Green Energy Scheduling for Demand Side Management in the Smart Grid

Demand side management (DSM) is an essential property of smart grid systems. Along with increasing expectations related to power quality from customers, and as new types of loads emerge, such as electric vehicles, local (renewable) energy generation, and stationary and mobile energy storage, it is critical to develop new methods for DSM. In this paper, we first construct a more efficient and reliable communication infrastructure in smart grid based on cognitive radio technology, which is an essential component for enabling DSM. Then, we propose a distributed energy storage planning approach based on game algorithm in DSM, which helps users select the appropriate size of storage units for balancing the cost in the planning period and during its use. Since planning problems may lead to consumer discomfort, we propose a cost function consisting of the billing, generation costs, and discomfort costs to balance users' preferences with the payment. Furthermore, a game theory-based distributed energy management scheme is developed in DSM without leaking user privacy, which is used as inner optimization in our proposed distributed energy storage planning approach. In this energy management scheme, Nash equilibrium is obtained with minimum information exchange using proximal decomposition algorithm. Simulation results show superior performance of our proposed DSM mechanism in reducing the peak-to-average ratio, total cost, user's daily payment, and energy consumption in smart grid communication networks.