A Bayesian Deep Reinforcement Learning-based Resilient Control for Multi-Energy Micro-gird
Aiming at a cleaner future power system, many regimes in the world have proposed their ambitious decarboniz-ing plan, with increasing penetration of renewable energy sources (RES) playing an alternative role to conventional energy. As a re-sult, power system tends to have less backup capacity and op...
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Published in | IEEE transactions on power systems Vol. 38; no. 6; pp. 1 - 16 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Aiming at a cleaner future power system, many regimes in the world have proposed their ambitious decarboniz-ing plan, with increasing penetration of renewable energy sources (RES) playing an alternative role to conventional energy. As a re-sult, power system tends to have less backup capacity and operate near their designed limit, thus exacerbating system vulnerability against extreme events. Under this reality, resilient control for the multi-energy micro-grid is facing the following challenges, which are: 1) the effect from the stochastic uncertainties of RES; 2) the need for a model-free and fast-reacting control scheme under extreme events; and 3) efficient exploration and robust performance with limited extreme events data .
To deal with the aforementioned challenges, this paper pro-poses a novel Bayesian Deep Reinforcement Learning-based resilient control approach for multi-energy micro-grid. In partic-ular, the proposed approach replaces the deterministic network in traditional Reinforcement Learning with a Bayesian probabilistic network in order to obtain an approximation of the value function distribution, which effectively solves the Q-value overestimation issue. Compared with the naive Deep Deterministic Policy Gra-dient (DDPG) method and optimization method, the effectiveness and importance of employing the Bayesian Reinforcement Learn-ing approach are investigated and illustrated across different operating scenarios. Case studies have shown that by using the Monte Carlo posterior mean of the Bayesian value function distribution instead of a deterministic estimation, the proposed Bayesian Deep Deterministic Policy Gradient (BDDPG) method achieves a near-optimum policy in a more stable process, which verifies the robustness and the practicability of the proposed approach. |
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ISSN: | 0885-8950 1558-0679 |
DOI: | 10.1109/TPWRS.2023.3233992 |