scholarly journals Combined Correlation and Cluster Analysis for Long-Term Power Quality Data from Virtual Power Plant

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 641
Author(s):  
Michał Jasiński
Keyword(s):  
Cluster Analysis ◽  
Power Plant ◽  
Power Quality ◽  
Quality Data ◽  
Virtual Power ◽  
Distributed Resources ◽  
Virtual Power Plant ◽  
And Cluster Analysis ◽  
Global Indicators

Analysis of the connection between different units that operate in the same area assures always interesting results. During this investigation, the concerned area was a virtual power plant (VPP) that operates in Poland. The main distributed resources included in the VPP are a 1.25 MW hydropower plant and an associated 0.5 MW energy storage system. The mentioned VPP was a source of synchronic, long-term, multipoint power quality (PQ) data. Then, for five related measurement points, the conclusion about the relation in point of PQ was performed using correlation analysis, the global index approach, and cluster analysis. Global indicators were applied in place of PQ parameters to reduce the amount of analyzed data and to check the correlation between phase values. For such a big dataset, the occurrence of outliers is certain, and outliers may affect the correlation results. Thus, to find and exclude them, cluster analysis (k-means algorithm, Chebyshev distance) was applied. Finally, the correlation between PQ global indicators of different measurement points was performed. It assured general information about VPP units’ relation in point of PQ. Under the investigation, both Pearson’s and Spearman’s rank correlation coefficients were considered.

scholarly journals Clustering Methods for Power Quality Measurements in Virtual Power Plant

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5902
Author(s):  
Fachrizal Aksan ◽  
Michał Jasiński ◽  
Tomasz Sikorski ◽  
Dominika Kaczorowska ◽  
Jacek Rezmer ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Power Plant ◽  
Power Quality ◽  
Optimal Number ◽  
Operating Conditions ◽  
Virtual Power ◽  
Number Of Clusters ◽  
Virtual Power Plant ◽  
Agglomerative Algorithm ◽  
Optimal Number Of Clusters

In this article, a case study is presented on applying cluster analysis techniques to evaluate the level of power quality (PQ) parameters of a virtual power plant. The conducted research concerns the application of the K-means algorithm in comparison with the agglomerative algorithm for PQ data, which have different sizes of features. The object of the study deals with the standardized datasets containing classical PQ parameters from two sub-studies. Moreover, the optimal number of clusters for both algorithms is discussed using the elbow method and a dendrogram. The experimental results show that the dendrogram method requires a long processing time but gives a consistent result of the optimal number of clusters when there are additional parameters. In comparison, the elbow method is easy to compute but gives inconsistent results. According to the Calinski–Harabasz index and silhouette coefficient, the K-means algorithm performs better than the agglomerative algorithm in clustering the data points when there are no additional features of PQ data. Finally, based on the standard EN 50160, the result of the cluster analysis from both algorithms shows that all PQ parameters for each cluster in the two study objects are still below the limit level and work under normal operating conditions.

Global Power Quality Index application in Virtual Power Plant

2020 ◽  
Author(s):  
Michal Jasinski ◽  
Tomasz Sikorski ◽  
Dominika Kaczorowska ◽  
Pawel Kostyla ◽  
Zbigniew Leonowicz ◽  
...  
Keyword(s):  
Power Plant ◽  
Power Quality ◽  
Quality Index ◽  
Virtual Power ◽  
Virtual Power Plant ◽  
Global Power

scholarly journals Short term Load Forecasting Considering Demand Response under virtual power plant mode

2021 ◽  
Vol 256 ◽  
pp. 02006
Author(s):  
Zhendong Du ◽  
Di Wu ◽  
Hua Bai ◽  
Zhengyong Wang ◽  
Yizong Guo ◽  
...  
Keyword(s):  
Power Plant ◽  
Demand Response ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Virtual Power ◽  
Short Term ◽  
Virtual Power Plant ◽  
Short Term Load Forecasting ◽  
Memory Network

In order to better manage demand response resources of user side and reduce short-term load forecasting error, a short-term load forecasting method considering demand response in virtual power plant mode is proposed. Firstly, the demand response mechanism of the virtual power plant is analyzed. Taking the maximum profit of the virtual power plant as the goal, considering the user’s energy consumption habits, self built photovoltaic, energy storage behavior and thermal electric coupling, the optimization model is established for each type of demand response resources. The CPLEX solver is called to solve the mixed integer linear programming problem after the model transformation, and the sub signals of each resource participating in the demand response are obtained. Then, based on this model, a long-term and short-term memory network model considering demand response signals is established to predict load power iteratively. At the same time, the long-term and short-term memory network model considering the demand response signals effectively makes up for the shortcomings of the traditional forecasting model without considering the demand response, and is more accurate in predicting the future trend of load change.

scholarly journals Optimal Sizing of Cascade Hydropower and Distributed Photovoltaic Included Virtual Power Plant Considering Investments and Complementary Benefits in Electricity Markets

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 952 ◽  
Author(s):  
Jichun Liu ◽  
Jianhua Li ◽  
Yue Xiang ◽  
Xin Zhang ◽  
Wanxiao Jiang
Keyword(s):  
Power Plant ◽  
Marginal Cost ◽  
Cost Model ◽  
Thermal Power ◽  
Arima Model ◽  
Decomposition Methods ◽  
Spot Price ◽  
Virtual Power ◽  
Virtual Power Plant

Due to an imminent fossil energy crisis and environmental pollution, renewable energy, such as photovoltaics, has been vigorously developing. However, the output of photovoltaic energy has strong volatility and intermittency. Thus, the photovoltaic generation system cannot constantly meet the load demand. To address this problem, a virtual power plant with hydro-photovoltaic-thermal generation is proposed in this paper. This virtual power plant utilizes the complementary characteristics of the output of the power sources to ensure a smooth and stable total output curve, and the power supply quality of the virtual power plant is improved. Further, the nonlinear operating cost model of the virtual power plant, with output changing over time, is established on the weighted output of hydro, photovoltaic, and thermal power; then, the corresponding marginal cost model of the virtual power plant is obtained. In the electricity market, three typical mid- to long-term electricity decomposition methods based on average, tracking load and spot price are constructed, and the spot price is predicted by the auto regressive moving average model (ARIMA) model, while the relationship between the spot price and the marginal cost of the virtual power plant is obtained; the marginal cost could also be adjusted based on the ARIMA model. Based on above factors, the sizing model of the virtual power plant is established, considering investment and complementary benefits. Finally, a case study is undertaken, where the sizing scheme for the increasing local load in the typical scenarios of the planning year and the corresponding annual rate of return are obtained. Sensitivity analysis of the influence for the above factors on the sizing of the virtual power plant is carried out. The optimal ratio of mid- to long-term electricity and its decomposition methods, as well as the capacity of the virtual power plant and the sizing ratio of hydropower, photovoltaic, and thermal power are obtained.

scholarly journals A Case Study on a Hierarchical Clustering Application in a Virtual Power Plant: Detection of Specific Working Conditions from Power Quality Data

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 907
Author(s):  
Michał Jasiński ◽  
Tomasz Sikorski ◽  
Dominika Kaczorowska ◽  
Jacek Rezmer ◽  
Vishnu Suresh ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Power Quality ◽  
Hierarchical Clustering ◽  
Working Conditions ◽  
Power Plants ◽  
General Information ◽  
Quality Data ◽  
Global Index ◽  
Virtual Power ◽  
Virtual Power Plants

The integration of virtual power plants (VPP) has become more popular. Thus, research on VPP for different issues is highly desirable. This article addresses power quality issues. The presented investigation is based on multipoint, synchronic measurements obtained from five points that are related to the VPP. This article provides a proposition and discussion of using one global index in place of the classical power quality (PQ) parameters. Furthermore, in the article, one new global power quality index was proposed. Then the PQ measurements, as well as global indexes, were used to prepare input databases for cluster analysis. The mentioned cluster analysis aimed to detect the short-term working conditions of VPP that were specific from the point of view of power quality. To realize this the hierarchical clustering using the Ward algorithm was realized. The article also presents the application of the cubic clustering criterion to support cluster analysis. Then the assessment of the obtained condition was realized using the global index to assure the general information of the cause of its occurrence. Furthermore, the article noticed that the application of the global index, assured reduction of database size to around 74%, without losing the features of the data.

A Virtual Power Plant with the use of the Energy Box in a Smart Grid concept

2013 ◽  
pp. 645-649
Author(s):  
C.S. Ioakimidis ◽  
L. Oliveira ◽  
K.N. Genikomsakis ◽  
P. Ryserski
Keyword(s):  
Power Plant ◽  
Smart Grid ◽  
Virtual Power ◽  
Virtual Power Plant
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