The general concept of AC Optimal Power Flow (ACOPF) refers to the economic dispatch planning under electric network constraints. Moreover, each instance with the entire network must be solved in real-time (i.e., every five minutes) to ensure cost-effective power system operation while satisfying power balance equation. As the operation of power systems penetrated with intermittent renewable energy becomes more complicated, this paper proposes Deep Neural Network (DNN) and Levenberg-Marquardt backpropagation-based Twin Delayed Deep Deterministic Policy Gradient (TD3) approach to improve computational performance of ACOPF. Specifically, because the ACOPF model shall consider prevailing constraints of the power system, including power balance equation, we set the appropriate reward vector in the training process to build our own policy. Furthermore, we add random Gaussian noise to individual net loads for representing uncertainty characteristics introduced by renewable energy sources. Finally, the proposed model is compared with the MAT-POWER solution on the IEEE 118-bus system to demonstrate its efficacy and robustness.

The high penetration level of renewable energy in large-scale power systems could adversely affect power quality, such as voltage stability and harmonic pollution. This paper assesses the impacts of Distribution Static Compensator (D-STATCOM), one of the Flexible AC Transmission System (FACTS) devices, on power quality of 4.16kV-level distribution systems via transient and steady-state analysis. Carrierbased Pulse Width Modulation (PWM) control in D-STATCOM generates d-q axis current reference via the PID (Proportional-Integral-Differential) controller to control d-q axis current and voltage. A new control method, via the Deep Deterministic Policy Gradient (DDPG) algorithm-based reinforcement learning (RL), is studied to create a new d-q axis current reference applying to the voltage control, which can improve voltage stability and transient response and derive fast convergence of current and voltage at the D-STATCOM bus. The real-time simulations on an IEEE 13-bus system show that the proposed approach can better control the D-STATCOM than the conventional control methods for enhancing voltage stability and transient performance.