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 decarbonizing plan, with increasing penetration of renewable energy sources (RES) playing an alternative role to conventional energy. As a result, 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 proposes a novel Bayesian Deep Reinforcement Learning-based resilient control approach for multi-energy micro-grid. In particular, 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 Gradient (DDPG) method and optimization method, the effectiveness and importance of employing the Bayesian Reinforcement Learning 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.