Search

Due to the volatility and intermittency of renewable energy, injecting large amounts of renewable energy into the grid will have a tremendous impact on the stability and security of the network. In this paper, we propose the hierarchical energy optimization of flywheel energy storage array system (FESAS) applied to smooth the power output of wind farms to realize source-grid-storage intelligent dispatching. The energy dispatching problem of the FESAS is described as a Markov decision process by the actor-critic (AC) algorithm. In order to solve the problems of stability and low sampling efficiency of the AC algorithm, the soft actor-critic (SAC) algorithm, a deep reinforcement learning (DRL) algorithm based on the model-free off-policy method of the maximum entropy framework, is adopted. Furthermore, SAC and prioritized experience replay (PER) are utilized to greatly improve learning efficiency and sample utilization. The experimental results show that SAC-PER has better performance and stability in energy optimization of the FESAS. [ABSTRACT FROM AUTHOR]

The flywheel energy storage (FES) array system plays an important role in smoothing the power output of wind farms. Therefore, how to allocate the total charging and discharging power of wind farms to individual FES system (FESS) units has long been a research hotspot. We propose a hierarchical coordinated control strategy applying the concept of the state of energy (SOE) of FESS units. Energy allocation is achieved by investigating the consensus algorithm of the SOE for both an undirected graph and unbalanced directed graph. Then, we propose another approach to convert the adjacency matrix into a column stochastic matrix using the idea of the PageRank algorithm for an unbalanced directed graph and achieve an SOE consensus of FESS units using the power iteration method. Simulation results demonstrate that the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

A voltage regulation scheme considering communication security is proposed for photovoltaic (PV) power system. The scheme is a two-level regulation to, respectively, reduce overall voltage deviation (VDE) and voltages difference (VDI). First, the evaluation indexes of VDE and VDI are built. Then, primary regulation through a powers compensation scheme is used. Considering communication topology change and delay under upper bound, secondary regulation through consensus protocol is developed. In addition, communication packet-loss and large delay are solved by predictive compensation. Finally, effectiveness of the proposed method is verified by simulation in MATLAB. [ABSTRACT FROM AUTHOR]

For the droop-controlled distributed generation units (DGs) in microgrids (MGs), the power sharing error associated with voltage difference of DGs can be attributed to the mismatched line impedance. To improve the power sharing accuracy in islanded MGs, a neighbor-prediction based hierarchical control strategy is proposed. In secondary control, a consensus protocol is used to regulate the DGs voltages interacted by communication network to the state of tracking synchronization. Thus, the error-free power sharing is obtained. Network data-loss and delay problems are considered. To stabilize the DGs’ inner-loop control in which there is a delayed input, the H∞ robustness criteria of a dynamic model of DGs is derived. If the DG i ’s voltage in network transmission loses, the prediction system of a neighbor will be activated to forecast the lost voltage for secondary consensus control by extreme learning machine (ELM). Thus, the voltages regulation will be back to normalization. Simulations in MATLAB confirm the effectiveness of proposed control in MGs. [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 reduce the power curtailment, a new PCA is used to realize coordinated RPC-APC in an optimal order. • To improve the voltage regulation ability, the PRN with the maximum voltage rise is always regulated. • To reduce the number of power control, the most sensitive PV is preferred for the RPC-APC for PRN. • To achieve an accurate RPC-APC, the real-time PRN update and exact power calculation are carried out. Increasing photovoltaic (PV) penetration has caused overvoltage problem in distribution systems. Power curtailment that includes power absorption of PV inverter and charge of battery energy storage is usually used to improve the voltage profile. Most methods prefer to use an excessive local power curtailment to regulate voltage. This inevitably reduces PV generation economy. Hence, a coordinated power compensation algorithm (PCA) is proposed in this paper to regulate bus voltage, aimed at reducing the total power curtailment while serving requisite load demand. Selecting primary control nodes and optimal PV compensation orders based on sensitivity sequence, our PCA achieves an effective voltage regulation with the minimal power curtailment. Final results on a tested PV system model verify the effectiveness. [ABSTRACT FROM AUTHOR]

The liquid air energy storage (LAES) technology has received widespread attention for its advantages of high energy storage density, a wide range of applications, safety, environmental protection and hygiene, flexible device regulation, and large energy storage scale and is developing toward high-parameter and large-capacity energy storage. However, due to the late start of research in China, large-scale LAES has not been put into commercial operation so far, and the lack of in-depth understanding of the dynamic characteristics of LAES has seriously impeded the development of the system design and regulation technology, which limits the development and popularization of LAES. In this study, a dynamic mathematical model of the liquid air energy storage system is established based on the SIMULINK platform of MATLAB software. The dynamic characteristics of the system in the energy release stage are analyzed, a control model of the grid-connected speed control system is established, and an optimized control strategy is obtained by using a linear active disturbance rejection controller and by analyzing the response under typical disturbances. The model and the results of this study provide a reference for the understanding of the dynamic characteristics of LAES and the control of the speed control system, and the control strategy can effectively reduce the problem of high overshooting of the rotor of the expander and reach a stable state in a shorter time, which is of practical significance for promoting the application and popularization of LAES. [ABSTRACT FROM AUTHOR]

Background: Emerging studies have revealed the potent functions of circRNAs in breast cancer tumorigenesis. However, the biogenesis, biofunction and mechanism of circRNAs in triple-negative breast cancer (TNBC) are largely unknown. Methods: High-throughput RNA sequencing was applied to identify dysregulated circRNAs in TNBCs and paired normal tissues. RNA pulldown and luciferase assays were performed to investigate the interaction between circular CD44 (circCD44, also annotated as hsa_circ_0021735) and miR-502–5p. RNA pulldown and RIP assays were used to investigate the interaction between circCD44 and IGF2BP2. Cell viability, colony formation, migration/invasion assays and in vivo tumorigenesis were used to investigate circCD44 biological functions. Results: CircCD44 is an uncharacterized circRNA, which is highly expressed in TNBC, and its expression is negatively correlated with the prognosis of TNBC patients. CircCD44 promotes TNBC proliferation, migration, invasion and tumorigenesis at least partially by sponging miR-502–5p and interacting with IGF2BP2. Conclusion: Our data suggested that overexpressed circCD44 promotes TNBC progression. CircCD44 is potentially a novel diagnostic and therapeutic marker for TNBC patients. [ABSTRACT FROM AUTHOR]

Background: Papillary thyroid cancer (PTC) is the most common type of thyroid cancer and the incidence of PTC has continued to increase over the past decades. Many studies have shown that obesity is an independent risk factor for PTC and obese PTC patients tend to have a relative larger tumor size and higher grade of tumor stage. Obesity is associated with disordered lipid metabolism and the relationship between serum lipids and PTC remains unclear. Therefore, this study aimed to investigate the association between serum lipid level and PTC. Methods: We retrospectively analyzed 1018 PTC patients diagnosed and treated in our hospital, all these cases were first diagnosed with PTC and had complete clinical information including ultrasound reports before surgery, serum lipid (CHOL, TG, HDL-c, LDL-c, Apo-A1, Apo-B, Apo-E) results, surgical records and pathological reports. Results: None of these lipid markers were associated with tumor size in the whole cohort and in the female group. In the male group, on crude analysis, Apo-A1 showed a marginally association with tumor size, [OR = 0.158 (0.021–1.777)], p = 0.072. After adjusting for age and multifocality, Apo-A1 showed a significant association with tumor size [OR = 0.126 (0.016–0.974)], p = 0.047. This association become more apparent in a young male subgroup, [OR = 0.051 (0.005–0.497)], p = 0.009. CHOL, TG, HDL-c, LDL-c, Apo-B, Apo-E did not show significant association with tumor size. As for LNM, neither in the male group nor in the female group were found to be associated with any serum lipid biomarkers. Conclusion: As PTC incidences continues to increase, our findings demonstrated a negatively association between PTC and apoA-1 in male PTC patients, which may contribute to further investigation concerning diagnosing and preventing this most common type of thyroid cancer. [ABSTRACT FROM AUTHOR]

This paper focuses on multi-agent-system-based bi-level bidding strategy of microgrid to make all power producers participate in electricity allocation as rational agents under electricity market transaction. Firstly, the multi-agent-system-based bidding system is constructed as bi-level MAS. Then, the bidding model is formulated as a bi-level optimization problem, which makes the best of renewable energy source, and subjects it to the object of obtaining maximum profits for all power producers. According to this model, electricity market is divided into two subsidiary markets, which involve the upper and the lower electricity market. The lower electricity market is composed of microgrids, which ally together in the upper electricity market to offset their shortages compared to traditional power producers, such as efficient market influence, the reliability of the energy supplying. Additionally, game theory is introduced for modeling and Nash equilibrium as the solution of bidding optimization problem is found through a series of iterative process based on the bi-level MAS. Finally, both the upper market and the lower market are illustrated in the case studies and corresponding proper conclusions are received. [ABSTRACT FROM AUTHOR]

Based on nonlinear busbar voltage in flywheel energy storage systems and frequent discharge characteristics, in order to improve the dynamic control derived from the analysis of a permanent magnet synchronous motor and its inverter set up model of DC bus and the active disturbance rejection principle and use the active disturbance rejection control (ADRC) technique to design the DC bus voltage adaptive controller, the first- and second-order ADRC controllers are compared with the traditional PI controller in MATLAB. In this work, we demonstrate that the voltage controller designed with the first- and second-order ADRCs was superior to a traditional PI in terms of dynamic performance control. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a distributed secondary control strategy based on finite‐time consensus protocol for islanded microgrids to ensure the stability of frequency and voltage. Simple droop control cannot achieve high‐precision adjustment due to its inherent shortcomings. Although traditional secondary control can solve this problem, the required conditions are harsh and the system has poor robustness. Based on finite‐time consensus protocol, the proposed distributed secondary control is completely distributed, and agents of network layer only communicate through peer‐to‐peer sparse networks without a central node, so the frequency and voltage remain stable and reach the nominal value within the set time that then improves the system reliability and robustness. Moreover, the improved extreme learning machine, where the empirical risk and structural risk are involved, is used to predict the missing part to deal with the problem of packet loss and communication interruptions. The effectiveness of the intended methods is verified by the simulation of an islanded microgrid. [ABSTRACT FROM AUTHOR]

In this paper, a decentralized coordinated control method based on multi-agent system is proposed to improve the voltage stability of micro-grid. In lower-level agents, the decentralized control is designed as double-loop controllers for the inverter of each distributed energy resource, including an outer-loop power controller based on droop control and an inner-loop voltage/current controller based on fractional order proportion-integral-derivative (PID). In upper-level agents, the distributed coordinated control is designed to make voltage consensus and proportional power sharing of all distributed energy resources. Since each distributed coordinated control only requires its own and neighboring information, the communication bandwidth can be saved. The simulation results have verified the effectiveness in terms of power sharing, voltage stability, and suppressing circulation current. [ABSTRACT FROM AUTHOR]

At present, the traditional islanded microgrid is changing to a cyber-physical system. And the data in its cyber layer is often used to solve practical problems in the physical layer through sensors and communicators. But the use of communication data also brings a lot of problems. Among them, the upload interruption problem (i.e., communicator or sensor failure) will make a heavy impact on the system. To improve the voltage and frequency stability of microgrid through secondary control considering data uploading interruption, an improved growing and pruning-radial basis function neural network-based secondary control strategy is designed in this study. The main designs can be summarized as below: (1) When the communicator cannot receive data, a prediction compensation part is designed directly in the corresponding communicator, i.e., the predicted data is used to complete the follow-up works; (2) When the sensor cannot send data, a nonlinear cyber-physical vulnerability assessment part and a neighbor transmission part are designed. In this way, a neighbor sensor is selected to send the interrupted data for the follow-up control process; (3) Based on these designs above, a novel secondary control is designed for microgrid. Finally, the simulation results show the effectiveness of the proposed strategy. [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]

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]

Wind power can effectively alleviate the energy crisis. However, its integration into the grid affects power quality and power grid stability. Accurate wind speed prediction is a key factor in the efficient use of wind power. Because of its intermittent and nonstationary nature, wind speed forecasting is difficult, and is the topic of much research, especially long-time multistep forecasts. In this paper, the multistep wind speed prediction problem is regarded as a sequence-to-sequence mapping problem, and a multistep wind speed prediction model based on a transformer is proposed. This model is based on an encoder–decoder architecture, where the encoder generates representations of historical wind speed sequences of any length, the decoder generates arbitrarily long future wind speed sequences, and the encoder and decoder are associated by an attention mechanism. At the same time, the encoder and decoder of Transformer are completely based on a multi-head attention mechanism. For easy modeling, a 1-dimensional original wind speed sequence is transformed to a 16-dimensional sequence by ensemble empirical mode decomposition (EEMD), and the multidimensional wind speed data are directly modeled with Transformer. We trained the model with very large-scale (19 years of data) wind speed data averaged at 10-minute intervals, and performed the evaluation over one-year wind speed data. Results show that our one-step forecast model achieved an average mean absolute error (MAE) and root mean square error (RMSE) of 0.167 and 0.221, respectively. To the best of our knowledge, our 3-, 6-, 12-, and 24-hour multistep forecast model achieves a new state of the art in wind speed forecasting, with respective MAEs of 0.243, 0.290, 0.362, and 0.453, and RMSEs of 0.326, 0.401, 0.513, and 0.651. It is believed that performance can be further improved with better model parameter optimization. • The multistep wind speed forecasting problem is regarded as a sequence to-sequence mapping problem. • A hybrid wind speed prediction model based on EEMD-Transformer is proposed. • Models were trained and evaluated on 20 years of large scale publicly available dataset. [ABSTRACT FROM AUTHOR]

• A CPHVA is proposed to evaluate the vulnerability of nodes in islanded MG. • The interruption problem in communicators is solved by path reconstruction. • The interruption problem in sensors is solved by predictive compensation. • A novel secondary control method is proposed to improve the control effect on DERs. Recently, more and more consensus-based hierarchical control methods are proposed in micro-grid. The main components of these methods are primary and secondary controllers. And they are usually used to adjust the output frequency and voltages of distributed energy resources. But the implementation of these methods is highly dependent on the communication network which is consisted by sensors and communicators. Once the data transmit of some sensors or communicators is interrupted, it will directly affect the control effect of hierarchical control. To solve the above problems, a cyber-physical cooperative response strategy is proposed in this paper. The main designs are as follows: (1) To solve the interruption problem among communicators, there are two defense methods proposed in the cyber layer: path planning and path reconstruction. Both methods are combined with cyber-physical hybrid vulnerability assessment and improved genetic algorithm; (2) To solve the interruption problem in the output channels of sensors, a distributed predictive compensation method combined with extreme learning machine and model predictive control is proposed in physical layer; (3) Based on the designs in (1) and (2), a novel secondary controller is proposed in this paper. Finally, the simulation results confirm the effectiveness of the proposed cooperative response strategy. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a cyber‐physical cooperative control strategy considering communication interruption is proposed to improve the control effect on output voltages and frequency obtained by droop control of distributed energy resources (DERs). The control structure consists of the physical and cyber layers. And in the two layers, the interruption occurs mostly in sensors and data aggregators. To solve the problem of interruption among data aggregators, a path‐planning method combined with improved Dijkstra algorithm (IDA) and improved genetic algorithm (IGA) is proposed in the cyber layer. To solve the problem of interruption among sensors, a predictive compensation method combined with extreme learning machine (ELM) and model predictive control (MPC) is proposed in the physical layer. Meanwhile, so as to improve the control effect of DERs, a virtual leader‐following consensus control (VLFCC) method combined with double sliding mode control (SMC) is first proposed in cyber layer and then applied to the physical layer to accomplish the secondary control on DERs. Finally, the simulation results show that under the condition of communication interruption, the output voltage and frequency of each DER can be adjusted to their respective reference values through the proposed cooperative control strategy. [ABSTRACT FROM AUTHOR]