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Power distribution systems are vulnerable to natural disasters and malicious attacks. An efficient and accurate online risk assessment tool is very necessary to provide timely warning information for emergency dispatch of resilient distribution systems. However, conventional analytical risk assessment methods are subject to known network information, while emerging datadriven methods rarely incorporate resilient resources into the risk assessment procedures, limiting their accuracies when applied to extreme events. To solve the problems, this paper proposes an improved online data-driven risk assessment method adaptive for resilient distribution systems. Twenty-five basic operational indexes from practical experience are chosen to indirectly reflect the system risk, and the complicated relationship between the indexes and risk is characterized by entropy weights and gray correlation degrees. The proposed method is validated on a modified 33-node system, and the results show that it has better accuracy compared with similar approaches in online risk assessment during extreme events. The whole scheme can be helpful for the software design and hardware layout of future resilient distribution systems.

Currently, the increasing wind power penetration, with consequent randomness and variability, presents great challenges to power system planning and operation. Probabilistic power flow (PPF) has been developed to calculate the power flow under uncertain circumstances. However, the current wind power models are subject to specific probability distributions, limiting their accuracies in wider applications. Additionally, the cumulant method (CM)-based PPF, if nonlinear relationship is considered in, would face an impractically high computational complexity. To address these problems in modeling and cumulant calculation, this article proposes a novel generalized density/distribution fitting method (GDFM) combining with the Copula function to establish a joint probability model for wind power generation. A special impulse- mixed probability density (IMPD) integration method is also introduced to derive the input cumulants from the model. Finally, a fast cumulant method (FCM) is proposed to reduce the computational burden of output cumulant calculation while retaining a high accuracy in a nonlinear context. Case study on the IEEE-118 test system validates the effectiveness of the proposed methods, and a real application to a provincial power grid in China provides some useful power flow risk information for decision making. The whole FCM-based PPF scheme can be helpful for future power flow examination in power system planning and operation.

Power systems are vulnerable to natural disasters and a proper load restoration strategy after blackouts is significant for the resilience enhancement. Current studies of load restoration including transient constraints have not considered the dynamic boundaries of microgrids and the software interaction between steady-state optimization and transient simulation has not yet been fully expounded on. To solve these problems, this paper proposes a load restoration scheme utilizing dynamic microgrids formation with transient constraints guaranteed. Specifically, an online closed-loop of steady-state optimization and transient simulation is established. For practical applications, the interaction between PSCAD and MATLAB is also presented in detail. The case study based on the IEEE 37-node test feeder shows the importance of integrating transient simulation verification and feedback in the load restoration strategies. In addition, the proposed scheme can provide guidance for further hardware-in-the-loop test and industrial controller design.

Power systems are vulnerable to natural disasters and thus a proper critical load restoration strategy after black-outs is significant for the resilience enhancement of power systems. Current studies regarding the restoration of PV-storage integrated distribution systems cannot handle the scenario-based stochastic optimization with rolling updated forecast uncertainty of PV power outputs. To fill this gap, this paper proposes a systematic online critical load restoration scheme, based on a novel Gaussian mixture model-based scenario generation method, and the mathematical optimization is also formulated with the second order cone programming form. The case study to the IEEE 37-node test feeder shows the importance of forecast uncertainty update to the improvement of restoration rate. The whole scheme can provide guidance of emergency dispatch under main grid outages for distribution system operators.

Coastal distribution systems are vulnerable to typhoon weather. An efficient and accurate real-time risk assessment is very necessary in providing timely warning information for decision making. However, the conventional risk assessment methods are subject to varied typology, parameters and uncertainties. While in current data-driven methods, the effect of complex factors such as resilient resources and emergency response are difficult to be analytically incorporated in risk assessment procedure. To solve the problems, this paper proposes an improved data-driven real-time risk assessment method for resilient distribution systems during typhoon weather. A comprehensive risk-oriented database with 25 indexes is constructed based on practical experience in system operation. And then the effects of various complex factors on distribution system risk are characterized by entropy weights and gray correlation degrees. The case study on a modified 33-node system has validated the proposed method in real-time risk assessment. The whole scheme can be helpful for the software and hardware update of resilient distribution systems.

As increasing penetration of renewable energy, stochastic power flow method becomes an essential tool to analyze the randomness and variation in power system. Conventional methods cannot count in the correlation between input random variables, which will lead to obvious error in most cases. Consequently, it’s necessary to study enhanced methods, and also conduct comparisons among them to provide criteria for method selection. This paper builds up the basic frame of stochastic power flow methods considering correlation and compares them, especially two typical cumulant methods in aspects of accuracy, efficiency and complexity. Useful conclusions are obtained for further study and application.

As increasing integration of stochastic factors such as renewable energy, storage system and controllable load, the consequent randomness and variability bring great challenges to power system planning and operation. Probabilistic power flow (PPF) is proposed to deal with those uncertainties by means of probability distributions and probabilistic methods. However, some stochastic factors like energy storage and controllable load are subject to the control strategies, which makes their distributions and correlations vary a lot and adds difficulties to PPF analysis. To address the problem, this paper combines alternative control strategies, a novel polynomial distribution fitting method and the Copula function theory to establish a general probability model. Based on the model, an enhanced probabilistic power flow procedure with all of these stochastic factors and correlations is performed, and a subsequent evaluation scheme for transmission expansion plans are developed. Case study shows that the proposed PPF method can help make more reasonable and reliable decisions to transmission expansion plans under multiple stochastic factors.

The ice storm, characterized by freezing rain, would cause great damages to power system transmission lines and towers, which significantly affects the normal power supply. Distributed generation (DG) installation is considered as an efficient way to improve the power system resilience against natural hazards. However, current studies have hardly focused on the reliability-based DG planning considering the extreme weathers. To address the problem, this paper establishes a complete scheme for DG planning under a specific ice storm weather example. After consecutive three steps of ice storm modelling, reliability evaluation and optimization, an optimal DG allocation plan is obtained to reduce the ice-storm-related economic loss as much as possible. Case study on IEEE 33 bus system shows that the DG plan by the proposed method will help system operator reduce nearly 2.8 million yuan’s loss a year, which shows great practicability and advantage in future analysis and decision of distribution system planning.

This paper aims to address problems in the development of a power system evaluation platform considering high proportions of renewable power integration. A new interface structure is proposed, which enables developers to enjoy both the powerful computation functions of MATLAB and the versatility of other primary programming languages. To ensure the efficient and reliable interactions between modules, as well as between the front-end and back-end of the software, a paradigm for data transmission is introduced. Finally, the proposed interface paradigm is applied to a real-world example, proving its efficacy and flexibility.

As increasing penetration of renewable energy, the unpredictable randomness and variability of generation pose challenges to power system planning and operation. Probabilistic power flow (PPF) is emerging as a valid method to deal with uncertainty of generation outputs. However, the conventional PPF methods considering correlated generations can only derive the probability distribution of single node voltage or transmission line power, failing to cover the correlation between multiple voltages and powers, which is also significant in power flow analysis. To address the problem, this paper proposed an enhanced cumulant-based probabilistic power flow method for correlation mining in order to obtain more detailed probability characteristics of node voltages and transmission powers. Test results showed that the proposed method performed better than common PPF methods in combined or conditional events analysis, thus is more practical for operation analysis and decision making.

With increasing renewable energies integrated into power grid, the variability and uncertainty in power system are becoming dramatic and unpredictable. Probabilistic Power Flow was proposed to handle the uncertainties, by introducing random variables with specific probability distributions into calculation. However, the current PPF usually adopts correlation coefficient matrix as its correlation model, which is incomplete for probabilistic modelling. And the deterministic distributions in PPF cannot fully describe the randomness of renewable energy. To address the problem, this paper proposed a fuzzy Copula model for building a more complete and real probability model. And based on the fuzzy model, a whole scheme of Fuzzy Probabilistic Power Flow (FPPF) was established. Case study showed that FPPF can help obtain more possible distribution scenarios of objective random variables, indicating its superior reliability and practicability than conventional PPF.

In this work, the CdSe&ndash, ZnO flower-rod core-shell structure (CSZFRs) was prepared by ion-exchange method. The surface of CSZFRs was modified by 3-mercaptopropionic acid (MPA), and then the DNA probe was immobilized on the surface via chemical bond between -NH2 of DNA probe and -COOH of MPA. Finally, the target norovirous (NV) RNA was combined with the probe according to the principle of complementary base pairing, resulting in a decrease of the photocurrent. The results show that the absorbance spectrum of visible light is enhanced for CSZFRs compared with pure ZnO. Under visible light irradiation, the photocurrent of CSZFRs is up to 0.1 mA, which can improve the sensitivity of the photoelectrochemical (PEC) biosensor. In the measurement range of 0&ndash, 5.10 nM, the measured concentrations (c) have a good linear relationship with the output photocurrent of the biosensor. The linear regression equation is expressed as I = 0.03256 &minus, 0.0033c (R2 = 0.99, S/N = 3) with a detection limit of 0.50 nM. Therefore, this work realizes a rapid and sensitive method for the detection of NV RNA.

Nowadays with enlargement of power grid and increasing renewable energy integration, uncertainties and randomness challenge traditional power system analysis methods. Scenarios are widely applied to deal with power system uncertainties. Especially power flow examination in transmission expansion planning needs typical scenarios to verify normal power flow, and provide useful information for auxiliary planning strategies by studying typical extreme scenarios. In order to perform large-scale and multidimensional scenario analysis in power flow examination, this paper proposes an improved initial-center-refined and weighted K-means (ICRW K-means) method to improve efficiency and reduce computational time. Taking extreme scenarios into account, an extreme scenario reduction method is presented to retain typical extreme scenarios, maintaining more features of scenario reduction results. A real network is studied using proposed ICRW K-means and extreme scenario reduction method to perform power flow examination in transmission expansion planning. Numerical results demonstrate the effectiveness of proposed method, providing suggestions for auxiliary planning strategies to enhance power grid in resisting extreme conditions and reducing economic losses.

Nowadays as quantities of renewable energy resources are integrated to the power grid, their fluctuation and uncertainties influence violently to power system operation. Stochastic power flow is utilized to handle this issue. However, conventional stochastic power flow did not consider correlation in renewable energies, especially wind farms, leading to errors in power flow analysis. To address this problem, this paper proposes a linear correlation clustering method, which decomposes the non-linear correlated wind power data to several linear models to solve. The data generated from the nonlinear probabilistic distribution is clustered according to similar linear property, still keeping their major features and characteristics. Through analysis in a case study, it is proved that the proposed linear correlation clustering method performs better than existing Gaussian mixture model in both accuracy and computational efficiency. It is insightful to be utilized for stochastic power flow calculation in power system planning and operation.