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Risk-Constrained Reinforcement Learning for Inverter-Dominated Power System Controls
Here, this paper develops a risk-aware controller for grid-forming inverters (GFMs) to minimize large frequency oscillations in GFM inverter-dominated power systems. To tackle the high variability from loads/renewables, we incorporate a mean-variance risk constraint into the classical linear quadratic regulator (LQR) formulation for this problem. The risk constraint aims to bound the time-averaged cost of state variability and thus can improve the worst-case performance for large disturbances. The resulting risk-constrained LQR problem is solved through the dual reformulation to a minimax problem, by using a reinforcement learning (RL) method termed as stochastic gradient-descent with max-oracle (SGDmax). In particular, the zero-order policy gradient (ZOPG) approach is used to simplify the gradient estimation using simulated system trajectories. Numerical tests conducted on the IEEE 68-bus system have validated the convergence of our proposed SGDmax for GFM model and corroborate the effectiveness of the risk constraint in improving the worst-case performance while reducing the variability of the overall control cost.
Reinforcement Learning-based Output Structured Feedback for Distributed Multi-Area Power System Frequency Control
Load frequency control (LFC) is a key factor to maintain the stable frequency in multi-area power systems. As the modern power systems evolve from centralized to decentralized paradigm, LFC needs to consider the decentralized scheme that considers limited information from the information-exchange graph for the generator control of each interconnected area. This paper aims to solve a data-driven constrained LQR problem with mean-variance risk constraints and output structured feedback, and applies this framework to solve the LFC problem in multi-area power systems. By reformulating the constrained optimization problem into a minimax problem, the stochastic gradient descent max-oracle (SGDmax) algorithm with zero-order policy gradient (ZOPG) is adopted to find the optimal feedback gain from the learning, while guaranteeing the convergence. In addition, to improve the adaptation of the proposed learning method to new or varying models, we construct an emulator grid that approximates the dynamics of a physical grid and performs training based on this model. Once the feedback gain is obtained from the emulator grid, it is applied to the physical grid with a robustness test to check whether the controller from the approximated emulator applies to the actual system. Numerical tests show that the obtained feedback controller can successfully control the frequency of each area, while mitigating the uncertainty from the loads, with reliable robustness that ensures the adaptability of the obtained feedback gain to the actual physical grid.
Fundamental mechanistic insights into the catalytic reactions of Li-S redox by Co single-atom electrocatalysts via operando methods
Lithium-sulfur batteries represent an attractive option for energy storage applications. A deeper understanding of the multistep lithium-sulfur reactions and the electrocatalytic mechanisms are required to develop advanced, high-performance batteries. We have systematically investigated the lithium-sulfur redox processes catalyzed by a cobalt single-atom electrocatalyst (Co-SAs/NC) via operando confocal Raman microscopy and x-ray absorption spectroscopy (XAS). The real-time observations, based on potentiostatic measurements, indicate that Co-SAs/NC efficiently accelerates the lithium-sulfur reduction/oxidation reactions, which display zero-order kinetics. Under galvanostatic discharge conditions, the typical stepwise mechanism of long-chain and intermediate-chain polysulfides is transformed to a concurrent pathway under electrocatalysis. In addition, operando cobalt K-edge XAS studies elucidate the potential-dependent evolution of cobalt’s oxidation state and the formation of cobalt-sulfur bonds. Our work provides fundamental insights into the mechanisms of catalyzed lithium-sulfur reactions via operando methods, enabling a deeper understanding of electrocatalysis and interfacial dynamics in electrical energy storage systems.
A method for crystallographic mapping of an alpha-beta titanium alloy with nanometre resolution using scanning precession electron diffraction and open-source software libraries
An approach for the crystallographic mapping of two-phase alloys on the nanoscale using a combination of scanned precession electron diffraction and open-source python libraries is introduced in this paper. This method is demonstrated using the example of a two-phase α/β titanium alloy. The data were recorded using a direct electron detector to collect the patterns, and recently developed algorithms to perform automated indexing and analyse the crystallography from the results. Very high-quality mapping is achieved at a 3 nm step size. The results show the expected Burgers orientation relationships between the α laths and β matrix, as well as the expected misorientations between α laths. A minor issue was found that one area was affected by 180° ambiguities in indexing occur due to this area being aligned too close to a zone axis of the α with twofold projection symmetry (not present in 3D) in the zero-order Laue Zone, and this should be avoided in data acquisition in the future. Nevertheless, this study demonstrates a good workflow for the analysis of nanocrystalline two- or multi-phase materials, which will be of widespread use in analysing two-phase titanium and other systems and how they evolve as a function of thermomechanical treatments.
Zero-Order Reaction Kinetics v. 3.4.hip
Zero-RK is a software package that simulates chemically reacting systems using sparse, preconditioned, adaptive matrix methods to achieve orders-of-magnitude reduction in simulation time while maintaining accurate results.
Zero-Order Reaction Kinetics v. 3.6
Zero-RK is a software package that simulates chemically reacting systems using sparse, preconditioned, adaptive matrix methods to achieve orders-of-magnitude reduction in simulation time while maintaining accurate results.