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

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.

[1]  Jimyung Kang,et al.  Electricity Customer Clustering Following Experts’ Principle for Demand Response Applications , 2015 .

[2]  Seung Ho Hong,et al.  A data mining-driven incentive-based demand response scheme for a virtual power plant , 2019, Applied Energy.

[3]  C. Nicolet,et al.  Virtual power plant with pumped storage power plant for renewable energy integration , 2014, 2014 International Conference on Electrical Machines (ICEM).

[4]  Danny Pudjianto,et al.  Microgrids and virtual power plants: Concepts to support the integration of distributed energy resources , 2008 .

[5]  Buhm Lee,et al.  Development of Power Quality Index Using Ideal Analytic Hierarchy Process , 2016 .

[6]  Xiaoyu Lyu,et al.  Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization , 2019, Applied Sciences.

[7]  Chuanwen Jiang,et al.  Multiple Objective Compromised Method for Power Management in Virtual Power Plants , 2011 .

[8]  Zbigniew Leonowicz,et al.  A Case Study on Power Quality in a Virtual Power Plant: Long Term Assessment and Global Index Application , 2020, Energies.

[9]  Wei Gu,et al.  A Multi-Time-Scale Economic Scheduling Strategy for Virtual Power Plant Based on Deferrable Loads Aggregation and Disaggregation , 2020, IEEE Transactions on Sustainable Energy.

[10]  Zita Vale,et al.  Distributed Energy Resources Scheduling and Aggregation in the Context of Demand Response Programs , 2018 .

[11]  Debasis Chaudhuri,et al.  An entropy-based initialization method of K-means clustering on the optimal number of clusters , 2020, Neural Computing and Applications.

[12]  C. De Capua,et al.  Imporvement of New Synthetic Power Quality Indexes: an Original Approach to Their Validation , 2005, 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings.

[13]  Tomasz Sikorski,et al.  Clustering as a tool to support the assessment of power quality in electrical power networks with distributed generation in the mining industry , 2019, Electric Power Systems Research.

[14]  Valeriy Vyatkin,et al.  Virtual Power Plant for Grid Services Using IEC 61850 , 2016, IEEE Transactions on Industrial Informatics.

[15]  Yu Shen,et al.  Robust stochastic optimal dispatching method of multi-energy virtual power plant considering multiple uncertainties , 2020 .

[16]  Zbigniew Leonowicz,et al.  The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation , 2020, Energies.

[17]  Zita Vale,et al.  Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response , 2019, Energies.

[18]  Georgios A. Vokas,et al.  Total Power Quality Index for Electrical Networks Using Neural Networks , 2015 .

[19]  J. Desmet,et al.  A Novel Feature Set for Low-Voltage Consumers, Based on the Temporal Dependence of Consumption and Peak Demands , 2020, Energies.