On Fast-Converged Deep Reinforcement Learning for Optimal Dispatch of Large-Scale Power Systems under Transient Security Constraints

Power system optimal dispatch with transient security constraints is commonly represented as Transient Security-Constrained Optimal Power Flow (TSC-OPF). Deep Reinforcement Learning (DRL)-based TSC-OPF trains efficient decision-making agents that are adaptable to various scenarios and provide solution results quickly. However, due to the high dimensionality of the state space and action spaces, as well as the non-smoothness of dynamic constraints, existing DRL-based TSC-OPF solution methods face a significant challenge of the sparse reward problem. To address this issue, a fast-converged DRL method for TSC-OPF is proposed in this paper. The Markov Decision Process (MDP) modeling of TSC-OPF is improved by reducing the observation space and smoothing the reward design, thus facilitating agent training. An improved Deep Deterministic Policy Gradient algorithm with Curriculum learning, Parallel exploration, and Ensemble decision-making (DDPG-CPEn) is introduced to drastically enhance the efficiency of agent training and the accuracy of decision-making. The effectiveness, efficiency, and accuracy of the proposed method are demonstrated through experiments in the IEEE 39-bus system and a practical 710-bus regional power grid. The source code of the proposed method is made public on GitHub.Comment: 10 pages, 11 figure

Neural ODE and DAE Modules for Power System Dynamic Modeling

The time-domain simulation is the fundamental tool for power system transient stability analysis. Accurate and reliable simulations rely on accurate dynamic component modeling. In practical power systems, dynamic component modeling has long faced the challenges of model determination and model calibration, especially with the rapid development of renewable generation and power electronics. In this paper, based on the general framework of neural ordinary differential equations (ODEs), a modified neural ODE module and a neural differential-algebraic equations (DAEs) module for power system dynamic component modeling are proposed. The modules adopt an autoencoder to raise the dimension of state variables, model the dynamics of components with artificial neural networks (ANNs), and keep the numerical integration structure. In the neural DAE module, an additional ANN is used to calculate injection currents. The neural models can be easily integrated into time-domain simulations. With datasets consisting of sampled curves of input variables and output variables, the proposed modules can be used to fulfill the tasks of parameter inference, physics-data-integrated modeling, black-box modeling, etc., and can be easily integrated into power system dynamic simulations. Some simple numerical tests are carried out in the IEEE-39 system and prove the validity and potentiality of the proposed modules.Comment: 8 pages, 4 figures, 1 tabl

A Transient Stability Numerical Integration Algorithm for Variable Step Sizes Based on Virtual Input

In order to reduce the online calculations for power system simulations of transient stability, and dramatically improve numerical integration efficiency, a transient stability numerical integration algorithm for variable step sizes based on virtual input is proposed. The method for fully constructing the nonhomogeneous virtual input for a certain integration scheme is given, and the calculation method for the local truncation error of the power angle for the corresponding integration scheme is derived. A step size control strategy based on the predictor corrector variable step size method is proposed, which performs an adaptive control of the step size in the numerical integration process. The proposed algorithm was applied to both the IEEE39 system and a regional power system (5075 nodes, 496 generators) in China, and demonstrated a high level of accuracy and efficiency in practical simulations compared to the conventional numerical integration algorithm

Power system preventive control aided by a graph neural network-based transient security assessment surrogate

In the context of the clean energy revolution and the high penetration of renewables and power electronics, the uncertainty level of operating states significantly increases, which brings new challenges to the safe and stable operation of power grids. Power system preventive control is an important measure to ensure the safe and stable operation of power systems by adjusting the active generation and nodal voltage of generators. In this paper, a power system preventive control algorithm aided by a Graph Neural Network (GNN)-based Transient Security Assessment (TSA) surrogate is proposed. A GNN-based fast contingency scanning model is constructed based on power network topology, which can predict the contingency scanning results rapidly only based on the power flow by transforming the original contingency scanning problem into the node classification problem on the GNN. The obtained GNN is then used as a surrogate model for TSA to speed up the solution of particle swarm optimization-based transient security-constrained optimal power flow. Numerical Tests are carried out in the IEEE-39 system and the test results indicate that the proposed method can significantly improve the efficiency without affecting the solution accuracy

Exploration of Artificial Intelligence-oriented Power System Dynamic Simulators

With the rapid development of artificial intelligence (AI), it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and AI. To explore the interaction mechanism of power system dynamic simulations and AI, a general design of an AI-oriented power system dynamic simulator is proposed, which consists of a high-performance simulator with neural network supportability and flexible external and internal application programming interfaces (APIs). With the support of APIs, simulation-assisted AI and AI-assisted simulation form a comprehensive interaction mechanism between power system dynamic simulations and AI. A prototype of this design is implemented and made public based on a highly efficient electromechanical simulator. Tests of this prototype are carried out under four scenarios including sample generation, AI-based stability prediction, data-driven dynamic component modeling, and AI-aided stability control, which prove the validity, flexibility, and efficiency of the design and implementation of the AI-oriented power system dynamic simulator.Comment: 10 pages, 8 figures, 1 table. Accepted by Journal of Modern Power System and Clean Energ

Exploration of Artificial-intelligence Oriented Power System Dynamic Simulators

With the rapid development of artificial intelligence (AI), it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and AI. To explore the interaction mechanism of power system dynamic simulations and AI, a general design for AI-oriented power system dynamic simulators is proposed, which consists of a high-performance simulator with neural network supportability and flexible external and internal application programming interfaces (APIs). With the support of APIs, simulation-assisted AI and AI-assisted simulation form a comprehensive interaction mechanism between power system dynamic simulations and AI. A prototype of this design is implemented and made public based on a highly efficient electromechanical simulator. Tests of this prototype are carried out in four scenarios including sample generation, AI-based stability prediction, data-driven dynamic component modeling, and AI-aided stability control, which prove the validity, flexibility, and efficiency of the design and implementation for AI-oriented power system dynamic simulators

Design and implementation of a general batch simulation tool of SOWFA and its application in training a single-turbine surrogate

Simulator fOr Wind Farm Applications (SOWFA) is a powerful wind farm simulation tool. It is widely used in wake effect research. However, the integration between AI and SOWFA is weak. Without AI, it is difficult for us to fully use SOWFA. The installation, parameter settings, and calculation procedures of SOWFA are complex, which hinders the application of SOWFA in practical power system engineering scenarios. To mitigate these issues, a batch simulation tool of SOWFA based on Docker and Python is designed and implemented. Firstly, the Docker engine is used to realize the virtualization container management of SOWFA. A SOWFA installation image is built to make it easy to migrate and deploy. Secondly, a Python application programming interface (API) of the SOWFA container is programmed to realize parameter editing, calculation process management, and batch simulations. Finally, a batch simulation post-processing Python API is constructed based on the ParaView library so that the SOWFA batch simulation results can be collated into data samples for downstream tasks. The above SOWFA image and Python API are tested in a simple single-turbine wake analysis scenario. With the samples generated by the batch simulations, an autoencoder-based single-turbine wake prediction surrogate model is trained, which verifies the effectiveness of the SOWFA batch simulation tool designed in this paper