A Fast Algorithm for Optimal Power Scheduling of Large-Scale Appliances With Temporally Spatially Coupled Constraints

Optimally scheduling power consumption of appliances is the essential feature of smart grid, which enables Demand Response Management (DRM) and helps to shape the power usage profile. This problem is often required to be solved in face of a large number of appliances and many time slots; thus the computational efficiency of solving a large scale optimal power scheduling problem with limited computational resources becomes the major concern of algorithm design. To this end, a novel algorithm is proposed based on Karush-Kuhn-Tucker (KKT) conditions to solve the optimal power scheduling problem with temporally spatially coupled constraints in a distributed manner. The proposed algorithm converts the original problem into equivalently solving an optimal KKT operator in a much lower dimension, thus the computation speed is greatly enhanced. In addition, the proposed method dose not require a step size in the iteration process, thus avoids the oscillation of numerical solution caused by problem parameter changes. Compared with the widely used conventional algorithms, e.g., interior point method and dual decomposition, the higher computational speed and less sensitivity to the problem parameter setting are observed in numerical simulations.