With continuous technological improvement and economic development of energy storage, distributed energy storage (DES) will be widely connected to the distribution network. If fragmented DES systems are aggregated to form a distributed energy storage aggregator (DESA), the DESA will have great potential to participate in the day-ahead energy and reserve market and the balancing market. The DESA could act as a mediator between the market and DES consumers, enabling beneficial coordination for DES owners and power systems. This paper presents a bilevel optimization model for DESAs in the energy and reserve market under wind power uncertainties. In the lower-level problem, generating companies, wind power plants (WPP), and DESAs are optimized for scheduling day-ahead (DA) energy and the reserve market. In the upper-level problem, operational strategies for DES systems and DESAs are designed to deal with wind power uncertainties in the balancing market. The DESA splits its resources between the energy and reserve markets so that it can reduce total power system consumption, and mutual profit for the system and end customers is achieved. This model is formulated as a mixed-integer linear programming (MILP) program, which can be solved with commercial software. The validity of the bilevel optimization model is verified by the eight-node test transmission system and IEEE-33 bus distribution system.

In adapting to the double-high development trend of high-voltage direct current (HVDC) receiving-end power systems and solving optimization problems in emergency frequency control (EFC) supporting virtual power plants (VPPs) in large-scale power systems, a parameter estimation method for a VPP frequency response model based on a successive linear programming (SLP) method is proposed. First, a “centralized/decentralized” hierarchical control architecture for VPP participation in EFC is designed. Second, the frequency response characteristics of multiple flexible resources are scientifically analyzed, and the system frequency response (SFR) model and equivalent model of VPP are constructed. Subsequently, parameter estimation of the VPP frequency response model is carried out based on the SLP method, aiming to balance the accuracy and computational efficiency of the model. Finally, the effectiveness of the proposed methodology is verified by using PSD-BPA to simulate and test the three-zone HVDC recipient area grid.

Mechanical elastic energy storage (MEES) system completes the energy storage process through permanent magnet synchronous motor (PMSM) rotates and tightens the energy storage boxes which contains large spiral torsion springs (STSs) to convert electrical energy into elastic potential energy. The torque of the storage box is gradually increased while the moment of inertia is gradually reduced in the process of energy storage. Thus, this paper proposed an backstepping control scheme with torque and inertia adaptive law and the stability was proved in the sense of Lyapunov theory. The effectiveness of the proposed scheme was verified through hardware experiments. The results indicated that the proposed scheme ensures the speed to track the given value rapidly and the energy storage process can be carried out smoothly.

The traditional direct torque control (DTC) of permanent magnet synchronous motor (PMSM) is characterized by the feature of inconstant switching frequency and large ripples of flux and torque which is due to the hysteresis controllers adopted in the system. Besides, the modification of motor parameters, especially the rise of stator temperature, and load torque disturbance further leads to the speed steady-state error and fluctuation. A novel DTC method of interior PMSM based on the adaptive backstepping control with load torque and stator resistance identification is proposed in this paper. In order to reduce the ripples of the traditional DTC, a speed backstepping controller is designed to replace PI speed controller, and torque and flux backstepping controllers are designed to replace torque and flux hysteresis controllers in this algorithm. Owing to the real-time and accurate estimation of the stator resistance and load torque, the control variables can be instantly and simultaneously modify to ensure the accurate and fast response of the speed. The effectiveness and correctness of the presented method are verified through multiple simulation experiments, and the results indicate that the dynamic and static performance of the PMSM can be significantly improved by using the method.

The Mechanical elastic energy storage (MEES) is a new type of physical energy storage. The energy storage medium is large-scale planar vortex spring (LSPVS), and the energy storage form is mechanical elastic potential energy. Multiple groups of LSPVS are fixed in a single energy storage box, and multiple energy storage boxes are connected in series to form a linked energy storage box group as the energy storage unit of MEES. In the process of energy storage, the torque and inertia of LSPVS change simultaneously. The permanent magnet synchronous motor (PMSM), as the driving device, is required to realize the fast response of torque change and effectively suppress the disturbance of inertia. In this paper, firstly, the mathematical model of linked energy storage box group is established. Secondly, combining with the direct torque control (DTC) model of PMSM and the backstepping control algorithm, the adaptive parameters of torque and inertia control strategy is designed. Finally, the experimental verification shows that the control strategy proposed in this paper has faster torque response and the disturbance of inertia is suppressed simultaneously, so as the energy storage process of MEES can be carried out smoothly and efficiently.