Abstract: With the integration of renewable energy sources into the multi-area power system, their uncertainty has greatly increased the complexity of the multi-area economic dispatching. It is a great challenge how to efficiently solve the multi-area economic dispatching containing the wind and solar energy (MAEDWS). In this paper, a novel Derivative Search-based Political Optimization (DSPO) algorithm is proposed to address the shortcomings of slow convergence speed, low solution accuracy, and so on in the existing optimization algorithms in solving the MAEDWS problems. A leader guide strategy and a derivative search mechanism are introduced into the political optimization algorithm. The former leads the candidate solutions to the more promising regions to speed up the convergence, while the latter derives some neighborhood solutions around the regional winners in the partisan election stage to enrich the diversity of the sollutions. The DSPO algorithm is applied to the MAEDWS problem and compared with the other six well-regarded algorithms applied to it respectively. The simulation results show that the DSPO algorithm achieves the overall best performance in terms of the convergence efficiency, the solution accuracy, and the stability.