The rotational inertia and primary frequency response capabilities of power systems are declining as synchronous generators (SGs) are replaced by inverter-based variable renewable energy (VRE) resources. Therefore, when a high VRE proportion is present, it becomes essential to include frequency security constraints in power system planning. Under this circumstance, the decline in SGs would deteriorate the system frequency response performance; thus, inverter-based resources are supposed to provide frequency response support. This paper presents a frequency-constrained stochastic planning method for high VRE share systems. Frequency response support from wind farms is considered in the planning process for the first time, and the unique characteristics of wind power support are addressed. Specifically, the uncertainty of and variations in wind power support capabilities are modeled from the field-measured data, and the optimal combination of various support schemes can be determined by introducing binary variables with novel linear constraints into the planning process. Moreover, the nonlinear frequency constraints are transformed and embedded in the planning model by the proposed computationally efficient adaptive piecewise linearization method. The planning is formulated as a stochastic optimization model to consider the VRE power uncertainty. Finally, case studies on the IEEE 30-bus system validate the proposed method, with results indicating time savings of 22%. Further, compared with the case that only SGs provide frequency support, the results based on the East China system demonstrate that considering wind power support can improve the VRE share from 49.5% to 79.7%.

The flexibility of power systems to manage variability and uncertainties is becoming increasingly important as the penetration of variable renewable energy continues to raise. Synergies from all parts of the existing system and new flexibility providers are required to balance demand and supply. However, because of fundamental limitations, existing test systems cannot fully support detailed and realistic studies. To fill this gap, this paper presents a 213-bus benchmark, called the FTS-213 system, dedicated for power system flexibility studies. This system has more than 38% of VRE penetration and includes real data at 15 minutes and 1 minute resolutions. The resources considered in the system cover all aspects of the generation, network, demand, energy storage and market. Specifically, to capture the intrinsic relationship between flexibility and renewable energy accommodation, the system contains integration grids, regional transmission grids and inter-regional tie lines. These assets reflect the full stack of utility-scale integration, transmission and consumption within and among regions. They cover multiple voltage levels, from 110 to 750 kV AC and ±400 to ±800 kV DC. The impacts of and the interactions between these flexibility factors are analyzed and three sets of planning results are provided.

Forecasting error amending is a universal solution to improve short-term wind power forecasting accuracy no matter what specific forecasting algorithms are applied. The error correction model should be presented considering not only the nonlinear and non-stationary characteristics of forecasting errors but also the field application adaptability problems. The kernel recursive least-squares (KRLS) model is introduced to meet the requirements of online error correction. An iterative error modification approach is designed in this paper to yield the potential benefits of statistical models, including a set of error forecasting models. The teleconnection in forecasting errors from aggregated wind farms serves as the physical background to choose the hybrid regression variables. A case study based on field data is found to validate the properties of the proposed approach. The results show that our approach could effectively extend the modifying horizon of statistical models and has a better performance than the traditional linear method for amending short-term forecasts.

Accurate wind power forecasting is crucial for setting reserve dimensions of a power system. From the perspective of power grid dispatcher, one of the most critical issues of dealing with the uncertainty brought by wind power forecast error (WPFE) is to analyse its extreme value to decide the new reserve requirements induced by variable wind power. This study targets at predicting the daily maximum of day‐ahead WPFE via extreme value analysis approach considering its application of operating reserve planning. Non‐stationary block maxima method (NS‐BMM) is applied to model the probabilistic characteristic of daily maxima WPFE via fitting the non‐linear relationship between distribution parameters and the covariates. To better shape the non‐stationarity, a conditional NS‐BMM (CNS‐BMM) via partition according to wind conditions is proposed. Moreover, indicators to evaluate the quality of applying NS‐BMMs in the day‐ahead reserve plan are also discussed. Finally, a case study based on the operational data from a typical wind farm in northern China is carried out. Results show that CNS‐BMM offers a better model of daily maximum WPFE compared to not only traditional quantile regression but also non‐CNS‐BMM, and applying the estimate of daily maximum WPFE on reserve plan demonstrates an advantage over conventional three‐sigma method.

The increasing penetration of new variable generations in power systems necessitates the modeling of the stochastic and variable characteristics, especially the modeling of wind power forecast error. A conditional probabilistic dependent method of modeling wind power forecast error of one single wind farm and multiple wind farms is presented in this paper, providing a range of potential forecast error for a given confidence level. Furthermore, a distribution generation algorithm of power forecast error based on Monte Carlo algorithm and genetic algorithm is proposed by applying the historical data and rank correlation of forecast error in multiple wind farms. Finally, the proposed method is verified in the case study by comparison of the conditional probabilistic model and the unconditional probabilistic model.