The distribution network reconfiguration (DNR) aims at minimizing the power losses and improving the voltage profile. Traditional model-based methods exactly need the network parameters to derive the optimal configuration of the distribution network. This paper proposes a DNR method based on model-free reinforcement learning (RL) approach. The proposed method adopts NoisyNet deep Q-learning network (DQN), by which the exploration can be automatically realized without need of tuning the exploration parameters, in order to accelerate the training process and improve the optimization performance. The proposed method is validated by the simulation results.

Conventionally, the frequency of the power system is controlled by matching the electric supply to the varying demand. Some recent studies demonstrated that demand response (DR) can also contribute to the frequency control. However, most of the research works focus on the overall control strategy of DR, whereas the DR parameters are usually randomised rather than carefully designed. In this study, a hybrid hierarchical DR control scheme is designed to support frequency control. The parameters settings are discussed in detail. Instead of randomising the parameters for individual DR controllers, this study proposes a systematic way to design the parameters such as frequency threshold and minimum off‐time for individual controllers, so that aggregation of DR can provide primary frequency control smoothly just like thermal generators. Furthermore, the control system is formulated as a multi‐objective optimisation problem. The parameters such as the recovery rate of DR and the integral gain of secondary frequency control is optimised so that the maximum deviation, overshoot and oscillations of the frequency can be properly minimised. The proposed method is verified on numerical examples.

Over the last few years, lots of attentions have been given to the demand response (DR) for the frequency control. DR can be incorporated with traditional frequency control method and enhance the stability of the system. In this paper, the frequency control strategy of DR for a multi-area power system is specially designed. In order to quickly stabilize the frequency of different areas, the tie-line power is adopted as the additional input signal of DR. To get the optimal parameters of the control system, the frequency control problem is formulated as a multi-objective optimization problem, and the parameters such as the integral gains of secondary frequency control, the frequency bias parameters, and coefficients of DR are optimized. Numerical results verify the effectiveness of the proposed method.

This paper presents a novel field-programmable gate array (FPGA) based method for empirical mode decomposition (EMD) in real time. Traditionally, EMD can be easily implemented and developed using a high-level computer language in a PC or DSP chip. However, it is difficult to implement EMD in a hardware environment. This paper develops EMD for real-time applications using a hardware-based FPGA. The proposed FPGA-based method calculates the upper and lower envelopes in EMD point by point by using a circular queue to temporarily store values of maxima and minima, from which the upper and lower envelopes in the EMD can be determined continuously. Additionally, an attempt is made to increase the efficiency of the computational process by cascading several identical modules as a serial pipeline structure in order to conduct an iterative loop for calculating the intrinsic mode functions in EMD. The fast process from the serial pipeline structure results in real-time computation with a sampling rate of up to 12.5 MHz and mitigation of the end effect. The proposed method is validated by the simulation results obtained by Quartus II and verified by FPGA (Altera Stratix III EP3SL150F1152C2) realization, revealing its effectiveness in real-time applications.

This paper presents a FPGA (field programmable gate array)-based design of Grid Friendly Appliance (GFA) controller. A GFA controller can monitor the frequency of the power system and switch on or off the electrical appliance accordingly, to support the stability of the power system. In this paper, FPGA chip is adopted to develop GFA controller for real-time applications. The proposed method detects the power system frequency by a digital phase-locked loop (PLL), and compares the frequency with a threshold. If the frequency drops below the threshold, the GFA controller output a switching off signal to shed some load. In order to compensate the trigging response delay, a proportional-derivative (PD) predictor is designed for the controller. The proposed method is validated by the co-simulation results of Quartus II and Matlab. The hardware-based GFA controller is developed in Quartus II, and the frequency response model is developed in Matlab software, separately. The co-simulation results show the effectiveness of the proposed method.

In order to improve the degree of renewable energy integration, the demand response resource, especially on the air conditioning load side, has received attention. Currently, most of the control modes for air conditioners are centralised control, which is difficult to be applied to widely distributed air conditioning. The current studies focus on central air conditioning, but there are few studies on split‐type inverter air conditioning, even though inverter air conditioning has become the most common split air system on the market. With this background, this study designs a consensus control strategy on the basis of distributed control mode, which for inverter air conditioners. The strategy is used for renewable energy integration in the studied region. The test results of various cases reveal the following findings: (i) the strategy adapts to the widely distributed air conditioning, (ii) the strategy can guarantee a fair allocation of power imbalance and user comfort, (iii) the control strategy can adapt to the uncertainties of air conditioners and the urban microclimate while efficiently consuming renewable energy. Therefore, the proposed consensus control strategy has practical value.