In recent years, much attention has been devoted to the development and applications of smart grid technologies, with special emphasis on flexible resources such as distributed generations (DGs), energy storages, active loads, and electric vehicles (EVs). Demand response (DR) is expected to be an effective means for accommodating the integration of renewable energy generations and mitigating their power output fluctuations. Despite their potential contributions to power system secure and economic operation, uncoordinated operations of these flexible resources may result in unexpected congestions in the distribution system concerned. In addition, the behaviors and impacts of flexible resources are normally highly uncertain and complex in deregulated electricity market environments. In this context, this paper aims to propose a DR based congestion management strategy for smart distribution systems. The general framework and procedures for distribution congestion management is first presented. A bi-level optimization model for the day-ahead congestion management based on the proposed framework is established. Subsequently, the robust optimization approach is introduced to alleviate negative impacts introduced by the uncertainties of DG power outputs and market prices. The economic efficiency and robustness of the proposed congestion management strategy is demonstrated by an actual 0.4 kV distribution system in Denmark.
In this paper, a novel short-term load forecasting method amalgamated with quantile regression random forest is proposed. Comprised with point forecasting, it is capable of quantifying the uncertainty of power load. Firstly, a bespoke 2D data preprocessing taking advantage of empirical mode decomposition (EMD) is presented. It can effectively assist subsequent point forecasting models to extract spatial features hidden in the 2D load matrix. Secondly, by exploiting multimodal deep neural networks (DNN), three short-term load point forecasting models are conceived. Furthermore, a tailor-made multimodal spatial–temporal feature extraction is proposed, which integrates spatial features, time information, load, and electricity price to obtain more covert features. Thirdly, relying on quantile regression random forest, the probabilistic forecasting method is proposed, which exploits the results from the above three short-term load point forecasting models. Lastly, the experimental results demonstrate that the proposed method outperforms its conventional counterparts.
To avoid serious damages caused by the dynamic environment, fault detection and health assessment are essential for an integrated robotic system. In this paper, we propose a fault detection algorithm and a health degree assessment approach for a robot manipulator system. Both the internal disturbance and the output measurement disturbance are considered in the proposed method. In addition, an adaptive observer is utilized to reconstruct the real system of robot manipulators. Under the proposed observer, the real system is estimated to detect the fault and obtain the health degree of the robot manipulator. The feasibility and reliability of the proposed fault detection algorithm and health degree assessment index for robot manipulator systems are proved by simulation experiments.
At present, the main way for electric power companies to check the accuracy of electric meters is that professionals regularly bring standard electric meters to the site for verification. With the widespread application of smart meters and the development of data processing technology, remote error estimation based on the operating data of smart meters becomes possible. In this paper, an error estimation method of smart meter based on clustering and adaptive gradient descent method is proposed. Firstly, the fuzzy c-means clustering method is used to preprocess the data to classify the operating conditions of each measurement, and then the adaptive gradient descent method is used to establish the error estimation model. The simulation results show that this method has high error estimation accuracy. This method has a small amount of calculation and high reliability and is suitable for large-scale power grids.
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