Mingjian Cui

Wind-Friendly Flexible Ramping Product Design in Multi-Timescale Power System Operations

With increasing wind power penetration in the electricity grid, system operators are recognizing the need for additional flexibility, and some are implementing new ramping products as a type of ancillary service. However, wind is generally thought of as causing the need for ramping services, not as being a potential source for the service. In this paper, a multi-timescale unit commitment and economic dispatch model is developed to consider the wind power ramping product (WPRP). An optimized swinging door algorithm with dynamic programming is applied to identify and forecast wind power ramps (WPRs). Designed as positive characteristics of WPRs, the WPRP is then integrated into the multi-timescale dispatch model that considers new objective functions, ramping capacity limits, active power limits, and flexible ramping requirements. Numerical simulations on the modified IEEE 118-bus system show the potential effectiveness of WPRP in increasing the economic efficiency of power system operations with high levels of wind power penetration. It is found that WPRP not only reduces the production cost by using less ramping reserves scheduled by conventional generators, but also possibly enhances the reliability of power system operations. Moreover, wind power forecasts play an important role in providing high-quality WPRP service.

An optimized swinging door algorithm for wind power ramp event detection

Significant wind power ramp events (WPREs) are those that influence the integration of wind power, and they are a concern to the continued reliable operation of the power grid. As wind power penetration has increased in recent years, so has the importance of wind power ramps. In this paper, an optimized swinging door algorithm (SDA) is developed to improve ramp detection performance. Wind power time series data are segmented by the original SDA, and then all significant ramps are detected and merged through a dynamic programming algorithm. An application of the optimized SDA is provided to ascertain the optimal parameter of the original SDA. Measured wind power data from the Electric Reliability Council of Texas (ERCOT) are used to evaluate the proposed optimized SDA. Results show that the proposed optimized SDA method provided better performance than the L1-Ramp Detect with Sliding Window (L1-SW) method but with significantly less (almost 1,400 seconds less) computational requirements, and it was also used as a baseline to determine the optimal value of the tunable parameter in the original SDA for ramp detection.

Statistical Representation of Wind Power Ramps Using a Generalized Gaussian Mixture Model

Wind power ramps are significantly impacting the power balance of the system operations. Understanding the statistical characteristics of ramping features would help power system operators better manage these extreme events. Toward this end, this paper develops an analytical generalized Gaussian mixture model (GGMM) to fit the probability distributions of different ramping features. The nonlinear least-squares method with the trust-region algorithm is adopted to optimize the tunable parameters of mixture components. The optimal number of mixture components is adaptively solved by minimizing the Euclidean distance between the GGMM and the actual histogram distribution. The probability distribution of ramping features is generally truncated due to the ramp definition with a specific threshold. Thus, a sign function is utilized to truncate the GGMM distribution. Then, the cumulative distribution function of the GGMM is analytically derived and utilized to design a random number generator for ramping features. Numerical simulations on publicly available wind power data show that the parametric GGMM can accurately characterize the irregular and multimodal distributions of ramping features.

A Methodology for Quantifying Reliability Benefits from Improved Solar Power Forecasting in Multi-Timescale Power System Operations

Solar power forecasting improvements are mainly evaluated by statistical and economic metrics, and the practical reliability benefits of these forecasting enhancements have not yet been well quantified. This paper aims to quantify reliability benefits from solar power forecasting improvements. To systematically analyze the relationship between solar power forecasting improvements and reliability performance in power system operations, an expected synthetic reliability (ESR) metric is proposed to integrate multiple state-of-the-art independent reliability metrics. The absolute value and standard deviation of area control errors (ACEs), and the North American Electric Reliability Corporation Control Performance Standard 2 (CPS2) score are calculated through a multi-timescale scheduling simulation, including the day-ahead unit commitment, real-time unit commitment, real-time economic dispatch, and automatic generation control sub-models. The absolute ACE in energy, CPS2 violations, CPS2 score, and standard deviation of the raw ACE are all calculated and combined as the ESR metric. Numerical simulations show that the reliability benefits of multi-timescale power system operations are significantly increased due to the improved solar power forecasts.

A Data-Driven Methodology for Probabilistic Wind Power Ramp Forecasting

With increasing wind penetration, wind power ramps (WPRs) are currently drawing great attention to balancing authorities, since these wind ramps largely affect power system operations. To help better manage and dispatch the wind power, this paper develops a data-driven probabilistic WPR forecasting (p-WPRF) method based on a large number of simulated scenarios. A machine learning technique is first adopted to forecast the basic wind power forecasting scenario and produce the historical forecasting errors. To accurately model the distribution of wind power forecasting errors, a generalized Gaussian mixture model is developed and the cumulative distribution function (CDF) is also analytically deduced. The inverse transform method based on the CDF is used to generate a large number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The p-WPRF is generated based on all generated scenarios under different weather and time conditions. Numerical simulations on publicly available wind power data show that the developed p-WPRF method can predict WPRs with a high level of reliability and accuracy.