6. Conclusion
In this paper, we proposed a scheme for energy management under different uncertainties concerning demand and price in a smart grid. The performance of the algorithms proposed in the existing literature on the issue of energy management, in general, suffers from uncertainty constraints. Therefore, we modeled the energy management scheme as a robust optimization approach using robust game theory to account for these uncertainty constraints. In the proposed model, the customers and the grid act as players of the game. The theoretical analysis of equilibrium of the game model is also presented. The simulation results showed that using the proposed approach, improved energy management over the existing ones, is achievable. The future extension of this work includes improvement in the expectation of the real-time demand from the customers in order to overcome the overestimation issue. We saw that the proposed scheme overestimates energy demand from customers in case of very low packet loss rate. Therefore, in future, we also plan to incorporate this issue in the smart grid systems. It also includes the establishment of a network architecture for smart grid to minimize packet loss in the communication network. This will enable us to achieve improved reliability and cost-effectiveness in energy management.
