Large intermittent generations have grown the influence on the grid security, system operation, and market economics. Although wind energy may not be dispatched, the cost impacts of wind can be substantially reduced if the wind energy can be scheduled using accurate wind forecasting. In other words, the improvement of the performance of wind power forecasting tool has significant technology and economic impact on the system operation with increased wind power penetration. Forecasting has been a vital part of business planning in today's competitive environment, especially in areas characterized by a high concentration of wind generation and a limited capacity of network. The target of this paper is to present a critical literature review and an up-to-date bibliography on wind forecasting technologies over the world. Various forecasting aspects concerning the wind speed and power have been highlighted. These technologies based on numeric weather prediction (NWP) methods, statistical methods, methods based upon artificial neural networks (ANNs), and hybrid forecasting approaches will be discussed. Furthermore, the difference between wind speed and power forecasting, the lead time of forecasting, and the further research will also be discussed in this paper.

During the 2001 Green Island Mesoscale Experiment (GIMEX), the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) was run at a horizontal resolution of 5 km twice a day and forced by initial and boundary conditions from the operational models of the Central Weather Bureau of Taiwan. The purpose of the paper is to evaluate the performance of the surface forecasts of the high-resolution numerical model and to verify quantitative precipitation forecasts (QPFs) over Taiwan Island within the 2-month period. The major errors in the forecasts are surface warm and dry biases. The model also tends to predict a stronger surface wind speed and an inland wind component, which suggest that the model overpredicted the sea breeze, a result that is consistent with the surface warm bias. The underprediction of the precipitation and poor skill scores are possibly due to the inadequate description of the humidity in the initial condition, and a spinup problem due to the steep Central Mountain... Abstract During the 2001 Green Island Mesoscale Experiment (GIMEX), the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) was run at a horizontal resolution of 5 km twice a day and forced by initial and boundary conditions from the operational models of the Central Weather Bureau of Taiwan. The purpose of the paper is to evaluate the performance of the surface forecasts of the high-resolution numerical model and to verify quantitative precipitation forecasts (QPFs) over Taiwan Island within the 2-month period. The major errors in the forecasts are surface warm and dry biases. The model also tends to predict a stronger surface wind speed and an inland wind component, which suggest that the model overpredicted the sea breeze, a result that is consistent with the surface warm bias. The underprediction of the precipitation and poor skill scores are possibly due to the inadequate description of the humidity in the initial condition, and a spinup problem due to the steep Central Mountain...

With the increasing proportion of renewable energy, some problems have gradually emerged. To reduce the operating cost and improve system reliability, renewable power forecasting is an indispensable part. Compared with the deterministic prediction, the probabilistic forecast considers the uncertainty, which helps manage power system operations. This study proposes a novel hour-ahead probabilistic forecasting method for wind power generation. It includes data preprocessing, adaptive neuro fuzzy inference system (ANFIS) training model with fuzzy c-means (FCM) clustering algorithm, and post processing of predicted-interval (PI). The input data of the proposed forecasting model include the numerical weather prediction (NWP) ensemble wind speeds, NWP spot wind speeds, and historical wind power measurements. The research results demonstrate that the proposed model supports better performance and prediction stability. Furthermore, this work reveals that data preprocessing and post processing of predicted interval are essential for wind power forecasting. These processes greatly improve the performance of the probabilistic wind power forecasts.

Because of climate changes, natural disasters are becoming more serious. For instance, the intensity of typhoons has been increasing in recent years. Typhoons and other natural disasters have high-impact low-probability (HILP) characteristics. Thus, procedures for preparing for natural disasters and increasing power system resilience are important issues. This paper proposes an all-inclusive process for system operators to make decisions for enhancing power system resilience and economic value during a severe weather event. This process first considers the typhoon track, the fragility curve and the recovery time of transmission lines. After collecting these data, system simulations and a calculated resilience index are implemented according to cases with and without disaster prevention. Next, the probability threshold and calculated economic value (EV) index are obtained based on numerical weather prediction (NWP) wind speeds and the cost-loss ratio. Finally, the two indexes are considered in combination to obtain the highest resilience with the greatest benefit. The proposed process helps system operators make decisions for appropriate preventive actions at the least cost. An actual Taiwan power system and a severe weather event are used as an example to demonstrate the proposed decision analysis. The simulation results indicate the feasibility of the proposed method, which can reduce potential risks caused by extreme weather events with the maximum economic benefits.

Typhoon WRF (TWRF) based on the Advanced Research Weather Research and Forecasting Model (ARW WRF) was operational at the Central Weather Bureau (CWB) for tropical cyclone (TC) predictions since 2010 (named TWRF V1). CWB has committed to improve this regional model, aiming to increase the model predictability toward typhoons over East Asia. In 2016, an upgraded version designed to replace TWRF V1 became operational (named TWRF V2). Compared with V1, which has triple-nested meshes with coarser resolution (45/15/5 km), V2 increased the model resolution to 15/3 km. Since V1 and V2 were maintained in parallel from 2016 to 2018, this study utilized the real-time forecasts to investigate the impact of model resolution on TC prediction. Statistical measures pointed out the superiority of the high-resolution model on TC prediction. The forecast performance was also found competitive with that of two leading global models. The case study further pointed out, with the higher resolution, the model not only advanced the prediction on the TC track and inner core structure but also improved the representativeness of the complex terrain. Overall, the high-resolution model can better handle the so-called terrain phase-lock effect and, therefore, improve the TC quantitative precipitation forecast over the complex Taiwanese terrain.

Recently, the Central Weather Bureau of Taiwan developed a WRF- and WRF data assimilation (WRFDA)-based convective-scale data assimilation system to increase model predictability toward high-impact weather. In this study, we focus on afternoon thunderstorm (AT) prediction and investigate the following questions: 1) Is the designation of a rapid update cycle strategy with a blending scheme effective? 2) Can surface data assimilation contribute positively to AT prediction under the complex geography of Taiwan island? 3) What is the relative importance between radar and surface observation to AT prediction? 4) Can we increase the AT forecast lead time in the morning through data assimilation? Consecutive ATs from 30 June to 8 July 2017 are investigated. Five experiments, each having 240 continuous cycles, are designed. Results show that employing continuous cycles with a blending scheme mitigates model spinup compared with downscaled forecasts. Although there are few radar echoes before AT initiation, assimilating radar observations is still crucial since it largely corrects model errors in cycles. However, assimilating surface observations is more important compared with radar in terms of extending forecast lead time in the morning. Either radar or surface observations contribute positively, and assimilating both has the highest QPF score. Assimilating surface observations systematically improves surface wind and temperature predictions based on 240 cases. A case study demonstrates that the model can capture the AT initiation and development by assimilating surface and radar observations. Its cold pool and outflow boundary prediction are also improved. In this case, the assimilation of surface wind and water vapor in the morning contributes more compared with temperature and pressure.