Power supply network failure

Abstract

A method for determining faults on a power supply network is disclosed, including determining which consumers are supplied by a particular substation. If a consumer is selected and the consumer's substation data is incomplete, a set of supply quality information is obtained for the consumer's facilities. This process is repeated for a number of other consumers, and the data for each of them is compared with the data for the first consumer. Once the subnetwork is correctly characterized, further supply quality information is analyzed to provide warnings about network problems, provide short-term and medium-term fixes for such problems, and also provide long-term solutions, including network redesign.

Description

Background Art

[0001] The power distribution network distributes energy from power generation companies such as power plants and wind farms to industrial and domestic users. To distribute electricity efficiently, electricity is transmitted at a high voltage, for example, 132 kV or higher, to reduce the resistive losses in the conductors. Electricity is usually distributed as alternating current (AC) which enables efficient conversion of voltage. The voltage is usually converted or stepped down in several discrete steps before being provided to consumers at 415V, 230V, 110V or in the vicinity thereof. The high voltage part of the network is usually called the transmission network, and the low voltage part (which supplies consumers) is usually called the distribution network.

[0002] Over the past half century, the requirements placed on the power distribution network have changed beyond recognition. Some changes, such as changes in work patterns that tend to spread demand throughout the day, have been beneficial, but most have been detrimental. Among these are the spread of solar panels (or other microgeneration) installed in domestic premises, and the increasing desire to charge electric vehicles. If domestic solar panels generate more electricity than is required at their own premises, they can be configured to output electricity to the power distribution network. This is done by increasing the voltage provided to the output of an inverter that converts the direct current (DC) generated by the solar panels to AC. This results in local variations in the voltage on the network. For example, in the UK, the nominal supply voltage is 240V, but local variations have been observed down to values as low as 235V and up to values as high as 248V. Variations in frequency also occur, with the frequency decreasing when the grid load is high and increasing when the grid load is low. Proposals for discharging from electric vehicles also increase the local voltage.

[0003] Such voltage and frequency fluctuations can be exacerbated by islanding. This may require cutting off areas of the grid due to the problem, resulting in smaller grids or islands. The smaller the island, the greater the voltage and frequency fluctuations. Islanding can improve grid stability, as can the use of inter-country interconnects.

[0004] Electricity providers are obligated to maintain the voltage and frequency of their supply within given specifications, and failure to do so often results in penalties (e.g., fines). Voltage and / or frequency fluctuations can disrupt the operation of equipment, shorten its lifespan, and / or trip protective devices, thereby creating inconvenient or dangerous situations for consumers. Network operators may need to form islands within their networks or use interconnectors to maintain power supply within specifications.

[0005] Distribution network operators (DNOs) may also refuse permission for consumers to power their distribution network from solar panels (or other micro-power sources) because the DNO believes it could destabilize the local network. However, such refusals (according to the UK's G99 form) are often unnecessary, as DNOs simply do not have accurate and up-to-date data on the network.

[0006] Fast charging for electric vehicles also places a high demand on the distribution network, as each vehicle can potentially generate a load of 7kW or more, which is two or three times the typical domestic demand. As a result, network operators are also forced to refuse permission for consumers to install fast-charging points for vehicles on the grounds that it could destabilize the local grid. This is particularly problematic as electric vehicles surge, with the UK, for example, projected to have 15 million such vehicles by 2030. Again, these refusals could be unnecessary.

[0007] Air conditioning places a considerable burden on the power supply. Another growing problem is the use of heat pumps, such as air-source heat pumps, because these typically consume 2-3 kilowatts continuously.

[0008] In the United States, load zones and hubs are used to set wholesale electricity prices locally, but this is usually done on fairly crude criteria. [Overview of the project] [Problems that the invention aims to solve]

[0009] The present invention aims to improve these problems. [Means for solving the problem]

[0010] According to a first aspect of the present invention, a method for predicting a fault in a power supply network is provided, the supply network comprising a distribution network and a plurality of subnetworks, each comprising at least one step-down transformer having a primary winding connected to the distribution network and a secondary winding connected to a plurality of consumer facilities via a plurality of conductors, the method is as follows: A step of receiving multiple samples of at least one power supply parameter selected from voltage, frequency, power factor, and power consumption from multiple detectors located at each consumer facility in a supply network, wherein the value of at least one parameter is sampled in multiple instances over a period of time. To determine which facilities are supplied by each transformer, the steps include correlating samples of at least one supply parameter received from different consumer facilities, For at least one facility that has been determined to be supplied by a specific step-down transformer, The process includes the step of comparing at least one value of at least one power supply parameter selected from voltage, frequency, power factor, and sinusoidal quality with one or more predetermined thresholds, wherein the comparison step, Alarm and, Commands to change the behavior of devices connected to a subnetwork, The predicted lifetime of the subnetwork, Generate a modified subnetwork design and at least one of the following.

[0011] In a preferred embodiment of the present invention, one or more of these four results can be combined in any combination. The power supply parameter used in the correlation step is preferably voltage. The multiple samples preferably include 10-second interval data available from a smart meter. The 10-second interval data is data value provided by the smart meter every 10 seconds, although the exact timing may differ between regions and jurisdictions. Here, the 10-second interval data means frequently supplied data separate from the billing data transmitted by the smart meter approximately every 24 hours. Therefore, the 10-second interval data is preferably provided at least every 5 minutes, preferably at least every 1 minute, and more preferably at least every 30 seconds.

[0012] The root cause of the problems identified above is the lack of visibility into the local behavior of the distribution network. Monitoring of the distribution network is carried out at intermediate (e.g., 11kV) levels, but monitoring at the lowest voltage levels is nonexistent or very limited. The state of the network also changes significantly over time, influenced by factors such as weather and television schedules. By utilizing the data received from the network of detectors, an accurate view of the distribution network can be obtained. This allows for both short-term measures and long-term planning. For example, it can be shown to the DNO that the addition of solar feed-in or electric vehicle charging infrastructure will not cause network instability.

[0013] Surprisingly, mature distribution networks generally grow over many years, and distribution network operators (DNOs) often do not know exactly which buildings are supplied by a particular transformer or substation. By correlating parameter measurements according to embodiments of the present invention, a precise understanding of the network layout can be obtained.

[0014] The solution could include warnings from short-term assessments, such as "the transformer is close to exploding," or long-term assessments, such as "the subnetwork will require a transformer upgrade in three years." The assessment could also offer alternatives, such as "if x amount of battery storage is currently available, the transformer upgrade can be delayed by y years."

[0015] Preferably, the step of correlating samples includes comparing, in time, the value of each of at least one power supply parameter received from a first consumer facility across multiple instances with the value of each of the supply parameters received from at least one other consumer facility. Preferably, the data is correlated from thousands or hundreds of thousands of different facilities.

[0016] A given threshold can include multiple samples that exceed a predetermined value within a specific duration. In other words, short-term excursions exceeding the limit cannot, in themselves, indicate a network failure. More sophisticated configurations can be based on logical formulas, such as taking appropriate action if parameter X exceeds X1, parameter Y falls below Y1, or parameter Z exceeds Z1.

[0017] Since all networks behave differently, one or more predetermined thresholds can preferably be derived from historical data. This can be achieved by a machine learning system to take into account the specific peculiarities of the network being monitored.

[0018] When commands are generated for devices within the network, these may include commands for at least one electric vehicle to reduce its charging current, or commands for at least one micro-generator to reduce its feed-in to the network. To fairly distribute the disadvantages of such measures, devices may be randomly selected within the subnetwork.

[0019] Commands to the equipment preferably include commands to the battery storage. The battery storage is charged when there is excess electrical energy available (detected by voltage and / or frequency exceeding a desired level) and discharged to support the network when demand is high (detected by voltage and / or frequency falling below a desired level). Thus, the battery storage operates to "smooth out" the demand.

[0020] Furthermore, battery storage providers may be separate commercial entities from distribution network operators (DNOs). In such cases, DNOs typically want to minimize their use of battery storage (to reduce costs). As a result, distribution network operators may be able to vary grid parameters more significantly (provided they meet their legal obligations) than if they owned the battery storage themselves.

[0021] If the result of an embodiment of the present invention is a predicted lifetime, the predicted lifetime of the subnetwork is derived by extrapolating power supply parameters using a predetermined algorithm. Such an algorithm may be based on the subnetwork's previous behavior modified by population-wide factors such as the expected proliferation of electric vehicles and energy-efficient appliances.

[0022] If the result of an embodiment of the present invention is a change in network design, this can include an increase in local battery storage, the installation of an uprated transformer, or both an increase in local battery storage and subsequent transformer upgrade. Cable wiring upgrades may also be provided. Requirements for such upgrades can be better determined when multiple detectors are distributed throughout the network. In particular, a comparison of voltage levels at facilities close to the transformer (with respect to cable length) and facilities far from the transformer can indicate that the capacity of the transformer is appropriate but the cable wiring is insufficient.

[0023] Since the responsibility for supply quality lies at the point of delivery, it is advantageous to place the measurements as close as possible to the end point within the country. Since the behavior of the local grid can vary within a single street, it is advantageous to measure the supply quality in as many buildings as possible. However, if the detectors are not inexpensive and easy to install, the cost can become excessive.

[0024] The detector can receive frequent data from smart meters and also has Internet connectivity to report the data to a remote server, so it is preferably equipped with a consumer access device (CAD). The data from the smart meter preferably includes data at 10 - second intervals. Sensing may be at a lower frequency provided that the minimum sensing frequency is not exceeded.

[0025] To reduce the demand on the communication and storage infrastructure, the data is preferably compressed or summarized before transmission to the server, in other words, many data points are transmitted in a combined message.

[0026] The method according to the first aspect of the present invention is preferably implemented on a computer.

[0027] According to a second aspect of the present invention, a method for operating a consumer access device is provided, the method comprising receiving at least one sampled parameter from a smart meter, determining whether the at least one sampled parameter complies with one or more predetermined thresholds, if the parameter complies with one or more thresholds, storing the value of the at least one sampled parameter, if the parameter does not comply with one or more thresholds, sending a message to a server, and sending a plurality of stored values to a server in response to the plurality of stored values of the at least one sampled parameter.

[0028] The CAD of this aspect operates in two modes. First, it accumulates data from the smart meter and sends the data (which may be a compressed or summarized version) to the server. Second, if the data is out of specification, it sends an emergency message to the server.

[0029] According to a third aspect of the present invention, a consumer access device for a smart energy system is provided, the consumer access device comprising a processor, a memory for storing at least one application, first communication means for receiving a plurality of samples representing at least one power supply parameter selected from voltage, frequency, power factor, and sine wave quality, and second communication means for communicating with at least one server to upload data representing the quality of the power supply.

[0030] To save transmission and storage overhead, the data representing the quality of the power supply preferably includes a summary of the data.

[0031] The processor is preferably programmed to urgently upload data if the power supply parameters do not conform to one or more predetermined thresholds.

[0032] While it is known that consumer devices in the home report electricity usage in kWh (usually for billing purposes), this information is insufficient to provide the precise mitigation and planning opportunities offered by embodiments of the present invention. At best, electricity usage is reported to the power supplier by a smart meter, typically once every 24 hours. Usage data does not include voltage and frequency information of the supply.

[0033] A fourth aspect of the present invention provides a computer-implementable method for determining the extent of a power distribution network, the method comprising: receiving at least a first set, a second set, and a third set of voltage values ​​over time from each facility; assigning the first set of voltage values ​​to a first power supply area; processing the first set or the second set of voltage values ​​to enable comparison; comparing the first set of voltage readings to a second set of voltage readings; assigning the second set of voltage values ​​to the first power supply area in response to a match within a predetermined threshold; and comparing the second set of voltage values ​​to at least a third set of voltage values ​​in response to a failure to match.

[0034] Aspects and embodiments of the present invention refer to consumer access devices or CADs.

[0035] Consumer access devices as referred to herein are distinct from smart utility meters and in-home displays (IHDs). Smart meters are interested in accurately and securely uploading data collected regarding utility usage within a facility. Since modifying this data (to deceive utility companies) would be beneficial to electricity consumers, smart meters must be secure devices. Typically, various tamper-proof and encryption technologies are deployed on smart meters to ensure that accurate and tamper-free data is transmitted to utility suppliers. For example, electricity consumers cannot update the data or software present on smart meters. Over-the-air (OTA) updates of smart meters may be performed, but this is a secure process managed entirely by or on behalf of the utility company. However, smart meters may provide data locally on a read-only basis, for example, via ZigBee®.

[0036] Home displays are typically devices that receive usage data from smart meters and display it to electricity consumers. Such devices can have different display modes, such as current electricity usage and past usage (daily, weekly, or monthly, etc.). They can also indicate when usage thresholds have been exceeded. However, they can only receive data from smart meters (e.g., over ZigBee®) and display it to the customer.

[0037] In contrast to smart meters, consumer access devices, or CADs, can be modified by consumers. Because CADs are run directly by smart meters, they do not need to be configured to securely relay utility usage data to the utility company's servers. This means that CADs can be modified in response to consumer requests (e.g., by software downloads).

[0038] In contrast to in-home displays, CADs are smart devices that can be programmed at the consumer's direction. In this context, the term “programmed” means the installation of software that was not included with the device when it was provided to the consumer. This is different from “programming” the device by, for example, selecting to display specific information from a menu. This selection or customization is a “dumb” process because the software providing all such displays is already installed, or at least not installed, at the consumer's request. The software for installation is preferably developed by a third-party developer unrelated to the utility company and selected by the consumer from a remote source such as an application store. The software is preferably downloaded via the internet (e.g., via a WiFi router in the facility) and never provided via the smart meter. This has the advantage of not employing a secure connection between the smart meter and the utility company for data traffic that does not require such a secure connection.

[0039] CAD can receive utility consumption information directly from smart meters, for example, via ZigBee®. This distinguishes CAD from other devices such as tablets or smartphones that receive utility consumption information from the utility company's servers (i.e., information already uploaded by the smart meter and provided by an app on the consumer's device). This allows CAD to operate with near real-time data, which would be impossible with existing infrastructure, since smart meters typically only upload data every 24 hours (usually 48 time slots of 30 minutes each). Theoretically, it would be possible to upload data from smart meters (e.g., via DCC) more frequently, but the load on the communication network would be unsustainable.

[0040] The software selected by the consumer preferably utilizes utility consumption data provided by a secure portion of the CAD that receives data directly from smart meters. This allows third-party software developers to develop applications directly relevant to consumers, such as improved displays of consumption data or energy-saving games. By providing "secure" and "open" portions to the CAD, the security of utility usage data is protected while allowing third-party developers to access the platform. Customer engagement is enhanced by empowering the development community to provide apps from consumers, which can also provide a revenue stream to utility providers (if consumers pay for the apps).

[0041] The present invention will be explained using the following diagram as an example. [Brief explanation of the drawing]

[0042] [Figure 1] This is a simplified diagram of the power transmission and distribution network. [Figure 2] This is a block diagram of a portion of the local power distribution network. [Figure 3] This is a block diagram of a consumer access device (CAD). [Figure 4] This is a flowchart of how CAD works. [Figure 5] This is a block diagram of the CAD and server network. [Figure 6] This is a flowchart of the steps taken by the server to correct problems detected on the local power distribution network. [Figure 7] This is a flowchart showing the process performed by the server to determine which consumers are connected to a particular subnetwork. [Figure 8] This is a block diagram of three local distribution networks connected to the transmission network via their respective transformers. [Figure 9]This figure shows graphs of data collection from a single local distribution network and from multiple local distribution networks. [Figure 10] This is a flowchart of the steps performed by a server to predict the lifespan of a local power distribution network. [Modes for carrying out the invention]

[0043] Figure 1 shows a simplified diagram of a power transmission and distribution network 100. A power plant 102 generates electricity from renewable resources such as fossil fuels, nuclear power, or wind power. The power plant outputs an AC voltage that is fed into transformers 104 (or a series of transformers) to step the voltage up to higher levels. In the UK, this voltage is 400kV or 275kV, which can be achieved in two or more steps (i.e., two or more transformers increase the voltage in several discrete steps). Since the power dissipated (i.e., lost) in the transmission and distribution networks is proportional to the square of the current flowing, it is important to reduce the current, which is done by increasing the voltage. The voltage is then gradually reduced to a level suitable for supplying consumers as close to their facilities as possible.

[0044] The high voltage is distributed by the transmission network 106 to several substations 108, one of which is shown. The term substation originates from the pre-historic era before the transmission grid, when electricity was generated in small power plants and consumed within a limited local area. Such substations now house step-down transformers rather than power plants. Substation 108 contains a transformer that steps down the voltage to a first intermediate level, which is 132kV in the UK.

[0045] The 132kV output of each substation 108 is supplied to several further substations 110, of which only one is shown for clarity. These substations drop the voltage down to a second intermediate level (the second to last level), which is typically 11kV in the UK. Occasionally, a further intermediate level of 33kV is also used. The 11kV output of each substation 110 is supplied to several substations 112, of which only one is shown. This is the last substation in the sequence and has a 415V phase-phase (three-phase) output, which is approximately 240V phase-neutral in the UK. This voltage output is supplied to consumer facilities such as houses 114, 116 and apartment buildings 118. The final link is typically provided by cables buried in towns and cities.

[0046] While this example focuses on the UK, other networks operate at different voltages and use different numbers of step-up and step-down transformers. However, the principle remains the same: the high-voltage core of the network supplies the continuous step-down transformers that ultimately supply the consumer.

[0047] At each step, the substations get smaller and more numerous, which means there is a considerable fan-out in the network. There are approximately 230,000 final step-down substations in the UK (population 67 million), each supplying up to about 500 buildings. In isolated areas, there are even more (about 350,000) smaller pole-mounted transformers, each supplying a few or even just one building.

[0048] The challenge for distribution network operators is to provide reliable power supply within defined voltage and frequency parameters 24 hours a day, 365 days a year. To do this effectively, operators must forecast or estimate information about demand and consumer behavior. Monitoring is provided on the network at higher voltage levels (11kV and above), and this voltage is supplied to large consumers such as factories. These consumers also typically have an obligation to provide operators with detailed and frequent information. However, network operators are typically blind to the behavior of localized 415 / 240V portions of the network. This means that serious and / or imminent problems may exist that are completely invisible to the operators.

[0049] Furthermore, to meet supply obligations, network operators may consider battery storage and load shedding. Both of these options have drawbacks. Battery storage can be connected to the distribution network at each voltage level to smooth out demand on the network. Battery storage is configured to charge when electricity demand is low and discharge when demand is high to power the network. Of course, it is essential to provide enough battery storage to meet load smoothing demand. However, determining the requirements for battery storage in local 415 / 240V portions of the network is extremely difficult. Because batteries are expensive, over-provisioning battery storage unnecessarily increases costs. Also, battery storage can be costly from a real estate perspective. However, under-provisioning battery storage does not solve the problem of fluctuations in the quality of power supply.

[0050] One solution to excess demand is load shedding, which can occur when certain high-usage customers, such as factories, are asked to reduce their demand for a certain period. This is something that power suppliers and network operators would clearly want to keep at an absolute minimum.

[0051] Figure 2 shows a portion of the local distribution network, namely the 415 / 240V portion of the UK. Substation 202 (corresponding to substation 112 in Figure 1) supplies electricity to seven consumer facilities shown: 210, 212, 214, 216, 218, 220, and 222. As previously mentioned, such a substation can typically supply up to 500 buildings. The entire monitoring system can include thousands or hundreds of thousands of buildings. Each building includes its own smart electricity meter 230, 232, 234, 236, 238, 240, and 242, which provide frequent supply information ("10-second interval data") over a short-range ZigBee® network. ZigBee® is a secure wireless communication standard, and further details can be found at www.zigbee.com. Available information typically includes voltage, frequency, power consumption, and power factor, which are sampled and transmitted every 10 seconds. Other means of measuring and communicating such information can also be used.

[0052] Each building is also provided with its own Consumer Access Devices (CADs) 230, 232, 234, 236, 238, 240, and 242. The CADs are configured to receive information from their respective smart meters via ZigBee® wireless connectivity and are also connected to the Internet (not shown) via, for example, a WiFi® router located within the building. Each CAD is configured to send data at 10-second intervals to a remote server (Figure 5) for processing. This data can be sent in real time or near real time, as will be described later. The CADs are also described in more detail with reference to Figure 3.

[0053] Three of the facilities shown in Figure 2 have additional features that distinguish them from basic facilities. Building 216 has solar panels 270 with the ability to supply surplus solar electricity to the distribution network. This is done by an inverter that converts the DC generated by the solar panels to AC. In order to supply surplus electricity to the distribution network, the output voltage of the inverter must be higher than the voltage level of local parts of the network. During periods of low electricity demand, this can cause the voltage to exceed the maximum allowable level. Other types of micro-power generation (such as wind power) cause similar problems. Buildings 218 and 220 have electric vehicles 280, 282 connected to the distribution network via fast-charging points. Such fast-charging points typically enable vehicle charging of 7 kW or more, such that the load presented by the electric vehicles during charging is about 2-3 times the normal domestic load from the building. If several electric vehicles are fast-charging within the local distribution network, this can cause the voltage and / or frequency of the power supply to fall below the minimum allowable level. Other high-power devices such as air conditioners can cause similar problems.

[0054] While businesses do not have information on when consumers are charging or attempting to charge their vehicles, they do have some limited information (from weather forecasts) about when significant feed-in activity from solar panels might be present.

[0055] Figure 2 also shows a battery storage 290 connected to the network at substation 202. This battery storage has the ability to store surplus energy when the local network load is low (and when voltage and frequency tend to increase) and return electricity to the network when demand is high. This type of local storage will be discussed further below.

[0056] In deregulated electricity markets like those in the UK, further complexities can arise. The electricity supplier to an individual consumer is a separate entity from the distribution network operator (DNO). In other words, the DNO has no direct relationship with the consumer. If a consumer has a smart meter (or other monitoring device) on their premises, the electricity supplier, not the DNO, is the one providing the usage data.

[0057] As a result, a local portion of a distribution network powered by a single substation will serve customers from several different electricity suppliers. However, each electricity supplier typically has enough customers within each local portion of the network for this method to work. Contracts between providers can also mitigate this problem.

[0058] Figure 3 is a block diagram of the Consumer Access Device (CAD) 300.

[0059] The processor 302 comprises a system-on-a-chip (SoC) including memory and one or more processors available from several semiconductor manufacturers. For communication, the processor interfaces with a ZigBee® transceiver 304, a WiFi transceiver 306, and a Bluetooth® transceiver 308. Each of these transceivers is shown as having its own antenna for clarity, although transceivers can share an antenna to save cost and space. Certain manufacturers also provide WiFi and Bluetooth® transceivers on a single chip. To interact with the consumer, the CAD also comprises a display 310 and an actuation button 312. In this example, the display is a 12x24 LED matrix, but other displays are equally suitable. The display shows images known as “cards.” In some embodiments, the display is optional, as information can be displayed to the consumer using a user device. Furthermore, the CAD may include a voice-activated interface (not shown). A 5-volt power supply 314 is provided via a USB socket 315, which may optionally have battery backup. If the CAD is equipped with a power line transceiver (not shown), the power supply can also transmit and receive data to and from, for example, equipment within the facility via electrical cables within the facility. Secure memory 320 is provided on the SoC or as a standalone entity, and optional alarms 322, such as audible alarms, are provided to warn consumers.

[0060] The diagram also shows several applications 318 stored in the memory of processor 302, within the dotted lines. These applications are managed and run by Docker® 316, which allows different software to run separately (placed in "containers") while sharing kernel functionality within the processor. A key point to note is that by placing data in a container for a specific application, that data can be made available to that specific application. Applications can maintain security by not accessing the contents of other applications' containers. Further information can be found at https: / / www.docker.com / . Docker® saves computing resources compared to the virtual machine (VM) architecture, although the VM architecture can be used in the same way.

[0061] Each CAD has a MAC address, installation code, serial number, and security certificate (for ZigBee® communication), all of which are unique to the device and are generally stored in secure memory 320.

[0062] Processor 302 has at least two roles: firstly, a secure role in which consumer consumption or usage data is received via ZigBee® transceiver 304 and stored in secure memory 320; and secondly, an application execution role in which application 318 is executed (in cooperation with Docker® 316).

[0063] The CAD includes an activation button 312 that is pressed to exit the CAD's sleep mode. Alternatively, the CAD may always be operational, in which case the activation button may be omitted. The activation button may also be used by the consumer to instruct the processor to switch to running a desired application or to instruct it to pair with a user device.

[0064] When activated, the CAD processor runs applications according to a policy set by the supplier, which may be customized by the consumer. In the simplest example, applications run in rotation, with each application given sufficient time to display any output to the consumer before the display goes blank and another application gains access to the processor and display. Alternatively, applications may run in order of priority and / or have override features that grant exclusive access to the processor to applications with important information to display. Some “background” applications may be configured to run continuously until overridden by a “foreground” application, such as a game. Any policy can be managed to some extent by the arrival of data from smart meters at 10-second intervals.

[0065] The display screens generated by the applications are known as cards. The consumer controls which cards are displayed by each application and what data is used to generate those cards (for example, through the user interface on a paired user device). For example, a consumer has the following three applications installed in CAD: -Data at 10-second intervals - Electric vehicle battery status -weather forecast.

[0066] The 10-second interval data application displays "live" energy consumption and includes several different cards using different graphic displays. The user selects one of these cards to set the 10-second application to display for at least 20 seconds.

[0067] Because the car battery's charge / discharge state changes little, users set the car's battery status application to run every minute and display the battery status for 10 seconds.

[0068] While the weather forecast itself may not change frequently, it requires more attention from consumers. In this case, consumers should set their weather forecast application to display the forecast every two minutes for 30 seconds. The exact scheduling may need to be slightly different to accommodate consumer requests.

[0069] Applications can be installed or removed as needed.

[0070] Preferably, the CAD is pre-programmed with at least one application, such as the Snake game. Consumers can control the game using controls on the CAD (not shown), such as buttons or a touchscreen. However, preferably, the user interface is provided using another device, such as a smartphone, which includes the CAD application and is paired with the CAD. Further CAD functionality is described with reference to the following flowchart.

[0071] A CAD having a display 310 and an alarm 322 is described, but these elements may be omitted.

[0072] CAD systems can be equipped with interfaces that allow for hardware upgrades, such as additional sensors and user interface functions. Those skilled in the art will be familiar with such configurations.

[0073] The CAD according to an embodiment of the present invention is programmed to upload supply quality information to a server, as described with reference to Figures 4 and 5.

[0074] Figure 4 shows a flowchart 400 of the operation of a CAD according to one embodiment of the present invention. The CAD is located in one of the consumer facilities shown in Figure 2. In addition to other functions, the CAD collects data from a smart meter (or other source) at 10-second intervals and, in a first mode, provides this to a supplier server for subsequent processing described later. In this example, the CAD can also operate in a second mode to immediately alert the supplier if the supply voltage or frequency deviates outside of predetermined limits. These two modes are preferably provided as applications on the CAD as described above, particularly as “background” applications that consumers are largely unaware of. Alternatively, the functionality may be provided at the firmware level.

[0075] The process begins in step 402, and in step 404, data is received from the smart meter at 10-second intervals. In step 406, the CAD monitors the quality of the supply by comparing the voltage and frequency values ​​to predetermined limits. If the values ​​meet the predetermined tolerance limits, the process proceeds to step 408, where the parameters are stored locally. In step 410, the CAD determines whether it has stored enough data to send to the server. If so, the data is sent in step 412, and the process returns to step 404. Otherwise, the process returns directly to step 404. If the parameters are found to be out of specification in step 406, the data is sent immediately in step 412. In this case, it is preferable that the message sent to the server is identified as urgent in some way.

[0076] Short-term fluctuations exceeding voltage and frequency limits may be permissible as long as they do not occur for longer than a predetermined time. Therefore, in another example, voltage and frequency may be compared to thresholds for the period prior to triggering transmission to the source.

[0077] In this example, data is sent every 30 minutes at 10-second intervals, resulting in data from 180 instances within that timeframe. To reduce the load on the reporting network, the CAD may be further configured to compress the data or generate a summary for transmission to the server, rather than sending the raw data.

[0078] Therefore, the CAD performs periodic and emergency data uploads if the quality of the supply does not meet one or more predetermined parameters. Although the described CAD effectively has two operating modes, it will be understood that this does not actually have to be the case, and the CAD can be configured to operate continuously in either of these two modes or other variations. Furthermore, the data collected and relayed to the server may not be limited to voltage and frequency. For example, the data may also include consumption and power factor, and / or may be collected and transmitted at intervals different from those described.

[0079] Figure 5 shows the configuration of CAD 500 and the server used to process the data collected from the CAD. CADs 510, 512, 514, 516, 518, 520, and 522 correspond to CADs located in different facilities as shown in Figure 2. Each CAD connects to the server 530 via the internet 532, such as via a WiFi router located at its respective facility. Other transmission technologies, such as SMS messaging, can also be used. It is conceivable to send data directly from the smart meter to the server (i.e., no separate device such as a CAD is required). The server may comprise smart meter infrastructure already installed for billing consumers, programmed to provide the additional functions described herein.

[0080] The server is also connected via the internet to control battery storage 540, which corresponds to battery storage 290 in Figure 2. Battery storage comprises a battery and an AC-DC converter (or rectifier) ​​used to charge the battery when there is a surplus in the local subnetwork, and a DC-AC converter (or inverter) that returns stored energy to the local network when there is a shortage. While standalone battery storage is shown as being related to a substation, battery storage may be distributed throughout the network, located in consumer facilities, or comprise batteries for electric vehicles.

[0081] Alternatively, battery storage may be locally controlled in response to locally detected network behavior. This is true when the battery operator and the network operator are separate entities. In other words, battery storage can operate autonomously.

[0082] Over time, the server accumulates data as shown in Table 1. [Table 1] The same applies to the following.

[0083] This can, of course, be stored in a database that also includes customer information such as address, credit and banking details. Further columns can be added to the table, such as indicators that an electric vehicle is being charged or that a small amount of electricity is being supplied to the network from the facility.

[0084] Figure 6 shows a flowchart 600 of the operation of a server (530, Figure 5) when resolving a problem on a local distribution network. The server will receive data from at least one CAD (or equivalent device) located within a facility supplied by the local network. Which facility is supplied by which substation (i.e., which particular local network it is located in) is predetermined, as illustrated, for example, with reference to Figure 7 below.

[0085] The process begins in step 602, where it receives live data from a CAD (or other device) representing the behavior of the local power distribution network. Step 604 determines whether the data conforms to the requirements, and if there are no problems, the process returns to step 602. If a problem is detected, the process proceeds to step 606 to determine whether the problem is due to high or low load.

[0086] If the problem is determined to be due to an overload (e.g., voltage and / or frequency is too low), the process proceeds to step 608, where the server determines whether any electric vehicles are being charged within the local distribution network. This can be done by querying the CADs within the same local network or by referring to data collected from facilities and stored in Table 1. If one or more electric vehicles are being charged, the server can instruct in step 610 to reduce (or "throttle") the charging rate to alleviate the load on the local network. If variable throttling is not possible or the network is severely overloaded, throttling may be set to 0 (i.e., charging stops). If only some electric vehicles are affected, they can be randomly selected to fairly share any inconvenience among consumers. The process returns to step 602, receiving the next data update from the CADs, and the performance of the local network is checked again in step 604.

[0087] If no electric vehicle is found charging in step 608, the process proceeds to step 612, where the server instructs the local battery storage (290 in Figure 2, 540 in Figure 5) to provide some support to the network regarding voltage. The process then returns to step 602.

[0088] While battery storage can autonomously increase grid voltage when it falls below a target value, it's worth noting that this can be a rather crude method. Preferably, battery storage is configured to increase the voltage of the local distribution network to a predetermined parameter range, rather than simply increasing the existing voltage by a specific amount.

[0089] If, in step 606, the problem is determined to be insufficient load (e.g., voltage and / or frequency are too high), the process proceeds to step 614. In step 614, the server determines whether any micro-power generation is being performed to supply power to the local network. This can be done by querying a CAD located within the local network or by checking the database as described above. If a micro-power generation feed-in is identified, the server instructs in step 616 to reduce the feed-in (possibly to zero), and the process returns to step 602. If no micro-power generation feed-in is identified in step 614, the process proceeds to step 618, where the battery storage is instructed to load the local network (i.e., charge its battery) in order to bring the network operating parameters back within specifications. Control then returns to step 602, and the process repeats.

[0090] Regardless of the repair applied, the process always returns to step 604, providing a feedback loop to ensure that the local power distribution network returns to operation within the specified parameters (restarting the interrupted device if possible).

[0091] While electric vehicles have been used as an example of the significant load they place on networks, the same principles apply to other high-power devices such as air conditioners.

[0092] The improvement steps described above are purely illustrative. For example, in the first instance, it may be preferable to utilize battery storage. Then, if the battery storage capacity is insufficient or already depleted, consumption or feed-in from micro-generation can be suppressed. It is also possible to encourage consumption at light loads, such as low-cost tariffs, and vice versa. For example, if the voltage and / or frequency are too high and this can be improved by stopping the feed-in from micro-generation, the feed-in tariff can be adjusted to zero. This means that consumers with micro-generation equipment will not receive payment for supplying power to the grid, which is intended to prevent feed-in.

[0093] The degree to which this process is automated can also be varied. In the simplest scenario, the server only needs to issue an alert if the local network is not functioning as specified. The corrective action can then be performed by a human operator.

[0094] The server can also receive data from two or more facilities served by the same transformer to derive a more accurate understanding of the local network's performance. Buildings farther from the substation (not in terms of exact geography, but in terms of longer cable extensions) typically record lower voltages than buildings closer to the substation due to conductor resistance losses. This is especially evident when the network load is high. The server can estimate the load on the local network by comparing the voltages reported by facilities closer to the substation with those farther away. This could be useful in at least two scenarios. First, the server may not receive consumption data from buildings (perhaps due to data protection concerns), and therefore can infer the load from the voltage drop. Second, even if the power supplier receives consumption data, they may only receive information from the proportion of facilities supplied by the local network. This second scenario may occur due to low CAD penetration among consumers or in deregulated electricity markets (where suppliers only receive information from their own customers).

[0095] Figure 6 shows a server performing remediation on a single distribution network basis, but the steps may be performed across multiple distribution networks.

[0096] Figure 7 shows a flowchart 700 of another process performed by the server that receives information from the CAD. This process is performed to determine which consumers are supplied by a particular substation. As mentioned above, and perhaps surprisingly, DNOs often do not know exactly which facilities are supplied by a particular substation.

[0097] The process begins in step 702, and in step 704, a first consumer is selected from the supplier database. This can be done in any suitable way, but it may be preferable to select a first consumer whose facility is geographically close to a particular substation, understanding that a particular consumer is likely to be supplied by a particular substation. In step 706, it is determined whether the relevant substation for the consumer has already been identified. If so, the process returns to step 704 to select another consumer. If the relevant substation has not yet been identified for that consumer, the process proceeds to step 708, where performance data received from the consumer's CAD is downloaded. In step 710, another consumer is selected. Any suitable selection criteria can be used, but it is preferable to select a consumer within a given geographical distance of the first consumer, i.e., a consumer that is likely to be supplied by the same substation. In step 712, it is checked whether the substation for that consumer has been identified, and if so, the process returns to step 710.

[0098] If another consumer's substation has not yet been identified, the process proceeds to step 714, where their performance data is downloaded. In step 716, the two sets of performance data are compared. If they match, the process proceeds to step 720. If they do not match, the process returns to step 710.

[0099] Unfortunately, there is no guarantee that data samples will be taken at the same moment in time, and therefore some extrapolation and / or interpolation may be performed between samples to enable comparison of data from different facilities. Linear interpolation may be used, or different methods are known to those skilled in the art. In principle, smart meters can be instructed to take samples at the same moment (e.g., aligned to Network Time Protocol (NTP)), but some further processing of the data may still be required due to processing and communication delays.

[0100] In step 720, the database is updated to confirm that there are matches between the data, i.e., both selected consumers are supplied by the same substation. In step 722, it is determined whether there is data for more consumers to compare with the data for the initially selected consumers. If so, the process returns to step 710; otherwise, the process continues to step 724. In step 724, it is determined whether further comparisons are to be made or whether all required consumers have had their substations identified. If more comparisons are needed, the process returns to step 704; otherwise, the process terminates in step 726.

[0101] In summary, to determine which consumers share a particular substation, a first consumer is selected, and the characteristics of their supply (e.g., if received from their CAD) are compared to those of other consumers. Ten-second interval data for the first consumer are compared to ten-second interval data for several other consumers. If the correlation is high, it is assumed that the consumer currently under consideration is supplied from the same substation as the first consumer. This can be further confirmed using location data, such as the consumer's zip code data.

[0102] One possibility is to compare the voltage for various consumers at each 10-second interval. However, as mentioned above, there may be some variation in the voltage detected at facilities supplied by the same substation. Buildings farther from the substation are more likely to detect lower voltages than buildings closer to the substation due to resistive losses in the distribution network. This is more likely if one or more consumers farther from the substation have high electricity usage at the time the voltage is sampled.

[0103] To accommodate this variation, the comparison in step 716 may be configured to compare voltage fluctuations between 10-second interval data from different consumers, rather than absolute voltages. Assume that consumers begin charging their electric vehicles during the sampling period. This may cause voltage drops detected in any building supplied by the same substation. If such drops are detected simultaneously and of similar magnitude (or proportion), it can be reasonably assumed that the buildings are supplied by the same substation, even if their absolute voltage values ​​are different. The same applies, of course, to voltage increases detected in different buildings.

[0104] When the comparison engine compares data from a first consumer with data from all consumers within a reasonable geographical distance of the first consumer's facility, the database contains a first list of consumers powered by the same substation and a list of consumers who are not. A second consumer is then selected from outside the first list, and the process is repeated for all consumers within a reasonable geographical distance of the second consumer who are not powered by the first substation.

[0105] This process continues until all consumers are assigned to substations. Information can be communicated in any suitable form, such as maps. Maps may be supplemented by historical performance data, particularly data collected when the network was strained. This represents the time when network operators struggled to comply with performance parameters and can be used to plan network upgrades, such as substation transformer upgrades or providing (more) battery storage.

[0106] Please note that in rural areas, transformers (usually mounted on poles) can only supply a small number of buildings. These buildings may have different power suppliers, so consumer data may not match data for other consumers. This simply means that there is only one consumer supplied by a particular supplier and particular transformer.

[0107] It is possible that such transformers exist that do not supply any consumers who are customers of a particular power supplier, in which case the supplier has no information about that part of the distribution network. However, this is not a problem as it represents only a small percentage of consumers on the network. Alternatively, power suppliers can collaborate to share data, especially in remote areas where there are fewer than 20, or even fewer than 10, facilities served by a particular transformer. As a further alternative, CAD (or other devices) can be provided and / or managed by a third party that can anonymize the data before sharing it with the supplier.

[0108] Figure 8 shows a portion of the distribution network 800, illustrating data collection from multiple local distribution networks. Part 802 of the distribution network supplies three substations 804, 806, and 808, each supplying its own local distribution networks 810 (DN1), 812 (DN2), and 814 (DN3). For example, performing the process in Figure 7 selects at least one CAD 816, 818, 820 (or other device) associated with each local distribution network to provide data to the server 824 via the internet 822. As previously mentioned, additional CADs associated with each local network can be selected to improve insights into the local networks and / or to provide backups in case a CAD stops transmitting data for any reason. Each CAD transmits data to the server, as illustrated with reference to Figure 4, for example. Thus, the server is provided with frequent data representing the behavior of each local network.

[0109] Figure 9(a) shows the voltage and frequency characteristics of a single local network over a period of time, illustrating a single instance where the characteristics were out of specification. Figure 9(b) shows instances where any of the characteristics were out of specification for three local networks. The thickness of the vertical lines corresponds to the degree to which the characteristics were out of specification. It can be seen that DN2 is plagued by more out-of-specification incidents than DN1 or DN3. DN2 requires some medium- to long-term remediation, such as providing battery storage or adding transformer capacity at the relevant substation 806 (Figure 8). Other remediation techniques, including the use of market levers to influence demand, would be obvious to those skilled in the art. One or more graphs, such as those shown in Figure 9, can be provided in real time, for example, on a network engineer's computer. The engineer can zoom in on any of the incidents to determine the nature of the problem and its duration. The graphs may be enhanced to show what remediation (such as those shown in Figure 6) has been applied at that point.

[0110] Figure 10 shows a flowchart 1000 of a method for predicting the remaining lifespan of a local distribution network and ranking local networks to determine upgrade priority. This may be performed by the server shown in Figure 8. The process begins in step 1002, and in step 1004, historical data for the local network is restored or downloaded. This data may be 10-second interval data for a period of one month or up to several years. This may include data from the most difficult month from consecutive years (typically January in cold climates of the Northern Hemisphere), or all available data. Alternatively, it may include characteristic data on failures where the network did not function as specified.

[0111] The process proceeds to step 1006, where network performance data is extrapolated into the future, typically over several years, to generate an estimate of how often the network will fail to function as specified. This can be done using any appropriate extrapolation technique that matches the nature of the data. The process then proceeds to a further forecast step 1008 (which may be optional or combined with the forecast step in step 1006). Step 1008 further processes the extrapolated data to take into account expected demographic changes across the entire population. This could include the expected percentage of consumers on the local network who will purchase electric vehicles or install heat pumps in their facilities. It could also include factors likely to decrease net consumption, such as increased use of energy-efficient appliances or the installation of solar panels.

[0112] In step 1010, the network's lifespan is predicted using extrapolated data. This lifespan can be determined as the point at which the frequency of out-of-specification incidents exceeds a given threshold, or the point at which the predicted peak consumption exceeds the design parameters of the local network. It can also be determined whether adding local battery storage would benefit the network, in particular whether adding battery storage is a cost-effective means of delaying more expensive upgrades to the network.

[0113] In step 1012, it is determined whether there are any more networks to analyze, and if so, the process returns to step 1004. If all relevant subnetworks have been analyzed, the process proceeds to step 1014, where an optional ranking is performed. This step compares the predicted lifetimes of each of the analyzed local networks to identify one or more networks that are most urgently in need of upgrade. The process ends in step 1016.

[0114] Therefore, the process described with reference to Figure 10 assigns network operators responsibility for multiple subnetworks and thus determines maintenance and upgrade priorities over many years. This can also assist in budgeting. Clearly, this situation is somewhat dynamic, and the process can be repeated at regular intervals, for example every six months, to take into account new data and possible changes in demographic forecasts.

Claims

1. A method for predicting faults in a power supply network, the supply network comprising a distribution network and a plurality of subnetworks, each having at least one step-down transformer having a primary winding connected to the distribution network and a secondary winding connected to a plurality of consumer facilities via a plurality of conductors, wherein the method A step of receiving multiple samples of at least one power supply parameter selected from voltage, frequency, power factor, and power consumption from multiple detectors located at each consumer facility within the supply network, wherein the value of the at least one parameter is sampled in multiple instances over a period of time. The steps include: correlating the samples of at least one supply parameter received from different consumer facilities in order to determine which facilities are supplied by each transformer; For at least one facility that has been determined to be supplied by a specific step-down transformer, The process includes the step of comparing at least one value of at least one power supply parameter selected from voltage, frequency, power factor, and power consumption with one or more predetermined thresholds, wherein the comparison step Alarm and, Commands to change the behavior of devices connected to the subnetwork, The predicted lifetime of the subnetwork, A method for generating at least one of the modified designs of the subnetwork, and

2. The method according to claim 1, wherein the step of correlating the samples includes comparing each value of the at least one power supply parameter received from a first consumer facility over a plurality of instances over a period of time with each value of the supply parameter received from a plurality of other consumer facilities over a plurality of instances over a period of time.

3. The method according to claim 1 or 2, wherein the same sample of at least one supply parameter is used in the correlating and comparing steps.

4. The method according to any one of claims 1 to 3, wherein the predetermined threshold includes a threshold for a parameter with a different threshold.

5. The method according to any one of claims 1 to 4, wherein the predetermined threshold includes a plurality of samples in which the predetermined value exceeds a predetermined value during a specific duration.

6. The method according to any one of claims 1 to 5, wherein one or more predetermined thresholds are derived from historical data.

7. The method according to any one of claims 1 to 6, wherein the instruction to the device includes an instruction to at least one electric vehicle for reducing the charging current.

8. The method according to any one of claims 1 to 7, wherein the command to the device includes a command to at least one micro-generator for reducing the feed-in to the network.

9. The method according to claim 7 or 8, wherein the device is randomly selected within the subnetwork.

10. The method according to any one of claims 1 to 9, wherein the command to the device is a command to the battery storage.

11. The method according to any one of claims 1 to 10, wherein the predicted lifetime of the subnetwork is derived by extrapolating the power supply parameters using a predetermined algorithm.

12. The method according to any one of claims 1 to 11, wherein the modified design includes an increase in local battery storage.

13. The method according to claim 12, wherein the modified design includes increased local battery storage and further includes a subsequent transformer upgrade.

14. The method according to any one of claims 1 to 12, wherein the modified design includes an improved transformer.

15. The method according to any one of claims 1 to 14, wherein the modified design includes an upgrade in cable routing.

16. The method according to any one of claims 1 to 15, wherein the comparison of at least one value of at least one power supply parameter selected from voltage, frequency, power factor, and power consumption with one or more predetermined thresholds includes a comparison of power supply parameters received from a plurality of consumer facilities.

17. The method according to any one of claims 1 to 16, wherein the plurality of samples of at least one supply parameter are received from a consumer access device.

18. The method according to any one of claims 1 to 17, wherein the plurality of samples of at least one supply parameter include data from a smart meter at 10-second intervals.

19. The method according to any one of claims 1 to 18, wherein the plurality of samples of at least one supply parameter are received in a combined message.

20. A method for operating a consumer access device, The steps include receiving at least one sampled parameter from a smart meter, The steps include determining whether the at least one sampled parameter conforms to one or more predetermined thresholds, If the parameter conforms to one or more thresholds, the step of storing the value of at least one sampled parameter, If the parameter does not conform to one or more thresholds, the step of sending a message to the server, A method comprising the step of transmitting to the server a plurality of stored values ​​in response to a plurality of stored values ​​of the at least one sampled parameter.

21. The method according to claim 20, wherein the step of transmitting the plurality of stored values ​​to the server includes the step of transmitting at least a summary of the plurality of stored values ​​to the server.

22. A consumer access device for smart energy systems, Processor and Memory to store at least one application, A first communication means for receiving multiple samples representing at least one power supply parameter selected from voltage, frequency, power factor, and sinusoidal quality, A consumer access device comprising: a second communication means for communicating with at least one server to upload data representing the quality of power supply;

23. The consumer access device according to claim 22, wherein the data, including the quality of power supply, includes a summary of the data.

24. The consumer access device according to claim 23 or 24, wherein the processor is programmed to urgently upload data if the power supply parameters do not conform to one or more predetermined thresholds.

25. A method implemented in a computer for determining the range of a power distribution network, The steps include receiving at least a first set, a second set, and a third set of voltage values ​​from each facility over time, The steps include: assigning the first set of voltage values ​​to a first power supply area; A step of processing the first set or the second set of voltage values ​​in order to enable comparison, The steps include comparing the first set of voltage readings with the second set of voltage readings, A step of assigning the second set of voltage values ​​to the first power supply area in response to a match within a predetermined threshold, A method comprising the step of comparing the second set of voltage values ​​with at least the third set of voltage values ​​in response to a failure to match.

Images