Due to the higher proportion of non-synchronous power resources in microgrids, adequate frequency regulation supports are required to maintain acceptable system frequency stability. As one of the sources for frequency regulation support in microgrid operation, via proper droop controller operations, wind energy conversion systems (WECS) could deliver kinetic energy stored in their turbines to the grid when needed. However, as the system inertia becomes smaller, the droop controller's response to system disturbances could cause the WECS to resonate with the grid and lead to torsional stress and fatigue damage in the WECS drivetrain. To protect the WECS against such damage, a resonance damper based on pole-zero cancellation technique is proposed in this paper. Simulation results depict the possible resonance phenomenon, and the reduction of undesired fatigue damage by the proposed damper retrofitted to the droop controller while improving the overall frequency regulation. Sensitivity analysis results are also provided to verify its robustness for practical applications.

A sustainable power grid involves variable and uncertain generation, load and equipment availability. In order to ensure the frequency quality, power system needs to maintain sufficient reserve capacity to meet the demand and generation variations, and to withstand contingency events. To limit the operation risk to an acceptable level, an operation practice driven and control performance based reserve allocation method is proposed in this paper to determine adequate frequency regulation reserve in an island power system with increasing renewable integrations. Analyses of on-line available frequency regulation reserves and control performance data recorded in Energy Management System are conducted to appraise and manage the risk of non-compliance of frequency control standard. The proposed method is based on the conditional probabilities of reaching certain Control Performance Standard 1 (CPS1) score under the availability of Automatic Generation Control ramping and droop response capability. Comparisons of estimated reserve requirements and expected performance of four dynamic reserve allocation schemes to comply with CPS1 are presented.

In a competitive power market, locations of future load growth have to be described with sufficient geographic precision to permit valid marketing strategy and siting of future T&D equipment. Small area load forecast, which provides information of future electric demand that includes spatial and temporal characteristics, is useful for T&D and market planning. Domain experts for spatial load forecast require long-term practicing and are difficult to find. In order to capture the meaningful associations between spatial data and the load changes, and to provide a useful tool for spatial load forecast, a data mining technique based on a knowledge discovery in database (KDD) procedure is proposed to determine automatically the preferential "scores" of land use changes. The proposed spatial modeling approach is an exploratory data analysis, trying to discover useful pattems in spatial data that are not obvious to the data user and are useful in the spatial load forecast.

Short-term load forecast (STLF) plays an important role in power system operations. This paper proposes a spline bases-assisted Recurrent Neural Network (RNN) for STLF with a semi-parametric model being adopted to determine the suitable spline bases for constructing the RNN model. To reduce the exposure to real-time uncertainties, interpolation is achieved by an adapted mean adjustment and exponentially weighted moving average (EWMA) scheme for finer time interval forecast adjustment. To circumvent the effects of forecasted apparent temperature bias, the forecasted temperatures issued by the weather bureau are adjusted using the average of the forecast errors over the preceding 28 days. The proposed RNN model is trained using 15-min interval load data from the Taiwan Power Company (TPC) and has been used by system operators since 2019. Forecast results show that the spline bases-assisted RNN-STLF method accurately predicts the short-term variations in power demand over the studied time period. The proposed real-time short-term load calibration scheme can help accommodate unexpected changes in load patterns and shows great potential for real-time applications.

Microgrids are designed to ensure reliability and quality of power supply. Reliable islanding transition is key to maintain uninterrupted supply of power to critical loads. Current islanding transition schemes assume fast response from local energy resources to establish secure islanding; however, such fast response systems are often expensive. Analysis of historical events shows that the islanding event, at most instances, can be predicted; by exploiting this feature, this paper proposes an adaptive optimal defense mechanism to establish secure islanding, without acquiring fast response energy resources. The dynamics involved in the islanding transition are incorporated into the search for optimal defense measures by using ‘simulation optimization” technique. A comparison between no defense approach and with defensive approach for the scenario when the islanding occurs demonstrates the effectiveness of the proposed adaptive defensive approach. The application of the adaptive defensive approach can help improve system performance. Sensitivity analyses are conducted to suggest improvements to reduce the operation cost of proposed defense plan.

Special protection systems (SPS) are sometimes perceived by the electric power industry as attractive alternatives to constructing new transmission facilities for maintaining system stability and security. One of the main concerns in the design of SPS is to assure whether the system could fit with the reliability requirements specification. Currently, there are only a few literatures that discuss the SPS reliability assessment. In one study conducted by the authors, some reliability evaluation techniques, including, reliability block diagram method, fault tree analysis, Markov modeling and Monte Carlo simulations, are applied to assess the probability that the decision process fails to respond as designed in an actual event-based SPS implemented by Taiwan Power Company (Taipower). Experiences acquired from using these methods for SPS reliability assessment and some recommendations are given in this paper.