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This paper develops a risk-aware controller for grid-forming inverters (GFMs)to minimize large frequency oscillations in GFM inverter-dominated powersystems. To tackle the high variability from loads/renewables, we incorporate amean-variance risk constraint into the classical linear quadratic regulator(LQR) formulation for this problem. The risk constraint aims to bound thetime-averaged cost of state variability and thus can improve the worst-caseperformance for large disturbances. The resulting risk-constrained LQR problemis solved through the dual reformulation to a minimax problem, by using areinforcement learning (RL) method termed as stochastic gradient-descent withmax-oracle (SGDmax). In particular, the zero-order policy gradient (ZOPG)approach is used to simplify the gradient estimation using simulated systemtrajectories. Numerical tests conducted on the IEEE 68-bus system havevalidated the convergence of our proposed SGDmax for GFM model and corroboratethe effectiveness of the risk constraint in improving the worst-caseperformance while reducing the variability of the overall control cost.

Risk-Constrained Reinforcement Learning for Inverter-Dominated Power System Controls