Mikhail Simonov

Event-Driven Communication in Smart Grid

The event-driven electricity metering method makes visible both processes and their components. It enables the detection and better understanding of processes that vary energy flows. The newly obtained process knowledge is shared grid-wide using communication channels. The changes in energy exchanges (processes) originate new information flows in smart grid. We use internal metering data to obtain meta-information needed to reduce said data flows. The paper explains the use of an event-driven communication method in metering.

Hybrid Scheme of Electricity Metering in Smart Grid

The hybrid scheme of electricity metering combining time- and event-driven approaches makes visible events. It enables the detection, understanding, and management of nonstationary flows of electricity as they vary by real-life processes. We use the internal metering data to supply electric values and additional meta-information being elicited, e.g., scalar values characterizing variations of power flows and their timing. The capability to recognize events and their sequences produces new information flows in smart grids. We discuss how to exploit the process knowledge in the grids control.

Gathering Process Data in Low-Voltage Systems by Enhanced Event-Driven Metering

Event-driven metering is an emergent paradigm enabling significant data compression enhancement with respect to the conventional time domain metering techniques. This paper first discusses the representation of energy-based data in low-voltage segments of smart grids by using a process-oriented approach, providing an original interpretation in terms of accumulated energy. This interpretation is used to construct an overall framework containing multiple options for representing energy-based data, encompassing uniform and nonuniform linear time finite elements. A specific representation of the information defined in the space of digital events is then presented under the established framework. An enhanced representation of energy-based data identified as digital events is introduced, leading to the formulation of the final event-based data gathering (EBDG) scheme. Dedicated tests are carried out on exemplificative data sets for residential customers, including a new challenging benchmark developed by using a fuzzy-controlled load for air conditioning. The results obtained validate the use of the EBDG scheme to mine the energy-varying processes occurring at the remote nodes of the distribution system. Finally, the approach leading to the EBDG scheme is discussed to show how it falls within the realm of Big Data and to highlight how the EBDG results can be used to enable analytic accounting of the energy consumed in the processes occurring in a given time interval.

Dynamic Partitioning of DC Microgrid in Resilient Clusters Using Event-Driven Approach

An energy distribution network is a critical infrastructure that any compromise has an enormous impact on daily lives and the economy. The objective of this work is a computerized tool for distributed monitoring, dynamic re-configuration and control of DC distribution topology. This paper describes the double bar bus DC system exploiting the event-driven, service-oriented architecture, and real-time metering with nonuniform time sampling as an example of neighborhood optimization. We build a system with the capability to assess the resilience of and to rebuild better resilient grid partitions at run-time. The result is an intelligent system distributing loads between two buses dynamically in a way to keep self-sustainable and/or non-interruptible portion running at one bus by moving few other loads to the second bus. In standalone modality, the tool assesses the survivability of microgrid with high penetration of renewable energy. Running in cooperation with grid management tools, the same software can reconfigure optimally the local topology at run-time.

Event-based hybrid metering feeding AMI and SCADA

This article presents an industrial application in which event-based smart meters supplies datasets feeding the Advanced Metering Infrastructure, legacy billing procedures, and Supervisory Control and Data Acquisition systems of smart grid. New generation hybrid meters interoperate by using the FI-WARE architectural framework personalized by FINESCE team in the context of Cloud-based Service-Oriented Architecture made available by the Future Internet Public-Private-Partnership. Authors illustrate an opportunity to make observable each node of the Low Voltage energy distribution topology by using new event-based real time toolkit. In this industrial use case, event-based meters fed billing subsystem at regular time intervals, and supplies statefull events in real-time to the control system. This way, both event-based and timer-based datasets are conveyed to the FI-WARE cloud first, that directs them to the respective recipients by using publish-subscribe features and Big Data tools.

Underlying concepts for event-driven energy metering

Event-driven energy metering has emerged recently with conceptual and practical implications, as well as with new manufactured technological solutions. This paper recalls the underlying concepts that have led to these improvements. The event-driven energy metering paradigm is addressed in terms of the definition of the major aspects leading to its interpretation, the type of information provided, and the prospects for its use. The characteristics of the approach are shown through specific application cases.

Event-Driven Energy Metering: Principles and Applications

Recent developments in smart metering applications have led to the conceptualization and construction of a new type of energy meter, operating on the basis of event-driven principles. The event-driven metering concepts are applied to represent the information on the electrical load patterns, which have an integral value. This paper explains why these concepts are different from the ones used for event-based applications in other domains, discusses the principles used in the new type of electricity meter, presents the data formats structured in such a way to provide detailed knowledge representation, and shows a number of results on real-case applications. A specific index is defined in order to represent the effectiveness of the event-driven metering scheme illustrated to represent the details of the metered pattern, comparing the results with the ones that could be reached in the most favorable case through regular timer-driven metering. The presentation of specific applications based on real-life datasets highlights the advantages of the event-driven energy metering over the traditional timer-driven metering scheme.

Information Processing in Smart Grids and Consumption Dynamics

This work suggests an effective approach for information management in smart power grids based on the introduction of a suitable theory of digital energy. It shows a possible way to effectively manage energy dynamics in real life systems in real time. Power grids hold real time information flows already, but the control systems currently adopted use other information sources. We discuss the use of the information and semantic technologies in order to balance the loads in storage-less electric energy domain, and the changes brought by Future Internet and its entities.

Learning Environment for Life Time Value Calculation of Customers in Insurance Domain

A critical success factor in Insurance business is its ability to use information sources and contained knowledge in the most effective way. Its profitability is obtained through the Technical management plus Financial management of the funds gathered on the market. The profitability of a given customer can be evaluated through its Life Time Value (LTV). We aim at applying evolutionary algorithms to the problem of forecasting the future LTV in the Insurance Business. The Framework developed within the Eureka cofunded research projects HPPC/SEA and IKF has been adapted to the Insurance Domain through a dedicated Genetic Engine. The solution uses RDF and XMLcompliant standard. The idea of using evolutionary algorithms to design fuzzy systems date from the beginning of the Nineties and a fair body of work has been carried out throughout the past decade. The approach we followed uses an evolutionary algorithm to evolve fuzzy classifiers of the data set.

Cooperative Recharge Method of Connected Electric Vehicles in Smart Grid

Knowledge sharing between Electric Vehicles and Smart Grid is a source for improved load management and control. Recharge stations using event-driven communication share the information about recharge processes going to occur, while optimization agent(s) might prepare the optimal energy use policies. In this paper, authors showcase one approach for better integrating electric vehicles into smart grid. A photovoltaic power production is considered, in order to take into account the variability of power availability. The use of event-driven approach highlights faster reaction to the changes occurring inside the electric grid.