Microgrid with hydrogen storage is an effective way to integrate renewable energy and reduce carbon emissions. This paper proposes an optimal operation method for a microgrid with hydrogen storage. The electrolyzer efficiency characteristic model is established based on the linear interpolation method. The optimal operation model of microgrid is incorporated with the electrolyzer efficiency characteristic model. The sequential decision-making problem of the optimal operation of microgrid is solved by a deep deterministic policy gradient algorithm. Simulation results show that the proposed method can reduce about 5% of the operation cost of the microgrid compared with traditional algorithms and has a certain generalization capability.

With the great increase of renewable generation as well as the DC loads in the distribution network; DC distribution technology is receiving more attention; since the DC distribution network can improve operating efficiency and power quality by reducing the energy conversion stages. This paper presents a new architecture for the medium voltage AC/DC hybrid distribution network; where the AC and DC subgrids are looped by normally closed AC soft open point (ACSOP) and DC soft open point (DCSOP); respectively. The proposed AC/DC hybrid distribution systems contain renewable generation (i.e., wind power and photovoltaic (PV) generation); energy storage systems (ESSs); soft open points (SOPs); and both AC and DC flexible demands. An energy management strategy for the hybrid system is presented based on the dynamic optimal power flow (DOPF) method. The main objective of the proposed power scheduling strategy is to minimize the operating cost and reduce the curtailment of renewable generation while meeting operational and technical constraints. The proposed approach is verified in five scenarios. The five scenarios are classified as pure AC system; hybrid AC/DC system; hybrid system with interlinking converter; hybrid system with DC flexible demand; and hybrid system with SOPs. Results show that the proposed scheduling method can successfully dispatch the controllable elements; and that the presented architecture for the AC/DC hybrid distribution system is beneficial for reducing operating cost and renewable generation curtailment.

In order to solve the capacity shortage problem in power system frequency regulation caused by large-scale integration of renewable energy, the battery energy storage-assisted frequency regulation is introduced. In this paper, an adaptive control strategy for primary frequency regulation of the energy storage system (ESS) was proposed. The control strategy combined virtual droop control, virtual inertial control, and virtual negative inertial control. The virtual inertial control was used to reduce the frequency change rate, and the virtual droop control was used to reduce the steady-state frequency deviation. The virtual droop control and the virtual inertia control were adopted in the frequency deterioration stage to slow down the frequency drop. While in the frequency recovery stage, the virtual negative inertia control worked together with the virtual droop control to accelerate the frequency recovery. Besides, the coefficients of the control methods were related to the state of charge (SOC) of ESS to avoid over-charging and over-discharging of the battery. Finally, a simulation model was built in MATLAB/SIMULINK, and case studies were conducted to verify the proposed control strategy. Results showed that the proposed method could meet the demand for frequency regulation and was beneficial to the cycle life of ESS.

With the large-scale development of electric vehicles, the accuracy of electric vehicle charging load prediction is increasingly important for electric power system. Accurate EV charging load prediction is essential for the efficiency of electric system planning and economic operation of electric system. This paper proposes an electric vehicle charging load predicting method based on variational mode decomposition and Prophet-LSTM. Firstly, the variational mode decomposition algorithm is used to decompose the charging load into several intrinsic mode functions in order to explore the characteristics of EV charging load data. Secondly, in order to make full use of the advantages of various forecasting methods, the intrinsic mode functions are classified into low and high frequency sequences based on their over-zero rates. The high and low frequency sequences are reconstructed to obtain two frequency sequences. Then the LSTM neural network and Prophet model are used to predict the high and low frequency sequences, respectively. Finally, the prediction results obtained from the prediction of high frequency and low frequency sequences are combined to obtain the final prediction result. The assessment of the prediction results shows that the prediction accuracy of the prediction method proposed in this paper is improved compared to the traditional prediction methods, and the average absolute error is lower than that of ARIMA, LSTM and Prophet respectively by 7.57%, 8.73%, and 46.02%. The results show that the prediction method proposed in this paper has higher prediction accuracy than the traditional methods, and is effective in predicting EV charging load.

The volatility and uncertainty of high-penetration renewable energy pose significant challenges to the stability of the power system. Current research often fails to consider the insufficient system flexibility during real-time scheduling. To address this issue, this paper proposes a flexibility scheduling method for high-penetration renewable energy power systems that considers flexibility index constraints. Firstly, a quantification method for flexibility resources and demands is introduced. Then, considering the constraint of the flexibility margin index, optimization scheduling strategies for different time scales, including day-ahead scheduling and intra-day scheduling, are developed with the objective of minimizing total operational costs. The intra-day optimization is divided into 15 min and 1 min time scales, to meet the flexibility requirements of different time scales in the power system. Finally, through simulation studies, the proposed strategy is validated to enhance the system’s flexibility and economic performance. The daily operating costs are reduced by 3.1%, and the wind curtailment rate is reduced by 4.7%. The proposed strategy not only considers the economic efficiency of day-ahead scheduling but also ensures a sufficient margin to cope with the uncertainty of intra-day renewable energy fluctuations.

This paper proposed a comprehensive control method for energy storage system (ESS) participating in primary frequency regulation (PFR). The integrated control strategy consists of PFR stage and “stage of charge” (SOC) recovery stage. In the PFR stage, the virtual droop control and virtual inertia control are combined and applied in the frequency deterioration phase to reduce the frequency deviation. The virtual negative inertia control coupled with virtual droop control is used in the frequency recovery phase to accelerate frequency recovery. In the SOC recovery stage, an adaptive SOC recovery control method is proposed. The SOC recovery control is performed while the frequency is within the dead zone, and the recovery power of ESS is based on the SOC to avoid overcharging and over-discharging. The proposed SOC recovery control can restore the SOC to a proper range which ensures the potential for ESS to participate in the next PFR process. It also reduces the number of SOC recovery starts compared with the fixed target SOC method and is beneficial to prolong the cycle life of ESS. Simulation outcomes demonstrate that the proposed comprehensive control method can efficiently enhance the frequency regulation performance and restore the SOC to the target range.