The stability of dual active bridge converter (DAB) is threatened when feeding the constant power loads (CPLs). This article proposes a deep reinforcement learning-based backstepping control strategy to solve this problem. First, a nonlinear disturbance observer is adopted to estimate the large-signal nonlinear disturbance. Then, a backstepping controller is used to stabilize the voltage response of the DAB under the large-signal disturbance. Finally, a compensation method based on deep reinforcement learning is developed to intelligently minimize output voltage tracking error and improve the operating efficiency of the system. The proposed controller can guarantee system stability under the large-signal disturbance of the CPL and achieve a fast dynamic response with accurate voltage tracking; it is more adaptive by using the deep reinforcement learning technique through the learning of its neural networks. The effectiveness of the proposed controller is verified by experiments.

The DC solid-state transformer (DCSST) is a key component of the DC microgrid, and stability issues due to operation with constant power loads (CPLs) have become increasingly prominent. To stabilize the system of DCSST with CPLs, in this paper, a novel intelligent backstepping controller based on a nonlinear disturbance observer (NDO) is proposed, and the control coefficients of the backstepping control are automatically adjusted by an auxiliary deep deterministic policy gradient (DDPG) controller. More specifically, the NDO estimates the large-signal nonlinear disturbance of the load power variation, and a backstepping controller is designed to improve the stability of the system under the estimated large-signal disturbance. Then, an input voltage equalizing compensator is added to ensure the identical input voltage to each sub-module of DCSST. Furthermore, a DDPG sub-controller is introduced into the backstepping control to intelligently adjust the control coefficients to improve the operating efficiency of the system. The results of the simulation and experiment show that the system is more robust due to the learning ability of the proposed controller, and the proposed controller can improve the stability of the system under the large-signal disturbance and intelligently ensure fast dynamic response with accurate tracking over different operating environments.

Owing to intermittency of wind power and slow ramp rates of conventional generators, a considerable amount of wind energy cannot be effectively utilized during frequency control processes. This paper proposes a technique for power system planners and operators to select or commit power capacity and energy capacity of battery energy storage system (BESS) for mitigating the effects of intermittent wind speeds on reliability and economics. A novel procedure is proposed to determine intermittent wind speeds based on the chaos theory and intermittency-related parameters. The frequency-related reliability and economic indices of a power system with BESS are formulated based on frequency control processes. The effects of BESS on dynamic reliability and economics of a practical power system are investigated using the proposed techniques and models.

This article proposes a circulating current and second harmonic current (SHC) suppression method by introducing virtual impedance into the circulating current and output current feedback control loops of the dc solid-state transformer (DCSST). The virtual impedance is employed to adjust impedance in the circulating current feedback control loop to decrease the circulating current when the transmission efficiency of each dual active bridge is different. And a SHC suppression method, which introduces the virtual impedance containing a bandpass filter, is adopted to reduce the SHC and improve the dynamic performance of the DCSST. Moreover, a relatively independent modular control method, which is conducive to the modular expansion of the DCSST, is proposed. Compared with traditional DCSST control methods, the proposed method can simultaneously realize the output current sharing and effectively reduce the SHC with modular control. Finally, a DCSST prototype is built and the results of the experiment verify the validity and effectiveness of the proposed control strategy and solutions.

As a promising way to achieve energy transition, electric vehicles (EVs) are rapidly advancing. Dynamic charging price of charging network operator (CNO) guides the owners to vary travel decisions, which in return affect the operation of power distribution networks (PDNs) and transportation networks (TNs). Therefore, an appropriate dynamic charging price can decrease the operating costs of PDN and TN, while increasing the profit of the CNO. This study proposes a tri-level dynamic pricing model. At the upper level, the CNO sets the charging price to maximize its profit considering the decision of EVs. And at the middle level, the gasoline vehicles (GVs) and EVs make their travel and charging decisions to minimize their travel cost by considering simultaneous charging, routing and departure time (SCRDT) choices, yielding a mixed dynamic user equilibrium (MDUE) pattern. Consequently, the charging loads are determined. At the lower level, the distribution network operator (DNO) calculates the power purchase price by solving a second-order cone ACOPF (SOC-ACOPF) problem with charging loads with temporal and geographical variations. Furthermore, a mixed genetic algorithm nested with the projection gradient method (PGM) and second-order cone programming (SOCP) is designed to solve the proposed model. Numerical examples validate the effectiveness of the proposed model, and analyze the impact of dynamic pricing on the operation of PDN and TN and CNO’s profit.

Considering the low environmental cost and good social benefits of clean energy power generation, we propose the concept of environmental-friendly model. We also describe power expansion planning model in detail, that helps to incorporate a variety of power generating units, including solar-power, and hydropower, and which appropriately addressed the restrictions of the energy market environment. Taking into account the constraints of cascade hydropower and the uncertainty of solar power, multiple objectives of economic efficiency, load tracking coefficient, and accommodation degree of solar power are formulated. An optimal scheduling model for hybrid solar-hydro power generation system in demand side is established. This model is solved by using an improved genetic algorithm. In various seasons, weather conditions, and time intervals, simulation examples are used to identify the optimal output of the source side and the minimum load transfer volume, as well as the validity and correctness of the suggested method. By means of comparing and analyzing the reliability indices, the impact of solar-hydro power on distribution system is researched and the results show that interconnection of solar power with hydro power can play a certain role in the improvement of power system reliability.