scholarly journals Model-Based Identification of Alternative Bidding Zones: Applications of Clustering Algorithms with Topology Constraints

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2763
Author(s):  
Pietro Colella ◽  
Andrea Mazza ◽  
Ettore Bompard ◽  
Gianfranco Chicco ◽  
Angela Russo ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Transmission Lines ◽  
Clustering Algorithms ◽  
Capacity Allocation ◽  
Congestion Management ◽  
Normal Operation ◽  
Electrical Network ◽  
Clustering Methods ◽  
Operational Conditions ◽  
Nodal Prices

The definition of bidding zones is a relevant question for electricity markets. The bidding zones can be identified starting from information on the nodal prices and network topology, considering the operational conditions that may lead to congestion of the transmission lines. A well-designed bidding zone configuration is a key milestone for an efficient market design and a secure power system operation, being the basis for capacity allocation and congestion management processes, as acknowledged in the relevant European regulation. Alternative bidding zone configurations can be identified in a process assisted by the application of clustering methods, which use a predefined set of features, objectives and constraints to determine the partitioning of the network nodes into groups. These groups are then analysed and validated to become candidate bidding zones. The content of the manuscript can be summarized as follows: (1) A novel probabilistic multi-scenario methodology was adopted. The approach needs the analysis of features that are computed considering a set of scenarios defined from solutions in normal operation and in planned maintenance cases. The weights of the scenarios are indicated by TSOs on the basis of the expected frequency of occurrence; (2) The relevant features considered are the Locational Marginal Prices (LMPs) and the Power Transfer Distribution Factors (PTDFs); (3) An innovative computation procedure based on clustering algorithms was developed to group nodes of the transmission electrical network into bidding zones considering topological constraints. Several settings and clustering algorithms were tested in order to evaluate the robustness of the identified solutions.

scholarly journals Transmission Congestion Management by using LMP Method in De-Regulated Electricity Market

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2017-2020
Keyword(s):  
Electricity Markets ◽  
Transmission Lines ◽  
Electricity Market ◽  
Power Flow ◽  
Congestion Management ◽  
Test System ◽  
Alternative Methods ◽  
Transmission Congestion ◽  
Advantages And Disadvantages ◽  
Primal Dual

The power transmission network has the problem of management due to congestion in the open access system. Power flow due to transactions in transmission lines can cause overloads. This condition is known as congestion. There are several alternative methods for congestion management which are suitable for different electricity markets. In this paper the Locational Marginal Pricing (LMP) method is discussed for an assessment of transmission congestion management and results are obtained to manage the transmission congestion such as redispatching existing generators outside the congested area to supply power to the customer. The primal dual IP algorithm is used to calculate the LMP’s and congestion cost values. The proposed work has been implemented on a 14-bus test system to illustrate the advantages and disadvantages of this method

scholarly journals Bridge Damage Detection Approach Using a Roving Camera Technique

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1246 ◽  
Author(s):  
Darragh Lydon ◽  
Myra Lydon ◽  
Rolands Kromanis ◽  
Chuan-Zhi Dong ◽  
Necati Catbas ◽  
...  
Keyword(s):  
Low Cost ◽  
Normal Operation ◽  
Laboratory Model ◽  
Traffic Patterns ◽  
Operational Conditions ◽  
Bridge Model ◽  
Detection Approach ◽  
Bridge Damage ◽  
Computer Vision Technology ◽  
Climate Events

Increasing extreme climate events, intensifying traffic patterns and long-term underinvestment have led to the escalated deterioration of bridges within our road and rail transport networks. Structural Health Monitoring (SHM) systems provide a means of objectively capturing and quantifying deterioration under operational conditions. Computer vision technology has gained considerable attention in the field of SHM due to its ability to obtain displacement data using non-contact methods at long distances. Additionally, it provides a low cost, rapid instrumentation solution with low interference to the normal operation of structures. However, even in the case of a medium span bridge, the need for many cameras to capture the global response can be cost-prohibitive. This research proposes a roving camera technique to capture a complete derivation of the response of a laboratory model bridge under live loading, in order to identify bridge damage. Displacement is identified as a suitable damage indicator, and two methods are used to assess the magnitude of the change in global displacement under changing boundary conditions in the laboratory bridge model. From this study, it is established that either approach could detect damage in the simulation model, providing an SHM solution that negates the requirement for complex sensor installations.

Sensitivity Based Approach for Transmission Congestion Management Utilizing Bids for Generation Rescheduling and Load Curtailment

2006 ◽  
Vol 7 (4) ◽  
Author(s):  
Himanshu Kumar Singh ◽  
S.C. Srivastava ◽  
Ashwani Kumar Sharma
Keyword(s):  
Electricity Markets ◽  
Power Flow ◽  
Reactive Power ◽  
Congestion Management ◽  
Test System ◽  
Hyperbolic Function ◽  
Flow Sensitivity ◽  
Fair Competition ◽  
Load Curtailment ◽  
The Impact

One of the most important tasks of System Operator (SO) is to manage congestion as it threatens system security and may cause rise in electricity price resulting in market inefficiency. In corrective action congestion management schemes, it is crucial for SO to select the most sensitive generators to re-schedule their real and reactive powers and the loads to curtail in extreme congestion management. This paper proposed the selection of most sensitive generators and loads to re-schedule their generation and load curtailment based on the improved line flow sensitivity indices to manage congestion. The impact of slack bus on power flow sensitivity factors has been determined to encourage fair competition in the electricity markets. Effect of bilateral and multilateral transactions, and impact of multi-line congestion on congestion cost has also been studied. The generators’ reactive power bid has been modeled by a continuous differentiable tangent hyperbolic function. The proposed concept of congestion management has been tested on a practical 75-bus Indian system and IEEE-118-bus test system.

Study of the influence of overvoltage on the quality of electricity in energy systems.

2020 ◽  
Vol 23 (2) ◽  
pp. 52-58
Author(s):  
S. SKRYPNYK ◽  
Keyword(s):  
Power Supply ◽  
Fire Hazard ◽  
Lightning Discharge ◽  
Quality Standard ◽  
Normal Operation ◽  
Electrical Network ◽  
Modern World ◽  
Electrical Networks ◽  
The World

Our world with its high technologies has long been deeply dependent on the quality of electricity supply. In most countries of the world there are national power grids that combine the entire set of generating capacity and loads. This network provides the operation of household appliances, lighting, heating, refrigeration, air conditioning and transport, as well as the functioning of the state apparatus, industry, finance, trade, health services and utilities across the country. Without this utility, namely electricity, the modern world simply could not live at its current pace. Sophisticated technological improvements are firmly rooted in our lives and workplaces, and with the advent of e-commerce began the process of continuous transformation of the way individuals interact with the rest of the world. But with the achievement of intelligent technologies, an uninterrupted power supply is required, the parameters of which exactly meet the established standards. These standards maintain our energy security and create a reliable power system, that is maintaining the system in a trouble-free state. Overvoltage is the deviation of the rated voltage from the value of the corresponding quality standard (frequency, sinusoidal voltage and compliance of harmonics). Overvoltage in terms of fire hazard is one of the most dangerous emergency modes of electrical equipment, which causes conditions that in most cases are sufficient for the occurrence of fire hazards (exceeding the allowable voltage leads to disruption of normal operation or possible ignition). Against the background of deteriorating engineering systems, increased power consumption and poor maintenance, power supply of electrical installations, the main causes of overvoltage in electrical networks are thunderstorms (atmospheric overvoltage), switching switches, uneven phase load in electrical networks, etc. The physical picture of internal overvoltage is due to oscillatory transients from the initial to the established voltage distributions in the conductive sections due to the different situation in the electrical circuit. In the conditions of operation of electric networks planned, mode or emergency situations are possible. Therefore, the ranges of overvoltage are determined by the range from several hundred volts to tens and hundreds of kilovolts, and depend on the types of overvoltage. Atmospheric overvoltage is considered to be one of the most dangerous types of emergency modes of operation of the electrical network. This overvoltage occurs as a result of lightning discharge during precipitation by concentrating electricity on the surface of the object, the introduction of potential through engineering networks and

Book Reviews

2014 ◽  
Vol 52 (2) ◽  
pp. 554-555
Keyword(s):  
Environmental Economics ◽  
Electricity Markets ◽  
Congestion Management ◽  
Electricity Industry ◽  
University Of Michigan ◽  
Catholic University ◽  
Generation Capacity ◽  
Environmental Economics And Policy ◽  
The University ◽  
Transmission Rights

Shaun McRae of the University of Michigan reviews “The Economics of Electricity Markets: Theory and Policy”, by Pippo Ranci and Guido Cervigni. The Econlit abstract of this book begins: “Six papers address the main issues that arise when competition is introduced into the electricity industry. Papers discuss wholesale electricity markets (Guido Cervigni and Dmitri Perekhodtsev); generation capacity adequacy (Cervigni, Andrea Commisso, and Perekhodtsev); congestion management and transmission rights (Perekhodtsev and Cervigni); competition policy in the electricity industry (Cervigni and Perekhodtsev); retail competition (Anna Creti and Clara Poletti); and climate change and the future of the liberalized electricity markets (Cervigni). Ranci is Professor of Economic Policy at the Catholic University of Milan and Chair of the Board of Appeal of the European Agency for the Cooperation of Energy Regulators. Cervigni is Research Director at the Center for Research on Energy and Environmental Economics and Policy at Bocconi University and Chief Economist at A2A S.p.A. Index.”

scholarly journals RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuan Zhao ◽  
Zhao-Yu Fang ◽  
Cui-Xiang Lin ◽  
Chao Deng ◽  
Yun-Pei Xu ◽  
...  
Keyword(s):  
Random Forest ◽  
Single Cell ◽  
Gene Selection ◽  
Clustering Algorithms ◽  
Selection Methods ◽  
Clustering Methods ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Random Forest Classification ◽  
Forest Classification

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods.

scholarly journals Comparison of dimensionality reduction and clustering methods for SARS-CoV-2 genome

2021 ◽  
Vol 10 (4) ◽  
pp. 2170-2180
Author(s):  
Untari N. Wisesty ◽  
Tati Rajab Mengko
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Reduction ◽  
Clustering Algorithm ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Reduction Process ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Clustering Methods

This paper aims to conduct an analysis of the SARS-CoV-2 genome variation was carried out by comparing the results of genome clustering using several clustering algorithms and distribution of sequence in each cluster. The clustering algorithms used are K-means, Gaussian mixture models, agglomerative hierarchical clustering, mean-shift clustering, and DBSCAN. However, the clustering algorithm has a weakness in grouping data that has very high dimensions such as genome data, so that a dimensional reduction process is needed. In this research, dimensionality reduction was carried out using principal component analysis (PCA) and autoencoder method with three models that produce 2, 10, and 50 features. The main contributions achieved were the dimensional reduction and clustering scheme of SARS-CoV-2 sequence data and the performance analysis of each experiment on each scheme and hyper parameters for each method. Based on the results of experiments conducted, PCA and DBSCAN algorithm achieve the highest silhouette score of 0.8770 with three clusters when using two features. However, dimensionality reduction using autoencoder need more iterations to converge. On the testing process with Indonesian sequence data, more than half of them enter one cluster and the rest are distributed in the other two clusters.

scholarly journals PRIVACY PRESERVING CLUSTERING BASED ON LINEAR APPROXIMATION OF FUNCTION

2013 ◽  
Vol 12 (5) ◽  
pp. 3443-3451
Author(s):  
Rajesh Pasupuleti ◽  
Narsimha Gugulothu
Keyword(s):  
Linear Approximation ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Privacy Preserving ◽  
Distance Measures ◽  
Clustering Methods ◽  
Sensitive Data ◽  
Processing Information ◽  
Data Objects ◽  
Approximation Of Function

Clustering analysis initiatives  a new direction in data mining that has major impact in various domains including machine learning, pattern recognition, image processing, information retrieval and bioinformatics. Current clustering techniques address some of the  requirements not adequately and failed in standardizing clustering algorithms to support for all real applications. Many clustering methods mostly depend on user specified parametric methods and initial seeds of clusters are randomly selected by  user.  In this paper, we proposed new clustering method based on linear approximation of function by getting over all idea of behavior knowledge of clustering function, then pick the initial seeds of clusters as the points on linear approximation line and perform clustering operations, unlike grouping data objects into clusters by using distance measures, similarity measures and statistical distributions in traditional clustering methods. We have shown experimental results as clusters based on linear approximation yields good  results in practice with an example of  business data are provided.  It also  explains privacy preserving clusters of sensitive data objects.

Short Text Clustering Algorithms for Weibo Topic Detection

2014 ◽  
Vol 971-973 ◽  
pp. 1747-1751 ◽  
Author(s):  
Lei Zhang ◽  
Hai Qiang Chen ◽  
Wei Jie Li ◽  
Yan Zhao Liu ◽  
Run Pu Wu
Keyword(s):  
Text Analysis ◽  
Semantic Information ◽  
Clustering Algorithms ◽  
Text Clustering ◽  
Massive Data ◽  
Topic Detection ◽  
Clustering Methods ◽  
Short Text ◽  
Short Text Clustering ◽  
Application Requirements

Text clustering is a popular research topic in the field of text mining, and now there are a lot of text clustering methods catering to different application requirements. Currently, Weibo data acquisition is through the API provided by big microblogging platforms. In this essay, we will discuss the algorithm of extracting popular topics posted by Weibo users by text clustering after massive data collection. Due to the fact that traditional text analysis may not be applicable to short texts used in Weibo, text clustering shall be carried out through combining multiple posts into long texts, based on their features (forwards, comments and followers, etc.). Either frequency-based or density-based short text clustering can deliver in most cases. The former is applicable to find hot topics from large Weibo short texts, and the latter is applicable to find abnormal contents. Both the two methods use semantic information to improve the accuracy of clustering. Besides, they improve the performance of clustering through the parallelism.

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