In order to currently motivate a wide range of various interactions between power network operators and electricity customers,residential load forecasting plays an increasingly important role in demand side response(D...In order to currently motivate a wide range of various interactions between power network operators and electricity customers,residential load forecasting plays an increasingly important role in demand side response(DSR).Due to high volatility and uncertainty of residential load,it is significantly challenging to forecast it precisely.Thus,this paper presents a short-term individual residential load forecasting method based on a combination of deep learning and k-means clustering,which is capable of effectively extracting the similarity of residential load and performing residential load forecasting accurately at the individual level.It first makes full use of k-means clustering to extract similarity among residential load and then employs deep learning to extract complicated patterns of residential load.The presented method is tested and validated on a real-life Irish residential load dataset,and the experimental results suggest that it can achieve a much higher prediction accuracy,in comparison with a published benchmark method.展开更多
A high-performance predictor for critical unstable generators(CUGs) of power systems is presented in this paper. The predictor is driven by the Map Reduce based parallelized neural networks. Specifically, a group of b...A high-performance predictor for critical unstable generators(CUGs) of power systems is presented in this paper. The predictor is driven by the Map Reduce based parallelized neural networks. Specifically, a group of back propagation neural networks(BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing,enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert.Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.展开更多
The operational environment of today's smart grids is becoming more complicated than ever before. A number of factors, including renewable penetration, marketization, cyber security, and hazards of nature, bring c...The operational environment of today's smart grids is becoming more complicated than ever before. A number of factors, including renewable penetration, marketization, cyber security, and hazards of nature, bring challenges and even threats to control centers. New techniques are anticipated to help dispatchers become aware of the accurate situations as they manipulate and navigate the situations as quickly as possible. To address the issues, we first introduce the background for this topic as well as the emerging technical demands of situational awareness in the dispatcher's environment. The general concepts and technical requirements of situational awareness are then summarized, aimed at offering an overview for readers to understand the state-of-the-art progress in this area. In addition, we discuss the importance of integrating the architecture of support tools in accordance with the dispatcher's thought process, which in fact guides correct and swift reactions in real-time operations. Finally, the prospects for situational awareness architecture are investigated with the goal of presenting situational awareness modules in an advanced and visualized manner.展开更多
基金supported by the Science and Technology Program of State Grid Corporation of China(Data Mining Technology of Potential High-Value Industrial Users for Data Operations,No.5700-202055267A-0-0-00)。
文摘In order to currently motivate a wide range of various interactions between power network operators and electricity customers,residential load forecasting plays an increasingly important role in demand side response(DSR).Due to high volatility and uncertainty of residential load,it is significantly challenging to forecast it precisely.Thus,this paper presents a short-term individual residential load forecasting method based on a combination of deep learning and k-means clustering,which is capable of effectively extracting the similarity of residential load and performing residential load forecasting accurately at the individual level.It first makes full use of k-means clustering to extract similarity among residential load and then employs deep learning to extract complicated patterns of residential load.The presented method is tested and validated on a real-life Irish residential load dataset,and the experimental results suggest that it can achieve a much higher prediction accuracy,in comparison with a published benchmark method.
文摘A high-performance predictor for critical unstable generators(CUGs) of power systems is presented in this paper. The predictor is driven by the Map Reduce based parallelized neural networks. Specifically, a group of back propagation neural networks(BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing,enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert.Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.
基金the National Natural Science Foundation of China(No.51437003)
文摘The operational environment of today's smart grids is becoming more complicated than ever before. A number of factors, including renewable penetration, marketization, cyber security, and hazards of nature, bring challenges and even threats to control centers. New techniques are anticipated to help dispatchers become aware of the accurate situations as they manipulate and navigate the situations as quickly as possible. To address the issues, we first introduce the background for this topic as well as the emerging technical demands of situational awareness in the dispatcher's environment. The general concepts and technical requirements of situational awareness are then summarized, aimed at offering an overview for readers to understand the state-of-the-art progress in this area. In addition, we discuss the importance of integrating the architecture of support tools in accordance with the dispatcher's thought process, which in fact guides correct and swift reactions in real-time operations. Finally, the prospects for situational awareness architecture are investigated with the goal of presenting situational awareness modules in an advanced and visualized manner.
