Spectrum sensing control for enabling cognitive radio based smart grid

This paper proposes a multiobjective (MO) approach for spectrum sensing control in a cognitive radio-based smart grid. An aggregator that serves power suppliers and residential users has two objectives: maximizing the aggregated benefit of power suppliers and users and minimizing the cost of the cognitive radio system. By increasing the sensing time for the available spectrum, the communication quality can be improved. Accurate information exchange further boosts the overall benefit of grid participants; however, larger sensing time yields higher communication cost, implying less profit for the aggregator. The above two objectives are conflicting, leading to a multiobjective optimization problem (MOP). By solving the MOP, a novel demand response management (DRM) system can be constructed. The MO framework motivates competition among power suppliers; hence, residential users can choose power suppliers probabilistically. For the aggregator, a reasonable cost of the cognitive radio system can be attained while communication quality of service is guaranteed. Simulations show that the proposed MO approach yields a stable DRM system. In contrast with a single-objective formulation, which combines multiple objectives, the MO formulation offers a broad perspective on optimality so that the conflicting features of the objectives can be readily visualized. A reasonable value of the sensing time can be further determined based on the obtained Pareto front.