Optimal scheduling of an active distribution system considering distributed energy resources, demand response aggregators and electrical energy storage

This paper presents a two-level optimization model for the optimal scheduling of an active distribution system in day-ahead and real-time market horizons. The distribution system operator transacts energy and ancillary services with the electricity market, plug-in hybrid electric vehicle parking lot aggregators, and demand response aggregators. Further, the active distribution system can utilize a switching procedure for its zonal tie-line switches to mitigate the effects of contingencies. The main contribution of this paper is that the proposed framework simultaneously models the arbitrage strategy of the active distribution system, electric vehicle parking lot aggregators, and demand response aggregators in the day-ahead and real-time markets. This paper's solution methodology is another contribution that utilizes robust and lexicographic ordering optimization methods. At the first stage of the first level, the optimal bidding strategies of plug-in hybrid electric vehicle parking lot aggregators and demand response aggregators are explored. Then, at the second stage of the first level, the day-ahead optimization process finds the optimal scheduling of distributed energy resources and switching of electrical switches. Finally, at the second level, the real-time optimization problem optimizes the scheduling of system resources. Different case studies were carried out to assess the effectiveness of the algorithm. The proposed algorithm increases the system's day-ahead and real-time revenues by about 52.09% and 47.04% concerning the case without the proposed method, respectively.

Automated summary

This paper presents a two-level optimization model for the optimal scheduling of an active distribution system in day-ahead and real-time market horizons. The proposed framework considers the arbitrage strategy of different stakeholders and uses robust and lexicographic ordering optimization methods.