Distributed optimal energy scheduling based on a novel PD pricing feedback strategy in smart grid

Pricing function plays an important role in optimal energy scheduling problem in smart grid systems. In this paper, we propose a novel real time pricing strategy named proportional and derivative (PD) pricing strategy. An optimal energy scheduling problem is then formulated by minimizing the total social cost of the overall power system. A distributed optimization algorithm based on finite-time consensus and projected gradient method is provided for each agent to iteratively determine an optimal solution to the problem. As iteration increases, the solutions from all the agents reach consensus and this solves the formulated optimal problem. A case study of heating ventilation and air conditioning (HVAC) system shows efficiency of the proposed algorithm.