Stability and Accuracy Assessment based Large-Signal Order Reduction of Microgrids

This paper aims to propose a novel large-signal order reduction (LSOR) approach for microgrids (MG) by embedding a stability and accuracy assessment theorem. Different from the existing order reduction methods, the proposed approach prevails mainly in two aspects. Firstly, the dynamic stability of full-order MG models can be assessed by only leveraging their derived reduced-order models and boundary layer models with our method. Specially, when the reduced-order system is input-to-state stable and the boundary layer system is uniformly globally asymptotically stable, the original MGs system can be proved to be stable under several common growth conditions. Secondly, a set of accuracy assessment criterion is developed and embedded into a tailored feedback mechanism to guarantee the accuracy of derived reduced model. It is proved that the errors between solutions of reduced and original models are bounded and convergent under such conditions. Strict mathematical proof for the proposed stability and accuracy assessment theorem is provided. The proposed LSOR method is generic and can be applied to arbitrary dynamic systems. Multiple case studies are conducted on MG systems to show the effectiveness of proposed approach

Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems

This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (MADRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in unbalanced distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions of the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the DQN agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DQN environment. Finally, the proposed multi-objective MADRL method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction