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Volt-VAR control is critical to keeping distribution network voltages within allowable range, minimizing losses, and reducing wear and tear of voltage regulating devices. To deal with incomplete and inaccurate distribution network models, we propose a safe off-policy deep reinforcement learning algorithm to solve Volt-VAR control problems in a model-free manner. The Volt-VAR control problem is formulated as a constrained Markov decision process with discrete action space, and solved by our proposed constrained soft actor-critic algorithm. Our proposed reinforcement learning algorithm achieves scalability, sample efficiency, and constraint satisfaction by synergistically combining the merits of the maximum-entropy framework, the method of multiplier, a device-decoupled neural network structure, and an ordinal encoding scheme. Comprehensive numerical studies with the IEEE distribution test feeders show that our proposed algorithm outperforms the existing reinforcement learning algorithms and conventional optimization-based approaches on a large feeder.

The microstructural evolution of Fe-0.2C-5Mn steel during intercritical annealing with holding time for up to 144 hours was examined by TEM and STEM. It was demonstrated by TEM that the martensite lath structure gradually transformed into a lamellar ferrite and austenite duplex structure. The partitioning of manganese from ferrite to austenite was found by STEM. Typical Kurdjumov-Sachs orientation relationship between austenite lath and ferrite lath was observed by electron back scattered diffraction (EBSD). Based on the analysis of the austenite lath thckening behavior, it was proposed that the Mn-partitioning in austenite dominated the microstructure evolution of the ultrafine lamellar ferrite and austenite duplex structure during annealing process.

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