Abstract: A method for intelligently adjusting a power flow based on a Q-learning algorithm includes: converting a variable, an action, and a goal in a power grid to a state, an action, and a reward in the algorithm, respectively; selecting an action from an action space, giving an immediate reward based on a result of power flow calculation, and correcting a next state; forwardly observing a next exploration action based on a strategy in the Q-learning algorithm; updating a Q value in a corresponding position in a Q-value table based on the obtained reward; if a final state is not reached, going back to step 2; otherwise, increasing the number of iterations by 1; if the number of iterations does not reach predetermined value K, that is, Episode<K, going back to step 2; otherwise, that is, Episode=K, outputting the Q-value table; and outputting an optimal unit combination.

Abstract: A method for intelligently adjusting a power flow based on a Q-learning algorithm includes: converting a variable, an action, and a goal in a power grid to a state, an action, and a reward in the algorithm, respectively; selecting an action from an action space, giving an immediate reward based on a result of power flow calculation, and correcting a next state; forwardly observing a next exploration action based on a strategy in the Q-learning algorithm; updating a Q value in a corresponding position in a Q-value table based on the obtained reward; if a final state is not reached, going back to step 2; otherwise, increasing the number of iterations by 1; if the number of iterations does not reach predetermined value K, that is, Episode<K, going back to step 2; otherwise, that is, Episode=K, outputting the Q-value table; and outputting an optimal unit combination.

Abstract: The present invention relates to a method for identifying parameters of a 10 kV static load model based on similar daily load curves. In the present invention, an optimization model for identifying full-period parameters of the static load model is proposed based on a large number of daily load curves with response characteristic, a structure and constraints of the static load model, and two theoretical basic assumptions about loads. Full-period (including 96 moments) static voltage model parameters of 10 kV loads are given through optimization solution. A rule that active power and reactive power of the loads at each moment change with voltage is obtained. In addition, a change rule of load constituents is obtained. The method delivers good applicability, satisfies actual demands, and is suitable for large-scale static model analyses for 10 kV loads.