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Accurate wind power prediction (WPP) is important for stable operation of power systems. However, the intermittent nature and high variability of wind causes many challenges. This paper proposes a three-layer WPP model considering the data from historical power measurements and numerical weather prediction (NWP) systems. The first layer uses a linear model to learn the wind power generation equation. The second layer includes several non-linear models to learn the seasonality and the inertia of wind turbines. The third layer uses stacked regression to learn a hybrid combination of predictors in the previous layer. We compared the proposed approach against the state-of-the-art algorithm as well as two neural network models. Experiment results show that our approach has the best performance.

Due to the high penetration of distributed energy resources (DERs) in the distribution system, there is an increasing need for advanced tools to thoroughly study the impacts of DERs on distribution networks under various DER control/modeling scenarios. This type of tools not only requires a powerful network simulation engine in distribution grids, but also a flexible and interactive environment for easy development of advanced analysis/control algorithms, e.g., cutting-edge machine learning packages. If the software can be open-sourced, the power industry can further enjoy transparency and faster-time-to-market transition to expedite renewable integration. Past work does not give a fully independent data structure to separate the simulation layer and the application layer. Therefore, this work aims at providing full independence while integrating the two most powerful open-source tools in distribution grid simulation and an extremely popular programming language: GridLAB-D and Python. Specifically, we carefully create (1) an open and flexible design, (2) easy-to-develop analytical application scenarios, and (3) compatibility with a variety of third-party tools. We demonstrate features (1) and (2) of this co-simulation framework with a use case study on integration capacity analysis (ICA) and we demonstrate feature (3) as an example to conduct graphical analysis in Python for distribution system analysis with a near-zero effort. A highly accurate and fast system-wide ICA result demonstrates the supreme data structure and easy-to-extend architecture for speeding renewable integration. The code is available for download.

Frequency regulation market, as one major application scenario for energy storage system (ESS), has been updating its market policies since FERC Order 755 was issued in 2011. The new pay-for-performance rules provide more opportunities for ESSs as fast ramping resources. This paper focuses on latest regulation market implementation in PJM. This paper outlines a simulation framework to estimate the potential maximum revenue of ESS from pay-for-performance regulation market participation. This framework consists of two different time-scale simulation layers, day-ahead hourly capacity scheduling layer and real-time dispatching layer. The sensitivity analysis was performed to investigate impacts of different parameter settings, e.g. regulation-up and regulation-down capacity factor, on storage resource operation in regulation market. Actual PJM regulation market data are applied and statistically analyzed in this study. The study results of this paper can be utilized to further improve BES optimal scheduling and operation.