**6. Conclusions**

A dynamic network tariff has been introduced in this paper that utilizes a hierarchical MAS-based architecture with distributed intelligence. The price-responsive behaviors of the loads have been modeled using a detailed bottom-up Markov Chain Monte Carlo approach. The loads are scheduled based on the dynamic prices available from the aggregator. If a case of network congestion is expected, the DSO adjusts the network tariff part of the price by varying it throughout the day. This is done on the basis of an estimated overloading cost resulting from the probable loading scenarios. The loading scenarios are generated using the Gaussian copula of the expected load from the aggregator and historical loading values of one month previous. The network tariff is raised around the peak hours while keeping the daily average fixed.

The proposed approach has been evaluated through simulations for 55 households in a modified form of the IEEE European LV test feeder. Simulation results reveal a notable relation between the dynamic price and congestion, which has been reported by a number of previous studies. The proposed approach appears to be efficient at managing congestions, as the total monthly congestion duration has been reduced up to 82%. The monthly simulation has been performed on an Intel Core i7 computer with 8 GB of RAM. The proposed approach requires a simulation time of approximately 15 min compared to 12 min for the uncontrolled case.

Future research on this topic will be directed to efficient methods for local voltage control. The functionalities of the aggregator will be further updated with smarter learning techniques for more accurate price adjustments.

**Author Contributions:** Literature Research, N.H. and A.T.; Conceptualization, N.H.; Methodology, N.H.; Visualization, N.H. and A.T.; Writing original draft, N.H. and A.T.; Writing review and editing, N.H., P.N. and G.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is part of the research program ERA-net Smart Energy Systems with project number 651.001.012, which is financed by the Netherlands Organization for Scientific Research (NWO).

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