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

Pricing function plays an important role in optimal energy scheduling problem in smart grid systems. The authors propose a novel real-time pricing (RTP) strategy named proportional and derivative (PD) pricing. Different from conventional RTP strategies, which only depend on the current total energy consumption, their proposed pricing strategy also takes the historical energy consumption into consideration, which aims to further fill the valley load and shave the peak load. An optimal energy scheduling problem is then formulated to minimise the total social cost of the overall power system. Two different distributed optimisation algorithms with different communication strategies are proposed to solve the problem. Several case studies implemented on a heating ventilation and air conditioning system are tested and discussed to show the effectiveness of both the proposed pricing function and distributed optimisation algorithms.