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Real-time knowledge about the energy being exchanged sustains energy efficiency applications and services. The event-based data-saving approach to the measurements of electric energy has emerged recently with conceptual and practical implications, also thanks to the manufacturing of a new technological solution. This paper explains the fundamental underlying concepts that have led to these improvements through real-case examples. This work borrows from ontology the distinction between the concepts of endurants and perdurants, associating these concepts to the quantities involved in the energy metering process. In the new event-based computational framework, energy metering is interpreted by detecting average power and accumulated energy variations, as well as highlighting the importance of the information provided by the rate of change of energy and by the rate of events gathered from meters.

Widespread renewable energy alters infrastructures and business by changing a way to balance between the demand and the offer. In customer-centered model, flexible economic behavior of small-scaled energy actors mitigates variability and uncertainty in flows of power and energy. Flexible cooperative behavior of many intraday market participants has a potential to reduce uncertainty in renewable energy flows. However, owners of small-scale renewable energy plants play limited market role. This chapter presents changed socio-economic and technology contexts and attracts attention to new challenges. New ICT enabler activates role of small-scale renewable energy actors by complementing their physical energy by structured information about the capacity and flexibility. In new market scenario, unpredictability of renewable energy is reduced by adding knowledge and exploiting better flexible behavior. Main conclusion is about using the information about flexibility to activate small-scale actors on real-time markets while improving ecological sustainability.

The transition to generation-led Smart Grid has determined a need to materialize the Demand-side Flexibility. In a changed market scenario, small-scale users will be enabled to sell the ancillary balancing services as a particular good that complements amounts of the energy-containing commodity. In Transactive Energy Control scenario, the market operations should be accompanied by the tools for analytical accounting of both parts of the Control as a Service, the energy amounts (a) and the flexible ramps (b). It asks for new measurement and control instruments, especially because of security implications. This work describes a prototype built in the context of an EU-funded project SUCCESS.

This is a work in progress on smart grid modeling aimed to support real time decentralized control of inertia-less forms of energy. Author discusses about the special steady-state case when the energy is converted at a rate of work described locally by a linear function. Author adopts the discrete wavelet transforms in order to infer about the associated derivative-based criteria. New workflow starts from the event-based measurements of energy. Computational process generates Step- and Delta-functions that model the energy transforming process. Wavelet approach maps them to a simplified derivative model then. In new model, the meaning of the demand of power is associated with a sequence of quantities demanded, a linearized stepwise Cadlag function.

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.

The Event-Driven Metering (EDM) method is about data-driven compression of the measurement data based on the replacement of the original arbitrary shape of the load by a simplified polygonal line that represents the same values of the energy per interval of time. EDM algorithm receives the Shannon data samples encoding the local measurements and reports the pre-processed knowledge to the controlling hosts in the form of Lebesgue-based events. This article explains the mathematical foundations of the invention that allows improved data efficiency. Method decomposes the global behavior of an energy system in a sequence of linearized chunks and exploits the energy conservation law in order to set up data-driven geometric integration. The result is an operator that maps a load shape into a data-efficient sequence of tokens representing exactly the same energy quantities as in the original pattern.

This paper discusses the conceptual and practical implications of adopting a new type of energy meter, based on the identification of events from measuring the power pattern and on sending information on the actual energy used in the time interval between two events. The event-driven metering (EDM) approach enables gathering data useful for setting up effective representations of the knowledge that can be extracted about the prosumers' energy usage during time. Specific comparisons between the EDM effectiveness in reconstructing the power patterns with respect to the classical time-driven metering at regular time intervals are introduced to show the remarkable advantages of the EDM approach. An enhanced average power pattern reconstruction mechanism is also presented, based on the direct reproduction of the average power at the fastest time step occurring before the event generation, and on averaging the power pattern for the remaining part of the corresponding time interval. The case study application is illustrated by using the highly variable power pattern of a residential user.

Since energy use is a type of consumer behavior reflecting the interests to maximize some objective function, the human being activities seen in energy terms might be used to create the social aggregations or groups. Electric energy generated from ecologic sources brings some unpredictability. Authors model the unpredictability of the distributed generation in order to create a tool for minimization. Authors propose the novel method to build real life smart micro grids in the distributed generation context characterized by zero emissions. The proposed tool becomes an instrument to create the social aggregation of users and negotiate locally the “social” energy in real time, strengthening and mastering a virtual neighborhood of the local community.

This work is about the in-process visualization of multi-dimensional datasets recorded by compressed event-driven energy metering methods. The main goal of this work is to provide easy visual form to better understand energy transformations caused by real-life processes.

This article reviews different approaches to electricity metering and proposes novel coarse-grained but cycle-accurate measurement method which preserves the existing Legacy billing applications but supplies historical series of true RMS measurements. New method traces energy variations with cycle-accuracy. To reduce the quantity of data but keep high precision, it uses geometric integration. It transmits accumulated time-series upon timer-driven events. The method is designed in a way to remain compliant with the open APIs developed by Future Internet PPP projects FINESCE and FI-WARE. The proposed approach bridges the Legacy approach and the service-oriented one.

This article describes geometrical numeric integration applied to coarse-grained cycle-precise event-driven measurements of electrical energy. The proposed simplectic method draws from Stormer-Verlet and leapfrog algorithms in order to provide operational intelligence tools to analyze the amounts of analytical energy data. The proposed tool is extremely quick. Being very simple in computational terms, it suites real-time usages. This work extends the event-driven measurement approaches.

The Event-Driven Metering (EDM) method for electricity enabled the detection and better understanding of variable energy-transforming processes from real life. This article generalizes the above-mentioned Lebesgue-based approach by translating it to the accounting of other energy vectors in the form of gases or liquids. Given the behavioral difference between the physical forms of vectors of energy, authors made an effort to keep newly proposed method compatible with the process knowledge being extracted and shared grid-wide by the EDM method previously declined to electricity. Authors exploit the symplectic properties deriving from the energy conservation law. Authors set up new compressive and sparse sensing method by moving from the point-like data to the segment-like entities remaining valid over longer finite time intervals.