Decentralized control of energy storage device charging and grid stability

Abstract

The edge computing devices may control load devices and distributed energy resource (DER) devices. The edge computing devices may be associated with electric meters installed at premises or distribution transformers installed at secondary substations. The control of the load devices and DER devices may be based in part on predictive pricing information that takes into account factors such as distribution facility equipment ratings, environmental conditions, historical patterns of generation and load, and user input.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims priority to U.S. Patent Application No. 63 / 145,660, filed on February 4, 2021, the entire content of which is incorporated herein by reference.

[0002] Technical Field The present disclosure generally relates to grid control systems. More specifically, the present disclosure relates to distributed control of charging of electric vehicles and energy storage devices to maintain grid stability.

Background Art

[0003] [[ID=Z19]] Background In a resource distribution system such as a power grid that supplies power, meters are used to measure and control the consumption at a customer's site. The meter can include a measurement module for measuring and monitoring the power consumption characteristics, a communication module for communicating with a central system such as a head - end system, and other modules and components.

[0004] When distributed energy resource (DER) devices such as solar panel arrays, wind turbines, hydro turbines, batteries, electric vehicle (EV) chargers, electric vehicles, energy storage devices, and generators are located on a customer's premises, the electricity generated or stored by these DER devices may be used on the premises or output to the power grid. Furthermore, electric vehicle chargers, electric vehicles, and energy storage devices may also receive power from the power grid for later storage and use. Depending on the system, a separate meter and meter socket may be required to connect the DER devices to the grid. With these individual devices, a central system, such as a headend system, may not be able to control the connection of the DER devices to the grid. In other systems, an integrated device may connect both the DER devices and the premises meter to the grid. However, these integrated devices may not provide optimal control of the DER devices. Therefore, improvements are needed in the systems that control and communicate with the premises DER devices. [Overview of the Initiative] [Means for solving the problem]

[0005] overview Embodiments and examples of systems and methods for controlling load devices and DER (Distributed Energy Resource) devices using one or more edge computing devices are disclosed. An electric meter may include an edge computing device, a communication module, and a port for connecting to a first load device. The meter's edge computing device can transmit load forecast information to an edge computing device associated with a distribution transformer. The transformer's edge computing device can generate price interval data based on the load forecast information and transmit it to the meter. The meter's edge computing device controls the load device based on the price interval data, operating it with normal operating parameters if the price interval data includes low-cost time intervals, and with energy-saving operating parameters if the price interval data includes high-cost time intervals. The electric meter's edge computing device can also control a DER device to output power to the grid if the price interval data includes high-cost time intervals.

[0006] In some examples, an edge computing device for an electric meter generates future price data. This future price data corresponds to time intervals following time intervals included in the price interval data and can be based on meter-specific data.

[0007] In some examples, edge computing devices are associated with distribution transformers and control chargers, such as EV chargers, on the site downstream of the transformers. The edge computing devices determine the predicted load of the transformers and the site, and use these predictions to control the chargers. The edge computing devices can control the charging speed of the chargers and when the chargers are charging.

[0008] These exemplary embodiments and features are mentioned not to limit or define the spirit of the invention described herein, but to provide examples that aid in understanding the concepts described in this application. Other embodiments, advantages, and features of the spirit of the invention described herein will become apparent after a thorough examination of the entire application. [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 is a block diagram illustrating an example of power connections between a power distribution network, meters, panels placed on the site, and multiple DER devices. [Figure 2] Figure 2 is a block diagram showing an example of a panel connected to a meter, multiple DER devices, and multiple loads. [Figure 3] Figure 3 is a block diagram showing a portion of an exemplary communication network for communication between the headend system, meters, and multiple DER devices. [Figure 4] Figure 4 is a block diagram showing a portion of an exemplary communication network topology for the communication network in Figure 3. [Figure 5] Figure 5 is a block diagram showing a portion of an exemplary communication network for communication between a headend system, gateway devices, meters, and multiple DER devices. [Figure 6] Figure 6 is a block diagram showing a portion of an exemplary communication and control network for communication between the headend system, an independent system operator, meters, and multiple DERs and load devices measured by the meters. [Figure 7] Figure 7 is a flowchart illustrating an example of a control method for load devices or DER devices in a power distribution network. [Figure 8] Figure 8 is a block diagram illustrating exemplary power and communication connections between a power distribution network, meters, panels placed on the site, and one or more DER devices. [Figure 9] Figure 9 is a block diagram illustrating exemplary power and communication connections between a power distribution network and multiple sites, including DER devices. [Figure 10A] Figure 10A is a block diagram showing an example of the operation of an aggregator and a control device. [Figure 10B] Figure 10B is a block diagram of an exemplary system used to regulate voltage on a grid using an aggregator and control devices. [Figure 11] Figure 11 is a block diagram illustrating an exemplary configuration of a multiport meter electrically coupled to a solar inverter. [Figure 12] Figure 12 is a block diagram illustrating an exemplary arrangement of a multiport meter electrically coupled to an inverter for solar panels, a battery storage system, or both. [Figure 13] Figure 13 is a block diagram showing an exemplary arrangement of a multiport meter electrically coupled to an electric vehicle charger. [Figure 14] Figure 14 shows an exemplary training and validation of a machine learning model. [Modes for carrying out the invention]

[0010] The present invention provides a system that supports communication between edge computing devices and between edge computing devices, and a headend system that controls load devices and DER (Distributed Energy Resource) devices. DER devices include devices that can supply energy to a power grid or site. Energy may be generated by devices such as solar panels or stored by devices such as batteries. Some DER devices, such as batteries, may have both energy supply and consumption functions. DER and load devices may be controlled at least in part based on forecast price information. Forecast price information includes, but is not limited to, many factors such as the ratings of equipment in distribution facilities such as distribution transformers, current and forecast weather conditions, historical patterns of occurrence and load, and user input. Device control may include operating the device using normal operating parameters, changing the device's operation to use energy-saving operating parameters, or disconnecting the device. Control of DER devices may include commands to connect the DER device to the grid, commands to disconnect the DER device from the grid, commands to connect or disconnect the DER device to or from the site, and commands to adjust the parameters of the DER device.

[0011] Figure 1 shows an exemplary power connection between a distribution network or grid 102, a site 104, a meter 106, a panel 108, and multiple DER devices 110a, 110b, ... 110n. In this example, at least three DER devices are located on the same site.

[0012] The meter 106 measures and controls the power between the grid and the site. The meter may include a measuring module 122, a communication module 132, a disconnect switch 142, and other components. The measuring module measures the consumption of electrical energy and may provide revenue grade measurement and load profiling. The communication module communicates with a central system or headend system via a communication network (not shown). The measuring module and the communication module may be separate modules or combined into a single module. The disconnect switch controls the flow of power from the grid through the meter to the site.

[0013] In Figure 1, each DER device 110a, 110b, and 110n may be connected to the grid and site via panel 108. Each DER device may include measurement modules 120a, 120b, and 120n, communication modules 130a, 130b, and 130n, and control devices 140a, 140b, and 140n (such as inverters and disconnect switches). The parameters and status of the control devices can be controlled by a controller. The measurement modules measure the power generated by the DER devices. This measures the amount of energy supplied to the site or grid by the DER devices and the duration of that energy supply. The measurements by the measurement modules may be accurate enough to provide revenue-level billing. Alternatively, the measurement modules may provide measurements or data for billing other than revenue grades. The measurement modules can provide load profiling using appropriate interval lengths (e.g., 1-minute intervals, 5-minute intervals, 15-minute intervals) required by the relevant regulatory authorities. They may also monitor the generated power and provide power data such as power quality and power factor data. In some systems, the measurement module is provided by a single module, such as a meter-on-chip (MOC) module. A communication module communicates with a central system via a communication network (not shown). The measurement module and communication module may be separate modules or combined into a single module. A control device controls the connection of the DER device's output to the panel.

[0014] Figure 1 shows DER devices 110a, 110b, 110n connected to panel 108, but alternative configurations are possible and can be supported by measurement modules 120a, 120b, 120n and communication modules 130a, 130b, 130n within the DER devices. As alternative means, there are methods of connecting one or more DER devices to a multiport meter or connecting the DER devices directly to the grid. In such examples, measurement modules 120a, 120b, 120n may form part of a meter 106 capable of measuring the individual loads of DER devices 110a, 110b, 110n.

[0015] The communication module of the DER device enables low-latency bidirectional communication with the DER device. These communications can provide more timely information regarding the operation of the DER device and enable better control of the DER device than currently available.

[0016] The measurement module and the communication module can be added to any type of DER device to enable the DER device to operate in the system of Figure 1. These modules (when combined, single modules) can be integrated into the hardware design of the DER device. There may be pads or connectors designed on the PCB (printed circuit board) of the DER device to accommodate the addition of the modules. In some implementations, the DER device is produced by a third party and the modules are provided by the same party that provides the meter. The modules are soldered onto the PCB or connected using connectors or wire harnesses. The measurement module and the communication module within the DER device can communicate with other components of the DER device via a wired connection, a wireless connection, or a combination thereof.

[0017] Meter 106 connects the site and the grid via panel 108. The panel can include not only the main circuit breaker that controls the connection to the site meter, but also additional circuit breakers for DER devices and loads on the site. FIG. 2 shows an example of panel 208 with main circuit breaker 202, circuit breakers for each DER device 204a, 204b, 204n, and circuit breakers for site loads 206a, 206b, 206n.

[0018] Communication Using the Meter

[0019] In addition to the power connections shown in FIGS. 1 and 2, the meter and DER devices are connected via one or more communication networks. FIG. 3 shows a part of one exemplary communication network. The communication network includes communication channels between meter 106 and DER devices 110a, 110b, 110n. Each communication module within the DER device communicates with the communication module within the meter. The communication channels can be wired or wireless and can use any type of communication protocol, including proprietary or non-proprietary protocols. One example is the Zigbee communication protocol. The communication module within the DER device can communicate information regarding the energy generated or supplied to the grid and information regarding the status of the device.

[0020] The communication module within the meter can receive commands or control instructions transmitted from the head-end system 302 and route those commands to the appropriate DER device. It can also receive communications from the DER device and transmit that communication to the head-end system.

[0021] In addition to or as an alternative to the routing of communications between the head-end system and the DER device, the meter can generate commands and transmit them to one or more communication modules within the DER device. The meter can also receive information from the DER device. This information can be provided to the head-end system or used by the meter.

[0022] In Figure 3, all communication between the DER device and other devices is routed through a meter on the same premises. The meter can communicate using the same network protocol as the DER device and other devices on the communication network, or it can communicate with the DER device using one network protocol and with other devices on the communication network using a different network protocol.

[0023] In some implementations, the meter receives information from the DER device, aggregates the information, and then sends the aggregated information to the headend system. In other implementations, the meter simply routes the information received from the DER device to the headend system without aggregating it.

[0024] As shown in Figure 3, as an alternative to communication between DER devices and the headend system passing through the meter, one or more DER devices can be configured so that communication between the headend system and the DER devices passes through nodes other than the meter. For example, communication between the headend system 302 and the DER device 110a can be routed through devices on the network other than the meter 106 on the same site.

[0025] In any of these examples, communication from other devices on the network may be routed through meter 106. As shown in Figure 4, the meter is connected to the network of other devices, and communication between the headend system 402 and meter 106 may be communicated between multiple devices via one or more networks.

[0026] In some networks, the communication modules of meters and / or DER devices may communicate with devices that provide edge computing services. Because the edge computing service or edge computing device 410 is topologically close to the DER device on the communication network, it can provide low-latency communication and control.

[0027] Communication using a gateway device

[0028] Figure 5 shows another exemplary communication network. In Figure 5, the communication modules within DER devices 110a, 110b, and 110n communicate with the gateway device 504. Any type of communication protocol can be used for communication with the gateway device, including proprietary or non-proprietary protocols. Examples include Zigbee, Wi-Sun, or WiFi communication protocols. The communication modules within the DER devices may communicate information about the energy being generated or supplied to the grid, as well as information about the status of the devices.

[0029] Communication between the meter and the headend system cannot be routed through a gateway device. Instead, the meter may communicate with the headend system as shown in Figure 3, or it may communicate with the headend system via a PLC network. Alternatively, the meter may communicate via a gateway device.

[0030] The gateway device may be installed on or near the site. The gateway device communicates with the headend system 502 via one or more networks. In addition to communication capabilities, the gateway device may be an edge computing device that provides edge computing services, such as services that support the measurement and control of the DER device. The headend system may communicate with the DER device via communications routed through the gateway device.

[0031] In some examples, the gateway device 504 is located on-site, such as a site gateway device or a home gateway device. Any type of communication protocol can be used for communication with the site gateway device 504, including proprietary or non-proprietary protocols. Examples include Zigbee, Wi-Sun, or WiFi communication protocols. The communication module within the DER device may communicate information about the energy being generated or supplied to the grid, as well as information about the device's status.

[0032] Site gateway devices can be installed on the site. For example, a home gateway device may be located inside or outside a building on the site. The home gateway device communicates with the headend system via one or more networks. In addition to communication capabilities, site gateway devices can also provide edge computing services, including measurement and control of DER devices.

[0033] In an additional example, the gateway device 504 may be a cellular base station that communicates with the meter and DER devices over a cellular network. The cellular network may be public or private. A communication module within the DER device may communicate information about the energy being generated or supplied to the grid, as well as information about the device's status.

[0034] The headend system 502 communicates with the meter and DER device via the cellular base station gateway device 504. Although not shown in Figure 5, communication between the cellular base station and the headend system may use additional networks and network devices. As an alternative to the meter's communication module communicating via the cellular base station, the meter may communicate with the headend system using a different network than that used by the DER device.

[0035] Edge computing devices for controlling loads and DER devices

[0036] Figure 6 is a block diagram showing an exemplary communication and control network for communication between a headend system 602, an independent system operator (ISO) 604, a meter 106, and multiple DERs and load devices measured by the meter 106. Communication between the components shown in Figure 6 can be performed using any of the techniques described with respect to Figures 1 through 5. As shown in the figure, the electric meter 106 includes multiple ports for electrical coupling, ports for communicative coupling, or multiple ports for electrically and communicatively coupling the electric meter 106 to the DERs and load devices. For example, the electric meter 106 may include ports for a battery storage device 610a, an inverter 610b connected to a solar panel, an electric vehicle (EV) charger 610c connected to an electric vehicle, a washer / dryer 612, an HVAC system 614, a home display or smart thermostat 616, a smart switch 618 connected to a device in an outlet, or other residential load devices 622. In one example, the electric meter 106 can monitor each coupled device and provide control commands to control the connection of the devices to the grid 102. Figure 6 shows one electric meter 106, but there may be multiple electric meters connected to distribution transformers associated with a secondary substation, each meter coupled to its own set of loads.

[0037] For example, a distribution transformer associated with a secondary substation controller 606 may become overloaded if multiple electric vehicle chargers 610c downstream of the distribution transformer start charging simultaneously. To prevent the distribution transformer from overloading, distributed intelligence, such as commands generated by an edge computing device 410 and delivered to networked devices, can be used to manage the charging of multiple electric vehicles or other battery storage devices 610a by reducing the duty cycle of the power supplied to the electric vehicle chargers 610c or battery storage devices 610a, or by reducing the charge levels of individual electric vehicles or battery storage devices 610a to manage overall demand.

[0038] If there are multiple meters connected to edge computing devices downstream of the same secondary substation, communication and coordination may occur between the edge computing devices to manage the devices connected to each meter. One option is to have one of the edge computing devices act as a master device. Another option is to allow each edge computing device to make its own decisions and communicate those decisions to the other edge computing devices so that when an edge computing device makes its own decisions, it can consider the actions that have been or should be taken by other edge computing devices.

[0039] In some examples, the demand for distribution transformers can be predicted by machine learning algorithms running on an edge computing device 410 located at meter 106 or on remote systems such as a secondary substation 606 or headend system 602. For example, a machine learning algorithm may learn over time the power consumption trends of devices downstream from the secondary substation controller 606, including distribution transformers that may be overloaded. Inputs to the machine learning algorithm may include, but are not limited to, current and historical energy demand, time of day, day of the week, date, geographical location, current and predicted weather conditions, historical generation by downstream DER devices, transformer and equipment ratings, a model of the distribution system or a portion of the distribution system including the distribution transformers, and a flexible load factor. The flexible load factor relates to loads that may or are predicted to be discharged. Once power consumption trends are learned, the edge computing device 410 may generate control commands for the devices during times when the distribution transformers may be stressed. For example, control commands could include controlling the charge rate of the electric vehicle charger 610c or the battery storage device 610a, or controlling the power supply from the electric vehicle, battery storage device 610a, and inverter 610b to the grid 102. Other control commands could also be implemented to reduce or increase energy consumption downstream from the distribution transformer by controlling the on / off switching of non-critical loads, discharging storage devices (e.g., energy storage devices and electric vehicles), or increasing the energy consumption of flexible loads (e.g., energy storage devices and electric vehicles). For example, control commands could include opening and closing circuit breakers to electrically isolate or couple the DER device to the grid 102 or other on-premises loads, as previously mentioned with respect to Figure 2.

[0040] For example, the electric vehicle charger 610c and other DER devices (e.g., battery storage 610a and inverter 610b connected to solar panels) can introduce significant phase imbalances into the multiphase power system provided by grid 102. This can occur, for instance, if several electric vehicles begin charging simultaneously or if the output of solar devices reaches its peak during peak times. Distributed intelligence can assist in timing the charging and discharging of DER devices 610a-610c to balance the load across phases. For example, machine learning techniques can learn over time the power consumption trends of devices downstream of the secondary substation controller 606 that introduce phase imbalances. Once power phase imbalance trends are learned, the edge computing device 410 can generate control commands for devices operating at various phases of grid 102 during the predicted period when phase imbalances are expected. For example, control commands could include controlling the charge rate of electric vehicles or battery storage device 610a, or controlling the supply of power from battery storage device 610a, inverter 610b, or electric vehicles to the grid. Other control commands can also be implemented to balance the load in the phases downstream from the distribution transformer.

[0041] Machine learning techniques enable automated decision-making based on load requirements, thus avoiding user intervention that can lead to inefficiencies and inaccuracies. Furthermore, machine learning techniques can provide predictive and preventative recommendations to avoid adverse effects on the grid, such as overloading distribution transformers.

[0042] In an additional example, combining the demand curve of grid 102 with the supply curve of solar power from inverter 610b and other factors can result in significant variability in the energy generation required. Distributed intelligence and control can provide a mechanism to smooth the demand and supply curves, potentially reducing overall energy production costs. Demand and supply curves can be relevant to areas of any size, from entire regions to microgrids. ISO 604 or edge computing device 410 can predict energy production costs for a given period. Energy production costs can be based on the load demand experienced by grid 102 during that period. For example, the demand and supply curves can be smoothed by performing actions that take advantage of low energy costs during low-cost periods, such as charging electric vehicles and battery storage 610a, and further reducing consumption during high-cost periods, such as using a vehicle-to-grid system to discharge electric vehicles and battery storage 610a and sell the energy back to grid 102, and further selling other available power from other DER devices back to grid 102. In some cases, predicting energy production costs allows us to maximize the use of available solar power while minimizing the use of non-renewable energy.

[0043] Such technologies can address the inefficient use of energy by end consumers, which generally leads to increased overall energy costs and carbon emissions. This technology could potentially incentivize customers to sell energy when needed in grid 102 (e.g., during high-cost periods) and consume energy at times optimal for grid 102 (e.g., during low-cost periods), in line with the true cost of energy production. This technology could also offer power companies the option to directly control energy use for the same purpose.

[0044] In some examples, the DER device can also address the effects of highly reactive loads and generators by regulating the voltage of grid 102. For example, if a highly reactive load begins to draw power from grid 102, the edge computing device 410 can provide a command to the DER device coupled to grid 102 via meter 106 to discharge so that the voltage of grid 102 is maintained at a desired level. The edge computing device can detect the presence of a highly reactive load by monitoring reactive power consumption or total harmonic distortion. As an example, if several electric vehicle charging stations are electrically coupled to charging stations with sufficient charge, several electric vehicle charging stations may be aggregated as sources of voltage regulation.

[0045] Furthermore, these device control systems can provide intelligence for smart home demand management, preventing excessive demand charges (e.g., by reducing demand during high-cost periods), providing real-time load monitoring, offering energy and billing forecasts, and providing alerts related to power consumption and associated costs. In some examples, an edge computing device 410 can automatically provide control commands to individual devices to stop or modify their operation when an overload condition is anticipated. For example, a washer / dryer 612, an HVAC system 614, a smart thermostat 616, a smart switch 618, and a load control device 620 can each receive control commands to prevent or limit operation during peak demand periods. In some examples, customers may agree to this direct control of various household devices in exchange for a lower overall unit cost of power consumption.

[0046] Examples of techniques for controlling loads or DER devices

[0047] As described above, by integrating communication modules into DER devices and other load devices, and connecting DER devices and other load devices to a communication network, we support improved device control and connectivity.

[0048] Figure 7 shows a typical method 700 for controlling DER devices and loads such as electric vehicles and energy storage devices. In block 702, method 700 includes ISO 604, a power company's head-end system 602, or an energy aggregator determining the selling and buying prices of energy for a predicted interval. ISO 604, the head-end system 602, or the energy aggregator may use machine learning techniques to learn past demand cycles, weather conditions, and other factors that contribute to energy production costs, and leverage those historical factors to set energy prices for the predicted interval. Inputs to the machine learning algorithm may include current and past energy demand, time of day, day of the week, date, geographical location, and other factors related to how the facility consumes energy. In some examples, the interval may be set as a 5-minute block where the price changes at the interval boundary. Shorter or longer intervals can also be used. Prices are predictable or can be set over extended periods. For example, it can provide the ability to predict prices for a day, a few days, or a few weeks, and map the cost-effective use of load devices coupled to the grid 102 to the edge computing device 410.

[0049] In block 704, method 700 includes the edge computing device 410 receiving price predictions from ISO 604 or the headend system 602. Other devices and users may also receive price predictions. For example, devices that can be controlled in accordance with price predictions, such as consumers, electric vehicle chargers, solar power inverters, and battery storage systems, may also receive price predictions.

[0050] In block 706, method 700 includes an edge computing device 410 that makes a decision to increase or decrease the energy received by the load, or to sell energy from energy storage and generation devices such as electric vehicles, battery storage systems, solar inverters, solar battery storage systems, and wind turbines. The edge computing device 410 can also predict the load on a site associated with the edge computing device 410 based on factors such as weather, historical load, time of day, and day of the week, and the edge computing device can generate load forecasts for a specific period. The period used by the edge computing device may be the same as, but does not have to be, the same as, the interval used in block 704. Furthermore, the edge computing device 410 can use historical price trends to predict future prices that exceed the forecast amounts generated by ISO 604, utility headend system 602, or energy aggregator. Alternatively, the edge computing device can also generate its own price forecasts based on received price forecasts.

[0051] In some examples, the edge computing device 410 sends control commands to load devices based on price and load forecasts. For example, during low-cost time intervals, the edge computing device 410 can send control commands to electric vehicle chargers and battery system chargers to initiate charging. During high-cost time intervals, the edge computing device 410 can send control commands to electric vehicle chargers and battery system chargers to either sell energy back to grid 102 using the vehicle-versus-grid system or supply energy to the site associated with the electric vehicle and battery system. Furthermore, the edge computing device 410 can control demand response devices such as pool pumps, washing machines, and HVAC systems so that they operate normally using normal operating parameters during low-cost time intervals and in a reduced manner using energy-saving operating parameters during high-cost time intervals. The edge computing device may consider user input or commands when controlling load devices. The user can specify whether to maintain power to a device during a specific time period or to charge a device to a specific level by a specific time. The user can also tolerate consumption of a particular device during high-cost time intervals or under specific conditions.

[0052] The edge computing device 410 may include the ability to directly control the actions described above, as well as other actions that affect the total load and return energy to the grid. These actions may affect the system's peak demand, voltage regulation, system balancing, etc. In some examples, control commands can be entered directly from a local controller, such as the utility's headend system 602 or a secondary substation controller 606. In the example of a hierarchical system, the headend system may send commands to the secondary substation specifying the amount of energy that needs to be reduced or is available. In addition to the commands received from the headend system, the secondary substation may send other commands to the edge control device downstream of the substation based on other factors such as available energy, instantaneous power, and grid topology. The edge control device may control the load based on the commands received from the substation and other factors. Furthermore, the user, utility, or both may have the ability to change or override how the edge computing device 410 controls the load. Additionally, the meter 106 may include load balancing, which enables more accurate load forecasting and provides a more efficient granularity for load control. Once a control command is provided by the edge computing device 410, method 700 can return to block 702 for further price predictions for additional time intervals.

[0053] In additional examples, a control group, such as a utility or energy aggregator, may enter into contracts with consumers to control load devices based on prices generated by ISO 604. ISO 604 can set prices for 5-minute intervals or other time interval lengths. Based on the ISO 604 prices, the control group can determine the amount of energy available and the optimal time to sell or use that energy. Based on this determination, the control group can bid on a certain amount of energy at specific time intervals, based on energy availability and the set price. The control group sends commands back to grid 102 at the required time based on the energy bids, enabling energy flow from individual energy storage devices. In some examples, the control group may also send commands for delayed loads, such as pool pumps or HVAC systems, based on the bids.

[0054] Monitoring and controlling DER loads, such as electric vehicles and battery storage systems, can serve power companies by managing charging and other energy consumption by DER loads, distributing the load over time, and ensuring a relatively flat load curve. For example, in areas where multiple electric vehicles are charging at the fastest possible rate, the load curve may spike, potentially overloading local substations. By managing electric vehicle charging, such as slowing down charging or reducing the number of electric vehicles charging simultaneously, it is possible to extract a manageable load level from substations.

[0055] Controlling load and DER devices using multiple edge computing devices

[0056] Figure 8 is a block diagram illustrating exemplary power and communication connections between a distribution grid, meters, panels placed on the site, and one or more DER devices. As shown in the figure, grid 102 supplies electricity to site 802 via a distribution transformer 804, an electric meter 106, and an electric panel 108. The distribution transformer operates in a similar manner to the secondary substation controller 606 in Figure 6. Communication can be provided from the utility headend system 302 to the edge computing device 806 of the distribution transformer 804 and the edge computing device 410 of the electric meter 106.

[0057] In some examples, price predictions may be determined using machine learning techniques at an edge computing device 806 at a distribution transformer 804 and provided to an edge computing device 410 at an electric meter 106. For example, the edge computing device 410 can control a DER device, such as an electric vehicle charger 808 at an electric vehicle 810, based on the price predictions received from the edge computing device at the distribution transformer. In some examples, the edge computing device 806 can communicate directly with and control a DER device connected to an electric meter. In that case, the electric meter may not require its own edge computing device.

[0058] In an additional example, the edge computing device 806 of the distribution transformer 804 can be removed. In such an example, the edge computing device 410 of the electric meter 106 can perform the operations previously performed by the distribution transformer 804. Furthermore, in an example where the electric meter 106 is a multi-port meter that can directly measure the energy consumed or output by the electric vehicle charger 808, the electric panel 108 can be bypassed by the electric vehicle charger 808.

[0059] Furthermore, in examples where both the head-end system 302 and the electric vehicle charger 808 are communicatively coupled to a data network, direct communication can be provided from the head-end system 302 to the electric vehicle charger 808. In some examples, both the electric meter 106 and the head-end system 302 can communicate directly with the electric vehicle charger 808. Figure 8 shows the electric vehicle charger 808 and the electric vehicle 810, but other examples may include an inverter coupled to battery storage. In some examples, the inverter, battery storage, or both may be connected to on-site solar panels.

[0060] Figure 9 is a block diagram illustrating exemplary power and communication connections between a distribution network and multiple sites, including DER devices. In some examples, edge computing device 806 can provide general information, such as dynamic peak pricing (i.e., price forecast information determined by edge computing devices associated with distribution transformers), to each site 902, 904, and 906. Edge computing devices 410a, 410b, and 410c can use the general information to determine more detailed commands at sites 902, 904, and 906. For example, edge computing devices 410a, 410b, and 410c can generate control commands for electric vehicle chargers 808a, 808b, and 808c based on dynamic peak pricing received from edge computing device 806 of a distribution transformer.

[0061] For example, a control device such as the distribution transformer 804 or an edge computing device 806 in the power company's headend system 302 can also monitor the load on the distribution transformer 804. With the increasing prevalence of electric vehicles 810, which require large amounts of current for charging, simultaneously charging electric vehicles 810a, 810b, and 810c at each site 902, 904, and 906 under the distribution transformer 804 could cause an overload on the distribution transformer. To prevent overloads, the control device monitors the overall load, including the loads of electric vehicle chargers 808a, 808b, and 808c, as well as individual site loads, through direct communication with multiport meters 106a, 106b, and 106c, electric vehicle chargers, or through site load balancing. The control device can predict the load using weather, historical load and generated data, time of day, day of the week, geographical location, and other factors. If the prediction exceeds a specified threshold (e.g., 80% of the distribution transformer capacity), the controller sends commands to some or all of the end devices, such as meters 106a, 106b, and 106c, which can then control the electric vehicle chargers 808a, 808b, and 808c through direct control, such as shut-off commands, commands to reduce the charge rate, or commands to prevent charging from starting. Direct control from the end devices can reduce the charge rate of the electric vehicles 810a, 810b, 810c or the electric vehicle chargers 808a, 808b, 808c to below a specific charge rate level (e.g., 10%, 50%, 90%) or stop charging the electric vehicles 810a, 810b, and 810c.

[0062] In addition to using predicted loads to control the electric vehicles 810a, 810b, and 810c, the control device can also use instantaneous demand values ​​to determine the level of control required for the electric vehicles. This allows the control device to respond if the distribution transformer 804 approaches or exceeds its limits.

[0063] In an additional example, the edge computing device 806 of the distribution transformer 804 can be removed. In such an example, the edge computing devices 410a, 410b, and 410c of each house 902, 904, and 906 can perform the operations previously performed by the distribution transformer 804. Furthermore, in an example where the electric meters 106a, 106b, and 106c are multi-port meters that can directly measure the energy consumed or output by the electric vehicle chargers 808a, 808b, and 808c, the electric panels 108a, 108b, and 108c can be bypassed by the electric vehicle chargers 808a, 808b, and 808c. In an example where the edge computing device 806 is removed, the headend system 302 can generate general information and provide it to the edge computing devices 410a, 410b, and 410c.

[0064] Furthermore, in an example where the headend system 302 and the electric vehicle chargers 808a, 808b, and 808c are communicatively coupled to a data network, direct communication can be provided from the headend system 302 to the electric vehicle chargers 808a, 808b, and 808c. Additionally, while Figure 9 shows the electric vehicle chargers 808a, 808b, and 808c and the electric vehicles 810a, 810b, and 810c, other examples may include inverters coupled to a battery storage system. In some examples, the inverter, battery storage system, or both may be coupled to on-site solar panels.

[0065] Power quality control using aggregators and control devices

[0066] Figures 10A and 10B illustrate how edge control devices control power quality. Figure 10B is a block diagram of system 1000 used to regulate load control and pricing at critical distribution points 1004 on the grid, using an aggregator and control device 1002, which includes edge software. Critical distribution points may correspond to secondary substations or distribution transformers. The aggregator and control device controls the aggregation of loads and distributed energy resources to optimize various outputs. Implemented as software, the aggregator and control device 1002 utilizes artificial intelligence and machine learning to input weather, time, date, historical demand and energy production, geographical location, and control outputs in the form of price changes or direct control. The outputs are sent to electricity meters downstream of the critical distribution point. The aggregator and control device 1002 can also monitor power quality, such as voltage stability, at critical distribution points 1004, such as distribution transformers located between the grid and power quality meters 1006. The aggregator and control device may be part of the power quality meter 1006.

[0067] For example, the aggregator and control device 1002 can predict the load and power quality, including active and reactive power and voltage, using weather, historical load and generation data, time, day of the week, geographical location, real-time power quality measurements of the grid 102 by an effective power and reactive power (PQ) meter 1006, and other factors, and adjust the voltage accordingly. If the aggregator and control device 1002 determines that adjustment of the grid's power quality is desirable, it can send commands to end devices, such as the meter 106 communicating with electric vehicle charging stations 808a, 808b, 808c, and 808d, to control the effective and reactive power necessary to reduce charging to a specific level (e.g., 10%, 50%, 90%), stop charging, start supplying energy to the grid, or adjust the voltage. The aggregator and control device 1002 can receive an indication of the effective power P injected into the grid 102 by the electric vehicle charging stations 808a, 808b, 808c, and 808d, and the reactive power Q absorbed from the grid 102 by the electric vehicle charging stations 808a, 808b, 808c, and 808d.

[0068] Figure 10A illustrates how EV charging stations are controlled to address power quality issues. A power quality meter 1006 can detect power quality issues and communicate them to control devices, such as an aggregator and a control device 1002. The aggregator and control devices control EV charging stations 808a-808d by sending control signals to the charging stations to make adjustments to address power quality issues. In response to receiving control signals, each EV charging station can charge or discharge the connected EV battery. The control signals sent to the EV charging stations may require different actions from the EV chargers. The aggregator and control devices combine the outputs from the EV chargers and send the output to the grid to control the effective power and reactive power at the grid nodes.

[0069] Examples of machine learning models

[0070] In the system described above, multiple machine learning models may be in operation. These models may include, but are not limited to, models that generate price interval data operating in the headend system or secondary substations, models that generate future price data operating in the electricity meter, and models that control loads and DER devices operating in the meter.

[0071] Models that generate price data can predict energy prices and can be implemented using regression models. Both linear and nonlinear regression models can be used. Each regression model can be trained and validated using a dataset. The dataset is based on pricing data collected from historical periods across various conditions, dates, and times. Model inputs can be defined using conditions, dates, and times, and model outputs can be defined using historical prices. The dataset used for a model operating on an electric meter may differ from the dataset used for a model operating on a headend system or secondary substation. The data for a model operating on an electric meter may include meter-specific data. Meter-specific data may include temperature information related to the meter or site, rather than temperature information for a broader geographical area.

[0072] The dataset is divided into a training dataset and a validation dataset. The training dataset is used to train the regression model, and the validation dataset is used to validate the trained model. Between training and validation, the weights used in the model are adjusted until the model provides an acceptable level of accuracy. Options for measuring the model's accuracy include determining the percentage of intervals in which the model correctly predicts the price, or the percentage of intervals in which the model predicts a price within an acceptable range. Once the model is validated, it can be deployed to edge computing devices. The performance of the deployed model is monitored, and additional data is collected so that the model can be retrained using a new dataset if necessary. The retrained model can then be deployed to replace the original model.

[0073] Models that control loads and DER devices may use training sets containing different types of data than those used in models that predict prices. The datasets can be based on control data collected from historical periods across various conditions and time periods. The data may include, but is not limited to, data related to specific types of loads or DER devices.

[0074] Figure 14 illustrates an exemplary method for training and validating a linear regression model for predicting pricing, which can be used to generate price interval data or future pricing data. The dataset 1402 includes the training dataset 1404 and the validation dataset 1406. The training dataset is used to train the linear regression model 1410. Once the trained model 1412 is available, the trained model is validated using the validation dataset. The model training and validation process involves many additional steps not shown in Figure 14, such as adjusting the weights used in the model to improve the accuracy of the price output in both training and validation. Once the model validation is complete, the model can be deployed.

[0075] Edge computing devices and multiport meters

[0076] Figure 11 is a block diagram showing the arrangement of a multiport meter 1102 electrically coupled to a solar inverter 1104. The solar inverter 1104 receives DC power from a photovoltaic solar panel 1106. The solar inverter 1104 converts the DC power to generate AC power, which it supplies to the multiport meter 1102. The multiport meter 1102 can supply the AC power from the solar inverter 1104 to the grid 102 or site 104. In one example, the multiport meter 1102 may receive instructions from an edge computing device 1108 to discharge energy production from the solar inverter 1104 as needed. Measurement of the solar inverter 1104 can be performed via the multiport meter 1102. Furthermore, the edge computing device 1108 can be placed in the multiport meter 1102 to perform edge decisions regarding the control of distributed energy resource devices such as the solar panel 1106 which is electrically coupled to the multiport meter 1102 via the solar inverter 1104.

[0077] Figure 12 is a block diagram showing the arrangement of a multiport meter 1102 electrically coupled to an inverter 1204 of a solar panel 1206, a battery storage system 1208, or both. The inverter 1204 receives DC power from the photovoltaic solar panel 1206 or the battery storage system 1208. The inverter 1204 converts the DC power to generate AC power, which is supplied to the multiport meter 1102. The multiport meter 1102 can supply the AC power from the inverter 1204 to the grid 102 or site 104. In one example, the multiport meter 1102 may receive instructions from an edge computing device 1108 to release energy production from the inverter 1204 as needed, for example. In such an example, the energy generated by the solar panel 1206 may be stored in the battery storage system 1208 for future use. Measurement of the inverter 1204 may be performed via the multiport meter 1102. Furthermore, the edge computing device 1108 can be placed in the multiport meter 1102 to make edge decisions regarding the control of distributed energy resource devices such as solar panels 1206 and battery storage systems 1208, which are electrically coupled to the multiport meter 1102 via inverter 1204.

[0078] Figure 13 is a block diagram showing the arrangement of a multiport meter 1102 electrically coupled to an electric vehicle charger 1304. Measurements of the electric vehicle charger 1304 can be performed through the multiport meter 1102. The electric vehicle charger 1304 can rectify the AC main power received by the multiport meter 1102 from the grid 102 and apply the rectified DC power to charge the electric vehicle 1306. In some examples, the electric vehicle charger 1304 can also convert the DC power from the electric vehicle 1306 in a way that enables the provision of AC power available for use in the grid 102 or site 104. In one example, an edge computing device 1108 can instruct the electric vehicle charger 1304 to charge or discharge the battery of the electric vehicle 1306 at the appropriate time. For example, the edge computing device 1108 can instruct the electric vehicle charger 1304 to control the charging or discharging level or voltage of the electric vehicle 1306 based on predicted energy prices or based on predicted overloads of distribution transformers associated with the site 104 of the multiport meter 1102.

[0079] The operation of the headend systems, gateway devices, measurement modules, electric meters, edge computing devices, aggregators and control devices, and communication modules described herein can be performed by any suitable computer system. The computing system may include one or more processing elements that execute computer-executable program code stored in a memory device. The memory device may include any suitable computer-readable medium for storing program code and data. The computing system may configure its processing elements to execute program code and perform one or more operations described herein. The computing system may include other components such as one or more network interface devices for establishing connectivity to a network and communicating over the network, and input / output devices such as display devices.

[0080] While the spirit of the present invention is described in detail with respect to certain embodiments, it will be recognized that those skilled in the art, having attained the foregoing understanding, can readily create modifications, variations, and equivalents to such embodiments. 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 spirit of the invention that would be readily understood by those skilled in the art.

Claims

1. An electric meter including an edge computing device related to the site, The electric meter is configured to control a first load device associated with the site, The site is located downstream of the distribution transformer. The aforementioned electric meter is A plurality of downstream edge computing devices located downstream of the distribution transformer, and a communication module for communicating with transformer edge computing devices associated with the distribution transformer, A first port for connecting the first load device to the electric meter, Includes, The edge computing device is Processor and Memory for storing computer-readable instructions and Includes, When executed by the aforementioned processor, the computer-readable instruction is: The load prediction information for the first load device and at least one second load device is transmitted to the transformer edge computing device via the communication module. Receiving price interval data from the transformer edge computing device via the communication module, wherein the price interval data provides a predicted price for each of a plurality of future time intervals, and receiving Controlling the first load device based on the price interval data, and by controlling the first load device, If the price interval data includes low-cost time intervals, the first load device can consume power and operate using one or more normal operating parameters during the subsequent time intervals, and If the price interval data includes high-cost time intervals, the first load device is controlled to operate using one or more energy-saving operating parameters during the subsequent time intervals. The first load device performs the following actions: controlling the first load device Configure the edge computing device to perform the following: The edge computing device further includes a second port for connecting a second load device related to the site to the electric meter, The aforementioned electric meter further, After receiving price interval data from the transformer edge computing device, the process involves determining future price data, wherein the future price data corresponds to a plurality of time intervals following the current time interval. Based on the aforementioned future price data, the second load device is controlled. It is configured to do, Electric meter.

2. The first load device is an electric vehicle (EV) charger, Controlling the first load device to operate using one or more energy-saving parameters during a future time interval includes reducing the charge level of the electric vehicle charger from its current charge level during a future time interval. The electric meter according to claim 1.

3. The edge computing device further includes a second port for connecting a second load device related to the site to the electric meter, The aforementioned electric meter further, Receiving user commands that provide instructions for controlling the second load device, Controlling the second load device based on the price interval data and the user command, and by controlling the second load device, If the price interval data includes high-cost time intervals and the user command allows the normal operation of the second load device, the second load device can operate using one or more normal operating parameters during the next time interval. Controlling the second load device and It is configured to do, The electric meter according to claim 1.

4. The edge computing device further includes a second port for connecting a distributed energy resource (DER) device associated with the site to the electric meter. The aforementioned electric meter further, If the price interval data includes high-cost time intervals, the DER device is controlled to output power to the power grid. It is configured to do, The electric meter according to claim 1.

5. The edge computing device is further configured to transmit information regarding the control of the first load device to other electric meters downstream of the distribution transformer. The electric meter according to claim 1.

6. The edge computing device is further configured to receive communications from the transformer edge computing device in order to control the first load device. The electric meter according to claim 1.

7. The price interval data is partially based on the rating of the distribution transformer. The electric meter according to claim 1.

8. A method for controlling at least one load device and at least one distributed energy resource (DER) device, Receiving price interval data at an edge computing device associated with an electric meter connected to the power grid and the site, wherein the price interval data provides a predicted price for each of several future time intervals. The edge computing device determines future price data, wherein the future price data corresponds to multiple time intervals following a future time interval. Controlling a plurality of load devices associated with the electric meter based on the price interval data and the future price data, wherein the load devices associated with the electric meter include the at least one load device and the at least one DER device, and by controlling the plurality of load devices associated with the electric meter, If both the price interval data and the future price data include low-cost time intervals, then at least one load device can consume power and operate using one or more normal operating parameters. If both the price interval data and the future price data include high-cost time intervals, control the at least one DER device to output power to the power grid, and If the price interval data includes high-cost time intervals and the future price data includes low-cost time intervals, the control of at least one load device to change its operation so that it operates using one or more energy-saving operating parameters during the upcoming time intervals and then operates using one or more normal operating parameters after the upcoming time intervals. This is done by a plurality of load devices associated with the electric meter, and controls the plurality of load devices associated with the electric meter. Methods that include...

9. The at least one DER device has a charging mode, Furthermore, If both the price interval data and the future price data include low-cost time intervals, then at least one DER device can consume power and operate in the charging mode. The method according to claim 8, including the method described in claim 8.

10. Furthermore, The edge computing device receives user commands for at least one load device, Controlling the at least one load device, and by controlling the at least one load device, If both the price interval data and the future price data include high-cost time intervals, then at least one load device can consume power and operate using one or more normal operating parameters. Controlling the at least one load device to perform the following: The method according to claim 8, including the method described in claim 8.

11. The aforementioned at least one load device is an electric vehicle (EV) charger, Controlling the at least one load device to change its operation to use one or more energy-saving operating parameters during a future time interval includes reducing the charge level of the electric vehicle charger from its current charge level during a future time interval. The method according to claim 8.

12. The price interval data and the future price data are determined separately, and the future price data is based on meter-specific data. The method according to claim 8.

13. The edge computing device receives communication from a transformer edge computing device for controlling at least one load device, and the electric meter is located downstream of the distribution transformer associated with the transformer edge computing device and receives the communication. The method according to claim 8, including the method described in claim 8.

14. In a method for controlling multiple chargers, Each charger is associated with a meter and site. The aforementioned method, To provide an edge computing device related to a power distribution transformer, wherein the power distribution transformer supplies power to each of the sites, The edge computing device predicts the predicted load associated with the distribution transformer, The edge computing device predicts the predicted load for each of the sites, Controlling the charger based on the predicted load predicted by the edge computing device and the predicted load of each individual site, and by controlling the charger, If the predicted load exceeds a threshold, the method involves controlling at least one charger of the charger to reduce the charge rate of the at least one charger of the charger, wherein the at least one charger of the charger is selected based on the predicted load of each site to the site related to the at least one charger of the charger, and, If the predicted load does not exceed the threshold, the charger will be charged. To control the charger and Methods that include...

15. Furthermore, The edge computing device receives load data from each meter related to the charger, Using the load data to predict the load of each of the aforementioned sites The method according to claim 14, including the method described in claim 14.

16. Furthermore, If the predicted load exceeds a threshold, the charger controls at least one of the chargers of the charger to reduce the charge level of that at least one charger, and then allows the second charger to maintain the second charge level. The method according to claim 14, including the method described in claim 14.

17. Furthermore, The process involves generating price interval data, wherein the price interval data provides a predicted price for each of a plurality of future time intervals. To communicate the price interval data to the meter associated with the charger. The method according to claim 14, including the method described in claim 14.

18. The method according to claim 14, wherein controlling at least one charger of the charger to reduce the charge rate of the at least one charger of the charger includes controlling the plurality of chargers to reduce the charge rate of each of the plurality of chargers.

19. The decrease in the charge rate of the first charger and the decrease in the charge rate of the second charger are different. The method according to claim 18.

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