentive-based method for coordinating a group of controllable devices that is practical, does not require complex, high-order models of the entire system, respects end-users privacy and quality of service (QoS), and can readily incorporate network conditions to ensure grid reliability. The proposed method includes algorithms at both the end-user level for controllable device operation and the aggregator level for managing the grid access requests. These algorithms are fast and with low computational burden which makes them suitable for the designed framework, reduces the implementation cost and increases the chance of scalability. The method is illustrated with a realistic test system consisting of a set of controllable heat pumps used in pool heating systems and uncontrollable loads placed in a distribution feeder and supplied by a distribution substation transformer. Simulation results highlight the effectiveness of the proposed method in satisfying the controllable device, end-users, and grid constraints. Comparing the results with similar existing methods shows that the method is 11% more cost-effective than traditional ON/OFF methods while reducing the number of rejected grid access requests from the devices, significantly. • Proposing a bottom-up methodology for coordinating grid access requests of devices. • Introducing a computationally lightweight incentive-based device controller algorithm. • Designing the method in such a way as to guarantee the quality of service for end-user. • Simulation-based implementation of the method on a set of swimming pool heat systems. • Decrease in electricity costs by 11% compared to conventional methods. [ABSTRACT FROM AUTHOR]