Partitioning Method of Virtual Microgrid Based on Electrical Coupling Strength

© 2019 Automation of Electric Power Systems Press. With the fast development in the research of smart grid and Energy Internet, more and more distributed renewable energy and energy storage devices are connected into distribution networks, intelligent development of distribution network has become an inevitable trend. It is a big challenge for large-scale conventional distribution networks to be consistent with the requirements of free, equal and flexible interaction. Virtual microgrids with high internal convergence are proposed based on electrical coupling strength, which is partitioned from conventional power distribution networks. Furthermore, an implementation framework of virtual microgrids based on extended cyber, physical and socioeconomic is put forward, three-stage research problems of boundary division, resource optimization deployment and collaborative capability management are introduced. According to the first problem, by defining the electrical coupling strength, the classical Newman fast partitioning algorithm is upgraded in complicated network to realize the automatic optimization of boundary in virtual microgrids. Through case studies, the proposed algorithm is verified to be reasonable and efficient

Partitioning Method of Virtual Microgrid Based on Electrical Coupling Strength

© 2019 Automation of Electric Power Systems Press. With the fast development in the research of smart grid and Energy Internet, more and more distributed renewable energy and energy storage devices are connected into distribution networks, intelligent development of distribution network has become an inevitable trend. It is a big challenge for large-scale conventional distribution networks to be consistent with the requirements of free, equal and flexible interaction. Virtual microgrids with high internal convergence are proposed based on electrical coupling strength, which is partitioned from conventional power distribution networks. Furthermore, an implementation framework of virtual microgrids based on extended cyber, physical and socioeconomic is put forward, three-stage research problems of boundary division, resource optimization deployment and collaborative capability management are introduced. According to the first problem, by defining the electrical coupling strength, the classical Newman fast partitioning algorithm is upgraded in complicated network to realize the automatic optimization of boundary in virtual microgrids. Through case studies, the proposed algorithm is verified to be reasonable and efficient

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Distributed Optimization for Power Systems with Radial Partitioning

This paper proposes group-based distributed optimization algorithms on top of intelligent partitioning for the optimal power flow (OPF) problem. Radial partitioning of the graph of a network is introduced as a systematic way to split a large-scale problem into more tractable sub-problems, which can potentially be solved efficiently with methods such as convex relaxations. The simple implementation of a Distributed Consensus Algorithm (DiCA) with very few parameters makes it viable for different parameter selection methods, which are crucial for the fast convergence of the distributed algorithms. The DiCA algorithm returns more accurate solutions to the tested problems with fewer iterations than component-based algorithms. Our numerical results show the performance of the algorithms for different power network instances and the effect of parameter selection. A software package DiCARP is created, which is implemented in Python using the Pyomo optimization package.Comment: 7 pages, 4 figure