Parallel autonomous optimization of demand response with renewable distributed generators

We propose a framework for demand response in smart grids that integrate renewable distributed generators (DGs). In this framework, some users have DGs and can generate part of their electricity. They can also sell extra generation to the utility company. The goal is to optimize the load schedule of users to minimize the utility company's cost and user payments. We employ parallel autonomous optimization, where each user requires only knowledge of the aggregated load of other users instead of the load profiles of individual users, and can execute distributed optimization simultaneously. We performed numerical examples to validate our algorithm. The results show that our method can significantly lower peak hour load and reduce the costs to users and the utility. Since the autonomous user optimizations are executed in parallel, our method also dramatically decreases the computation time, management complexity, and communication costs.