Fast real-time pricing method based on improved dual decomposition for smart grid

Real-time pricing scheme is an important means of demand side management strategies and is an ideal demand response mechanism to achieve the supply and demand balance in smart grid. It can bring out peak clipping and valley filling. Thus, we propose a distributed real-time pricing algorithm for the smart grid, that is, proximal center algorithm based on an improved dual decomposition, which can solve the optimization problem that maximizes the difference between the utilities of all users' and the cost of the energy provider. In the algorithm, Lagrangian multiplier of the dual problem is the energy price. Through updating the real-time pricing the interactions are activated between real-time pricing of the energy provider and real-time energy consumption of users. The algorithm finds the optimal energy consumption levels for each user (the maximization of the users' utilities), and meanwhile the minimum of the cost for the energy provider. It not only preserves the separability of the problem but also acceler