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 / 145660, filed February 4, 2021, the entire contents of which are incorporated herein by reference.

[0002] Technical Field The present disclosure relates generally to grid control systems and, more particularly, to distributed control of charging of electric vehicles and energy storage devices to maintain grid stability. [Background technology]

[0003] background Resource distribution systems, such as power grids that supply electricity, use meters to measure and control consumption at customer premises. Meters may include a metering module for measuring and monitoring power consumption characteristics, a communications 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, water turbines, batteries, electric vehicle (EV) chargers, electric vehicles, energy storage devices, and generators, are located on a customer's premises, power generated or stored by the DER devices may be used on the premises or output to the power grid. Additionally, electric vehicle chargers, electric vehicles, and energy storage devices may receive power from the power grid for later storage and use. In some systems, separate meters and meter sockets are required to connect the DER devices to the grid. These separate devices may not allow a central system, such as a head-end system, to control the connection of the DER devices to the grid. In other systems, integrated devices 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. As such, improved systems that control and communicate with the premises' DER devices are needed. Summary of the Invention [Means for solving the problem]

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

[0006] In some examples, an edge computing device of an electric meter generates future price data, which corresponds to a time interval after the time interval included in the price interval data and may be based on meter-specific data.

[0007] In some examples, an edge computing device is associated with a distribution transformer and controls chargers, such as EV charging, at premises downstream of the distribution transformer. The edge computing device determines a predicted load on the transformer and a predicted load on the premises and uses the predictions to control the chargers. The edge computing device can control the charging speed of the chargers or when the chargers charge.

[0008] These exemplary aspects and features are mentioned not to limit or define the presently described inventive subject matter, but to provide examples to aid in understanding the concepts described in this application. Other aspects, advantages, and features of the presently described inventive subject matter will become apparent after review of the entire application. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram illustrating example power connections between an electric grid, a meter, a panel located on a site, and multiple DER devices. [Diagram 2] FIG. 2 is a block diagram illustrating an example of a panel connected to a meter, multiple DER devices, and multiple loads. [Diagram 3] FIG. 3 is a block diagram illustrating a portion of an example communications network for communication between a head-end system, a meter, and multiple DER devices. [Figure 4] FIG. 4 is a block diagram illustrating a portion of an example communication network topology for the communication network of FIG. [Diagram 5] FIG. 5 is a block diagram illustrating a portion of an example communications network for communication between a headend system, a gateway device, a meter, and multiple DER devices. [Figure 6] FIG. 6 is a block diagram illustrating a portion of an example communication and control network for communication between a head-end system, an independent system operator, meters, and multiple DER and load devices metered by the meters. [Figure 7] FIG. 7 is a flow diagram illustrating an example method for controlling load or DER devices in an electrical grid. [Figure 8] FIG. 8 is a block diagram illustrating example power and communication connections between the electric grid, a meter, a panel located on a site, and one or more DER devices. [Figure 9] FIG. 9 is a block diagram illustrating example power and communication connections between an electric grid and multiple premises that contain DER devices. [Figure 10A] FIG. 10A is a block diagram illustrating an example of the operation of an aggregator and a control device. [Figure 10B] FIG. 10B is a block diagram of an example system used to regulate voltage on a grid using an aggregator and a control device. [Figure 11] FIG. 11 is a block diagram illustrating an exemplary arrangement of a multiport meter electrically coupled to a solar inverter. [Figure 12] FIG. 12 is a block diagram illustrating an exemplary arrangement of a multiport meter electrically coupled to an inverter for a solar panel, battery storage, or both. [Figure 13] FIG. 13 is a block diagram illustrating an exemplary arrangement of a multiport meter electrically coupled to an electric vehicle charger. [Figure 14] FIG. 14 is a diagram illustrating training and validation of an exemplary machine learning model. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The present invention provides a system for supporting communication between and among edge computing devices, and a head-end system for controlling load devices and distributed energy resource (DER) devices. DER devices include devices that can supply energy to a power grid or a site. Energy can be generated by devices such as solar panels or stored by devices such as batteries. Some DER devices, such as batteries, may have the ability to both supply and consume energy. DER and load devices can be controlled at least in part based on predictive pricing information. Predictive pricing information includes many factors such as, but is not limited to, ratings of distribution utility equipment such as distribution transformers, current and predicted weather conditions, historical patterns of generation and load, and user input. Controlling a device can include operating the device using normal operating parameters, modifying the operation of the device to use energy saving operating parameters, or disconnecting the device. Controlling a DER device can include commands to connect the DER device to the grid, disconnect the DER device from the grid, connect or disconnect the DER device to a site, and adjust parameters of the DER device.

[0011] 1 illustrates an example power connection between an electrical 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 premises. The meter may include a metering module 122, a communications module 132, a disconnect switch 142, and other components. The metering module measures the consumption of electrical energy and may provide revenue-grade metering and load profiling. The communications module communicates with a central or head-end system via a communications network (not shown). The metering module and communications 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 premises.

[0013] In FIG. 1, each DER device 110a, 110b, 110n may be connected to the grid and the premises via a panel 108. Each DER device may include a measurement module 120a, 120b, 120n, a communication module 130a, 130b, 130n, and a control device 140a, 140b, 140n (such as an inverter or a disconnect switch). The parameters or states of the control device may be controlled by a controller. The measurement module measures the power generated by the DER device. It measures the amount of energy provided by the DER device to the premises or grid and the time the energy is provided. The measurement by the measurement module may be accurate enough to provide revenue level billing. Alternatively, the measurement module may provide measurements or data for billing other than revenue grade. The measurement module may provide load profiling that may use appropriate interval lengths (e.g., 1 minute intervals, 5 minute intervals, 15 minute intervals) as required by the relevant regulator. It 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. The communication module communicates with a central system via a communication network (not shown). The measurement module and communication module may be separate modules or may be combined into a single module. The control device controls the connection of the outputs of the DER devices to the panels.

[0014] 1 shows the DER devices 110a, 110b, 110n connected to the panel 108, alternative configurations are possible and may be supported by the measurement modules 120a, 120b, 120n and communication modules 130a, 130b, 130n in the DER devices. Alternatives include connecting one or more DER devices to a multi-port meter or connecting the DER devices directly to the grid. In such an example, the measurement modules 120a, 120b, 120n may form part of the meter 106 that can measure the individual loads of the DER devices 110a, 110b, 110n.

[0015] The communications module of the DER device allows for low latency two-way communications with the DER device. These communications may provide more timely information regarding the operation of the DER device and may allow for greater control of the DER device than is currently available.

[0016] Measurement and communication modules can be added to any type of DER device to enable the DER device to operate in the system of Figure 1. These modules (or standalone modules, if combined) can be integrated into the hardware design of the DER device. There may be pads or connectors designed into 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 and communication modules in the DER device can communicate with other components of the DER device via wired connections, wireless connections, or a combination thereof.

[0017] The meter 106 connects the premises to the grid through a panel 108. The panel may include a main circuit breaker that controls the premises' connection to the meter as well as additional circuit breakers for the DER devices and loads at the premises. Figure 2 shows an example panel 208 with a main circuit breaker 202, circuit breakers for each DER device 204a, 204b, 204n, and circuit breakers for the premises loads 206a, 206b, 206n.

[0018] Metered communications

[0019] In addition to the power connections shown in Figures 1 and 2, meters and DER devices are connected through one or more communication networks. Figure 3 shows a portion of one example communication network. The communication network includes communication channels between the meter 106 and the DER devices 110a, 110b, 110n. Each communication module in the DER device communicates with a communication module in 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 modules in the DER devices can communicate information about the energy generated or delivered to the grid and information about the status of the device.

[0020] The communications module in the meter can receive commands or control instructions sent from the head-end system 302 and route the commands to the appropriate DER device. It can also receive communications from the DER devices and send the communications to the head-end system.

[0021] In addition to, or as an alternative to, routing communications between the head-end system and the DER device, the meter may generate commands and send them to one or more communication modules in the DER device, and the meter may receive information from the DER device that may be provided to the head-end system or used by the meter.

[0022] In Figure 3, all communications between the DER device and other devices are 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 communications network, or the meter can communicate with the DER device using one network protocol and with other devices on the communications network using a different network protocol.

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

[0024] As an alternative to communications between the DER devices and the head-end system going through the meter as shown in Figure 3, one or more DER devices can be configured so that communications between the head-end system and the DER devices go through a node other than the meter. For example, communications between the head-end system 302 and the DER device 110a can be routed through a device on the network other than the meter 106 on the same premises.

[0025] In any of these examples, communications for other devices on the network may be routed through the meter 106. As shown in Figure 4, the meter may be connected to a network of meters and other devices, and communications between the head-end system 402 and the meter 106 may be communicated between multiple devices over one or more networks.

[0026] In some networks, the communication modules of the meters and / or DER devices may communicate with devices that provide edge computing services. The edge computing services or edge computing devices 410 may be located topologically close to the DER devices on the communications network and thus provide low latency communication and control.

[0027] Communication using a gateway device

[0028] 5 illustrates another example communication network. In FIG. 5, communication modules in the DER devices 110a, 110b, 110n communicate with a gateway device 504. The communication with the gateway device can use any type of communication protocol, including proprietary or non-proprietary protocols, such as Zigbee, Wi-Sun, or WiFi communication protocols. The communication modules in the DER devices can communicate information about the energy generated or provided to the grid and information about the status of the devices.

[0029] Communications between the meter and the head-end system may not be routed through a gateway device. Instead, the meter may communicate with the head-end system as shown in FIG. 3, or may communicate with the head-end system through a PLC network, or the meter may communicate through a gateway device.

[0030] The gateway device may be installed at or near the premises. The gateway device communicates with the head-end system 502 over one or more networks. In addition to communication functions, the gateway device may be an edge computing device that provides edge computing services, such as services supporting metering and control of the DER devices. The head-end system may communicate with the DER devices via communications routed through the gateway device.

[0031] In some examples, the gateway device 504 is located on the premises, such as a premises gateway device or a home gateway device. Communication with the premises gateway device 504 can use any type of communication protocol, including proprietary or non-proprietary protocols, such as Zigbee, Wi-Sun, or WiFi communication protocols. A communication module in the DER device can communicate information about the energy generated or provided to the grid and information about the status of the device.

[0032] A premises gateway device may be installed at the premises. For example, a home gateway device may be located inside or outside a building at the premises. The home gateway device communicates with the head end system over one or more networks. In addition to communication functions, the premises gateway device may also provide edge computing services including metering and control of DER devices.

[0033] In an additional example, the gateway device 504 may be a cellular base station that communicates with the meters and the DER devices over a cellular network, which may be a public or private cellular network. A communication module in the DER device may communicate information about the energy generated or provided to the grid and information about the status of the device.

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

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

[0036] FIG. 6 is a block diagram illustrating a portion of an example communication and control network for communication between a head-end system 602, an independent system operator (ISO) 604, a meter 106, and multiple DER and load devices metered by the meter 106. Communication between the components illustrated in FIG. 6 can be performed using any of the techniques described above with respect to FIGS. 1-5. As illustrated, the electric meter 106 includes multiple ports for electrically coupling, communicatively coupling, or electrically and communicatively coupling the electric meter 106 to the DER and load devices. For example, the electric meter 106 can include ports for a battery storage device 610a, an inverter 610b connecting to a solar panel, an electric vehicle (EV) charger 610c connected to an electric vehicle, a washer / dryer 612, an HVAC system 614, an in-home display or smart thermostat 616, a smart switch 618 connected to an outlet device, 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 device to the grid 102. Although FIG. 6 includes one electric meter 106, there may be multiple electric meters connected to a distribution transformer associated with a secondary substation, with each meter coupled to a respective set of loads.

[0037] For example, a distribution transformer associated with the secondary substation controller 606 may become overloaded if multiple electric vehicle chargers 610c downstream of the distribution transformer begin charging simultaneously. To prevent overloading of the distribution transformer, distributed intelligence, such as commands generated by the edge computing device 410 and distributed to networked devices, may be used to manage the charging of multiple electric vehicles or other battery storage devices 610a by reducing the duty cycle of power provided 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] When there are multiple meters with edge computing devices connected downstream of the same secondary substation, communication and coordination can 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 have each edge computing device make its own decisions and communicate those decisions to the other edge computing devices so that an edge computing device can take into account actions taken or to be taken by other edge computing devices when determining its own actions.

[0039] In some examples, the demand for the distribution transformer may be predicted by a machine learning algorithm running on the edge computing device 410 at the meter 106 or a remote system such as the secondary substation 606 or the head-end system 602. For example, the machine learning algorithm may learn the power consumption trends over time 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 past energy demand, time of day, day of the week, month, geographic location, current and forecasted weather conditions, past production by downstream DER devices, transformer and equipment ratings, a model of the distribution system or part of the distribution system that includes the distribution transformer, and flexible load factors. The flexible load factors are related to loads that may be or are forecasted to be discharged. Once the power consumption trends are learned, the edge computing device 410 may generate control commands for devices during times that may stress the distribution transformer. In one example, the control commands may include controlling the charging rate of the electric vehicle charger 610c or the battery storage device 610a, or controlling the delivery of power from the electric vehicle, the battery storage device 610a, and the inverter 610b to the grid 102. Other control commands may also be implemented to reduce or increase energy consumption downstream from the distribution transformer by controlling non-essential loads on or off, discharging storage devices (e.g., energy storage devices or electric vehicles), or increasing the energy consumption of flexible loads (e.g., energy storage devices or electric vehicles). For example, the control commands may include commands to open or close circuit breakers to electrically isolate or couple the DER devices to the grid 102 or other on-premises loads, as described above with respect to FIG. 2.

[0040] In one 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 imbalance into the multi-phase power system provided by the grid 102. For example, if several electric vehicles start charging at once or if the output of a solar device peaks during a peak period. Distributed intelligence can assist in timing the charging and discharging of the DER devices 610a-610c to provide a balancing effect on the loads between the phases. As an example, machine learning techniques can learn over time the power consumption trends of devices downstream of the secondary substation controller 606 that result in phase imbalance. For example, once the power phase imbalance trends are learned, the edge computing device 410 can generate control commands for devices operating in various phases of the grid 102 during a forecast period when a phase imbalance is expected. As an example, the control commands can include controlling the charging rate of the electric vehicle or battery storage device 610a, or controlling the delivery of power from the battery storage device 610a, the inverter 610b, or the electric vehicle to the grid. Other control commands may also be implemented to balance the load of phases downstream from the distribution transformer.

[0041] Machine learning techniques enable automated decision making based on load demand, avoiding user involvement that can lead to inefficiencies and inaccuracies. Additionally, machine learning techniques can provide prediction and preventative recommendations to avoid adverse impacts to the grid, such as overloading distribution transformers.

[0042] In an additional example, the demand curve for the grid 102 combined with the supply curve for solar power generation from the inverter 610b and other factors can result in large variations in the required energy generation. Using distributed intelligence and control can provide a mechanism to smooth the demand and supply curves, potentially reducing the overall energy production cost. The demand and supply curves can relate to an area of ​​any size, from an entire area to a microgrid. The ISO 604 or edge computing device 410 can forecast the energy production cost for a period of time. The energy production cost can be based on the load demand experienced by the grid 102 during the period of time. As an example, the demand and supply curves can be smoothed by performing operations that take advantage of low energy costs during low-cost times, such as charging the electric vehicle and battery storage 610a, and further reducing consumption during high-cost times, such as discharging the electric vehicle and battery storage 610a using a vehicle-to-grid system to sell energy to the grid 102, and further selling other available power from other DER devices to the grid 102. In some instances, forecasting energy production costs can help maximize utilization of available solar power production and minimize use of non-renewable energy.

[0043] Such technology can address inefficient use of energy by end consumers that typically results in increased overall energy costs and carbon emissions. This technology could provide incentives to customers to sell energy when it is needed by the grid 102 (e.g., during high cost times) and consume energy when it is optimal for the grid 102 (e.g., during low cost times), in line with the true cost of energy production. This technology could also provide utilities with the option to directly control energy usage for the same purpose.

[0044] In some examples, DER devices can also address the effects of highly reactive loads and generators by regulating the voltage of the grid 102. For example, if a highly reactive load begins to consume power from the grid 102, the edge computing device 410 can provide commands to DER devices coupled to the grid 102 via the meters 106 to discharge so that the voltage of the 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, several electric vehicle charging stations can be aggregated as a source of voltage regulation if electric vehicles with sufficient charge are electrically coupled to the charging stations.

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

[0046] Examples of Technologies for Controlling Loads or DER Devices

[0047] As described above, incorporating communication modules into DER devices and other load devices and connecting the DER devices and other load devices to a communication network supports improved device control and connectivity.

[0048] An exemplary method 700 for controlling DER devices and loads, such as electric vehicles and energy storage devices, is shown in FIG. 7. At block 702, the method 700 includes an ISO 604, a utility head-end system 602, or an energy aggregator determining energy selling and purchasing prices for a forecasted interval. The ISO 604, head-end system 602, or energy aggregator may use machine learning techniques to learn past demand cycles, weather conditions, and other factors that contribute to energy generation costs and leverage those past factors to set energy prices for the forecasted interval. Inputs to the machine learning algorithm may include current and past energy demand, time of day, day of the week, day of the month, geographic location, and other factors related to how the facility consumes energy. In some examples, the intervals may be set as five-minute blocks with prices changing at the interval boundaries. Shorter or longer intervals may also be used. Prices may be predictable or set over extended periods of time. For example, prices can be forecasted for a day, several days, several weeks, etc., providing the edge computing device 410 with the ability to map cost-effective usage of load devices coupled to the grid 102.

[0049] At block 704, the method 700 includes the edge computing device 410 receiving the price prediction from the ISO 604 or the head-end system 602. Other devices or users may also receive the price prediction. For example, consumers, electric vehicle chargers, solar power inverters, battery storage systems, and other equipment that can be controlled in response to the price prediction may also receive the price prediction.

[0050] At block 706, the method 700 includes the edge computing device 410 making a decision to increase or decrease the energy received at the load or sell energy from an energy storage and generation device, such as an electric vehicle, a battery storage system, a photovoltaic inverter, a photovoltaic battery storage system, a wind turbine, etc. The edge computing device 410 may also forecast the load for a premises associated with the edge computing device 410 based on factors such as weather, historical load, time of day, day of week, etc., and the edge computing device may generate a load forecast for a particular time period. The time period used by the edge computing device may be, but need not be, the same as the interval used in block 704. Additionally, the edge computing device 410 may use historical price trends to forecast future prices that exceed forecast amounts generated by the ISO 604, the utility head end system 602, or an energy aggregator. Alternatively, the edge computing device may generate its own price forecast based on the received price forecast.

[0051] In some examples, the edge computing device 410 sends control commands to the load devices based on price and load predictions. For example, during low-cost time intervals, the edge computing device 410 can send control commands to the electric vehicle charger and the battery system charger to initiate charging. During high-cost time intervals, the edge computing device 410 can send control commands to the electric vehicle charger and the battery system charger to sell energy back to the grid 102 using a vehicle-to-grid system or to supply energy to a premises associated with the electric vehicle and the battery system. Additionally, the edge computing device 410 can control demand response devices, such as pool pumps, washing machines, and HVAC systems, to 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 can take user input or commands into account when controlling the load devices. A user can specify whether to maintain power on a device during a certain time period or to charge the device to a certain level by a certain time. A user can also tolerate consumption of a particular device during high-cost time intervals or under certain conditions.

[0052] The edge computing device 410 may include the ability to directly control the above actions and other actions that affect the total load and return energy to the grid. These actions may affect system peak demand, voltage regulation, system balancing, and the like. In some examples, the control commands may come directly from a local controller, such as the utility's head-end system 602 or secondary substation controller 606. In an example of a hierarchical system, the head-end system may send commands to the secondary substation specifying the amount of energy that needs to be curtailed or is available. In addition to the commands received from the head-end system, the secondary substation may send other commands to an edge control device downstream of the substation based on other factors such as available energy, instantaneous power, grid topology, and the like. The edge control device may control the load based on commands received from the substation and other factors. Additionally, the user, the utility, or both may have the ability to modify or override how the edge computing device 410 controls the load. Additionally, the meter 106 may include load balancing, which allows for more accurate load forecasting and provides more efficient granularity for load control. Once the control command is provided by the edge computing device 410, the method 700 may 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, can enter into a contract with consumers to control load devices based on a price generated by the ISO 604. The ISO 604 can set prices for 5-minute intervals or other time interval lengths. The control group can determine the amount of energy available and the optimal time to sell or use the energy based on the ISO 604 price. Based on this determination, the control group can bid a certain amount of energy for a particular time interval based on the energy availability and the set price. The control group sends commands back to the grid 102 to enable energy flow from individual energy storage devices at the times needed based on the energy bids. In some examples, the control group may also send commands for delayed loads, such as pool pumps and HVAC systems, based on the bids.

[0054] Monitoring and control of DER loads, such as electric vehicles and battery storage systems, can provide a service to utilities to manage charging and other energy consumption by DER loads, spreading the load over time to ensure a relatively flat load curve. For example, an area with multiple electric vehicles charging at their fastest charging rates can create a spike in the load curve and overload the local substation. Managing the charging of electric vehicles, such as by slowing down charging or reducing the number of electric vehicles charging at the same time, can draw an acceptable load level from the substation.

[0055] Controlling Load and DER Devices Using Multiple Edge Computing Devices

[0056] 8 is a block diagram illustrating example power and communication connections between an electric grid, a meter, a panel located on a premises, and one or more DER devices. As shown, the grid 102 provides electricity to a premises 802 via a distribution transformer 804, an electric meter 106, and an electric panel 108. The distribution transformer operates in a manner similar to the secondary substation controller 606 of FIG. 6. Communications may be provided from the utility head-end system 302 to an edge computing device 806 of the distribution transformer 804 and to an edge computing device 410 of the electric meter 106.

[0057] In some examples, price predictions may be determined using machine learning techniques at the edge computing device 806 at the distribution transformer 804 and provided to the edge computing device 410 at the electric meter 106. For example, the edge computing device 410 may 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 may directly communicate with and control the DER devices connected to the electric meter. In that case, the electric meter may not need 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 operations previously performed at the distribution transformer 804. Furthermore, in examples 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] Additionally, in examples where both the head-end system 302 and the electric vehicle charger 808 are communicatively coupled to a data network, communications may be provided directly 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 may be in direct communication with the electric vehicle charger 808. While FIG. 8 illustrates an electric vehicle charger 808 and an electric vehicle 810, other examples may include an inverter coupled to battery storage. In some examples, the inverter, the battery storage, or both may be connected to solar panels on the premises.

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

[0061] In one example, a control device such as a distribution transformer 804 or an edge computing device 806 in the utility head-end system 302 can also monitor the load on the distribution transformer 804. With the proliferation of electric vehicles 810 that require large amounts of current to charge, simultaneously charging electric vehicles 810a, 810b, 810c at each site 902, 904, 906 under the distribution transformer 804 can cause an overload on that distribution transformer. To prevent overload, the control device monitors the overall load and individual site loads, including the loads on electric vehicle chargers 808a, 808b, 808c, via multiport meters 106a, 106b, 106c, direct communication with the electric vehicle chargers, or load balancing for the sites. The control device can forecast the load using weather, historical load and generation data, time of day, day of the week, geographic location, and other factors. If the prediction exceeds a specified threshold (e.g., 80% of the distribution transformer capacity), the controller can send 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 a shutoff command, a command to reduce the charge rate, or a command to prevent charging from starting. Direct control from the end devices can cause the charge rate of electric vehicles 810a, 810b, 810c or electric vehicle chargers 808a, 808b, 808c to drop below a particular charge rate level (e.g., 10%, 50%, 90%, etc.) or stop charging of electric vehicles 810a, 810b, 810c.

[0062] In addition to using the predicted load to control electric vehicles 810a, 810b, and 810c, the control device can also use the instantaneous demand values ​​to determine the level of control required for the electric vehicles, allowing the control device to react if 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, 410c of each residence 902, 904, 906 can perform the operations previously performed at the distribution transformer 804. Furthermore, in an example where the electric meters 106a, 106b, 106c are multi-port meters that can directly measure the energy consumed or output by the electric vehicle chargers 808a, 808b, 808c, the electric panels 108a, 108b, 108c can be bypassed by the electric vehicle chargers 808a, 808b, 808c. In an example where the edge computing device 806 is removed, the head-end system 302 can generate and provide general information to the edge computing devices 410a, 410b, 410c.

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

[0065] Controlling power quality using aggregators and control devices

[0066] 10A and 10B show how edge control devices control power quality. FIG. 10B is a block diagram of a system 1000 using an aggregator and control device 1002 including edge software to regulate load control and pricing at critical distribution points 1004 on the grid. The 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. When implemented as software, the aggregator and control device 1002 utilizes artificial intelligence and machine learning, inputting weather, time, date, historical demand and energy production, geographic location and control output in the form of price changes or direct control. The output is sent to an electric meter 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 a power quality meter 1006. The aggregator and control device may be part of the power quality meter 1006 .

[0067] In one example, the aggregator and control device 1002 can use weather, historical load and generation data, time of day, day of the week, geographic location, real-time power quality measurements of the grid 102 by real and reactive power (PQ) meters 1006, and other factors to predict load and power quality, including real and reactive power and voltage, and adjust voltage accordingly. If the aggregator and control device 1002 determines that an adjustment to the power quality of the grid is desirable, the aggregator and control device 1002 can send commands to end devices, such as meters 106 in communication with electric vehicle charging stations 808a, 808b, 808c, and 808d, to reduce charging below a particular level (e.g., 10%, 50%, 90%, etc.), to stop charging, to begin supplying energy to the grid, or to control the real and reactive power required to adjust voltage. The aggregator and control device 1002 may receive an indication of the real 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] FIG. 10A illustrates a method for controlling an EV charging station to address a power quality issue. A power quality meter 1006 can detect a power quality issue and communicate the issue to a control device, such as an aggregator and control device 1002. The aggregator and control device can send control signals to the charging stations to control the EV charging stations 808a-808d and make adjustments to address the power quality issue. In response to receiving the control signals, each EV charging station can charge or discharge the connected EV battery. The control signals sent to the EV charging stations can request different actions by the EV chargers. The aggregator and control device can combine the output from the EV chargers, send the output to the grid, and control the real and reactive power at the grid nodes.

[0069] Example of a machine learning model

[0070] In the above system, multiple machine learning models may operate, including, but not limited to, models that operate at the head-end system or secondary substations to generate price interval data, models that operate at the electric meter to generate future price data, and models that operate at the meter to control loads and DER devices.

[0071] The models that generate the pricing data predict energy prices and may be implemented using regression models. Both linear and nonlinear regression models may be used. Each regression model may be trained and validated using a dataset. The dataset is based on pricing data collected from historical periods across various conditions, dates, and times. The conditions, dates, and times may be used to define the model inputs and historical prices may be used to define the model outputs. The dataset used for models that operate on electric meters may differ from the dataset used for models that operate on head-end systems or secondary substations. Data for models that operate on electric meters may include meter-specific data. Meter-specific data may include temperature information related to a meter or premises rather than temperature information for a broader geographic area.

[0072] The dataset is separated 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. During training and validation, the weights used in the model are adjusted until the model provides an acceptable level of accuracy. Options for measuring the accuracy of the model include determining the percentage of intervals where the model correctly predicts the price, or the percentage of intervals where the model predicts the price within an acceptable range. Once the model is validated, it may be deployed to an edge computing device. The performance of the deployed model is monitored and additional data is collected so that the model can be retrained with a new dataset, if necessary. The retrained model can be deployed to replace the original model.

[0073] Models that control loads and DER devices may use training sets that include different types of data than the data used in models that predict prices. The data sets may be based on control data collected from historical periods over a variety of conditions, dates and times. The data may include, but is not limited to, data related to specific types of loads or DER devices.

[0074] FIG. 14 illustrated an exemplary method for training and validating a linear regression model for predicting pricing that can be used to generate price interval data or future pricing data. A dataset 1402 includes a training dataset 1404 and a validation dataset 1406. The training dataset is used to train a linear regression model 1410. Once a trained model 1412 is available, the validation dataset is used to validate the trained model. The process of training and validating a model includes many additional steps not shown in FIG. 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] FIG. 11 is a block diagram illustrating the placement 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 and provides it to the multiport meter 1102. The multiport meter 1102 can provide the AC power from the solar inverter 1104 to the grid 102 or the site 104. In one example, the multiport meter 1102 can be instructed by an edge computing device 1108 to, for example, drain energy production from the solar inverter 1104 as needed. Measurement of the solar inverter 1104 can be made via the multiport meter 1102. Additionally, the edge computing device 1108 can be located at the multiport meter 1102 to make edge decisions regarding control of distributed energy resource devices, such as the solar panel 1106, that are electrically coupled with the multiport meter 1102 via the solar inverter 1104.

[0077] FIG. 12 is a block diagram illustrating an arrangement of a multiport meter 1102 electrically coupled to an inverter 1204 of a solar panel 1206, a battery storage 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 and supplies it to the multiport meter 1102. The multiport meter 1102 can supply the AC power from the inverter 1204 to the grid 102 or the site 104. In one example, the multiport meter 1102 can be instructed by the edge computing device 1108 to, for example, release energy production from the inverter 1204 as needed. In such an example, the energy generated by the solar panel 1206 can be stored in the battery storage system 1208 for future use. Metering of the inverter 1204 can be performed via the multiport meter 1102. Additionally, an edge computing device 1108 can be disposed 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 inverters 1204.

[0078] FIG. 13 is a block diagram illustrating an arrangement of a multiport meter 1102 electrically coupled to an electric vehicle charger 1304. Metering of the electric vehicle charger 1304 can be performed through the multiport meter 1102. The electric vehicle charger 1304 can rectify AC mains power received at 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 DC power from the electric vehicle 1306 in a manner that enables provision of available AC power for use on the grid 102 or the site 104. In one example, the edge computing device 1108 can provide instructions to 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 a predicted energy price or based on a predicted overload of a distribution transformer associated with the premises 104 of the multiport meter 1102.

[0079] The operations of the head-end system, gateway device, metering module, electric meter, edge computing device, aggregator and control device, and communication module described herein may 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 the processing elements to execute the 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 connections to and communicating over a network, and input / output devices, such as a display device.

[0080] Although the gist of the present invention has been described in detail with respect to certain embodiments thereof, it is recognized that those skilled in the art, upon gaining an understanding of the foregoing, can easily make modifications, variations, and equivalents to such embodiments. It should therefore be understood that the present disclosure has been presented for purposes of illustration and not limitation, and is not intended to preclude the inclusion of modifications, variations, and / or additions to the gist of the present invention as would be readily apparent to one skilled in the art.

Claims

1. 1. An electric meter including an edge computing device associated with a premises, the electric meter configured to control a first load device associated with the premises; The site is located downstream of a distribution transformer; The electricity meter comprises: a communications module for communicating with a plurality of downstream edge computing devices located downstream of the distribution transformer and a transformer edge computing device associated with the distribution transformer; a first port for connecting the first load device to the electric meter; The edge computing device; Including, The edge computing device includes: A processor; a memory for storing computer readable instructions; Including, When executed by the processor, the computer readable instructions transmitting load forecast information for the first load device and at least one second load device to the transformer edge computing device via the communication module; receiving, via the communications module, price interval data from the transformer edge computing device, the price interval data providing a predicted price for each of a plurality of future time intervals; controlling the first load device based on the price interval data, if the price interval data includes a lower cost time interval, the first load device may consume power and operate using one or more normal operating parameters during a future time interval; and If the price interval data includes a costly time interval, the first load device is controlled to operate using one or more energy saving operating parameters during a future time interval. controlling the first load device such that the first load device performs the and configuring the edge computing device to: Electricity 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 decreasing a charge rate of the electric vehicle charger from a current charge rate during a future time interval.

2. The electricity meter of claim 1.

3. The edge computing device further includes a second port for connecting a second load device associated with the premises to the electric meter; The electricity meter further comprises: receiving a user command providing an instruction for controlling the second load device; controlling the second load device based on the price interval data and the user command, wherein controlling the second load device comprises: if the price interval data includes a costly time interval and the user command allows normal operation of the second load device, the second load device may operate using one or more normal operating parameters during a future time interval. Controlling the second load device.

4. The method of claim 3, 2. The electricity meter of claim 1.

4. The edge computing device further includes a second port for connecting a second load device associated with the premises to the electric meter; The electricity meter further comprises: determining future price data after receiving price interval data from the transformer edge computing device, the future price data corresponding to a plurality of time intervals after the future time interval; controlling the second load device based on the future price data; and 4. The method of claim 3, 2. The electricity meter of claim 1.

5. The edge computing device further includes a second port for connecting a distributed energy resource (DER) device associated with the premises to the electric meter; The electricity meter further comprises: and controlling the DER device to output power to a power grid if the price interval data includes a high cost time interval.

4. The method of claim 3, 2. The electricity meter of claim 1.

6. The edge computing device is further configured to transmit information regarding controlling the first load device to another electric meter downstream of the distribution transformer.

2. The electricity meter of claim 1.

7. the edge computing device is further configured to receive communications from the transformer edge computing device to control the first load device.

2. The electricity meter of claim 1.

8. the price interval data is based in part on a rating of the distribution transformer; 2. The electricity meter of claim 1.

9. 1. A method for controlling at least one load device and at least one distributed energy resource (DER) device, comprising: receiving price interval data at an edge computing device associated with an electric meter connected to an electric grid and to a premises, the price interval data providing a predicted price for each of a plurality of future time intervals; determining, by the edge computing device, future price data, the future price data corresponding to a plurality of time intervals after a future time interval; and controlling a plurality of load devices associated with the electric meter based on the price interval data and the future price data, the load devices associated with the electric meter including the at least one load device and the at least one DER device, by controlling the plurality of load devices associated with the electric meter; when both the price interval data and the future price data include a lower cost time interval, the at least one load device is capable of consuming power and operating using one or more normal operating parameters; controlling the at least one DER device to output power to a power grid if both the price interval data and the future price data include a high cost time interval; and if the price interval data includes a high cost time interval and the future price data includes a low cost time interval, controlling the at least one load device to change operation to operate using one or more energy saving operating parameters during the future time interval and to operate using one or more normal operating parameters after the future time interval. a plurality of load devices associated with the electric meter performing the above-mentioned control; A method comprising:

10. The at least one DER device has a charging mode, Furthermore, and wherein the at least one DER device is operable to consume power and operate in the charging mode when both the price interval data and the future price data include a low cost time interval.

10. The method of claim 9, comprising:

11. Furthermore, receiving, at the edge computing device, a user command for the at least one load device; Controlling the at least one load device, wherein controlling the at least one load device comprises: and wherein the at least one load device is operable to consume power and operate using one or more normal operating parameters if both the price interval data and the future price data include a high cost time interval. and controlling the at least one load device to 10. The method of claim 9, comprising:

12. The at least one load device is an electric vehicle (EV) charger, controlling the at least one load device to modify its operation to operate using one or more energy saving operating parameters during a future time interval includes decreasing a charge rate of the electric vehicle charger from a current charge rate during a future time interval.

10. The method of claim 9.

13. said price interval data and said future price data are determined separately, said future price data being based on meter specific data; 10. The method of claim 9.

14. receiving, at the edge computing device, a communication from a transformer edge computing device for controlling the at least one load device, the electric meter being located downstream of a distribution transformer associated with the transformer edge computing device; 10. The method of claim 9, comprising:

15. 1. A method for controlling a plurality of chargers, comprising: Each charger is associated with a meter and a site. The method comprises: providing an edge computing device associated with a distribution transformer, the distribution transformer providing power to each of the premises; forecasting, by the edge computing device, a predicted load associated with the distribution transformer; forecasting, by the edge computing device, individual site predicted loads for each of the sites; Controlling the chargers based on the predicted load and the predicted load of the individual premises predicted by the edge computing device, wherein controlling the chargers controlling at least one of the chargers to reduce a charging rate of the at least one of the chargers if the predicted load exceeds a threshold, the at least one of the chargers being selected based on a predicted load of the individual premises for the premises associated with the at least one of the chargers; and controlling at least one of the chargers to reduce a charging rate of the at least one of the chargers; If the predicted load does not exceed a threshold, then causing the charger to charge. and controlling the charger to perform the A method comprising:

16. Furthermore, receiving, by the edge computing device, load data from each meter associated with the charger; using the load data to predict a projected load for the individual premises; 16. The method of claim 15, comprising:

17. Furthermore, controlling at least one of the chargers to reduce a charging rate of the at least one of the chargers and subsequently enable a second of the chargers to maintain a second charging rate if the predicted load exceeds a threshold.

16. The method of claim 15, comprising:

18. Furthermore, generating price interval data, the price interval data providing a predicted price for each of a plurality of future time intervals; communicating the price interval data to the meter associated with the charger; 16. The method of claim 15, comprising:

19. The method of claim 15, wherein controlling at least one of the chargers to reduce the charging rate of the at least one of the chargers includes controlling the multiple chargers to reduce the charging rate of each of the multiple chargers.

20. The decrease in the charging rate of the first charger is different from the decrease in the charging rate of the second charger.

20. The method of claim 19.

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