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The liquid air energy storage (LAES) technology has received widespread attention for its advantages of high energy storage density, a wide range of applications, safety, environmental protection and hygiene, flexible device regulation, and large energy storage scale and is developing toward high-parameter and large-capacity energy storage. However, due to the late start of research in China, large-scale LAES has not been put into commercial operation so far, and the lack of in-depth understanding of the dynamic characteristics of LAES has seriously impeded the development of the system design and regulation technology, which limits the development and popularization of LAES. In this study, a dynamic mathematical model of the liquid air energy storage system is established based on the SIMULINK platform of MATLAB software. The dynamic characteristics of the system in the energy release stage are analyzed, a control model of the grid-connected speed control system is established, and an optimized control strategy is obtained by using a linear active disturbance rejection controller and by analyzing the response under typical disturbances. The model and the results of this study provide a reference for the understanding of the dynamic characteristics of LAES and the control of the speed control system, and the control strategy can effectively reduce the problem of high overshooting of the rotor of the expander and reach a stable state in a shorter time, which is of practical significance for promoting the application and popularization of LAES. [ABSTRACT FROM AUTHOR]

Due to the volatility and intermittency of renewable energy, injecting large amounts of renewable energy into the grid will have a tremendous impact on the stability and security of the network. In this paper, we propose the hierarchical energy optimization of flywheel energy storage array system (FESAS) applied to smooth the power output of wind farms to realize source-grid-storage intelligent dispatching. The energy dispatching problem of the FESAS is described as a Markov decision process by the actor-critic (AC) algorithm. In order to solve the problems of stability and low sampling efficiency of the AC algorithm, the soft actor-critic (SAC) algorithm, a deep reinforcement learning (DRL) algorithm based on the model-free off-policy method of the maximum entropy framework, is adopted. Furthermore, SAC and prioritized experience replay (PER) are utilized to greatly improve learning efficiency and sample utilization. The experimental results show that SAC-PER has better performance and stability in energy optimization of the FESAS. [ABSTRACT FROM AUTHOR]

Based on nonlinear busbar voltage in flywheel energy storage systems and frequent discharge characteristics, in order to improve the dynamic control derived from the analysis of a permanent magnet synchronous motor and its inverter set up model of DC bus and the active disturbance rejection principle and use the active disturbance rejection control (ADRC) technique to design the DC bus voltage adaptive controller, the first- and second-order ADRC controllers are compared with the traditional PI controller in MATLAB. In this work, we demonstrate that the voltage controller designed with the first- and second-order ADRCs was superior to a traditional PI in terms of dynamic performance control. [ABSTRACT FROM AUTHOR]

The flywheel energy storage (FES) array system plays an important role in smoothing the power output of wind farms. Therefore, how to allocate the total charging and discharging power of wind farms to individual FES system (FESS) units has long been a research hotspot. We propose a hierarchical coordinated control strategy applying the concept of the state of energy (SOE) of FESS units. Energy allocation is achieved by investigating the consensus algorithm of the SOE for both an undirected graph and unbalanced directed graph. Then, we propose another approach to convert the adjacency matrix into a column stochastic matrix using the idea of the PageRank algorithm for an unbalanced directed graph and achieve an SOE consensus of FESS units using the power iteration method. Simulation results demonstrate that the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]