C-DAX supports a heterogeneous set of co-existing smart grid applications that use the same communication platform. This allows a more efficient usage of the underlying communication infrastructure. Three realistic and pertinent use cases were selected from different domains to guide the design of the C-DAX platform and provide validation criteria to assess the performance of the platform.
Use case 1: RTU/IEDs at Distribution Substations
This use case concerns the integration of SCADA systems in the C-DAX cloud. SCADA systems are used by utilities for collecting power grid data at periodic intervals as well as reporting of asynchronous events (i.e., alarms) in the grid based on detected faults and for automatically controlling operations of actuating elements such as circuit breakers. For that purpose, RTUs are deployed at the substations that communicate with the SCADA Master Control and other systems in the utility DCCs.
SCADA systems use their own specific technologies for communication. At this moment substations are evolving to support the IEC 61850 Power Utility Automation set of standards. Consequently, the various actors are not able to directly interact with the C-DAX cloud. Therefore, it is necessary to have an adaptation layer in the C-DAX clients to assure interoperability between the technologies used by the actors and the operation of the C-DAX cloud. The responsibility of this layer is to interface the existing infrastructure with the C-DAX cloud. So hardware and software compliant to existing standards can be used with C-DAX with little configuration changes and C-DAX can be transparent for legacy hardware and software.
Use case 2: Pervasive Synchrophasor Deployment at MV Level
This use case concerns the wide-spread deployment of synchrophasors throughout the distribution grid. Phasor Measurement Units (PMUs) provide phasor measurements in the order of 50 or 60 frames per second which allows three phase real-time state estimation assessing the distribution network state within the order of tens of milliseconds and thus greatly improving the observability of the grid. This real-time state estimation allows applications such as real-time optimal voltage control, line congestion control, fault detection and localization, post-fault management, local load balancing and minimization of grid losses.
The C-DAX platform supports this use case by providing a scalable and resilient communication infrastructure for the timely delivery of high volume and continuous synchrophasor measurements, collected from geographically dispersed PMU locations.
This first video gives a short overview of the C-DAX platform as well as a live prototype demo in the lab of EPFL in Lausanne. The lab setup consists of an simulated feeder of Alliander with 10 real PMUs sending data over C-DAX to two different applications: (i) a real-time state estimation of the simulated MV grid and (ii) a fault localization application. These applications run on top of 2 Phasor Data Concentrators (PDCs) that act as C-DAX clients. The PDC serving the real-time state estimation application is interested in the data coming from all 10 PMUs whereas the second PDC is only interested in a subset of the PMUs. The C-DAX platform takes care of managing the connections and of forwarding the (filtered) data to the 2 clients. The data publishers (PMUs) are completely unaware of this and just provide their data to the platform. The video also shows the resilience functionality of C-DAX. When the primary data broker goes down, the secondary data broker almost immediately takes over and the interruption is hardly noticeable at the application side.
This use case will be evaluated in the field within the MS Livelab of Alliander where PMU devices from National Instruments will be installed and C-DAX will be validated in a live electricity network with real world monitoring and control requirements.
Something similar, but without using C-DAX as communication platform, has already been implemented by EPFL on their campus. They equipped part of the medium-voltage grid of the campus with PMUs. At this moment one feeder is equipped with PMUs at every of 5 substations. In the future this will be extended to over 20 substations in total. The 5 PMUs synchronously estimate the frequency, derivative of frequency (the so-called ROCOF), amplitude and phase of the 3-phase nodal voltages and nodal injected currents at each substation and streams these values to a concentration point via dedicated telecom copper cables. The concentration point (PDC) time-aligns the PMU data and make them available to the real-time state estimation process that accurately estimates the state (i.e. the voltage phasors in every node) of the monitored feeder. The video shows the developed GUI on top of the PDC displaying the PMU measurements together with the results of the state estimation.
The first part of the video shows the PDC connection setup to the 5 PMUs installed on the campus and, on the right, the buffer that stores the latest PMU measurements (each led represents a received data frame and each column corresponds to a streaming PMU).
The second part shows the feeder schematic, together with the instantaneous content of each PMU data frame. Each data frame contains the estimated values of frequency/ROCOF and 3-phase voltage and current phasors (amplitude and phase) together with a time-stamp value and additional information. At the bottom you can see the real-time profiles of the voltage and current amplitudes in the first bus of the feeder (please note some strong dynamics the PMU can detect).
The last part of the video shows the feeder schematic together with the results of the state estimation (in the top of the screen). The estimated state is used in this case to estimate the 3-phase active and reactive power injections/absorptions in every node of the grid (see the 2 meter graphs below each estimated state, the top one for active and the bottom one for reactive power). In the bottom of the screen you can see the latency due to the real-time state estimation(SE latency).
Use case 3: Retail Energy Transactions
Retail Energy Transactions (RETs) are the transactions between energy consumers and suppliers in the Retail Energy Markets (REMs). These transactions may have several forms, ranging from the matching of consumers’ demand requests with supply offers, to pricing negotiations for Demand Response (DR), and from the itemized billing and settlement for individual consumers to transactions for power rebalancing.
Within the C-DAX project, we selected a subset of these transaction types, in the form of specific application scenarios with the purpose of revealing the basic challenges emerging in the context of REMs that need to be addressed by the C-DAX architecture, while preserving the simplicity and focus of the use case framework. These application scenarios include Demand Response (use of pricing mechanisms to control the demand of end users with as goal the reduction of peak loads, voltage support, etc.), Flexibility Offerings (matching of demand and supply for environments with lots of Distributed Energy Resources (DERs) connected to the grid) and Electric Vehicle Support (the focus is on smart charging of the EVs with as goal timely serving of the customers as well as providing ancillary services to the grid).