Deep Reinforcement Learning-Based Tie-Line Power Adjustment Method for Power System Operation State Calculation
Open Access
- 24 October 2019
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 7, 156160-156174
- https://doi.org/10.1109/access.2019.2949480
Abstract
Operation state calculation (OSC) provides safe operating boundaries for power systems. The operators rely on the software-aid OSC results to dispatch the generators for grid control. Currently, the OSC workload has increased dramatically, as the power grid structure expands rapidly to mitigate renewable source integration. However, the OSC is processed with a lot of manual interventions in most dispatching centers, which makes the OSC error-prone and personnel-experience oriented. Therefore, it is crucial to upgrade the current OSC in an automatic mode for efficiency and quality improvements. An essential process in the OSC is the tie-line power (TP) adjustment. In this paper, a new TP adjustment method is proposed using an adaptive mapping strategy and a Markov Decision Process (MDP) formulation. Then, a model-free deep reinforcement learning (DRL) algorithm is proposed to solve the formulated MDP and learn an optimal adjustment strategy. The improvement techniques of stepwise training and prioritized target replay are included to decompose the large-scale complex problems and improve the training efficiency. Finally, five experiments are conducted on the IEEE 39-bus system and an actual 2725-bus power grid of China for the effectiveness demonstration.Keywords
Funding Information
- Adaptive Generation and Interactive Adjustment Technology of Power Grid Operation State Based on Online Data (5442XT190011)
This publication has 30 references indexed in Scilit:
- Transient Stability Enhancement of Power Grid With Integrated Wide Area Control of Wind Farms and Synchronous GeneratorsIEEE Transactions on Power Systems, 2017
- Calculation of TTC for multi‐area power systems based on improved Ward‐PV equivalentsIET Generation, Transmission & Distribution, 2017
- The irreversible momentum of clean energyScience, 2017
- Reinforcement Learning Applied to an Electric Water Heater: From Theory to PracticeIEEE Transactions on Smart Grid, 2016
- Improving methods for evaluating the stability of electrical systems with distributed generationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Convolutional Neural Networks for Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load ControlIEEE Transactions on Smart Grid, 2016
- Mastering the game of Go with deep neural networks and tree searchNature, 2016
- Realistic and Transparent Optimum Scheduling Strategy for Hybrid Power SystemIEEE Transactions on Smart Grid, 2015
- Control Policy with Autocorrelated Noise in Reinforcement Learning for RoboticsInternational Journal of Machine Learning and Computing, 2015
- Human-level control through deep reinforcement learningNature, 2015