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When several microgrids are interconnected into microgrid cluster (MGC), the network topology of MGC system would be more complex. Traditional hierarchical control based on droop control tends to make full use of all networks so that the costs of energy exchange could be high. Considering the voltage stability and power sharing of MGC system, a distributed cooperative control method based on network topology optimisation for MGC is proposed to control the power flow of multi‐microgrid system. Specially, preliminary Q‐leaning algorithm is utilised with establishment of new adjacency matrices for the distributed cooperative control to decrease the costs. Moreover, multi‐level controls based on consensus protocol are proposed in the distributed cooperative MGC system to eliminate the necessity of a central controller. The adaptive regulation based on the state of charge among the battery groups will be taken as the first‐level control. The second‐level control including the current controller, voltage controller and power controller is implemented among the DGs and a novel third‐level control based on consensus is applied in eliminating the voltage deviation caused by the first‐level control and the second‐level control. The effectiveness of the proposed method is verified through the simulation by simulink. [ABSTRACT FROM AUTHOR]

Communication data in the communication network of the islanded micro‐grid can be disturbed in many ways, such as data attack or packet loss. To improve the control effect on the voltages and frequencies obtained by droop control of distributed energy resources (DERs) facing communication data disturbance (CDD), a response hierarchical control strategy is proposed in this study. Based on the concept of the cyber physical system, the control structure is divided into two layers: the cyber layer and physical layer. In the cyber layer, firstly, the effect of the CDD on the micro‐grid system is analysed and then, an event‐triggered data compensation method combining the back‐propagation neural network and extreme learning machine is proposed in this layer to solve the problem of the CDD. In the physical layer, firstly, the droop control is used as the primary control to control the voltages and frequencies of DERs and then, a novel virtual leader‐following consensus control method considering time‐delay is proposed in this layer. Also, it is used to complete the secondary control of the voltage and frequency obtained by primary control. In the end, the simulation results confirm the effectiveness of the proposed hierarchical control strategy under CDD. [ABSTRACT FROM AUTHOR]

Berberine, a widely used isoquinoline alkaloid in traditional Chinese medicine, has been proved to be a potential candidate in liver cancer therapy. However, the low therapeutic dose in the tumor target which is due to the poor solubility and oral bioavailability has limited its clinical application. In this study, fluorescent self‐carried Berberine microrods (Ber‐MRs) were prepared in gram‐scale through a facile and cheap antisolvent precipitation method. Ber‐MRs exhibited good optical properties, pH‐responsive drug release behavior and selective and safe antitumor performance in vitro and in vivo without obvious toxicity. These findings have demonstrated that Ber‐MRs are promising for efficient and safe liver cancer therapy. © 2018 BioFactors, 44(5):496–502, 2018 [ABSTRACT FROM AUTHOR]

In order to improve the control effect on voltage and frequency of each distributed energy resource (DER) and improve the stability of the islanded micro‐grid, a novel hierarchical control strategy combined sliding mode control (SMC) and consensus control is proposed in this study. The control structure is divided into two layers: the physical layer and the cyber layer. In the physical layer, in order to improve the dynamic control effect, an improved droop control based on consensus theory with considering packet loss is proposed in this study. And the control can be completed in finite time by selecting the approximate parameters in the controller; in order to improve the stability of voltage loop and current loop, an improved double closed‐loop structure based on the SMC is proposed. Moreover, the control can also be completed in the finite time. In the cyber layer, an event‐triggered SMC‐virtual leader–following consensus protocol is proposed in this study to solve the problem of channel noise (disturbance). For improving the control effect, the protocol is used to complete the secondary control on voltage and frequency of each DER. Finally, the effectiveness of the novel control strategy is verified by simulation results. [ABSTRACT FROM AUTHOR]

Accurate renewable resource and load forecasting plays a key role in the progress of power grid planning schemes. In this study, a hybrid short‐term forecasting method based K ‐means clustering and variational mode decomposition (VMD) technique is proposed to deal with the problem of forecasting accuracy. K ‐means clustering is a means of data mining approach and used for classifying data into several clusters. A cluster selection method is adopted to extract similar features from historical days. To better analyse the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies. Self‐adaptive evolutionary extreme learning machine as a novel and fast regression tool is trained and used for predicting each component. Eventually, the forecasting result generated by reconstructing all the predicted components values. The performance of the proposed hybrid forecasting model is evaluated by using real data from National Renewable Energy Laboratory. The simulation results show that it can obtain better forecasting accuracy than some previously reported methods. [ABSTRACT FROM AUTHOR]

Droop control is a common method in the universal microgrid applications. Conventional droop control is unpractical for low‐voltage microgrid, where the line impedance among distributed generation units (DGs) is mainly resistive to generate the active and reactive power of DG is coupled. Besides, accurate reactive power sharing is not achieved due to the voltage deviation of DGs caused by disparate line impedance in microgrid. An improved droop control based on the virtual power source (VPS) and composite virtual impedance, which is constituted by a negative resistance and a negative inductance, is proposed for low‐voltage microgrid. The virtual negative resistance counteracts the line resistance to decouple power. The reactive power sharing accuracy based on the virtual negative inductance and VPSs control increases through analysing the line and virtual voltage drop. Finally, the fractional‐order PID controller is adopted to control the inverter voltage. The optimal controller parameters are obtained by the differential evolution algorithm. To verify the viability and availability of improved control strategy, simulations are carried out on MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

In this Letter, a novel voltage regulation method is proposed for ensuring voltage security in photovoltaic (PV) distribution systems. It is a two‐level regulation to reduce overall voltage deviation (VDE) and voltage difference (VDI). Firstly, evaluation indexes for VDE and VDI are built. Then, in order to reduce VDE, primary regulation by sensitivities‐based power compensation is developed. Furthermore, in order to reduce VDI, secondary regulation by using PI coordinate compensator is built. Compared with other methods, the proposed method can achieve high regulation accuracy. The effectiveness is verified in a PV platform. [ABSTRACT FROM AUTHOR]

To solve the overvoltage problem in high penetration photovoltaic (PV) system, a new voltage regulation method is proposed. First, the node voltage sensitivity to PV power is calculated. Next, an optimal power compensation scheme using a new compensation mechanism is obtained so that the node voltage can be acceptably regulated with minimum power curtailment. The maximum tolerable communication delay is also estimated based on PV power mutation during communication delay (CD). Because extreme learning machine (ELM) algorithms exhibit fast and precise prediction, they can be used here to predict PV power. Based on the predicted power, the power compensation can be appropriately corrected to achieve effective voltage regulation under abnormal CD (ACD). Finally, the results indicate that voltage deviation can be regulated to an acceptable range of 1.00–1.05 per unit by using the proposed power compensation approach to curtail reactive power or even active power. The maximum PV power error predicted by ELM algorithm is only 2.67%, which is an acceptable basis for power compensation correction under ACD. Hence, by curtailing additional PV power again, the effective voltage regulation can be finally ensured under power mutation with ACD. [ABSTRACT FROM AUTHOR]