Adaptive metering in smart grids

Abstract

A utility meter implementation is connected to resources and customer premises. The utility meter includes a sensor, a converter, and a radio. The sensor is configured to detect characteristics of resource usage by the customer premises. The converter is configured to convert the characteristics into raw consumption data describing resource usage. The radio is configured to transmit the raw consumption data output by the converter to a remote processing system. The remote processing system includes one or both of a fog and a cloud. The fog is associated with the geographic region of the utility meter and performs data processing on the raw consumption data and regional raw consumption data received from other endpoints in the geographic region. The cloud performs data processing on the raw consumption data and data received from other endpoints across various geographic regions.

Description

Technical Field

[0001] Some implementations described herein are related to utility meters, and more specifically, to an adaptive meter ring implemented as a sensor to which a utility meter is connected in an adaptive smart grid environment.

Background Art

[0002] A smart grid is an electric grid that utilizes some aspects of intelligence. For example, a smart grid includes a series of utility meters, i.e., smart meters, and each utility meter is configured to provide data necessary for grid intelligence. In a smart grid, advanced metering infrastructure (AMR) may be implemented in utility meters. A utility meter with AMR periodically transmits its consumption data to a central processing system, also referred to as a head-end system. Specifically, after the end of each interval, the utility meter transmits a data packet containing corresponding consumption data describing the resource consumption of that interval to the head-end system. Further, the utility meter periodically transmits a meter snapshot indicating the state of the meter to the head-end system. The head-end system can use the consumption data and the meter snapshot to generate bills or analyze the connectivity and other aspects of the smart grid. Data transmission via the smart grid is performed from the utility meter to the head-end system by radio frequency (RF) mesh, RF point-to-multipoint technology, or power line technology.

Summary of the Invention

[0003] In one implementation, a utility meter connects to the power grid and the customer's facility. The utility meter includes a sensor, an analog-to-digital (A / D) converter, and a radio. The sensor is configured to detect the electrical characteristics of the customer's use of electricity in the power grid. The A / D converter is configured to convert the electrical characteristics into raw consumption data that describes the customer's use of electricity. The radio is configured to transmit the raw consumption data output by the A / D converter to a teleprocessing system. The teleprocessing system includes a fog processing system with one or more fog devices. The fog processing system is associated with the geographical region of the utility meter and is configured to perform data processing on the raw consumption data and the region's raw consumption data received from other endpoints within the geographical region.

[0004] In an alternative implementation, the system includes a utility meter and a teleprocessing system. The utility meter is connected to resources and customer facilities. The utility meter includes sensors, converters, and radios. The sensors are configured to detect characteristics of resource usage by customer facilities. The converters are configured to translate characteristics into raw consumption data describing usage by customer facilities. The radios are configured to transmit the raw consumption data. The teleprocessing system includes a fog processing system and a cloud processing system and is configured to receive raw consumption data from the utility meter. The fog processing system includes one or more fog devices associated with the geographical region of the utility meter and is configured to perform data processing on the raw consumption data and regional raw consumption data received from other endpoints within the geographical region. The cloud processing system includes one or more cloud devices. The cloud processing system is configured to perform centralized data processing on the raw consumption data and various raw consumption data from additional endpoints outside the geographical region as well as from other endpoints within the geographical region.

[0005] In yet another implementation, the method performed by the utility meter involves connecting to the power grid and the customer facility. In this method, the utility meter uses sensors to determine electrical characteristics that indicate the customer facility's electricity usage on the power grid. The utility meter converts the electrical characteristics into raw consumption data that describes the customer facility's electricity usage. The utility meter sends the raw consumption data describing the electricity usage to a fog processing system, which processes the regional consumption data it receives from a set of first endpoints within a geographic area associated with the fog processing system. The utility meter also potentially sends the consumption data describing the electricity usage to a cloud processing system via the fog processing system, which centrally processes various consumption data it receives from a set of first endpoints within a geographic area and a second set of endpoints outside the geographic area.

[0006] These exemplary embodiments and features are mentioned not to limit or define the inventions relating to this disclosure, but to provide examples that aid in understanding the concepts of this disclosure. Other embodiments, advantages, and features of the inventions relating to this disclosure will become apparent after a review of the entire application. [Brief explanation of the drawing]

[0007] These and other features, aspects, and advantages of this disclosure will be better understood by reading the following detailed description with reference to the accompanying drawings. [Figure 1] Figure 1 shows an example of a smart grid with some of the implementations described here. [Figure 2] Figure 2 illustrates another example of a smart grid, based on some of the implementations described here. [Figure 3] Figure 3 illustrates yet another example of a smart grid, based on some of the implementations described here. [Figure 4] Figure 4 is a flowchart illustrating how data is processed via a smart grid using several implementations described here. [Figure 5]Figure 5 shows a diagram of a smart grid utility meter, based on some of the implementations described here. [Modes for carrying out the invention]

[0008] A utility service provider may have numerous utility meters, also known as meters, serving a large number of customers. Each utility meter represents an expense for that utility service provider. Furthermore, the cost of the meters increases as the utility service provider attempts to integrate increased intelligence into the utility meters to enable more intelligent functions within the smart grid. Overall, the cost of meters under the management of a utility service provider can represent a significant expense, such as reducing the cost of individual meters, which can lead to substantial savings across various meters.

[0009] However, recent advancements in computing power and the availability of communication bandwidth have made it possible to shift the location where intelligence is implemented in smart grids. Specifically, intelligence can be shifted away from utility meters and headend systems. In some implementations described here, the cost of meters can be reduced by moving processing that was traditionally done in meters to a fog processing system, also known as a fog system, or a cloud processing system, also known as a cloud system.

[0010] In some implementations, the meter itself can reduce the amount of hardware or software (e.g., firmware) and reduce the processing load compared to conventional meters. For example, the utility meter described here may also be called an adaptive meter and may be an Internet of Things (IoT) sensor configured to sense resource consumption characteristics (e.g., current or voltage) and publish raw consumption data to a remote processing system based on those characteristics. Raw consumption data may be, for example, a digital representation of characteristics obtained as a result of analog-to-digital (A / D) conversion. The remote processing system may include a cloud processing system and one or more fog processing systems. In some implementations, the fog processing system may be associated with a geographical region and provide real-time or near real-time regional processing on the raw consumption data provided by meters in that geographical region. In contrast, the cloud processing system may provide more centralized processing outside of the headend system for meters across various geographical regions. In such implementations, smart grid intelligence is shifted from the meter itself to the fog processing system or cloud processing system. Furthermore, since intelligence can be migrated from the head-end system, the load on the head-end system can be reduced, allowing it to specialize in specific meter-related tasks rather than performing the majority of meter-related tasks.

[0011] Some implementations here provide an adaptive, intelligent IoT metering solution architecture based on low-complexity, low-cost endpoint hardware (e.g., utility meters) combined with advanced communications. Examples of utility meters include simplified, low-cost wirelessly connected IoT smart sensors. Furthermore, some implementations include a suite of fog-based or cloud-based services capable of performing a significant portion of the information processing required to support the operation of the system and services. Fog and cloud-based services can be implemented using a variety of technologies, such as data processing, analytics, machine learning, and other artificial intelligence. By reducing complexity and the cost of utility meters, the overall cost of the smart grid can be reduced, while significant benefits can be gained by placing services in the fog or cloud. Advantages of some implementations include improved modularity of software services, potential optimization of the overall communication bandwidth used, simplified software update processes, and increased flexibility in system configuration, operation, and management of utility meters. Communication bandwidth optimization is particularly useful when the smart grid utilizes irreversible networks such as radio frequency (RF) mesh. Furthermore, as discussed here, some implementations offer improvements over existing smart grids in terms of flexibility, scalability, or adaptability.

[0012] Figure 1 is a diagram of an example of a smart grid system 100, also referred to here as smart grid 100, according to some implementations described herein. In some implementations, smart grid 100 is an electric grid supported by intelligence implemented through various devices. Smart grid 100 may include one or more adaptive meters 110 and a teleprocessing system 130. The adaptive meter 110 is a utility meter, as described here. As shown in Figure 1, the teleprocessing system 130 may include one or both of a cloud processing system 140, also called a cloud, and a fog processing system 150, also called a fog.

[0013] Throughout this disclosure, we refer to the adaptive meter 110 as an electric meter. However, the adaptive meter 110 may be a gas meter, a water meter, or any other type of meter. In implementations where the adaptive meter 110 is not an electric meter, it is understood that the smart grid may be replaced by another type of network connecting the adaptive meter 110, the remote processing system 130, and other devices, as described herein.

[0014] In some implementations, the adaptive meter 110 is configured to determine and transmit data that may include raw data, such as raw consumption data. For this purpose, the adaptive meter 110 may include sensors, converters, a microprocessing unit (MCU) or other processing unit, and a radio. The MCU or other processing unit may include memory as needed to perform the tasks described herein or other tasks of the adaptive meter 110, or the adaptive meter 110 may include a separate memory device. The sensors may detect characteristics of resource consumption by (i.e., occurring at) the customer facility associated with the adaptive meter 110. For example, if the adaptive meter 110 is an electric meter, the sensors of the adaptive meter 110 may detect electrical characteristics such as voltage or current as an indicator of electric consumption. The converter may be an A / D converter that converts the detected characteristics into raw sensing data, which may be numerical or other digital data describing resource consumption. The raw sensing data may be used as consumption data, or a microprocessor may provide further processing, such as converting the raw sensing data into an appropriate format for transmission, in order to generate raw consumption data. Depending on the type of adaptive meter 110 (e.g., electricity, water, gas), the raw consumption data may describe the consumption of the applicable resource being measured (e.g., electrical energy, water, gas) along with the associated timestamp. Using a radio, the adaptive meter 110 can output the raw consumption data as streaming data.

[0015] In addition to or alternative to the above, in some implementations, the MCU of the adaptive meter 110 performs some other minimal processing on the raw sensing data to generate raw consumption data. For example, the MCU may aggregate the raw sensing data based on short intervals to form raw consumption data. For example, each interval may be 30 seconds, 1 minute, 2 minutes, or less than 2 minutes. The MCU may average or aggregate the raw sensing data for each such interval, and the resulting average or other aggregated value can be used as raw consumption data for the corresponding interval. In that case, the adaptive meter 110 can output a stream of values ​​as raw consumption data, where each value represents the aggregated (e.g., average) resource consumption for the corresponding time interval. However, in some implementations, the meter 110 does not perform aggregation on the raw sensing data, so the raw consumption data is not yet aggregated.

[0016] In some implementations, the adaptive meter 110 may output other data in addition to the raw consumption data, such as other raw data. For example, such other raw data may include information detected about the adaptive meter's neighbors in the smart grid 100 (e.g., other adaptive meters 110 or other devices that the adaptive meter 110 can communicate with). More generally, the adaptive meter 110 may detect information relevant to itself and expose that information for processing by the fog processing system 150 or the cloud processing system 140, or both. The adaptive meter 110 may either not process such data or perform limited processing on it before exposing it, so as not to require the same amount of computing resources as a conventional meter.

[0017] To facilitate the above in some implementations, the adaptive meter 110 can be connected to multiple networks. For example, the adaptive meter 110 communicates with neighbors (e.g., other meters 110 or gateway 120) within the smart grid 100 via a resource distribution network, and the adaptive meter 110 communicates with a teleprocessing system 130 via another communication network. In some implementations, the adaptive meter 110 can use a single radio for each such network. However, the adaptive meter 110 can also communicate via the resource distribution network using a first radio and via the other communication network using a second radio. Various implementations are possible and are within the scope of this disclosure.

[0018] Because the meter 110 determines the raw data and performs minimal processing on the raw data other than A / D conversion, some implementations of the adaptive meter 110 may have reduced functionality and computing resources compared to conventional meters. For example, the adaptive meter 110 does not need to include a display or display driver. The adaptive meter 110 does not need to include an optical port or a driver for such a port. As another example, the adaptive meter 110 may have a smaller memory device compared to conventional meters because it does not need to temporarily store raw data during processing and requires less storage to stream the raw data. Rather, in some implementations, the adaptive meter 110 is essentially an IoT sensor with limited computing power. For example, an example of an adaptive meter 110 includes a sensor and a system-on-a-chip (SoC) component that performs A / D conversion or other digital signal processing and transmits the results.

[0019] Some implementations described here reduce (e.g., minimize) the cost of endpoints (e.g., adaptive meters 110) while maintaining the functionality of connected wireless IoT measurement sensors at the endpoints. For example, the adaptive meter 110 examples described here cost half or one-third the cost of conventional utility meters. To this end, some or all data processing, management, decision-making, analysis, or other services may be separated from and therefore migrated from the adaptive meter 110. This reduces the computing resources required by the adaptive meter 110. Additionally, by utilizing fog 150 or cloud 140, the adaptive meter 110 can be associated with value-added services that leverage the data it generates. In many cases, utility service providers own thousands or millions of endpoints. Therefore, some implementations described here can significantly reduce the utility service provider's equipment costs by potentially maintaining or adding available services that utilize data from those endpoints while reducing the computing resources required for each endpoint.

[0020] Within the smart grid 100, the adaptive meter 110 may communicate with one or more neighbors, such as other meters or gateways 120, to enable peer-to-peer monitoring or to provide an ad-hoc communication network within the smart grid 100. In some implementations, gateway 120 routes communication to and from the adaptive meter 110 in the smart grid 100. For example, the adaptive meter 110 can send raw consumption data or other data to a remote processing system 130 by routing raw consumption data through gateway 120, and the adaptive meter 110 can receive commands or other data from the remote processing system 130 through gateway 120. Thus, in some implementations, gateway 120 can facilitate communication of the adaptive meter 110 within the smart grid 100, including communication between the adaptive meter 110 and the remote processing system 130. Therefore, it is understood that references in this disclosure to adaptive meters 110 that transmit or receive data may include, but are not required, routing via gateway 120.

[0021] In some implementations, the adaptive meter 110 exposes its raw consumption data or other data (e.g., other raw data), in other words, makes the raw consumption data or other data available to one or more nodes in the teleprocessing system 130. Exposure of raw consumption data can be performed in one or more different ways. For example, the adaptive meter 110 can send the raw consumption data to the teleprocessing system 130, such as the cloud processing system 140 (e.g., to one or more cloud nodes 145), the fog processing system 150 (e.g., to one or more fog nodes 155), or both. In another example, the adaptive meter 110 indirectly sends the raw consumption data to the cloud processing system 140 by sending it to the fog processing system 150, which in turn sends it to the cloud processing system 140. In yet another example, the adaptive meter 110 transmits its raw consumption data to a storage device, such as a network-attached storage device or other device containing storage, which is accessible to the fog processing system 150 or the cloud processing system 140. Various implementations are possible and are within the scope of this disclosure.

[0022] In some implementations, data is shared across the smart grid 100 via a message bus 160. Generally, a message bus is a messaging infrastructure that allows various devices to use a shared interface. For example, to implement the message bus 160 used in some implementations, nodes in the teleprocessing system 130 can utilize the common data model by operating internally with it or by converting data to that common data model before sending it to another device. Furthermore, in some implementations, either or both of the adaptive meter 110 and the gateway utilize this common data model by operating internally with it or by converting data to that common data model before sending it to another device. For example, the gateway 120 converts data from the adaptive meter 110 into data suitable for transmission over the message bus 160 and then routes that data on behalf of the adaptive meter 110, and if necessary, the gateway 120 converts data received from the teleprocessing system 130 via the message bus 160 into a format that the adaptive meter 110 can understand and then forwards the resulting converted data to the adaptive meter 110. Therefore, the adaptive meter 110 can use the message bus 160 (for example, via gateway 120) to expose data such as raw consumption data, and the fog processing system 150 and cloud processing system 140 can use the message bus 160 to receive that data, pass data between nodes, or send data to the adaptive meter 110. Various other uses of the message bus 160 are possible and are within the scope of this disclosure. Depending on the implementation, communication between devices within the smart grid 100 may use one or more of various communication technologies or standards, such as 4G, 5G, ZigBee, Wireless Fidelity (WiFi), and Wireless Smart Utility Network (Wi-SUN), via the message bus 160 or otherwise.

[0023] As described above, the remote processing system 130 can include a cloud processing system 140 and a fog processing system 150. The cloud processing system 140 can provide centralized processing for various adaptive meters 110. The cloud processing system 140 can include one or more computing devices (i.e., nodes), herein referred to as cloud devices or cloud nodes 145, configured to perform processing for providing cloud-based services. The cloud processing system 140 can be configured to make determinations for the adaptive meters 110 within the smart grid 100. In contrast, the fog processing system 150 can provide processing in a decentralized manner and may be closer to the connectivity or geographically to the adaptive meters 110. As a result, the fog processing system 150 may be suitable for real-time or near real-time decision-making. The fog processing system 150 can include one or more computing devices (i.e., nodes), also referred to as fog devices or fog nodes 155, configured to perform processing for providing fog-based services. The fog processing system 150 can perform processing for, or related to, the adaptive meters 110 located within a geographical area associated with the fog processing system 150 and thus near the fog nodes 155. In some implementations, the smart grid 100 includes multiple fog processing systems 150, each including a respective fog processing system 150 for each geographical area on the smart grid 100. In that case, each fog processing system 150 processes data (e.g., raw consumption data) that is nearby within the geographical area associated with the fog processing system 150, more specifically associated with the adaptive meter 110. The fog processing system 150 can be configured to make determinations for the adaptive meters 110 associated with that fog processing system 150 (i.e., within the geographical area associated with the fog processing system 150).

[0024] Some implementations described here reduce the cost of the meter 110 by removing processing functions from the network edge (i.e., from the meter 110) and placing such processing functions in a remote processing system 130 located outside the meter 110 itself. Some implementations use a fog processing system 150, which is located close to the meter 110 where the fog processing system 150 performs processing, allowing the smart grid 100 to perform processing in real time or near real time. Furthermore, using multiple fog processing systems 150 distributes specific processing in a way that achieves effective load balancing. Additionally, for tasks with fewer time constraints, such as cost reductions through integration or data aggregation across meters 110 in various geographical regions, or for tasks where centralized processing is desired for any reason, a cloud processing system 140 enables centralized processing.

[0025] The remote processing system 130 can perform various tasks based on data provided by the associated adaptive meter 110. Each of these tasks can take raw data, such as raw consumption data, as input, or data obtained as a result of other processing performed within the remote processing system 130 (for example, on raw data). Various techniques may be used to process data in the remote processing system 130. For example, such techniques may include machine learning techniques, and the techniques used may change over time. Examples of such tasks performed by the remote processing system 130 include load profiling, time-of-use (TOU) analysis, load balancing, grid state monitoring, grid topology and mapping, and grid analysis. In the example implementation, the fog processing system 150 performs processing tasks related to load profiling and grid state monitoring (because these tasks are time-sensitive). The cloud processing system 140 performs processing tasks related to TOU analysis, load balancing, grid topology and mapping, and grid analysis (because these tasks are not time-sensitive).

[0026] As executed by the remote processing system 130, load profiling may involve determining load profile data that describes the electrical profile of the load (i.e., the customer facility) being monitored by the adaptive meter 110. For example, the remote processing system 130 can perform load profiling on raw consumption data by aggregating (e.g., averaging) the raw consumption data at intervals to form load profile data. Thus, in the load profile data, the values represent the electrical energy consumption that occurred during the corresponding time intervals. If the raw consumption data has already been aggregated at short time intervals by the adaptive meter 110, the load profile data may include values that are further aggregated based on longer time intervals. For example, the time intervals represented in the load profile data can be 5 minutes, 15 minutes, 30 minutes, 1 day, 30 days, or the length of a month.

[0027] The load profile data can provide detailed insights into how energy is consumed and how power flows through the smart grid 100. The load profile data can deepen the understanding of the smart grid 100 and enhance its efficiency and robustness, such as enabling the identification of bottlenecks and the estimation of the amount of renewable energy generation that can be safely accommodated. In some implementations, the fog processing system 150 or the cloud processing system 140, or both, can utilize the load profile data, such as by applying one or more machine learning models, to perform tasks such as identifying bottlenecks, estimating the amount of renewable energy that can be generated, determining price models, fixing problems, or other tasks. A better understanding of the smart grid 100 provided by the load profile data can be translated into more targeted and effective investments in grid upgrades.

[0028] In some implementations, the remote processing system 130 can perform load profiling at a meter level or at a higher level across multiple meters 110. For example, the fog processing system 150 can perform load profiling on a meter-by-meter basis, or it can aggregate raw consumption data across multiple meters 110 within a geographic area associated with the fog processing system 150 and perform load profiling on multiple meters 110 as a set. Similarly, the cloud processing system 140 can perform load profiling across multiple meters 110 across geographic areas of the smart grid 100. Specifically, for example, load profile data can be aggregated from all meters 110 connected to a transformer to better understand the load of that transformer, or load profile data can be aggregated from all meters 110 connected to a transformer connected to a substation to better understand the load of that substation. In some implementations, such load profile data determination across multiple meters 110 can be performed more efficiently by the remote processing system 130 rather than on individual meters as in the past. Furthermore, it may be more economical to allocate the computing power required to generate such load profile data to a remote processing system 130 rather than to individual meters or clusters of individual meters.

[0029] As performed by the remote processing system 130, TOU analysis may include determining when electrical energy consumption occurs. For example, as part of TOU analysis, the remote processing system 130 can calculate macro-metrics such as total annual electricity consumption, peak consumption, average peak consumption, and the distribution of peak times for electricity consumption. In some implementations, TOU analysis accepts raw consumption data or load profile data as input. Either or both of these data can be provided at the meter 110 or elsewhere (e.g., the fog processing system 150) at intervals synchronized with a clock determined by the adaptive meter 110 or load profiling. In the former case, one or more nodes performing TOU analysis can receive raw consumption data directly or indirectly from the adaptive meter 110. In the latter case, one or more nodes performing TOU analysis can receive load profile data from one or more fog nodes 155 or cloud nodes 145 that have performed load profiling on the raw consumption data.

[0030] The output of TOU analysis, also known as TOU data, can be used in various ways within the remote processing system 130, outside the remote processing system 130, or both. For example, TOU data may be used by headend systems or other systems for billing purposes. In one example, if TOU data indicates that peak demand is in the morning and evening, TOU tariffs can be formed across those time slots, and different rates can be set to curb consumption during those time slots. As a result of TOU tariffs, power operators can optimize generation and consumption through peak reduction, thereby lowering costs for consumers.

[0031] Load balancing, as performed by the remote processing system 130, determines which appliances (i.e., which particular load) are being used at the customer facility. For example, the fog processing system 150 or the cloud processing system 140 can apply machine learning or other techniques to identify specific consumption signatures in the raw consumption data, each such consumption signature corresponding to a specific appliance being used by (i.e., at) the customer facility.

[0032] The fog processing system 150, the cloud processing system 140, or both may perform grid state monitoring, grid topology and mapping, and other grid analysis. More specifically, in some implementations, the fog processing system 150 may perform grid state monitoring and grid topology and mapping, which may be time-critical, while the cloud processing system 140 may perform other grid analysis that is not time-critical. The fog processing system 150, or other embodiments of the remote processing system 130, may monitor the state of the grid and determine the grid topology and mapping (i.e., determine the connectivity map of devices in the smart grid 100) based on raw data provided by the adaptive meter 110 or other data regarding connectivity with other devices in the smart grid 100. This information may be used to provide efficient communication within the smart grid 100 and to remediate connectivity issues as needed. The cloud processing system, or other embodiments of the remote processing system 130, may perform other grid analysis that is less time-critical, such as those that are unlikely to require remediation.

[0033] Through the performance of grid analysis, the fog processing system 150 or cloud processing system 140 can provide one or more of a variety of remediation techniques. For example, the fog processing system 150 can receive data from multiple meters 110 associated with a common transformer, for example, via grid topology or mapping, and the fog processing system 150 can therefore monitor the load on that transformer to ensure that the load is within the transformer's specifications. If the load is not within specifications, the fog processing system 150 can issue an alarm (for example, to a utility service provider) to manage potential aging or explosion of the transformer. In some implementations, the fog processing system 150 implements intelligent management of the transformer so that it can isolate one or more homes from the transformer if the transformer's capacity is compromised. As another example, grid analysis can be used to detect power theft or to detect devices that could be used for malicious use or damage, such as solar power inverters (PV) or other consumer-owned equipment that could be used to make it appear as if power is flowing towards the grid. As yet another example, charging electric vehicles can place a significant burden on the once-widely used power grid. However, using grid analysis, the fog processing system 150 or the cloud processing system 140 can identify the source of this strain and issue messages to consumers requesting reduced billing during peak periods. A variety of other practical applications are possible and are within the scope of this disclosure.

[0034] In addition to, or as an alternative to, the remote processing system 130 can perform other processing tasks, such as safety and power quality, volt / VAR (voltage-ampere-response) control, sub-second polling related to quality of service (QoS), distributed energy resource (DER) management, and phase identification. For example, in a given implementation, the fog processing system 150 performs processing tasks related to safety and power quality, volt / VAR control, and sub-second polling related to QoS, as these are time-sensitive tasks. For example, volt / VAR analysis may reveal PV inverters that are out of phase, which can cause problems such as damage to connected equipment, load imbalance and instability in the power grid, and even power grid outages. As a result of discovering such PV inverters, the fog processing system 150, or additionally or alternatively, one or more nodes in the cloud processing system 140, can issue a message requesting remediation to the affected consumer, including a request to shut down the PV inverter. In some implementations, the cloud processing system 140 performs processing tasks related to DER management and phase identification, which are less time-sensitive. Other processing tasks may be performed additionally or alternatively by the remote processing system 130.

[0035] Through the migration of work from the adaptive meter 110 or headend system to the remote processing system 130, some implementations described herein offer various advantages over existing smart grids 100. These advantages are derived in terms of flexibility, scalability, adaptability, or revenue. In terms of flexibility, because services are decoupled from endpoints, endpoints can include a variety of hardware, software, or firmware, and service implementations are not tied to a specific endpoint vendor. In some implementations, smart grid 100 endpoints do not need to be manufactured by the same entity and may have different hardware, software, or firmware. Furthermore, services delivered in fog 150 or cloud 140 do not need to depend on the specific endpoint used, and the hardware, software, or firmware used to provide such services in fog 150 or cloud 140 may differ over time or between services without affecting the operation of the endpoints. This allows for scalability of such services by adding new nodes or modifying existing nodes, for example, without affecting the endpoints themselves. As will be discussed later, endpoints are highly adaptable in that services associated with an endpoint can be changed without affecting, or significantly affecting, the endpoint itself, as it is not necessary to modify the endpoint to add, remove, or change services. Furthermore, or alternatively, Smart Grid 100 supports subscription services. For example, service providers offering services on Fog 150 or Cloud 140 can offer those services through a subscription model, allowing them to generate recurring revenue similar to that used for Software as a Service (SaaS) services. There are many other benefits, which are within the scope of this disclosure.

[0036] Figure 2 is a diagram of another example of a smart grid 100 following some of the implementations described here. As shown in Figure 2, the smart grid 100 may include a headend system 210 in addition to including one or more adaptive meters 110 and a teleprocessing system 130. The headend system 210 can perform centralized data processing. Data processed by the headend system 210 may include, for example, raw consumption data, other raw data, or data obtained as a result of processing raw consumption data by the teleprocessing system 130 (e.g., load profile data, TOU data). In some implementations, the headend system 210 is associated with and managed by a utility service provider associated with the smart grid 100.

[0037] In some implementations, the headend system 210 performs various intensive tasks, such as billing tasks, for some or all of the adaptive meters 110 within the smart grid 100. To enable the headend system 210 to perform such tasks, it can be configured to receive data from the adaptive meters 110 or the teleprocessing system 130. In one example, the adaptive meters 110 send raw consumption data to the headend system 210, which can then process the raw consumption data to perform billing and other intensive tasks. In another example, one or more fog nodes 155 or cloud nodes 145 send data to the headend system 210, which may be based on raw consumption data, and the headend system 210 further processes that data to perform billing and other intensive tasks.

[0038] As shown in Figure 2, the headend system 210 can utilize the same message bus 160 used in other embodiments of the smart grid system 100. Thus, the headend system 210 is configured to communicate with the fog processing system 150, the cloud processing system 140, and the adaptive meter 110 as needed. The headend system 210 can be configured to communicate with the adaptive meter 110 or the remote processing system 130 via the message bus 160 using one or more different communication technologies. Such communication technologies include, for example, 4G, 5G, ZigBee, WiFi, or Wi-SUN.

[0039] In typical existing smart grids, there is no remote processing system 130 for processing data related to utility meters; utility meters are managed entirely centrally by a headend system. The headend system not only performs billing tasks but is also associated with update servers that push firmware updates to utility meters when their functionality changes, or push firmware updates to utility meters. As a result of this configuration, some or all services related to utility meters must communicate with the utility meters via the headend system. For example, the headend system manages such services, or the headend system 210 facilitates communication between the utility meters and the relevant servers that manage such services.

[0040] In contrast, according to some implementations, the headend system 210 is not required to be responsible for all services related to the adaptive meter 110. Thus, a utility service provider associated with the headend system 210 can focus on and specialize in specific services while leaving other services to other service providers (i.e., vendors). For example, the headend system 210 can manage billing tasks, while other service providers can operate the fog node 155 or cloud node 145 to provide a variety of other services.

[0041] Furthermore, while conventional headend systems are responsible for pushing firmware to individual meters 110 when their services require modification, some implementations described here enable updates by instead modifying a set of nodes within the teleprocessing system 130. For example, to add a service to meter 110, one or more fog nodes 155 or cloud nodes 145 responsible for running that service are instructed (e.g., by appropriate servers associated with those nodes and operated by the service provider) to run that service based on the raw data provided by meter 110, or meter 110 is instructed to provide that raw data to one or more such fog nodes 155 or cloud nodes 145. To change the service provided to meter 110, one or more fog nodes 155 or cloud nodes 145 responsible for running that service can be updated with the applicable changes. Similarly, to remove a service from meter 110, one or more fog nodes 155 or cloud nodes 145 responsible for running that service may be instructed (for example, by an appropriate server associated with those nodes and operated by a service provider) to no longer run that service based on the raw data provided by meter 110, or meter 110 may be instructed to stop providing its raw data to such one or more fog nodes 155 or cloud nodes 145. In this way, services associated with the adaptive meter 110 can be changed easily and relatively inexpensively without firmware updates to the adaptive meter 110 itself. Similarly, testing of new services is also simplified in some implementations, as testing may require changes to the fog nodes 155 or cloud nodes 145 rather than changes to the adaptive meter 110.

[0042] Changes to services provided via the remote processing system 130 typically do not require firmware updates on the meter 110, although there may be cases where firmware updates on the meter 110 are necessary. For example, firmware may be required to fix bugs or update the drivers for the adaptive meter 110's radio. If the adaptive meter 110 itself requires a firmware update, the headend system 210 or another device can push the firmware update to the meter 110 as needed. However, because the functionality of the meter 110 is reduced, its firmware update is likely to be smaller in scale compared to firmware updates for conventional meters. Therefore, the computing resources and error margin required for the firmware update can be reduced as needed.

[0043] Figure 2, like the other figures here, shows a non-exclusive example of the smart grid 100, and it will be understood that the headend system 210 does not necessarily have to be included in the smart grid 100. For example, in some implementations, tasks that were traditionally performed by the headend system 210 may be performed by nodes of the remote processing system 130, such as one or more fog nodes 155 or cloud nodes 145. For example, the cloud processing system 140 can perform billing operations such as determining billing data, generating invoices, and issuing invoices, instead of the headend system 210. Therefore, some implementations described here shift tasks that were traditionally performed by the headend system 210 to the cloud processing system 140 or fog processing system 150.

[0044] Figure 3 is a diagram of yet another example of the smart grid 100 following some of the implementations described here. As shown in Figure 3, multiple fog processing systems 150 can be included in the smart grid 100. Each fog processing system 150 can be associated with a set of meters 110, which are geographically or connectivity-wise close to the fog processing system 150. This allows the fog processing system 150 to process raw consumption data received from these meters 110 in real time or near real time. In this example, the cloud processing system 140 is configured to process raw consumption data from some or all of the adaptive meters 110, regardless of which fog processing system 150 is associated with which of the adaptive meters 110. Although a headend system 210 is not shown in Figure 3, the smart grid system 100 can include one or more headend systems 210, regardless of the number of fog processing systems 150 used.

[0045] The example in Figure 3 shows two fog processing systems 150, but the smart grid 100 can include more or fewer fog processing systems 150. In this example, both the first adaptive meter 110aa and the second adaptive meter 110ab are located in geographical region A and consist of one or more geographical regions that are closely communicatively coupled to the first fog processing system 150a. Thus, both of these adaptive meters 110 are assigned to the first fog processing system 150a. Thus, the first fog processing system 150a processes raw consumption data from the first adaptive meter 110aa and the second adaptive meter 110ab. The first fog processing system 150a can perform such processing in near real time because it is in close proximity and receives raw consumption data from these adaptive meters 110 in near real time.

[0046] In this example, both the third adaptive meter 110ba and the fourth adaptive meter 110bb are located in geographical region B, which consists of one or more geographical regions that are closely communicated with the second fog processing system 150b. Therefore, both of these adaptive meters 110 are assigned to the second fog processing system 150b. Thus, the second fog processing system 150b processes the raw consumption data from the third adaptive meter 110ba and the fourth adaptive meter 110bb. The second fog processing system 150b can perform such processing in near real-time by proximity and by receiving the raw consumption data from these adaptive meters 110 in near real-time.

[0047] Because geographical regions do not need to have strict boundaries, the geographical regions of different fog processing systems 150 may overlap. Rather, in this disclosure, geographical regions are defined by the associated fog processing system 150. A geographical region is a region or set of regions that is communicatively close enough for an adaptive meter 110 to a particular fog processing system 150 so that the fog processing system 150 can process raw consumption data from such adaptive meter 110 in real time or near real time. More specifically, for example, an adaptive meter 110 in a geographical region associated with a fog processing system 150 may be communicatively closer to that fog processing system 150 than the adaptive meter 110 is connected to the cloud processing system 140. In other words, communication from the adaptive meter 110 reaches the fog processing system 150 associated with that adaptive meter 110 faster than it reaches the cloud processing system 140.

[0048] The adaptive meter 110 can be assigned to a fog processing system 150 using one or more different methods. In some implementations, the adaptive meter 110 is assigned to the fog processing system 150 that is closest in terms of connectivity, and it may not necessarily have to be the closest in terms of geographical location. In one example, the adaptive meter 110 unilaterally selects its fog processing system 150 (i.e., the fog processing system 150 to which the adaptive meter 110 sends raw consumption data), and the fog processing system 150 responds to the adaptive meter's registration request based on this. For example, the adaptive meter 110 broadcasts a registration request (e.g., via the message bus 160), and the fog processing system 150 that receives the broadcast responds, confirming that such a fog processing system 150 is nearby and available. In some implementations, if multiple fog processing systems 150 respond, the adaptive meter 110 may select to use the fog processing system 150 that receives the response first, since the order in which such responses are received may indicate proximity of communication.

[0049] In some implementations, a manual or automated administrator, such as one running on the headend system 210, assigns the adaptive meter 110 to a fog processing system 150. In one example, the administrator assigns the adaptive meter 110 to a nearby fog processing system 150 when the adaptive meter 110 is installed. In this case, the adaptive meter 110 may be programmed with information (e.g., an Internet Protocol (IP) address) to reach the fog processing system 150 to which it is assigned. In another example, the administrator assigns the adaptive meter 110 to a nearby fog processing system 150 when the adaptive meter 110 connects to the smart grid 100 (e.g., when the adaptive meter 110 comes online and registers with the headend system 210 or cloud processing system 140). In this case, the gateway 120 or other devices within the smart grid 100 can provide the adaptive meter 110 with the information necessary to communicate with the assigned fog processing system 150. Furthermore, or alternatively, the administrator assigns the adaptive meters 110 to the fog processing systems 150 in a manner that achieves load balancing across the various fog processing systems 150. For example, the administrator can enforce a maximum number of adaptive meters 110 per fog processing system 150, or assign the adaptive meters 110 to maintain approximately the same number of adaptive meters 110 per fog processing system 150.

[0050] In additional or alternative implementations, the assignment of where intelligence is processed (i.e., the assignment of the adaptive meter 110 to the appropriate fog processing system 150 that processes data from the adaptive meter 110) is determined based on the communication technology between the adaptive meter 110 and the fog processing system 150. For example, if the communication technology used is an RF mesh, the rules applicable to the formation of a stable RF mesh are applied to match the adaptive meter 110 and the fog processing system 150. For example, some aspects of the smart grid 100, such as the gateway 120 for the adaptive meter 110, assign the adaptive meter 110 to the fog processing system 150, where high received signal strength indicator (RSSI) signal strength is achieved between the adaptive meter 110 and its associated fog processing system 150, combined with low latency between the adaptive meter 110 and the fog processing system 150. However, various implementations are possible for assigning the adaptive meter 110 to a suitable fog processing system 150, and these are within the scope of this disclosure.

[0051] As shown in Figure 3, various configurations of the gateway 120 and adaptive meters 110 are possible. For example, adaptive meters 110 assigned to a common fog processing system 150 do not need to have a common gateway, as shown in the third adaptive meter 110ba and the fourth adaptive meter 110bb. However, adaptive meters 110 may have a common gateway 120, as shown in the first adaptive meter 110aa and the second adaptive meter 110ab. The gateway 120 may not only function as a router for communications but also as a collector. More specifically, the gateway 120 can collect raw consumption data from various connected adaptive meters 110 and provide that raw consumption data to other devices such as the headend system 210, the fog processing system 150, or the cloud processing system 140. Various configurations of the gateway 120 are possible and are within the scope of this disclosure.

[0052] Figure 4 is a flowchart of method 400 for processing data via smart grid 100, following several implementations described herein. Method 400 is provided for illustrative purposes only and does not limit the various possible implementations or functions of smart grid 100.

[0053] As shown in Figure 4, in block 405, the adaptive meter 110 senses electrical characteristics related to energy consumption. For example, the adaptive meter 110 can utilize its sensors to sense voltage or current between the power grid and the load (i.e., customer facility). Depending on the implementation, this sensing may occur continuously.

[0054] In this example of Method 400, the adaptive meter 110 is an electric meter and therefore senses electrical characteristics. However, the adaptive meter 110 could instead be another type of meter, in which case the sensed characteristics would be related to the resource whose consumption is being measured by the adaptive meter 110. For example, if the adaptive meter 110 is a water meter, the sensed characteristics would indicate water consumption, and if the adaptive meter 110 is a gas meter, the sensed characteristics would indicate gas consumption. Various implementations are possible and are within the scope of this disclosure.

[0055] In block 410, the adaptive meter 110 converts electrical characteristics into raw consumption data. For example, as mentioned above, the adaptive meter 110 may include a converter configured to convert analogs, such as voltage or current detection, into digitals, such as numerical representations of voltage or current. Thus, to convert electrical characteristics into raw consumption data, at least the electrical characteristics must be converted into digital data. Raw consumption data may be a digital representation of the sensed electrical characteristics. For example, consumption data is a stream of numbers, where each number represents a measurement of the electrical characteristic at a corresponding time. For example, the numbers may be values ​​representing electrical characteristics (e.g., voltage or current) and may correspond to time intervals of sub-seconds or seconds.

[0056] In block 415, the adaptive meter 110 exposes raw consumption data. For example, to expose consumption data, the adaptive meter 110 can send raw consumption data to one or more fog nodes 155 or cloud nodes 145 in the teleprocessing system 130. Additionally, or alternatively, if a headend system 210 is included in the smart grid 100, the adaptive meter 110 can send raw consumption data to the headend system 210. Additionally, or alternatively, the adaptive meter 110 can also expose other raw data, such as data describing connectivity with other meters, gateways 120, or other devices in the smart grid 100.

[0057] The adaptive meter 110 can make the raw consumption data available to the remote processing system 130 or the headend system 210 in various ways. For example, in some implementations, the adaptive meter 110 sends the raw consumption data to the fog processing system 150, which performs further processing on the raw consumption data and then sends the resulting processed consumption data, along with the raw consumption data as needed, to the cloud processing system 140 or the headend system 210, or both. In some other implementations, the adaptive meter 110 sends the raw consumption data to both the fog processing system 150 and the cloud processing system 140, which both process the raw consumption data and exchange the processed consumption data with each other as needed. In such implementations, the fog processing system 150 or the cloud processing system 140, or both, may send the processed consumption data to the headend system 210 as needed. Various implementations are possible and are within the scope of this disclosure.

[0058] In some implementations, blocks 405, 410, and 415 occur in parallel because they are in progress. That is, during the normal operation of the adaptive meter 110, the sensor continuously senses electrical characteristics, the converter continuously converts the electrical characteristics into digital data to generate raw consumption data, and the radio continuously outputs that consumption data as streaming data.

[0059] In block 420, the fog processing system 150 accesses raw consumption data from the adaptive meters 110 and performs distributed processing on that raw consumption data, as well as raw consumption data from other adaptive meters 110 associated with the fog processing system 150. As previously mentioned, the fog processing system 150 can provide distributed processing close to the edge (i.e., close to the meters 110 themselves) and generate insights in real time or near real time. For example, in some implementations, the fog processing system 150 may perform processing based on the raw consumption data to perform one or more of the following tasks: load profiling, grid state monitoring, safety and power quality analysis, volt / VAR control, or sub-second polling for QoS. Furthermore, in some implementations, the fog processing system 150 receives raw consumption data associated with a first set of adaptive meters 110, such as adaptive meters 110 within a geographical area. Therefore, the fog processing system 150 can be configured to perform processing to determine aggregated data across a first set of meters 110, or to determine insights regarding the first set of meters 110.

[0060] In block 425, the cloud processing system 140 accesses raw consumption data from the adaptive meters 110 and performs intensive processing on that raw consumption data and raw consumption data from other adaptive meters 110 associated with the cloud processing system 140 (e.g., some or all other adaptive meters 110 in the smart grid 100). As described above, the cloud processing system 140 can provide intensive processing for time-insensitive tasks so that the computing resources required for intensive processing do not need to be replicated across multiple meters 110 or multiple fog processing systems 150. For example, in some implementations, the cloud processing system 140 may perform processing based on the raw consumption data to perform one or more tasks such as TOU analysis, load balancing, grid topology and mapping, grid analysis, DER management, or phase identification. Furthermore, in some implementations, the cloud processing system 140 receives raw consumption data related to the entire set of adaptive meters 110, such as all adaptive meters 110 within the smart grid 100, or all adaptive meters 110 associated with services running on the cloud processing system 140. Therefore, the cloud processing system 140 can be configured to determine aggregated data across the entire set of meters 110, or to perform processing to determine insights about the entire set of meters 110.

[0061] In some implementations, blocks 420 and 425 occur in parallel with each other, as well as in parallel with blocks 405, 410, and 415, because they are in progress. For example, the fog processing system 150 may process raw consumption data from the adaptive meter 110, other raw data from the adaptive meter 110, or other data (e.g., data resulting from processing the raw consumption data), while the cloud processing system 140 is also processing raw consumption data from the adaptive meter 110, other raw data from the adaptive meter 110, or other data.

[0062] Figure 5 shows an adaptive meter 110 in a smart grid, according to some implementations described herein. For example, but not limited to, the adaptive meter 110 may be an electricity meter, a water meter, a gas meter, or another type of meter that measures the consumption of resources 510 at a facility 520. As previously mentioned, the adaptive meter 110 may include a sensor 530, a converter 540, an MCU 550 or other processing unit, and a radio 560. The system bus 570 can connect the converter 540, the MCU 550, and the radio 560 to each other so that these components can communicate with each other as needed for the operation of the adaptive meter 110.

[0063] In some implementations, the sensor 530 detects a signal (i.e., electrical characteristics) indicating the use of resource 510, and the converter 540 converts that signal into digital data and inputs it to the MCU 550. The MCU 550 can instruct the radio 560 to transmit raw consumption data, which can be based on the digital data. In some implementations, the raw consumption data is the same as or minimally processed as the digital data received by the MCU 550. For example, the MCU 550 may aggregate the digital data into short intervals, or (if required for, for example, the message bus 160) the MCU 550 may simply convert the digital data into an appropriate format and transmit it. The radio 560 can be configured to utilize 4G, 5G, ZigBee, WiFi, Wi-SUN, or other communication technologies, and can transmit the raw consumption data using one or more such communication technologies.

[0064] In some implementations, the adaptive meter 110 may also include memory 580, which is volatile memory (e.g., random access memory (RAM)), non-volatile memory (e.g., flash storage), or both. As shown in Figure 5, memory 580 may be integrated with the MCU 550. The MCU 550 can store digital or raw consumption data in memory 580 as needed to generate raw consumption data and enable transmission of the raw consumption data by the radio 560. There may be regulatory or customer requirements for the adaptive meter 110 to store raw consumption data locally for a certain period, in which case memory 580 can be used to store the raw consumption data for at least the required time. In additional or alternative implementations, memory 580 may be separated from the MCU 550 rather than integrated with the MCU as shown in Figure 5.

[0065] More generally, the adaptive meter 110 may include components configured for basic photometric and communication processes, but it may lack certain other components that are typically incorporated into a conventional utility meter. For example, the adaptive meter 110 may lack one or more of the following: an integrated display and its driver, a local optical port, or a local demand reset switch. In addition, or instead, compared to a conventional utility meter, the adaptive meter 110 may include a reduced amount of storage (e.g., a smaller memory 580), a slower MCU 550, or other computing resources that are less powerful or efficient in some way. Various implementations are possible and are within the scope of this disclosure.

[0066] Numerous specific details are provided herein in order to fully understand the present invention. However, those skilled in the art will understand that the present invention can be carried out without these specific details. In other instances, methods, apparatus, or systems that would be known to a person of the ordinary skill are not described in detail so as not to obscure the present invention.

[0067] The functions described herein are not limited to any particular hardware architecture or configuration. A computing device may include any suitable arrangement of components that provide a result conditioned on one or more inputs. Suitable computing devices include multipurpose microprocessor-based computer systems that access stored software (i.e., computer-readable instructions stored in the computer system's memory) to program or configure the computing system, ranging from general-purpose computing devices to specialized computing devices that implement one or more aspects of the present invention. Any suitable programming, scripting, or other type of language or combination of languages ​​may be used to implement the teachings contained herein in the software used to program or configure the computing device.

[0068] Embodiments of the methods disclosed herein can be implemented in the operation of such computing devices. The order of the blocks shown in the above example can be changed in various ways. For example, the blocks can be rearranged, combined, or divided into subblocks. Certain blocks or processes can be executed in parallel.

[0069] The use of “adapted” or “configured” here means an open and inclusive language that does not exclude devices adapted or configured to perform additional tasks or steps. Furthermore, the use of “based on” means that it is open and inclusive, and if a process, step, calculation, or other action “based” on one or more cited conditions or values, it may actually be based on additional conditions or values ​​beyond those cited. The headings, lists, and numbering provided herein are for illustrative purposes only and are not limiting.

[0070] While the present invention is described in detail with respect to its particular aspects, those skilled in the art will recognize that modifications, variations, and equivalents to such aspects can be readily created once the foregoing understanding is obtained. Therefore, it should be understood that this disclosure is presented for illustrative purposes only, not limitation, and does not preclude the inclusion of modifications, variations, and / or additions to the invention that would be readily understood by those skilled in the art.

Claims

1. In utility meters connected to the power grid and customer facilities, A sensor configured to detect the electrical characteristics of electricity usage in the power grid by a customer facility, An analog-to-digital (A / D) converter configured to convert electrical characteristics into raw consumption data describing electricity usage by the customer facility, and A wireless device configured to transmit raw consumption data output by an A / D converter to a remote processing system and a headend system. Includes, The remote processing system is A fog processing system comprising one or more fog devices associated with a geographical region of a utility meter, configured to perform data processing on raw consumption data and regional raw consumption data received from a first set of endpoints within the geographical region, wherein the first set of endpoints includes a utility meter, and A cloud processing system comprising one or more cloud devices, configured to perform centralized data processing on raw consumption data and various raw consumption data from a first set of endpoints within a geographical area and from other endpoints outside the geographical area. Includes, The headend system is configured to perform billing tasks. Utility meter.

2. The wireless device transmits raw consumption data as a data stream consisting of a series of numerical values, where each numerical value represents the respective value of the electrical characteristic at the corresponding time. The utility meter according to claim 1.

3. The aforementioned wireless device transmits raw consumption data as a real-time data stream. The utility meter according to claim 2.

4. The utility meter is associated with one or more subscription services provided by the remote processing system. The utility meter according to claim 1.

5. In the system, A utility meter connected to resources and customer facilities, A sensor configured to detect the characteristics of resource usage by customer facilities. A converter configured to transform characteristics into raw consumption data describing usage by customer facilities, and A radio configured to transmit raw consumption data. Includes a utility meter, A remote processing system configured to receive raw consumption data from the aforementioned utility meter, wherein the remote processing system is A fog processing system including one or more fog devices associated with the geographical region of the utility meter, configured to perform data processing on raw consumption data and regional raw consumption data received from a first set of endpoints within the geographical region, wherein the first set of endpoints includes the utility meter and the fog processing system. A cloud processing system comprising one or more cloud devices, configured to perform centralized data processing on raw consumption data and various raw consumption data from a first set of endpoints within a geographical area and from other endpoints outside the geographical area, and A headend system configured to perform billing tasks, Remote processing systems and including, system.

6. The converter is an analog-to-digital (A / D) converter. The raw consumption data transmitted by the aforementioned wireless device is a real-time data stream of numerical values ​​output by the A / D converter. The system according to claim 5.

7. The fog processing system performs real-time processing on raw consumption data. The system according to claim 6.

8. moreover, Including a second utility meter, The remote processing system further includes a second fog processing system associated with a second geographical region of the second utility meter, The second fog processing system is configured to perform data processing on second raw consumption data received from the second utility meter and on second regional consumption data received from a second set of endpoints within a second geographical area, the second set of endpoints including the second utility meter. The system according to claim 5.

9. The fog processing system is configured to make decisions regarding a first set of endpoints within a geographical area. A second fog processing system is configured to make decisions regarding a second set of endpoints within a second geographical area. The system according to claim 8.

10. The fog processing system is configured to calculate the interval of load profile data based on raw consumption data. The system according to claim 5.

11. The fog processing system is configured to calculate usage time data based on the interval of load profile data. The system according to claim 10.

12. The fog processing system is configured to perform load balancing, determining which equipment will be used by the customer facility based on raw consumption data. The system according to claim 10.

13. The utility meter, the fog processing system, and the cloud processing system are configured to communicate with each other via a common message bus. The system according to claim 5.

14. The server updates the features associated with the utility meter by updating the services within the remote processing system. The system according to claim 5.

15. The server adds features to the utility meter by configuring a service within the remote processing system to process consumption data from the utility meter. The system according to claim 5.

16. The server removes features from the utility meter by configuring a service in the remote processing system to stop processing consumption data from the utility meter. The system according to claim 15.

17. The utility meter connects to the power grid, The utility meter connects to the customer's facility, By using a utility meter, a sensor is used to determine the electrical characteristics that indicate the electricity usage of the power grid by the customer's facility. The utility meter converts electrical characteristics into raw consumption data that describes the electricity usage by the customer's facility, For data processing of regional consumption data received from a first set of endpoints within a geographic area related to a fog processing system, a utility meter transmits raw consumption data describing electricity usage to the fog processing system, the fog processing system includes one or more fog devices, and transmits to the fog processing system. For centralized data processing of various consumption data received from a first set of endpoints within a geographical area and a second set of endpoints outside the geographical area, a utility meter transmits consumption data describing electricity usage to a cloud processing system, the cloud processing system includes one or more cloud devices, and The utility meter transmits raw consumption data describing electricity usage to a headend system that performs billing tasks, separate from the cloud processing system. Methods that include...

18. Sending raw consumption data to the fog processing system means sending a real-time data stream describing electricity usage to the fog processing system at intervals of less than one second. The method according to claim 17, including the method described in claim 17.

19. moreover, We receive requests to add new subscription features to the utility meter, Configuring a utility meter to send raw consumption data to a node of the fog processing system upon request, wherein the node of the fog processing system configures a utility meter associated with a new subscription feature. The method according to claim 17, including the method described in claim 17.

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