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Smart generation control based on multi-agent reinforcement learning with the idea of the time tunnel

The more importance to solve the haze problem is that large-scale renewable energy is adopted in power system in order to reduce carbon emission. However, the large-scale integration of new energy will result in stochastic disturbance to interconnected power grid. The PDWoLF-PHC(λ) based on the idea of time tunnel is to be proposed in this paper, which, based on the variable learning rate, can obtain the optimal strategy, deal with stochastic disturbance caused by massive integrations of new energy and distributed energy sources to the interconnected power grid, which is difficult for traditional centralized AGC, as well as make the new energy compatible with power system. The result of simulation on the modified IEEE standard two-area load-frequency control power system model, the smart distribution network model, and the Central China Power Grid model shows that the proposed algorithm can reduce carbon emission and enhance utilization rate of the new energy. Compared with the traditional smart ones, it is characterized with faster convergence and stronger robustness.