Smart-grids create a new paradigm in energy distribution, transmission and consumption and their operations domain aim to balance the demand with the production in a flexible and cost-efficient manner. Energy providers can use Demand Response (DR) to control electricity load during peak hours and can have the ability to shift energy demand from one time period to another to achieve smooth consumption patterns. This work presents the development of an incentive based Demand Response (DR) method. The preferences and behaviour of each residential user are modelled using an Internet of Things (IoT) reference architecture, while the DR actions are derived by the solution of a dynamic optimization problem. The overall energy management is performed by a local aggregator system and relies on this optimization-based approach with priorities that are dynamically updated based on the status of each consumer. A simulation scenario is presented that demonstrates how a DR program can affect the consumer’s behaviour in an optimal manner. The aggregator managed one of the appliances (Air Condition) of the participants to reduce the necessary amount of power. As a result, the power reduction was on average 23 % while the demand for reduction was 20 %, which is a clear indication that the applied actions by the aggregator fulfilled the energy provider’s request. The key result is the customer empowerment which is achieved by the reduction of the cost of electricity while maintaining the desired level of comfort. On the other hand, DR provides energy operators and utilities additional operational and enhancement of their resource capacity.