A scalable and robust approach to demand side management for smart grids with uncertain renewable power generation and bi-directional energy trading

This paper investigates the energy cost minimization problem for smart grids with distributed renewable energy resources. Unlike earlier research studies that either have assumed all the appliance jobs are interruptible or power-shiftable and that the electricity prices as well as the availability of renewable resources are known, this paper focuses on more challenging scenarios in which appliance jobs are non-interruptible and non-powershiftable, the electricity prices vary with the overall load of the entire grid in real-time, and the renewable power generation is uncertain. Because home solar systems are widely available, this paper assumes that each consumer in the grid can have a photovoltaic system and a side battery. Collected solar energy can be used to meet a consumer's individual power demand, stored in the battery for future use, or sold back to the grid during peak hours to lower electricity bills and the overall load on the entire grid. To solve this problem, a two-stage robust optimization model is proposed, and the C&CG method is utilized to solve it. However, to solve the problem more efficiently when the number of consumers and appliance jobs is large, a second approach called SRDSM is proposed. The SRDSM algorithm consists of two parts: The first part is a job scheduling algorithm that minimizes electricity costs for all consumers. The second part is a power management algorithm based on dynamic programming that reduces the energy cost further by utilizing renewable energy. The numerical results show that, although the C&CG method produces optimal solutions, the SRDSM algorithm is much more scalable and efficient when the problem size is large.