A robust optimization approach to evaluate the impact of smart grid technologies on generation plans

In this paper how the electric power system generation plans change and improve based on the availability of smart grid technologies is investigated. The smart grid technologies mainly consist of renewable energy generation, carbon capture and storage (CCS), demand response and electric vehicles in this research. The approach uses robust optimization to determine the generation technology options in the context of smart grids under uncertainties. Robust optimization approach focuses on the consideration of parameter uncertainty in a mathematical programming problem. This paper explores the technical and economic feasibility of improving the utilization of smart grid technologies in the generation plan model. Since smart grid technologies have more uncertainties than conventional technologies, more estimation errors will appear and influence the optimal value of the generation planning problem. Therefore, the application of a robust optimization approach is proposed. In the adopted robust methodology, the optimal decision-making will search for the trade-off between the robustness and the optimality. The new model specifically considers the availability of smart grid technologies improving the performance of the generation system. In the proposed model, the objectives of the proposed model include investment, operational and maintenance cost, generation cost, reliability cost and carbon emission cost. Moreover, the constraints of both the electricity grid and the customer sector are considered in the generation planning. Due to the existing data uncertainties in the constraints, the robust linear programming problem is solved to find the corresponding robust counterpart, which means the uncertainty optimization problem will be transformed into a deterministic optimization problem. The derived robust counterpart model shows the multi-objective and multi-period generation expansion plan problem, and it will be solved by linear programming technique. The proposed optimization model is justified and described in some detail, apply it to the reference cases, to support the generation planning with smart grid technology applications.