Initialization-free Privacy-guaranteed Distributed Algorithm for Economic Dispatch Problem
It addresses the need for privacy-preserving and initialization-free distributed optimization in power systems, enabling online adaptation to changing grid conditions.
The paper proposes a distributed algorithm for the economic dispatch problem that minimizes total generation cost under constraints, requiring no initialization and preserving privacy. Simulations on the IEEE 118 bus system demonstrate robustness to network variations.
This paper considers the economic dispatch problem for a network of power generators and customers. In particular, our aim is to minimize the total generation cost under the power supply-demand balance and the individual generation capacity constraints. This problem is solved in a distributed manner, i.e., a dual gradient-based continuous-time distributed algorithm is proposed in which only a single dual variable is communicated with the neighbors and no private information of the node is disclosed. The proposed algorithm is simple and no specific initialization is necessary, and this in turn allows on-line change of network structure, demand, generation constraints, and even the participating nodes. The algorithm also exhibits a special behavior when the problem becomes infeasible so that each node can detect over-demand or under-demand situation of the power network. Simulation results on IEEE 118 bus system confirm robustness against variations in power grids.