DISTRIBUTED DATA COLLECTION FOR UTILITY GRIDS

TECHNICAL FIELD

The present disclosure relates generally to utility control systems, e.g., to “smart grid” technologies.

BACKGROUND

Utility control systems and data processing systems have largely been centralized in nature. Energy Management Systems (EMSs), Distribution Management Systems (DMSs), and Supervisory Control and Data Acquisition (SCADA) systems reside in control or operations centers and rely upon what have generally been low complexity communications to field devices and systems. There are a few distributed control systems for utility applications, including a wireless mesh system for performing fault isolation using peer-to-peer communications among devices on feeder circuits outside of the substations. In addition, certain protection schemes involve substation-to-substation communication and local processing. In general however, centralized systems are the primary control architecture for electric grids.

Moreover, utility grids have generally relied on self-contained sensor devices that are configured to produce a final “sensed” value. Often, the computation is complex, and by individualizing the computation, various types of aggregate computations (e.g., phasor measurement) are not readily available or sometimes even accurate.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:

FIG. 1 illustrates an example simplified utility grid hierarchy;

FIG. 2 illustrates an example simplified communication network based on a utility grid (e.g., a “smart grid” network);

FIG. 3 illustrates an example simplified device/node;

FIG. 4 illustrates an example table showing challenges associated with complexity for smart grids at scale;

FIG. 5 illustrates an example of a smart grid core functions stack;

FIG. 6 illustrates an example of various feedback arrangements;

FIG. 7 illustrates an example chart showing a latency hierarchy;

FIG. 8 illustrates an example table of data lifespan classes;

FIG. 9 illustrates an example of an analytics architecture;

FIG. 10 illustrates an example of types of distributed analytic elements;

FIG. 11 illustrates an example data store architecture;

FIGS. 12A-12E illustrate an example layered services architecture model (“stack”);

FIG. 13 illustrates an example logical stack for a distributed intelligence platform

FIGS. 14A-14D illustrate an example of a layered services platform;

FIG. 15 illustrates an example of a distributed data collection system; and

FIG. 16 illustrates an example of a simplified procedure for distributed data collection.

DESCRIPTION OF EXAMPLE EMBODIMENTS
Overview

According to one or more embodiments of the disclosure, a system that provides distributed data collection for sensor networks in a utility grid comprises one or more data collection agents, one or more grid data collection service devices, and one or more points of use. The one or more data collection agents may be configured to generate grid data values that comprise raw grid data values, processed grid data values, and/or any combination thereof. The one or more data collection agents may be configured to communicate the grid data values using a communication network in the utility grid to the one or more grid data collection service devices, which may be configured to receive the grid data values in a time-synchronized manner, and to distribute the time-synchronized grid data values in substantially real-time to the one or more points of use.

Description

Electric power is generally transmitted from generation plants to end users (industries, corporations, homeowners, etc.) via a transmission and distribution grid consisting of a network of interconnected power stations, transmission circuits, distribution circuits, and substations. Once at the end users, electricity can be used to power any number of devices. Generally, various capabilities are needed to operate power grids at the transmission and distribution levels, such as protection, control (flow control, regulation, stabilization, synchronization), usage metering, asset monitoring and optimization, system performance and management, etc.

FIG. 1 illustrates an example simplified utility grid and an example physical hierarchy of electric power distribution. In particular, energy may be generated at one or more generation facilities 110 (e.g., coal plants, nuclear plants, hydro-electric plants, wind farms, etc.) and transmitted to one or more transmission substations 120. From the transmission substations 120, the energy is next propagated to distribution substations 130 to be distributed to various feeder circuits (e.g., transformers) 140. The feeders 140 may thus “feed” a variety of end-point “sites” 150, such as homes, buildings, factories, etc. over corresponding power-lines.

Note that the illustrative structure of the utility grid is shown as a highly simplified hierarchy, e.g., a hierarchy with generation at the top, transmission substations as the next tier, distribution substation as the next, etc. However, those skilled in the art will appreciate that FIG. 1 is merely an illustration for the sake of discussion, and actual utility grids may operate in a vastly more complicated manner (e.g., even in a vertically integrated utility). That is, FIG. 1 illustrates an example of power-based hierarchy (i.e., power starts at the generation level, and eventually reaches the end-sites), and not a logical control-based hierarchy. In particular, in conventional environments, transmission and primary distribution substations are at the same logical level, while generation is often its own tier and is really controlled via automatic generation control (AGC) by a Balancing Authority or other Qualified Scheduling Entity, whereas transmission lines and substations are under the control of a transmission operator Energy is Management System (EMS). Primary distribution substations may be controlled by a transmission EMS in some cases and are controlled by a distribution control center, such as when distribution is via a Distribution System Operator (DSO). (Generally, distribution feeders do logically belong to primary distribution substations as shown.)

In the case of distributed control, that is, in terms of control-based hierarchy, substations may be grouped so that some are logically higher level than others. In this manner, the need to put fully duplicated capabilities into each substation may be avoided by allocating capabilities so as to impose a logical control hierarchy onto an otherwise flat architecture, such as according to the techniques described herein. In such cases, transmission substations may be grouped and layered, while primary distribution substations may be separately grouped and layered, but notably it is not necessary (or even possible) that distribution substations be logically grouped under transmission substations.

In general, utility companies can benefit from having accurate distribution feeder (medium voltage/low voltage or “MV/LV” circuit) connectivity information in their software applications and data stores. This is especially useful for outage management and for convenient application to planning, construction, operations, and maintenance. It is, however, very challenging to try to construct or approximate the circuit model within a geographic information systems (GIS) environment due to the complexity of modeling the dynamic nature of an electrical network. That is, while the utility may have an “as-built” database, it may differ from the actual grid for various reasons, including inaccurate or incomplete data capture on grid construction, changes to circuits that are not reflected in updates to the database, and structural damage to the grid. In addition, circuit topology may change dynamically as feeder switches are operated in the course of either normal or emergency operations. Such changes result in an “as-operated” topology that is dynamic and is not reflected in the “as-built” database.

To assist in control of the utility grid, various measurement and control devices may be used at different locations within the grid 100. Such devices may comprise various energy-directing devices, such as reclosers, power switches, circuit breakers, etc. In addition, other types of devices, such as sensors (voltage sensors, current sensors, temperature sensors, etc.) or computational devices, may also be used. Electric utilities use alternating-current (AC) power systems extensively in generation, transmission, and distribution. Most of the systems and devices at the high and medium voltage levels operate on three-phase power, where voltages and currents are grouped in threes, with the waveforms staggered evenly. The basic mathematical object that describes an AC power system waveform (current of voltage) is the “phasor” (phase angle vector). Computational devices known as Phasor Measurement Units (PMUs) have thus been commercialized by several companies to calculate phasors from power waveforms. Because phase angle is a relative quantity, it is necessary when combining phasors taken from different parts of a power grid to align the phase angle elements to a common phase reference; this has been typically done in PMUs through the use of GPS timing signals. Such phasors are known as synchrophasors.

FIG. 2 is a schematic block diagram of a communication network 200 that may illustratively be considered as an example utility grid communication network. The network 200 illustratively comprises nodes/devices interconnected by various methods of communication, such as wired links or shared media (e.g., wireless links, Power-line communication (PLC) links, etc.), where certain devices, such as, e.g., routers, sensors, computers, etc., may be in communication with other devices, e.g., based on distance, signal strength, current operational status, location, etc. Those skilled in the art will understand that any number of nodes, devices, links, etc. may be used in the computer network, and that the view shown herein is for simplicity. Data packets may be exchanged among the nodes/devices of the computer network 200 using predefined network communication protocols such as certain known wired protocols, wireless protocols (e.g., IEEE Std. 802.15.4, WiFi, Bluetooth®, DNP3 (distributed network protocol), Modbus, IEC 61850, etc.), PLC protocols, or other protocols where appropriate. In this context, a protocol consists of a set of rules defining how the nodes interact with each other.

Illustratively, a control center 210 (and backup control center 210a) may comprise various control system processes 215 and databases 217 interconnected via a network switch 219 to a system control network 205. Additionally, one or more substations 220 may be connected to the control network 205 via switches 229, and may support various services/process, such as a distributed data service 222, grid state service (e.g., “parstate”, a determination of part of the whole grid state) 223, control applications 225, etc. The substations 220 may also have a GPS clock 221 to provide timing, which may be distributed to the FARs 250 (below) using IEEE Std. 1588. Note that a monitoring center 230 may also be in communication with the network 205 via a switch 239, and may comprise various analytics systems 235 and databases 237. The substations 220 may communicate with various other substations (e.g., from transmission substations to distribution substations, as mentioned above) through various methods of communication. For instance, a hierarchy of wireless LAN controllers (WLCs) 240 and field area routers (FARs) 250 may provide for specific locality-based communication between various portions of the underlying utility grid 100 in FIG. 1. WLCs 240 (which may also be considered as a type of higher grid level FAR) may comprise various services, such as data collection 245, control applications 246, etc. Generally, grid devices on shared feeder sections (e.g., FAR 250-X) may communicate with both involved substations (e.g., both WLCs 240, as shown). Further, FARs 250 may also comprise data collection services 255 themselves, and may collect data from (or distribute data to) one or more end-point communication devices 260, such as sensors and/or actuators (e.g., home energy controllers, grid controllers, etc.).

Specific details of the operation of the smart grid devices are described below. Note that while there is a general correlation between the communication network 200 and underlying utility grid 100 (e.g., control centers, substations, end-points, etc.), such a correlation may only be generally assumed, and is not a necessity. For instance, FARs 250 may be associated with feeder circuits 140, or may be more granular such as, e.g., “pole-top” routers. In other words, the hierarchies shown in FIG. 1 and FIG. 2 are not meant to be specifically correlated, and are merely examples of hierarchies for illustration.

FIG. 3 is a schematic block diagram of an example node/device 300 that may be used with one or more embodiments described herein, e.g., as any capable “smart grid” node shown in FIG. 2 above. In particular, the device 300 is a generic and simplified device, and may comprise one or more network interfaces 310 (e.g., wired, wireless, PLC, etc.), at least one processor 320, and a memory 340 interconnected by a system bus 350, as well as a power supply 360 (e.g., battery, plug-in, etc.).

The network interface(s) 310 contain the mechanical, electrical, and signaling circuitry for communicating data over links coupled to the network 200. The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. Note, further, that the nodes may have two different types of network connections 310, e.g., wireless and wired/physical connections, and that the view herein is merely for illustration. Also, while the network interface 310 is shown separately from power supply 360, for PLC the network interface 310 may communicate through the power supply 360, or may be an integral component of the power supply. In some specific configurations the PLC signal may be coupled to the power line feeding into the power supply.

The memory 340 of the generic device 300 comprises a plurality of storage locations that are addressable by the processor 320 and the network interfaces 310 for storing software programs and data structures associated with the embodiments described herein. Note that certain devices may have limited memory or no memory (e.g., no memory for storage other than for programs/processes operating on the device and associated caches). The processor 320 may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures 345. An operating system 342, portions of which are typically resident in memory 340 and executed by the processor, functionally organizes the device by, inter alia, invoking operations in support of software processes and/or services executing on the device. These software processes and/or services may comprise one or more grid-specific application processes 348, as described herein. Note that while the grid-specific application process 348 is shown in centralized memory 340, alternative embodiments provide for the process to be specifically operated within the network elements or network-integrated computing elements 310.

It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while the processes have been shown separately, those skilled in the art will appreciate that processes may be routines or modules within other processes.

As noted above, utility control systems and data processing systems have largely been centralized in nature. Energy Management Systems (EMS's), Distribution Management Systems (DMS's), and Supervisory Control and Data Acquisition (SCADA) systems reside in control or operations centers and rely upon what have generally been low complexity communications to field devices and systems. Both utilities and makers of various grid control systems have recognized the value of distributed intelligence, especially at the distribution level.

Generally, distributed intelligence is defined as the embedding of digital processing and communications ability in a physically dispersed, multi-element environment (specifically the power grid infrastructure, but also physical networks in general). In the area of sensing, measurement and data acquisition, key issues are:

- Sensing and measurement—determination of quantities to be sensed, type and location of sensors, and resulting signal characteristics;
- Data acquisition—collection of sensor data, sensor data transport;
- System state and observability—key concepts that can be used to guide the design of sensor systems for physical systems with topological structure and system dynamics; and
- Sensor network architecture—elements, structure, and external properties of sensor networks.
  
  Key elements of distributed intelligence comprise:
- Distributed data collection and persistence—measurement of electrical grid state, power quality, asset stress and utilization factors, environmental data, real-time grid topology, and device operating states, as opposed to central SCADA;
- Distributed data transformation and analytics—processing of measured data and event messages generated by smart grid devices and systems to extract useful information, prepare data for use by applications, or to correlate and filter data and events for aggregation purposes, as opposed to data center processing; and
- Distributed control—execution of actual control algorithms, with control commands being sent directly to grid control actuators for relatively local controllers, as opposed to central control.

By establishing the network as a platform (NaaP) to support distributed applications, and understanding the key issues around sensing and measurement for dynamic physical network systems, key capabilities of smart communication networks may be defined (e.g., as described below) that support current and future grid applications. In particular, as ICT (Information Communication Technology) networks converge with physical power grids and as “smart” functions penetrate the grid, centralized architectures for measurement and control become increasingly inadequate. Distribution of intelligence beyond the control center to locations in the power grid provides the opportunity to improve performance and increase robustness of the data management and control systems by addressing the need for low latency data paths and supporting various features, such as data aggregation and control federation and disaggregation.

In particular, there are a number of compelling arguments for using distributed intelligence in smart power grids, and in large scale systems in general, such as:

- Low Latency Response—A distributed intelligence architecture can provide the ability to process data and provide it to the end device without a round trip back to a control center;
- Low Sample Time Skew—Multiple data collection agents can easily minimize first-to-last sample time skew for better system state snapshots;
- Scalability—No single choke point for data acquisition or processing; analytics at the lower levels of a hierarchical distributed system can be processed and passed on to higher levels in the hierarchy. Such an arrangement can keep the data volumes at each level roughly constant by transforming large volumes of low level data into smaller volumes of data containing the relevant information. This also helps with managing the bursty asynchronous event message data that smart grids can generate (example: last gasp messages from meters during a feeder momentary outage or sag). The scalability issue is not simply one of communication bottlenecking however—it is also (and perhaps more importantly) an issue of data persistence management, and a matter of processing capacity. Systems that use a central SCADA for data collection become both memory-bound and CPU-bound in a full scale smart grid environment, as do other data collection engines; and
- Robustness—Local autonomous operation, continued operation in the presence of fragmentation of the network, graceful system performance and functional degradation in the face of failures, etc.

Standard approaches to distributed processing suffer from shortcomings relative to the electric grid environment. These shortcomings include inability to handle incremental rollout, variable distribution of intelligence, and applications not designed for a distributed (or scalable) environment. Further, existing approaches do not reflect the structure inherent in power grids and do not provide integration across the entire set of places in the grid where intelligence is located, or across heterogeneous computing platforms. Current systems also suffer from inability to work with legacy software, thus requiring massive software development efforts at the application level to make applications fit the platform, and also lack zero-touch deployment capability and requisite security measures.

For instance, one major obstacle in the adoption of distributed intelligence, now that IP communications and embedded processing capabilities are becoming available in forms that utilities can use, is that utilities cannot make large equipment and system changes in large discrete steps. Rather they must go through transitions that can take years to complete. This is due to the nature of their mission and the financial realities utilities must deal with. In practice, utilities must be able to transition from centralized to distributed intelligence, and must be able to operate in a complicated hybrid mode for long periods of time, perhaps permanently. This means that the utility must be able to roll out distributed intelligence incrementally while maintain full operations over the entire service area, and must be able to modify the distributed architecture appropriately over time and geography. Simply having a distributed architecture implementation is not sufficient; it must be easily and continually mutable in terms of what functionality is distributed to which processing locations in the grid and must be capable of coexisting with legacy control systems where they remain in place. Therefore, there exist various kinds of variable topology for effective distributed intelligence:

- Transition Variability—Rollout of distributed intelligence functions will be uneven both geographically (topologically) and over time, and there is no one-size-fits-all solution, even for a single utility;
- End State Variability—Not every distributed intelligence function will be pushed to every end node of the same class, and distributed intelligence functions and distributions will have to change over the life of the system;
- Operational Variability—Users must be able to change locations of functions to deal with failures and maintenance, etc.

Additionally, design and implementation of smart grids at scale poses a number of challenging architecture issues. Many of these issues are not apparent or do not show significant effects at pilot scale, but can become crucial at full scale. Note that generally herein, “at full scale” means one or more of:

- Endpoint scale—the number of intelligent endpoints is in the millions per distribution grid;
- Functional complexity scale—the number and type of functions or applications that exhibit hidden layer coupling through the grid is three or more; or the number of control systems (excluding protection relays) acting on the same feeder section or transmission line is three or more; and
- Geospatial complexity—the geographical/geospatial complexity of the smart grid infrastructure passes beyond a handful of substation service areas or a simple metro area deployment to large area deployments, perhaps with interpenetrated service areas for different utilities, or infrastructure that cuts across or is shared across multiple utilities and related organizations.

In the table 400 shown in FIG. 4, some of the challenges arising from these levels of complexity for smart grids at scale are illustrated. For instance, ultra large scale (ULS) characteristics of smart grids at scale are usually associated with decentralized control, inherently conflicting diverse requirements, continuous evolution and deployment, heterogeneous, inconsistent, and changing elements, and various normal failure conditions. Also, hidden couplings via the grid exist, since systems and controls are inherently coupled through grid electrical physics and therefore interact in ways that may be unaccounted for in system designs. The grid may further be viewed at scale as a multi-objective, multi-control system, where multiple controls affecting the same grid portions, and where some of the controls actually lie outside of the utility and/or are operating on multiple time scales. Moreover, bulk or aggregate control commands, especially as regards secondary load control and stabilization, may not consider the specific localities within the grid, and are not broken down to the feeder or even section level, taking into account grid state at the level. Lastly, smart grid-generated data must be used on any of a number of latency scales, some of which are quite short, thus precluding purely centralized processing and control approaches. Note that there are additional issues affecting architecture for smart grids at scale than those that are shown in FIG. 4, but these are representative of some of the key challenges.

The smart grid has certain key attributes that lead to the concept of core function classes supported by the smart grid. These key attributes include:

- A geographically distributed analog infrastructure;
- A digital superstructure consisting of digital processing layered on top of the analog superstructure, along with ubiquitous IP-based digital connectivity; and
- Embedded processors and more general smart devices connected to the edges of the smart grid digital superstructure and the analog infrastructure; these include both measurement (sensor) and control (actuator) devices.

Given this environment, and given our present understanding of the nature of the desired behavior of the power grid, we may identify a number of key function classes; functional groups that arise inherently from the combination of desired smart grid behavior, grid structure, and the nature of the digital superstructure applied to the grid. An understanding of these core function groups is key to developing a view toward a layered network services architecture for smart grids. A model is presented herein in which smart grid applications of any type are built upon a logical platform of core function classes that arise from the grid itself.

FIG. 5 illustrates the concept and the function classes themselves. For instance, to support distributed intelligence for electric power grids or any physical network, the concept of network services may be extended to become a stack of service groups, where the services become increasingly domain-oriented as one moves up the stack. This means that the lower layer contains ordinary network services. The next layer contains services that support distributed intelligence. The third layer provides services that support domain specific core functions. The top layer provides services that support application integration for real-time systems.

Specifically, as shown in the model of FIG. 5, the function classes are divided into four tiers.

1) The base tier 510 is:

- Power Delivery Chain Unification: use of digital communications to manage secure data flows and to integrate virtualized information services at low latency throughout the smart grid; enable N-way (not just two-way) flow of smart grid information; provision of integration through advanced networking protocols, converged networking, and service insertion. Note that this layer is based on advanced networking and communication, and in general may be thought of as system unification. In this model, networking plays a foundational role; this is a direct consequence of the distributed nature of smart grid assets.

2) The second tier 520 is:

- Automatic Low Level Control 521—digital protection inside and outside the substation, remote sectionalizing and automatic reclosure, feeder level flow control, local automatic voltage/VAr regulation, stabilization, and synchronization; and
- Remote Measurement 522—monitoring and measurement of grid parameters and physical variables, including direct power variables, derived element such as power quality measures, usage (metering), asset condition, as-operated topology, and all data necessary to support higher level function classes and applications.

3) The third tier 530 is:

- Control Disaggregation 531—control commands that are calculated at high levels must be broken down into multiple commands that align with the conditions and requirements at each level in the power delivery chain; the process to accomplish this is the logical inverse of data aggregation moving up the power delivery chain, and must use knowledge of grid topology and grid conditions to accomplish the disaggregation; and
- Grid State Determination 532—electrical measurement, power state estimation, and visualization, voltage and current phasors, bus and generator phase angles, stability margin, real and reactive power flows, grid device positions/conditions, DR/DSM available capacity and actual response measurement, storage device charge levels, circuit connectivity and device parametrics.

4) The fourth tier 540 is:

- Fault Intelligence 541—detection of short or open circuits and device failures; fault and failure classification, characterization (fault parameters), fault location determination, support for outage intelligence, support for adaptive protection and fault isolation, fault prediction, fault information notification and logging;
- Operational Intelligence 542—all aspects of information related to grid operations, including system performance and operational effectiveness, as well as states of processes such as outage management or fault isolation;
- Outage Intelligence 543—detection of service point loss of voltage, inside/outside trouble determination, filtering and logging of momentaries, extent mapping and outage verification, root cause determination, restoration tracking and verification, nested root cause discovery, outage state and process visualization, crew dispatch support;
- Asset Intelligence 544—this has two parts:
  - asset utilization intelligence—asset loading vs. rating, peak load measurement (amplitude, frequency), actual demand curve measurement, load/power flow balance measurement, dynamic (real-time) de-rating/re-rating, real-time asset profitability/loss calculation; and
  - asset health/accumulated stress intelligence—device health condition determination, online device and system failure diagnostics, device failure or imminent failure notification, asset accumulated stress measurement, Loss of Life (LoL) calculation, Estimated Time to Failure (ETTF) prediction, Asset Failure System Risk (AFSR) calculation; and
- Control Federation 545—grid control increasingly involves multiple control objectives, possible implemented via separate control systems. It is evolving into a multi-controller, multi-objective system where many of the control systems want to operate the same actuators. A core function of the smart grid is to federate these control systems that include Demand Response and DSM, voltage regulation, capacitor control, power flow control, Conservation Voltage Reduction (CVR), Electric Vehicle Charging Control, Line Loss Control, Load Balance Control, DSTATCOM and DER inverter VAr control, reliability event control, Virtual Power Plant (VPP) control, and meter connect/disconnect and usage restriction control.

These function classes may support one or more smart grid applications 550. In general, therefore, smart grid networks, that is, the combination of a utility grid with a communication network, along with distributed intelligent devices, may thus consist of various type of control, data acquisition (e.g., sensing and measurement), and distributed analytics, and may be interconnected through a system of distributed data persistence. Examples may include, among others, distributed SCADA data collection and aggregation, grid state determination and promulgation, implementation of distributed analytics on grid data, control command delivery and operational verification, control function federation (merging of multiple objective/multiple control systems so that common control elements are used in non-conflicting ways), processing of events streams from grid devices to filter, prevent flooding, and to detect and classify events for low latency responses, and providing virtualization of legacy grid devices so that they are compatible with modern approaches to device operation and network security.

In particular, there may be a number of types of control, such as sequence control (e.g., both stateless and stateful, typified by switching systems of various kinds), stabilizers (e.g., which moderate dynamic system behavior, typically through output or state feedback so that the system tends to return to equilibrium after a disturbance), and regulators (e.g., in which a system is made to follow the dynamics of a reference input, which may be dynamic or static set points). Quite often, all three of these are present in the same control system. In terms of electric power grids, flow control is sequence control, whereas model power oscillation damping and volt/VAr control represent stabilization and regulatory control, respectively.

For most control systems, feedback is a crucial component. FIG. 6 illustrates output feedback 610 and state feedback 620, both of which are quite common. FIG. 6 also illustrates a slightly more complex feedback arrangement 630 intended to be used when a system exhibits two very different sets of dynamics, one fast and one slow. There are a great many extensions of the basic control loop and the volume of mathematics, theory, and practice is enormous and widely used.

Regarding data acquisition, sensing and measurement support multiple purposes in the smart grid environment, which applies equally as well to many other systems characterized by either geographic dispersal, or large numbers of ends points, especially when some form of control is required. Consequently, the sensing system design can be quite complex, involving issues physical parameter selection, sensor mix and placement optimization, measurement type and sample rate, data conversion, sensor calibration, and compensation for non-ideal sensor characteristics.

Additionally, collection of the data in large scale systems such as smart grids presents issues of cycle time, data bursting, and sample skew. There are multiple modes of data collection for large scale systems and each presents complexities, especially when the system model involves transporting the data to a central location. In the typical round-robin scanning approach taken by many standard SCADA systems, the time skew between first and last samples represents an issue for control systems that is insignificant when the scan cycle time is short compared to system dynamics, but as dynamics increase in bandwidth with advanced regulation and stabilization, and as the number of sensing points increases, the sample time skew problem becomes significant.

Data is consumed in a variety of ways and places in a power grid; most of these are not located at the enterprise data center and much grid data does not enter the data center. Some of it does not even enter the control/operations center, as it must be consumed “on the fly” in grid devices and systems. Consequently it is important to classify data according to the latency requirements of the devices, systems, or applications that use it and appropriate persistence (or lack thereof) must also be defined. Note that much grid data has multiple uses; in fact, it is an element of synergy that has significant impact on smart grid economics and system design (networking, data architecture, analytics) to ensure that data is used to support as many outcomes as possible.

FIG. 7 is a chart 700 that illustrates the issue of latency, as latency hierarchy is a key concept in the design of both data management and analytics applications for physical networks with control systems or other real-time applications. In particular, in the example (and non-limiting) chart 700, grid sensors and devices are associated with a very low latency, where high-speed/low-latency real-time analytics may require millisecond to sub-second latency to provide results through a machine-to-machine (M2M) interface for various protection and control systems. The latency hierarchy continues toward higher latency associations as shown and described in chart 700, until reaching a very high latency at the business data repository level, where data within days to months may be used for business intelligence processing, and transmitted via a human-machine interface (HMI) for various reporting, dashboards, key performance indicators (KPI's), etc. Note that the chart does not illustrate that a given data element may in fact have multiple latency requirements, depending on the various ways it may be used, meaning that any particular datum may have multiple destinations.

The latency hierarchy issue is directly connected to the issue of lifespan classes, meaning that depending on how the data is to be used, there are various classes of storage that may have to be applied. This typically results in hierarchical data storage architecture, with different types of storage being applied at different points in the grid that correspond to the data sources and sinks, coupled with latency requirements.

FIG. 8 illustrates a table 800 listing some types of data lifespan classes that are relevant to smart grid devices and systems. In particular, transit data exists for only the time necessary to travel from source to sink and be used; it persists only momentarily in the network and the data sink and is then discarded. Examples are an event message used by protection relays, and sensor data used in closed loop controls; persistence time may be microseconds. On the other hand, burst/flow data, which is data that is produced or processed in bursts, may exist temporarily in FIFO (first in first out) queues or circular buffers until it is consumed or overwritten. Examples of burst/flow data include telemetry data and asynchronous event messages (assuming they are not logged), and often the storage for these types of data are incorporated directly into applications, e.g., CEP engine event buffers. Operational data comprises data that may be used from moment to moment but is continually updated with refreshed values so that old values are overwritten since only present (fresh) values are needed. Examples of operational data comprise grid (power) state data such as SCADA data that may be updated every few seconds. Transactional data exists for an extended but not indefinite time, and is typically used in transaction processing and business intelligence applications. Storage of transactional data may be in databases incorporated into applications or in data warehouses, datamarts or business data repositories. Lastly, archival data is data that must be saved for very long (even indefinite) time periods, and typically includes meter usage data (e.g., seven years), PMU data at ISO/RTO's (several years), log files, etc. Note that some data may be retained in multiple copies; for example, ISO's must retain PMU data in quadruplicate. Just as with latency hierarchy, grid data may progress through various lifetime classes as it is used in different ways. This implies that some data will migrate from one type of data storage to another as its lifetime class changes, based on how it is used.

Distributed analytics may be implemented in a fully centralized manner, such as usually done with Business Intelligence tools, which operate on a very large business data repository. However, for real-time systems, a more distributed approach may be useful in avoiding the inevitable bottlenecking. A tool that is particularly suited to processing two classes of smart grid data (streaming telemetry and asynchronous event messages) is Complex Event Processing (CEP) which has lately also been called streaming database processing. CEP and its single stream predecessor Event Stream Processing (ESP) can be arranged into a hierarchical distributed processing architecture that efficiently reduces data volumes while preserving essential information embodies in multiple data streams.

FIG. 9 shows an example of such analytics architecture. In this case, the analytics process line sensor data and meter events for fault and outage intelligence. In particular, various line sensors 905 may transmit their data via ESPs 910, and may be collected by a feeder CEP 915 at a substation 920. Substation CEPs 925 aggregate the feeder CEP data, as well as any data from substation devices 930, and this data may be relayed to a control center CEP 935 within a control center 940. Along with meter events from meter DCE 945 and other data from database 950, the control center CEP 935 may thus perform a higher level of analytics than any of the below levels of CEPs, accordingly.

In general, distributed analytics can be decomposed into a limited set of analytic computing elements (“DA” elements), with logical connections to other such elements. Full distributed analytics can be constructed by composing or interconnecting basic analytic elements as needed. Five basic types of distributed analytic elements are defined herein, and illustrated in FIG. 10:

- 1. Local loop 1010—an analytic element operates on data reports its final result to a consuming application such as a low latency control;
- 2. Upload 1020—an analytic element operates on data and then reports out its final result;
- 3. Hierarchical 1030—two or more analytic elements operate on data to produce partial analytics results which are then fused by a higher level analytics element, which reports the result;
- 4. Peer to peer 1040—two or more analytics elements operate on data to create partial results; they then exchange partial results to compute final result and each one reports its unique final analytic; and
- 5. Database access 1050—an analytic element retrieves data from a data store in addition to local data; it operates on both to produce a result which can be stored in the data store or reported to an application or another analytic element
- A sixth type, “generic DA node” 1060, may thus be constructed to represent each of the five basic types above.

Given the above-described concept of distributed analytics, including the database access element 1050 shown in FIG. 10, it becomes useful to consider distributed data persistence as an architectural element. Low level and low latency analytics for smart grids (mostly related to control) require state information and while local state components are generally always needed, it is often the case that elements of global state are also necessary. Operational data (essentially extended system state) may be persisted in a distributed operational data store. The reason for considering a true distributed data store is for scalability and robustness in the face of potential network fragmentation. In power systems, it is already common practice to implement distributed time series (historian) databases at the control center and primary substation levels. The techniques described herein may incorporate this and the distributed operational data store into an integrated data architecture by employing data federation in conjunction with various data stores.

FIG. 11 illustrates a data store architecture 1100 that federates distributed and centralized elements in order to support a wide range of analytics, controls, and decision support for business processes. In particular, a control center 1110 may comprise various centralized repositories or databases, such as a waveform repository 1112, an operational (Ops) data database 1114, and a time series database 1116. For instance, common interface model (CIM) services 1118 within the control center 1110 may operate based on such underlying data, as may be appreciated in the art. The data itself may be federated (e.g., by data federation process 1119) from various transmission substation databases 1120, primary distribution substation databases 1130, secondary substation databases 1140, distribution feeder (or other distributed intelligence point) database 1150. Typically, edge devices (end-points, sites, etc.) need not have further database or storage capabilities, but may depending upon various factors and considerations of a given implementation.

Notably, the architecture herein may build upon the core function groups concept above to extend grid capabilities to the control center and enterprise data center levels, using the layer model to unify elements and approaches that have typically been designed and operated as if they were separate and unrelated. This model may also be extended to provide services related to application integration, as well as distributed processing. This yields a four tier model, wherein each tier is composed of multiple services layers. The four tiers are as follows (from the bottom of the stack upward), where each of the layers and tiers is intended to build upon those below them:

1. Network services;

2. Distributed Intelligence services;

3. Smart Grid Core Function services; and

4. Application Integration services.

FIGS. 12A-12E illustrates the Layered Services Architecture model (“stack”)

1200. In particular, FIG. 12A shows a full stack model for the layered services. Application Integration Services 1210 comprises services that facilitate the connection of applications to data sources and each other. Note that at this top layer the stack splits into two parallel parts as shown in FIG. 12B: one for enterprise level integration 1212 and one for integration at the real-time operations level 1214. For the enterprise level, there are many available solutions, and the use of enterprise service buses and related middleware in a Service Oriented Architecture (SOA) environment is common. For the real-time operations side, the architecture herein relies less on such middleware tools and much more on network services. This is for two reasons: network-based application integration can perform with much lower latencies than middleware methods, and the use of middleware in a control center environment introduces a layer of cost and support complexity that is not desirable, given that the nature of integration at the real-time operations level does not require the more general file transfer and service composition capabilities of the enterprise SOA environment. The enterprise side of the application integration layer is not actually part of the distributed intelligence (DI) platform; it is shown for completeness and to recognize that interface to this form of integration environment may be needed as part of a fully integrated computing platform framework.

Additionally, the Smart Grid Core Function Services layer 1220 (detailed in FIG. 12C) generally comprises the components listed above in FIG. 5, namely services that derive from or are required by the capabilities of the smart grid superstructure. Moreover, the Distributed Intelligence Services layer 1230 (FIG. 12D) comprises support for data processing and data management over multiple, geographically dispersed, networked processors, some of which are embedded. Lastly, Network Services layer 1240 (FIG. 12E) comprises IP-based data transport services for grid devices, processing systems, and applications. Note that CEP is illustratively included here because it is fundamental to network management in the core grid architecture model.

Another way of approaching the layered services stack as shown in FIGS. 12A-12E above is from the perspective of the devices themselves, particularly as a logical stack. For instance, a logical stack 1300 for the distributed intelligence platform is illustrated in FIG. 13. Note that not all parts of this stack 1300 are intended to be present in every processing node in a system. FIG. 13 is correlated with the layered services stack 1200 of FIGS. 12A-12E, but the logical stack 1300 also shows placement of two types of data stores (historian 1365 to store a time series of data, thus maintaining a collection of (e.g., all of) the past values and database 1336 to store generally only the most recent (e.g., periodically refreshed) values of a set of operational variables), as well as an API layer 1340 to expose certain capabilities of the platform to the applications and to upper levels of the platform stack. Generally, at the base of the stack 1300 is the known IPv4/v6 protocol stack 1310, above which are grid protocols 1320 and peer-to-peer (P2P) messaging protocols 1325. Further up the stack 1300 are standard network services 1332, embedded CEP engines 1334, and the distributed database 1336. Through the API layer 1340, the stack 1300 reaches distributed intelligence services 1350 and unified computing/hypervisor(s) 1355, upon which rest grid-specific network services 1360 and historians 1365. Application integration services/tools 1370 tops the stack 1300, allowing for one or more applications 1380 to communicate with the grid devices, accordingly.

Based on the description above, a layered services platform may be created, which is a distributed architecture upon which the layered services and smart grid applications may run. The distributed application architecture makes use of various locations in the grid, such as, e.g., field area network routers and secondary substation routers, primary substations, control centers and monitoring centers, and enterprise data centers. Note that this architecture can be extended to edge devices, including devices that are not part of the utility infrastructure, such as building and home energy management platforms, electric vehicles and chargers, etc.

FIGS. 14A-14D illustrate an example of the layered services platform described above. For instance, as detailed in FIGS. 14A-14D, enterprise data centers 1410 may comprise various business intelligence (BI) tools, applications (enterprise resource planning or “ERP,” customer information systems or “CIS,” etc.), and repositories based on a unified computing system (UCS). Other systems, such as meter data management systems (MDMS) may also be present. Via a utility tier network 1420, the enterprise data centers 1410 may be in communicative relationship with one or more utility control centers 1430, which comprise head-end control and other systems, in addition to various visualization tools, control interfaces, applications, databases, etc. Illustratively, a services-ready engine (SRE), application extension platform (AXP), or UCS may structurally organize the utility control centers 1420. Through a system control tier network 1440, one or more primary substations 1450 may be reached by the control centers 1430, where a grid connected router (GCR) interconnects various services (apps, databases, etc.) through local device interfaces. Utility FANs (field area networks) 1460 (or neighborhood area networks (NAN's)) may then bridge the gap to pole top FARs 1470, or else (e.g., in Europe) secondary distribution substations 1480 to reach various prosumer (professional consumer) assets 1490, accordingly.

Distributed Data Collection for Utility Grids

As noted above, utility grids have generally relied on self-contained sensor devices that are configured to produce a final “sensed” value. Often, the computation may be complex, and by individualizing the computation within a self-contained sensor device, various types of aggregate computations such as, for example, phasor measurement, are not readily available or sometimes even accurate.

The techniques herein provide distributed data collection for sensor networks, which may be particularly useful for phasor measurement unit (PMU) measurement, sensor calibration, and the like (e.g., sensor virtualization). Distributed data collection, as disclosed herein, may comprise both distributed synchronized data collection and distributed processing of raw sensor data. For example, the techniques herein use the network itself as a distributed database to store information in the network.

Said differently, the techniques herein provide for router-integrated distributed data collection engines that are capable of generating low sample skew grid state to be stored in a distributed database as part of a larger grid data architecture. That is, the techniques herein may use the abilities of network devices to run software (e.g., third party software) to implement the distributed data acquisition and distributed database, thus enhancing the use of the network as a platform (NaaP) (e.g., with illustrative reference to FIG. 2 above).

Specifically, according to one or more embodiments of the disclosure as described in detail below, the techniques herein provide a system of distributed data collection for sensor networks in a utility grid that comprises one or more data collection agents, one or more grid data collection service devices, and one or more points of use. The one or more data collection agents may be configured to generate grid data values that comprise raw grid data values, processed grid data values, and/or any combination thereof. The one or more data collection agents may be configured to communicate the grid data values using a communication network in the utility grid to the one or more grid data collection service devices, which may be configured to receive the grid data values in a time-synchronized manner, and to distribute the time-synchronized grid data values in substantially real-time to the one or more points of use.

Illustratively, with reference again to FIG. 3, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with the grid-specific application process 348, which may contain computer executable instructions executed by the processor 320 to perform functions relating to the techniques described herein. For example, the techniques herein may be treated as “distributed data collection,” and may be located across a distributed set of participating devices 300, such as grid sensors, data collection agents, data collection service devices, and the like, as described herein, with functionality of the process 348 specifically tailored to the particular device's role within the techniques of the various embodiments detailed below.

Operationally, the techniques herein allow for data collection within a utility grid by either polling (or “pulling”) or pushing data with time synchronized sampling. Illustratively, as shown in FIG. 15, data collection agents 1510 may comprise multiple thin data collection service instances 1512 residing on endpoint routers 1514 (e.g., a field area router) that may collect data from grid sensors 1505, which is then subject to synchronization process 1516 (e.g., by using a precision time protocol such as IEEE 1588) to perform time-synchronized data collection. Remote grid sensors (“GS”) 1505 may communicate with one or more data collection agents 1510, or a grid sensor(s) 1505 may be integrally associated with data collection agent (DCA) 1510. The endpoint routers may then publish the data (e.g., via PIM/SSM) to distribute the collected data to one or more grid data collection service devices 1520; alternatively, they may also store the collected data in local storage 1518 or a shared distributed database 1508. The grid data collection service devices 1520 may then route the data in the most efficient manner available to one or more points of use such as, for example, a control center 1550, monitoring center 1540, sub-station 1530, or any other device, system, application, or process that has authorized access and need for the data. By using DCAs 1510 with data collection service 1512 at each endpoint router 1512 in time synchrony via synchronization 1516, the network may acquire all data without significant sampling time skew, and can scale to large numbers of endpoints without suffering from round robin cycle time growth. In addition, the techniques herein provide for conversion of data distribution from polling to streaming (e.g., via PIM/SSM), which effectively creates a network-based publish and subscribe system for utility grid data.

The techniques herein allow distribution of time-synchronized data to the one or more points of use in substantially real time via the processes of distributed synchronized data collection and distributed processing of raw sensor data, which may act in concert to provide high level ordered data to support complex analytics services such as, for example, complex event processing (CEP), grid topology, grid state determination and/or the like. In other words, the techniques herein allow the use of a utility grid network comprising grid sensors 1505, DCAs 1510, and data collection service devices 1520 as a massively parallel SCADA collection engine that may reduce or eliminate sample skew in collected grid data, while simultaneously providing large grid scalability.

Time-synchronization by synchronization 1516 of DCA 1510 may occur by, for example, a process that implements a precision time protocol such as IEEE 1588 and GPS clock 1542. For example, DCA 1510 may communicate with one or more grid sensors 1505 to acquire data on schedule. It is contemplated within the scope of the disclosure that DCA 1510 may have low-level signal/data processing 1506 capability as necessary (e.g., for a distributed PMU service), which may be particularly beneficial in cases where grid sensors 1505 may be programmed to emit data on schedule. In such a case, low-level data processing 1506 at each DCA 1510 may receive the data from grid sensor 1505 and perform the necessary processing before providing the data to the data collection service device 1520. It should be noted that the techniques herein accommodate any mixture of grid devices on the one hand (e.g., IEEE 1588 capable devices, as well as devices that lack IEEE 1588 capability), and support any kind of grid application/process/function on the other, including those that may want to receive data feeds directly, and not through a grid state service.

Illustratively, the distributed data collection methods described herein may be extended to provide distributed phasor measurement unit (PMU) measurement at the distribution level. For example, line sensing of voltage and current waveforms results in digital waveform data streams that can be continually processed to calculate synchrophasors. The phasor calculations may be done at the point of sampling (e.g., the sensor/node), or the sampled data may be propagated to a higher functionality node (e.g., a DCA, data collection service device, etc.) in the network where the calculations may be performed.

Of note, this technique includes converting raw sensed data into useful (calibrated/converted) values, which can occur anywhere in the network, not just at the sensor (e.g., a type of “sensor virtualization”). For instance, a sensor may generally be configured to produce an output value in terms of a voltage level, a binary bit sequence, etc., based on one or more sensed characteristics (e.g., temperature). Without calibration, for example, the value created by a sensor may simply be on a relative scale (e.g., 60 on a scale of 0-128, or 3.2V on a scale of 0-5V), and then a calibration process (e.g., scales and/or formulas) may be used to convert that value to actual data (e.g., 20 degrees Celsius). In some instances, such conversion can be a complex process. As such, by virtualizing the sensors according to the techniques herein, such calibration and conversion may be performed by more capable devices, rather than at the (often low powered) sensors themselves.

As another example, the techniques herein may facilitate grid state determination. Generally, grid state determination may require several kinds of data aggregation, depending on what state elements are needed, and how they are to be determined. For example, raw instant voltage or current samples may be aggregated so that they may be processed into RMS values and analyzed for harmonic content. As another example, aggregate voltage samples taken at various points in a meter network may be used to generate a voltage profile as a function of electrical distance from a feeder. If network meters can measure real and reactive power, data values may be aggregated to determine power flows or DRAC values at various points on a feeder. Current and power flow data values may also be aggregated from points to feeder segments to feeder sections to substations to transmission lines to control areas. Due to the complexity of distribution grids and the cost of sensor installation, implementing proper grid state determination is not a trivial exercise. For each utility grid or sub-grid, a grid sensing strategy must be implemented that results in an efficient sensor network design for that particular grid. This ensures that sufficient data measurement is done to provide the data values to allow grid state determination, while minimizing the total cost of the sensor network (including not only material costs but also installation and service labor). The techniques herein provide data collection techniques that will enable grid state determination.

FIG. 16 illustrates an example simplified procedure for distributed data collection for sensor networks in accordance with one or more embodiments described herein. The procedure 1600 may start at step 1605, and continues to step 1610, where, as described in greater detail above, a plurality of DCAs may generate grid data values such as, for example, raw data values, processed grid data values, or any combination thereof. In step 1615, the DCAs determine whether or not to communicate the grid data values. If the DCAs determine to communicate the grid data values then, as shown in step 1620, a communication network may be used to communicate the grid data values to a plurality of grid data collection service devices configured to receive the grid data values in a time-synchronized manner. In step 1625, the grid data collection service devices determine whether or not to distribute the grid data values. If the grid data collection service devices determine to distribute the grid data values then, as shown in step 1630, they may distribute the grid data values to one or more points of use in substantially real time. The procedure 1600 may then illustratively end in step 1635, though notably with the option to return to any appropriate step described above based on the dynamicity of the forward and reverse clouding as detailed within the disclosure above.

It should be noted that while certain steps within procedure 1600 may be optional as described above, the steps shown in FIG. 16 are merely examples for illustration, and certain other steps may be included or excluded as desired. Further, while a particular order of the steps is shown, this ordering is merely illustrative, and any suitable arrangement of the steps may be utilized without departing from the scope of the embodiments herein.

The techniques described herein, therefore, provide distributed data collection for utility grids (e.g., a sensor fabric in a utility grid). In particular, the techniques herein allow intelligent processing of raw sensed data anywhere in the network, and also allow for more intelligent aggregated computation (e.g., PMUs), which together provide a number of benefits for a sensor network. For example, they dramatically improve network energy utilization, efficiency, scalability, and latency because raw and processed sensor data is available for consumption by an application/process/user at the point of generation.

Notably, a layered services architecture approach addresses complexity management for smart grids at scale, one of the most challenging smart grid design issues. Short term adoption of a layered services architecture allows for efficient transition to new control systems that are hybrids of distributed elements with centralized management. Later, as smart grid implementations approach full scale (in any given dimension), complexity management and the other smart grid architecture issues will benefit from a layered services architecture.

Said differently, now that communications and embedded processing capabilities are becoming available in forms that utility companies can use, a major obstacle in the adoption of distributed intelligence is that utility companies cannot make large changes in their systems in discrete steps. Rather they must go through transitions that can take years to complete. This is due to the nature of their mission and the financial realities utility companies face. In practice, utilities need to transition from centralized to distributed intelligence, and to operate in a complicated hybrid mode for long periods of time, perhaps permanently. This means that the utility service provider needs to be able to roll out distributed intelligence incrementally while maintaining full operations over the entire service area, and be able to modify the distributed architecture appropriately over time and geography. Simply having a distributed architecture implementation is not sufficient; it needs to be easily and continually mutable in terms of what functionality is distributed to which processing locations in the grid and be capable of coexisting with legacy control systems where they remain in place.

The present disclosure thus presents one or more specific features of a distributed intelligence platform that supports variable topology over both time and geography. The platform provides the mechanisms to locate, execute, and re-locate applications and network services onto available computing platforms that may exist in control and operations centers, substations, field network devices, field edge devices, data centers, monitoring centers, customer premises devices, mobile devices, and servers that may be located in power delivery chain entities external to the Transmission and Distribution utility. These techniques use a communication network as a future-proofed platform to incrementally and variably implement distributed intelligence and thereby achieve the associated benefits without being forced to make an untenable massive switchover or to use a single fixed architecture everywhere in its service area.

While there have been shown and described illustrative embodiments that provide for distributed data collection for sensor networks, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, the embodiments have been shown and described herein with relation to electric grids. However, the embodiments in their broader sense are not as limited, and may, in fact, be used with other types of utility grids, such as gas, water, etc., or specific types of “smart” networks where appropriate. For example, in addition to utility grids, recent trends indicate that the future will progress towards sensor-actuator based automation in various sectors including buildings, communities/cities, transportation, energy, etc. Experts predict that in the coming decades there will be a fabric of trillions of sensor-actuator devices embedded into our surroundings. This fabric will bring about integrated automation that will greatly improve the efficiency of the environment/resources as well as the quality of living for the human and living being within the environment. In addition, while certain protocols are shown, other suitable protocols may be used, accordingly.

Illustratively, the techniques herein can span the entire power delivery chain out to and including networks outside of the utility but connected to it. In addition, the techniques herein apply to all of the other adjacencies, such as:

- Rail systems—electric rail power control and monitoring, all rail and car condition monitoring, route control, accident detection/prevention, mobile WiFi, control centers;
- Roadways/highways—hazard detection (fog/ice/flooding/earthquake damage), bridge/overpass structural condition, congestion monitoring, emergency response support, transit control facilities;
- Rivers and canals—locks and dams, flooding detection/extent measurement, dikes and levees, flow/depth, traffic flow;
- Sewage/wastewater/storm drain systems—treatment plants, flow/blockage monitoring, leak/spill detection; Etc.

The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the embodiments herein.

DISTRIBUTED DATA COLLECTION FOR UTILITY GRIDS

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