A Novel Automatic Generation Control Method Based on the Ecological Population Cooperative Control for the Islanded Smart Grid

Abstract

To achieve automatic generation control coordination in the islanded smart grid environment resulted from the increasing penetration of renewable energy, a novel ecological population cooperative control (EPCC) strategy is proposed in this paper. The proposed EPCC, based on the new win-loss criterion and the time tunnel idea, can compute the win-loss criterion accurately and converge to Nash equilibrium rapidly. Moreover, based on a multiagent system stochastic consensus game (MAS-SCG) framework, a frequent information exchange between agents (AGC units) is implemented to rapidly calculate optimal power command, which achieves the optimal cooperative control of the islanded smart grid. The PDWoLF-PHC(λ), WPH strategy (wolf pack hunting), DWoLF-PHC(λ), Q(λ)-learning, and Q-learning are implemented into the islanded smart grid model for the control performance analysis. Two case studies have been done, including the modified IEEE standard two-area load frequency control power system model and the islanded smart grid model with distributed energy and microgrids. The effectiveness, stronger robustness, and better adaptability in the islanded smart grid of the proposed method are verified. Compared with five other smart ones, EPCC can improve convergence speed than that of others by nearly 33.9%–50.1% and the qualification rate of frequency assessment effectively by 2%–64% and can reduce power generation cost.