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 conduct power flow calculation under uncertain circumstances. However, the current wind power models are subject to specific probability distribution, limiting their accuracy in wider applications. Additionally, the CM-based PPF calculation, if nonlinear relationship is considered, would face an impractically high computational complexity. To address these problems in modeling and cumulant calculation, this paper 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 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.

As the scale of wind power bases rises, it becomes significant in power system planning and operation to provide detailed correlation characteristic of wind speed in different geographical hierarchies, that is among wind turbines, within a wind farm and its regional wind turbines, and among different wind farms. A new approach to analyze the correlation characteristics of wind speed in different geographical hierarchies is proposed in this paper. In the proposed approach, either linear or nonlinear correlation of wind speed in each geographical hierarchy is firstly identified. Then joint sectionalized wind speed probability distribution is modeled for linear correlation analysis while a Copula function is adopted in nonlinear correlation analysis. By this approach, temporal-geographical correlations of wind speed in different geographical hierarchies are properly revealed. Results of case studies based on Jiuquan Wind Power Base in China are analyzed in each geographical hierarchy, which illustrates the feasibility of the proposed approach.

This paper proposes a novel approach to evaluate the renewable energy accommodation capacity (REAC) of power system with wind power and photovoltaic (PV) integration. Based on Copula theory, the wind power output model is proposed considering uncertainty and the spatial correlation of wind speed in different wind farms. The PV output model is presented based on solar irradiance and weather influence factor. The solar irradiance depicts the periodicity and correlation of PV output while the new concept of weather influence factor is introduced to reflect the uncertainty of PV output. And the REAC evaluation model considering reliability of power system with wind power and PV integration is proposed with Monte-Carlo method. The proposed method comprehensively considers the characteristics of renewable energy and evaluates power system REAC more accurately. Furthermore, the influences of multiple factors on REAC of power system are analyzed. Results of case studies based on IEEE Case 39 test system and actual historical renewable energy data of northern China illustrate the feasibility of proposed approach.

The metamaterial with artificial synthetic gauge field has been proved as an excellent platform to manipulate the transport of the electromagnetic wave. Here we propose an inhomogeneous spoof surface plasmonic metasurface to construct an in‐plane pseudo‐magnetic field, which is generated by engineering the gradient variation of the opened Dirac cone corresponding to spatially varying mass term. The chiral zeroth‐order Landau level is induced by the strong pseudo‐magnetic field. Based on the bulk state propagation of the chiral Landau level, the photonic Dirac waveguide is designed and demonstrated in the experimental measurement, in which the unidirectionally guided electromagnetic mode supports the high‐capacity of energy transport. Without breaking the time‐reversal symmetry, our proposal structure paves a new way for realizing the artificial in‐plane magnetic field and photonic Dirac waveguide in metamaterial, and have potential for designing integrated photonic devices in practical applications.