Dynamic Clustering for Imbalance Management through Local Supply and Demand Balancing

The electrical power system has been facing a fundamental shift of paradigm with the integration of renewable energy sources (RES) and price responsive loads. These changes introduce higher uncertainties and pose implications for both network and market actors. While the network operators face frequent operational challenges in terms of voltage limit violations and network congestions, the market parties need deal with a higher risk of imbalance. In this paper, a dynamic clustering method has been proposed to procure flexibility from the smallscale residential prosumers in order to minimize the imbalance cost. An agent-based coordination mechanism has been adopted to implement a demand response program based on local supply and demand balancing. A bottom-up simulation analysis was performed incorporating 600 residential prosumers. Simulation results indicate that a dynamic and adaptive clustering scheme can efficiently minimize the imabalance cost resulting from wind power forecast errors while maintaining the comfort constraints of the prosumers.