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Smart city article details
| Title | Deep Reinforcement Learning-Based Data-Driven Active Power Dispatching For Smart City Grid |
|---|---|
| ID_Doc | 18065 |
| Authors | Shi L.; Lu S.; Feng T. |
| Year | 2023 |
| Published | 2023 IEEE International Conference on Power Science and Technology, ICPST 2023 |
| DOI | http://dx.doi.org/10.1109/ICPST56889.2023.10165401 |
| Abstract | In order to enhance the frequency regulation performance of islanded microgrids, a data-driven active power dispatching (DDB-APD) method is proposed to realize the multi-objective comprehensive In addition, a proximal policy optimization (PPO) algorithm is introduced to achieve load frequency control in microgrids through data-driven policy gradient optimization. In this method, the original active power controller is replaced with an agent to achieve fast active power control by setting a reasonable reward function. Simulations of an isolated microgrid in the Southern Power Grid illustrate that the proposed method can effectively reduce microgrid frequency deviations and generation costs. © 2023 IEEE. |
| Author Keywords | Active power dispatching; controller; deep reinforcement learning; microgrid; proximal policy optimization algorithm |
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