With more and more distributed photovoltaic (PV) plants access to the distribution system, whose structure is changing and becoming an active network. The traditional methods of voltage regulation may hardly adapt to this new situation. To address this problem, this paper presents a coordinated control method of distributed energy storage systems (DESSs) for voltage regulation in a distribution network. The influence of the voltage caused by the PV plant is analyzed in a simple distribution feeder at first. The voltage regulation areas corresponding to DESSs are divided by calculating and comparing the voltage sensitivity matrix. Then, a coordinated voltage control strategy is proposed for the DESSs. Finally, the simulation results of the IEEE 33-bus radial distribution network verify the effectiveness of the proposed coordinated control method.

Lithium-ion batteries are widely used in electric vehicles and renewable energy storage systems due to their superior performance in most aspects. Battery parameter identification, as one of the core technologies to achieve an efficient battery management system (BMS), is the key to predicting and managing the performance of Li-ion batteries. However, due to the complex chemical reactions and thermodynamic processes inside lithium-ion batteries, coupled with the influence of the external environment, accurate identification of lithium-ion battery parameters has become an urgent problem to be solved. In addition, data-driven parameter identification can enable battery models to better understand battery behavior, which is one of the focuses of future research. For this reason, this paper comprehensively reviews the application of data-driven parameter identification methods in different scenarios. Firstly, the research briefly explains the working principle of lithium-ion batteries and the key parameters affecting their performance. Secondly, this paper deeply discusses data-driven methods for parameter identification, which are widely used nowadays, and provides improvement ideas to address the shortcomings of traditional methods. Finally, the paper discusses the challenges faced by parameter identification technology for lithium-ion batteries and envisages future prospects.

Energy storage technologies have been rapidly evolving in recent years. Energy storage plays different roles in various scenarios. For electricity consumers, they are concerned with how to use the energy storage system (ESS) to reduce their costs of electricity or increase their profits. In this paper, a stochastic optimization method for energy storage sizing based on an expected value model for consumers with Photovoltaic Generation (PV) is proposed. Firstly, the Gaussian mixture model clustering method is used to cluster the historical load and PV data and calculate the probability of each cluster. Secondly, the optimal model of total system profit is established. Finally, according to the expected value model, the optimal ESS power and capacity are determined. Two case studies are used to demonstrate the calculation of optimal ESS capacity. The results obtained by the method proposed in this paper are compared with the results produced by the deterministic method. Through the analysis and comparison, the validity and superiority of the method proposed in this paper are verified. The profits obtained by the method proposed in this paper are 0.87% to 127.16% more than the deterministic method.

The goal of this study is to develop a disturbance observer-based prescribed performance fault-tolerant controller (PPFTC) for a multi-area interconnected power system (MIPS) with a hybrid energy storage system (HESS). The mathematical model of the MIPS with HESS is introduced first. Then the load disturbance estimation is obtained using a disturbance observer (DO) approach. By introducing two additional functions, the tracking error is bounded to achieve the desired response. The PPFTC is further developed based on the DO ensuring that the area control error (ACE) of the MIPS asymptotically converges to zero. Finally, the effectiveness of the given PPFTC and DO are inspected through the use of the Lyapunov theory and simulated results.

With the high penetration of wind power, the power system has put forward technical requirements for the frequency regulation capability of wind farms. Due to the energy storage system’s fast response and flexible control characteristics, the synergistic participation of wind power and energy storage in frequency regulation is valuable for research. This paper established a frequency characteristic model of a power system, including wind power and energy storage, and analyzed the influence of different frequency regulation methods on system stability. Based on the established model, a fuzzy PID-based energy management strategy was designed for different disturbance scenarios, which offered the advantages of simple parameter design and easy online operation of the project. This paper used a case based on the actual parameters for verification. The energy storage, rated at 10% of the wind farm’s rated power, provided 56% frequency drop suppression and 89% frequency fluctuation rate suppression, according to the proposed model in this paper. The proposed fuzzy PID control strategy achieved adaptive control of the controller parameters under strong disturbances and was able to provide an additional frequency rejection capability of 10–25%.