**6. Conclusions**

Using renewable energy resources and distributed generation has an important role to reduce the peak of greenhouse gas emissions. Innovative management strategies, such as integrating demand response programs, are required. This paper presented a precise vision of the demand response timeline in an aggregator model. The proposed aggregator has been considered as a third party between the upstream and downstream sides of the network, to aggregate small scale demand response resources. The time needed in the short and real-time demand response programs to notify all participants, to wait for their response, and evaluate the available resources is addressed.

For real-time simulation, a set of resistive loads to emulate the actual demand reduction of some demand response participants have been used. The numerical results highlight that the costs related to the periods prior to the event, such as ramp period, should be taken into account as in the most of model, demand response costs are only related to the period between the starting and ending point of the event. It should always be considered that normally the aggregator does not reach the desired reduction level immediately, and it requires some time to reach the desired reduction level. Regarding the remuneration, while the consumption is being reduced, consumers expect to receive remunerations for the related consumption reduction, even if the reduction has occurred prior to the starting of the event.

The experimental results obtained through emulation of loads indicate that there is a gap between the expected and actual results. In this way, laboratory tests play an important role to reveal technical issues of any model under practical challenges, namely voltage variations, frequency instabilities, and other electrical grid conditions.

**Author Contributions:** Conceptualization, P.F.; investigation, O.A.; methodology, P.F.; project administration, Z.V.; resources, O.A.; writing—original draft, O.A.; writing—review and editing, P.F. and Z.V. All authors have read and agreed to the published version of the manuscript.

**Acknowledgments:** The present work was done and funded in the scope of the following projects: UIDB/00760/2020, CEECIND/02887/2017, and COLORS PTDC/EEI-EEE/28967/2017, funded by FEDER Funds through the COMPETE program. This work has also received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066).

**Conflicts of Interest:** The authors declare no conflict of interest.
