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Frequency scanning is a powerful and versatile approach for subsynchronous resonance (SSR) screening in power grids. Among different numerical and analytical frequency scanning techniques, the harmonic injection method is well suited for the scan of black-box models with active elements, such as in wind farms and solar plants. The results can be used for SSR risk assessment, in which accuracy performs a decisive role. The ideal situation for this method, which leads to the most accurate results, is to perform one simulation per single-frequency injection. However, for SSR studies with a wide range of frequencies and simulations with very small time steps (scale of μs), this comes at the expense of increased simulation time. This article proposes a technique to improve the accuracy of the harmonic injection method through the optimization of the crest factor while injecting all frequencies at one shot, therefore also reducing simulation time. The proposed technique was tested on a wind farm connected to a radial test case and on a portion of a Texas synthetic grid with multiple active elements, including two wind farms and a VSC-based STATCOM. The frequency scan results were benchmarked with both time-domain transient simulations and the ideal multiple single-frequency injection case and compared with other techniques. The results show that the proposed method is superior in accuracy when compared to the other techniques and is 11.71 times faster when compared to multiple single-frequency injections. [ABSTRACT FROM AUTHOR]

The demand side management (DSM) has been conceived to improve the efficiency and flexibility of the power grids. Currently, different types of DSM programs, such as demand response, the efficiency of power delivery, heating, ventilation, and air conditioning (HVAC) improvements and distributed energy storage, have been developed and implemented by different utility companies. Since DSM is not a one-size-fit-all program, detailed consumers’ consumption data are required to both develop and improve effective DSM programs for different consumers. The rapid expansion of the advanced metering infrastructure in recent years has produced an enormous amount of data which can be utilized by utility companies for the DSM applications. Based upon the consumption characteristics of different types of consumers, various DSM programs can be developed and applied accordingly. This article analyzes and studies the practical smart meter data of commercial consumers from New York City considering the potential of commercial consumers’ participation for DSM applications, and it provides a better understanding of the power consumption pattern of commercial consumers in the urban area. Both utility companies and commercial consumers could benefit from this article to develop and participate in DSM programs, respectively. [ABSTRACT FROM AUTHOR]

Petrochemical industry not only consumes a large amount of electricity every year but also requires high-quality power supplies. Any unexpected power failure could shut down the entire production line and cause significant financial loss. Meanwhile, due to the large electricity consumption and intensive power demand, in-house cogeneration plants have been equipped for most petrochemical facilities. Thus, any power interruption or disturbance between a petrochemical facility and interconnected power grids could affect both sides of the power systems. Therefore, having high power quality becomes one of the most critical parts for petrochemical facilities. To avoid unexpected power failure and to provide a better understanding of the power system at petrochemical facilities, a fast, precise, and reliable power monitoring system is required. A novel real-time web-based power monitoring system on field-programmable gate array platform is developed in this paper for a power substation at the petrochemical facility. Besides the data collecting with event-triggering mechanism and measurement data recording functions, subsynchronous oscillation detection application is also developed in this system. This monitoring system could provide precise data to help engineers with insightful analysis of the electric system to prevent a power failure, and it also could help system operators to have a better understanding of the system operation characteristics. [ABSTRACT FROM AUTHOR]

With the wind power energy largely interconnected with power grid, it is necessary to develop a robust dynamic equivalent model of the wind farm for the system stability analysis and control. In this paper, a detailed wind farm model with doubly fed induction generators is established according to Western Electricity Coordinating Council benchmark test system. Then, the parameters identification process based on aggregation technique consisting of a hybrid algorithm combined improved-particle swarm optimization with improved-genetic algorithm is proposed for dynamic equivalent model development by using the measurement data from phasor measurement unit at the point of interconnection. Additionally, the robustness of the developed equivalent model is analyzed. The simulation results of the benchmark test system and IEEE-9 bus system demonstrate the effectiveness and robustness of the proposed equivalent modeling and identification methodology. [ABSTRACT FROM AUTHOR]