Optimal Demand Response and Real-Time Pricing by a Sequential Distributed Consensus-Based ADMM Approach

This paper proposes a novel optimization model and a novel approach to derive new demand response (DR) and real-time pricing schemes for smart grid in which renewable energy and power losses are taken into account. In our proposed optimization model, a time-varying load constraint is introduced to better capture the consumption variation of customers and hence gives our approach an adaptive feature as well as facilitates DR. Then our approach enables all generation and demand units to actively collaborate in a distributed manner to obtain the optimal electric price and their optimal power updates in real-time while achieving their best profits. To do so, the total welfare in the grid is maximized and the optimization problem is analytically solved using the alternating direction method of multipliers and consensus theory for multi-agent systems. Moreover, the power balance constraint is guaranteed in every iteration of the proposed algorithm. Next, the effects of renewable energy to conventional generation, consumer consumption, and electric price are theoretically revealed which show the essential role of renewable energy for peak load shifting. Finally, simulations on the IEEE 39-bus system are introduced to illustrate the effectiveness of the proposed approach.