Privacy-Preserving Demand Response Framework: Prime Dual Method of Multipliers

In the context of modern electric grids, the concept of demand response has become increasingly important. With the advent of liberalized markets, maintaining information privacy has become a key concern for responsive load aggregators. However, existing privacy-preserving demand response programs have encountered various challenges, such as slow convergence, subpar performance when dealing with numerous demand aggregators, and sensitivity to auxiliary parameters. To tackle these issues, this conference paper introduces a distributed graph-based optimization algorithm known as the primal-dual method of multipliers (PDMM). The proposed method aims to optimize the consumption of shiftable and curtilabe loads while ensuring customer satisfaction. To validate its effectiveness and superiority, empirical data from London is employed for demonstrating the proposed method's capabilities.