Power network system and method with region automatic control

Abstract

The invention discloses a power grid system and method with regional autonomous control. A power grid system comprising a plurality of power transmission regions is provided herein. Each power transmission region of the plurality of power transmission regions may include a region measurement device configured to measure a region operating parameter of an assigned power transmission region of the plurality of power transmission regions. Each of the plurality of power transmission regions also includes a region controller associated with an assigned power transmission region and in communication with a region measurement device. The zone controller is configured to receive zone operation data for an assigned power transmission zone from the zone measurement device. The zone controller is further configured to determine a zone orchestration index based on the zone operation data. Further, the zone controller is configured to determine an adaptive control action for the control device in the assigned power transmission zone based on the zone orchestration index, and to deliver a command to the control device based on the adaptive control action.

Description

TECHNICAL FIELD

[0001] The teachings generally relate to power grids, and more particularly to operation and management of power grids. BACKGROUND

[0002] In recent decades, power grids have increased in complexity. The rise of variable energy sources and bulk power electronic control devices in transmission systems, and controllable loads and variable energy sources in distribution systems, have led to increased complexity of power grids. Such factors increase the number of controllable node points and circuit configurations possible in power grids. The increased complexity of power grids can present challenges in the case of centralized control. For example, the increased complexity of power grids increases the number of node points to monitor, manage, and control. BRIEF DESCRIPTION OF DRAWINGS

[0003] Various needs are at least partially met through the provision of power grid systems with regional autonomous control as described in the following detailed description, particularly when studied in conjunction with the drawings. The complete disclosure of the description makes apparent to those of ordinary skill in the art the entire disclosure of the aspects of the description claimed as being new, embodying the best modes presently contemplated of carrying out the aspects of the description, including the best mode of practicing the aspects of the description, referencing particularly to the drawings, in which:

[0004] Figure 1 Block diagram of a power grid system including various embodiments in accordance with the teachings;

[0005] Figure 2 Block diagram of a transmission region controller including various embodiments in accordance with the teachings;

[0006] Figure 3 Flowchart of a method of operating a transmission region controller to generate an adaptive control action or a predictive control action for a transmission region in a power grid including various embodiments in accordance with the teachings;

[0007] Figure 4 Diagram of an exemplary transmission region in a power grid including various embodiments in accordance with the teachings;

[0008] Figure 5 Map of an exemplary transmission region in a power grid including various embodiments in accordance with the teachings;

[0009] Figure 6 Block diagram of a distribution region controller including various embodiments in accordance with the teachings;

[0010] Figure 7 Flowchart of a method of operating a distribution region controller to generate an adaptive control action or a predictive control action for a distribution region in a power grid including various embodiments in accordance with the teachings;

[0011] Figure 8 Flowchart of a method of operating a distribution area controller including various embodiments in accordance with these teachings;

[0012] Figure 9 Block diagram of a distribution area in a power grid including various embodiments in accordance with these teachings;

[0013] Figure 10 Diagram of a distribution area with sub-areas and clusters including various embodiments in accordance with these teachings;

[0014] Figure 11 Flowchart of a method of operating a transmission area controller including various embodiments in accordance with these teachings;

[0015] Figure 12 Flowchart of a method of operating a transmission area controller including various embodiments in accordance with these teachings;

[0016] Figure 13 Flowchart of a method of operating a transmission area controller including various embodiments in accordance with these teachings;

[0017] Figure 14 Flowchart of a method of operating a transmission area controller including various embodiments in accordance with these teachings;

[0018] Figure 15 Block diagram of a joint grid management system for a power grid including various embodiments in accordance with these teachings;

[0019] Figure 16 Flowchart of a method of operating a joint grid management system including various embodiments in accordance with these teachings;

[0020] Figure 17A 、 17B Flowchart of a method of operating a joint grid management system including various embodiments in accordance with these teachings; 17C

[0021] Figure 18 Flowchart of a method of operating a power grid system with multiple areas including various embodiments in accordance with these teachings;

[0022] Figure 19 Flowchart of a method of operating a power grid system with multiple areas including various embodiments in accordance with these teachings;

[0023] Figure 20 Exemplary computer system for a power grid system including various embodiments in accordance with these teachings.

[0024] The elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions and / or relative positioning of some of the elements in the figures can be exaggerated relative to other elements to help to improve understanding of various embodiments of the present teachings. Furthermore, common but well-understood elements that are useful or necessary in a commercially feasible embodiment are often not depicted in order to facilitate a less obstructed view of these various embodiments of the present teachings. Certain actions and / or steps can be described or depicted in a particular order of occurrence while those skilled in the art will understand that such specificity is to be understood in more tolerable terms, such as within a range of occurrence. DETAILED DESCRIPTION

[0025] The power grid systems and methods described herein divide a power grid system into various regions, for example, within a distribution and / or transmission power grid. In some aspects, the systems and methods utilize regionally autonomous control in order to manage the power grid as a collection of mutually coordinated regions. The power grid systems and methods described herein can also use a federated grid model that provides a federated data fabric such that there is a data fidelity and consistent modeling of the grid that is common across distribution and transmission systems. The federated grid model can provide an overall grid orchestration and form autonomously controlled regions based on centrally defined policies. The federated grid model can set policies and push the policies to the regions for local execution. The regionally autonomous control can be performed by a region controller that has data organized, mapped, stored, and / or learned in a standardized manner to help achieve data fidelity. The federated grid model can receive data from various region controllers.

[0026] Conventional approaches for power grid system control and management have been top-down approaches with a central system that communicates across the power grid with many, sometimes thousands, of points. The power grid system control and management approaches provided herein use a decentralized control and automation solution to provide the ability to manage growing power grids with increasing complexity.

[0027] The approaches provided herein also provide an integrated solution for configuring and managing assets in a power grid system, including both primary assets and secondary assets, whereas conventional approaches have used separate management solutions for primary assets and secondary assets. Regions are formed and it is determined for each region what assets, e.g., primary assets, exist and what intelligent electronic devices (IEDs) are connected to the assets, e.g., secondary assets. For each region, data is collected about the primary assets and the secondary assets and various analyses are computed based on the collected data. From the analyses, the primary assets and the secondary assets can be ranked in the region for asset valuation and management.

[0028] Reference Figure 1 A power grid system 100 is shown in FIG.Figure 1 In particular, the power grid system 100 is in communication with the joint grid management system 134, an advanced distribution management system (ADMS) 144, and a plurality of databases.

[0029] The power grid system 100 can be divided into a plurality of zones. For example, a transmission power grid can be divided into transmission zones 102, and a distribution power grid can be divided into distribution zones 104. In some aspects, the power grid system 100 includes a plurality of transmission zones 102 and a plurality of distribution zones 104.

[0030] The power grid system 100 can be divided into a plurality of transmission zones 102, for example, based on the assets, boundaries, and operational domains of the zones. The operational domain of a zone can refer to the primary functionality of the zone, such as power generation, transmission, or distribution. The operational domain of a zone can depend on what assets are present within the zone. As discussed, the operational domain of a zone can be a consideration in dividing the power grid system 100 into a plurality of transmission zones. For example, when a portion of the power grid system 100 has an operational domain of power generation, that portion of the power grid system 100 can be designated as a power generation zone 180B (see, e.g., FIG. 1). In another example, a microgrid in the power grid system 180 can be designated as its own zone, for example, as a microgrid / distributed energy resource (MG / DER) zone. Figure 4

[0031] As used herein, a transmission zone can be a sub-portion of a transmission power grid network having a particular logical structure and boundaries. As shown in FIG. 1, each of the plurality of transmission zones 102 can have a transmission zone controller 112 that is dedicated to the assigned transmission zone. Each transmission zone also includes transmission zone measurement devices 108 and transmission zone control devices 110, which provide a measurement and control node or transmission zone. The transmission zone measurement devices 108 and transmission zone control devices 110 can be used to identify and control operational and non-operational violations within the transmission zone. In addition, the transmission zone controllers 112 can communicate or coordinate with each other to improve the reliability and resiliency of the power grid system. Figure 1

[0032] The transmission zone 102 can include the main substation equipment 106, one or more of the transmission zone measurement devices 108, one or more of the transmission zone control devices 110, and the transmission zone controller 112. The transmission zone measurement devices 108 and the transmission zone control devices 110 are operatively coupled to the main substation equipment 106. The transmission zone measurement devices 108 and the transmission zone control devices 110 are in communication with the transmission zone controller 112.

[0033] ​​Primary substation equipment 106 can include any primary equipment at a substation, such as, for example, transformers, switchgear (e.g., circuit breakers), high voltage direct current (HVDC) controllers, flexible alternating current transmission systems (FACTS) devices, or current and voltage transformers. The primary equipment can be any equipment through which power flows at the substation.

[0034] Power zone measurement devices 108 can include sensors, intelligent electronic devices (IEDs), smart meters, etc. at a substation that provide monitoring functions for primary substation equipment 106. Power zone measurement devices 108 can be operable to measure, sense, or otherwise determine one or more power zone operating parameters. Power zone operating parameters can include, for example, voltage, frequency, inertia, congestion, reactive power load, or power factor.

[0035] Power zone control devices 110 can include intelligent electronic devices (IEDs), gateways, or any auxiliary equipment at a substation that provide control or maintenance functions for primary substation equipment 106. In some examples, power zone control devices 110 can include protection relays. Power zone control devices 110 can be operable to adjust an operating parameter of at least one of a primary transformer, a switch, a current transformer, a voltage transformer, a circuit breaker, a voltage regulator, a flexible alternating current transmission system (FACTS) device, a power electronic controller, an HVDC link controller, a renewable power source, or a grid forming inverter in a substation.

[0036] Power zone controller 112 can be operable to receive data from power zone measurement devices 108 and control power zone control devices 110. For example, power zone controller 112 can communicate commands to power zone control devices 110 to implement control actions. Power zone controller 112 can be in communication with joint grid management system 134. In some examples, at least one power zone 102 of a plurality of power zones includes power zone controller 112. In other examples, each of a plurality of power zones includes power zone controller 112. According to some embodiments, Figure 2 Power zone controller 112 is shown in further detail in FIG. 1C.

[0037] Distribution area 104 may include main equipment 114, one or more distribution area measuring devices 116, one or more distribution area control devices 118, and distribution area controller 120. Distribution area 104 may be a sub-part of a distribution power grid network with a specific logical structure and boundaries. Distribution area measuring devices 116 and distribution area control devices 118 are operatively coupled to main equipment 114. Distribution area measuring devices 116 and distribution area control devices 118 communicate with distribution area controller 120. Distribution area 104 may also include one or more interconnections with another distribution area.

[0038] Major equipment 114 may include any power distribution system asset and may be any equipment through which power flows at a substation. Power distribution assets may include, for example, substation equipment, distribution radial feeders, or outgoing lines. Substation equipment may include, for example, transformers, switchgear, voltage regulators, capacitor banks, or power factor control devices.

[0039] Distribution area measurement device 116 may include sensors or intelligent electronic devices (IEDs) at the substation that provide monitoring functionality for main equipment 114. Distribution area measurement device 1114 may be operable to measure, sense, or otherwise determine one or more distribution area operating parameters. Distribution area operating parameters may include, for example, voltage, power factor, active / reactive power, load per node, battery-based energy storage system (BESS) capacity, distributed energy generation (DER), renewable energy generation (REN), frequency, electric vehicle (EV) load, microgrid generation, microgrid load, feeder voltage, feeder current, feeder load imbalance, power quality data, etc.

[0040] The distribution area control device 118 may include intelligent electronic devices (IEDs) or any auxiliary devices at the substation that provide control or maintenance functions for the main equipment 114.

[0041] Distribution area controller 120 is operable to receive data from distribution area measurement device 116 and control distribution area control device 118. For example, distribution area controller 120 can transmit commands to distribution area control device 118 to implement control actions. In some examples, at least one distribution area 104 of a plurality of distribution areas includes distribution area controller 120. In other examples, each of the plurality of distribution areas includes distribution area controller 120. According to some embodiments, Figure 6 Further details of the distribution area controller 120 are shown in the image.

[0042] The power grid system 100 may be coupled to multiple local, remote, and / or cloud databases to retrieve data to perform the various functions described herein and / or store generated data. In some embodiments, the data stored in the databases may include a training database 122, a regional prediction / planning database 128, a regional operations database 130, a regional orchestration index database 132, a power restoration feasibility index database 124, and a regional strategy database 125.

[0043] Training database 122 may store training data for training any machine learning model described herein. Training database 122 may also store historical data that can be used for training purposes to train the machine learning models described herein.

[0044] The regional forecasting / planning database 128 may contain forecasting or planning data for one or more distribution areas and / or one or more transmission areas. Forecasting data may include, for example, forecasted load data, forecasted weather data, or forecasted distributed energy resources (DER) data. Planning data may include, for example, plans for adding, repairing, upgrading, removing, or shrinking transmission or distribution networks within the power grid system 100.

[0045] The regional operations database 130 can store any operational data related to the operation of transmission areas 102 and / or distribution areas 104 of the power grid system 100. The regional operations database 130 can communicate with any component of the power grid system 100, such as transmission area controller 112, transmission area measurement device 108, transmission area control device 110, distribution area controller 120, distribution area measurement device 116, and / or distribution area control device 118. The regional operations database 130 can also communicate with the combined grid management system 134, enabling it to transmit regional operations data to one or more components of the combined grid management system 134. The regional operations database 130 can also communicate with one or more machine learning models among the machine learning models described herein, enabling the operational data from the regional operations database 130 to be used for training purposes.

[0046] The regional orchestration index database 132 can store regional orchestration indices determined by one or more transmission area controllers 112, which will be referenced below. Figure 2 Further discussion. The regional orchestration index database 132 can communicate with one or more transmission area controllers 112 or their components, and in some respects, with the combined grid management system 134.

[0047] The power restoration feasibility index database 124 can store power restoration feasibility indices determined by one or more distribution area controllers in the distribution area controllers 120, which will refer to... Figure 13Further discussion. The regional orchestration index database 132 can communicate with one or more of the distribution area controller 120 or its components, and in some respects with the combined grid management system 134 and / or with ADMS 144.

[0048] The regional policy database 125 may contain at least one of the following: a transmission region policy for transmission region 102, a distribution region policy for distribution region 104, or a regional prediction policy for transmission region 102, or a regional prediction policy for distribution region 104. The policy may contain at least one of the following: an adaptive control source, how to map the adaptive control source to violations, a predictive control source, how to map the predictive control source to violations, information on how to operate the multiple distribution regions, how to form sub-regions or clusters based on the power grid topology, machine learning objectives for the region, computational indices related to the operation of the region, or information on at least one of the following: coordination between multiple regions.

[0049] ADMS 144 can communicate with one or more distribution area controllers in distribution area controller 120. ADMS 144 can be configured to dynamically classify the distribution power network into multiple distribution areas. In some methods, ADMS 144 can classify or identify multiple distribution areas in the distribution power network based on at least one of the topology of the distribution power network or predefined logic. ADMS 144 can be operable to receive distribution operation data from distribution area controller 120. In some examples, ADMS 144 can also be configured to dynamically classify distribution area 104 into multiple sub-areas 250 based on one or more sub-zoning rules. In other examples, ADMS 144 can also be configured to dynamically classify sub-areas 250 into multiple clusters 258 based on one or more clustering rules or policies. Sub-zoning rules or policies and clustering rules or policies can be defined in ADMS 144 or in the area policy database 125. Sub-area and cluster references Figure 9 and Figure 10 It is described in detail.

[0050] In some embodiments, the power grid system 100, the integrated grid management system 134, and the ADMS 144 can be, for example, Figure 20 The computer system shown is the computer system 510.

[0051] exist Figure 20In this embodiment, computer system 510 includes a processor 511, memory 512, input / output (I / O) adapter 513, and network adapter 514 communicating on a bus. In some embodiments, computer system 510 may include other common components of a processor-based device. Processor 511 is configured to execute computer-readable instructions stored on memory 512 to perform one or more functions described herein. Processor 511 is also configured to receive and / or transmit data via I / O adapter 513 and / or network adapter 514. In some embodiments, input device 515 and output device 516 may include user interface devices such as a display screen, touchscreen, keyboard, microphone, speaker, camera, motion sensor, etc. In some embodiments, computer system 510 may communicate with one or more other devices or databases via network adapter 514 through network 517. In some embodiments, network 517 may include a local area network or wide area network, such as the Internet. Although computer system 510 is shown as having a single processor 511 and memory 512, in some embodiments, computer system 510 may be implemented on a cloud-based computer with multiple distributed processors and memory.

[0052] According to some embodiments Figure 1 Further details of the system's executable functions are described in this document and referenced. Figures 2-19 It was further shown.

[0053] Transmission operations, management, and control

[0054] In some embodiments, the systems and methods provided herein can be used to operate, manage, or control transmission power grids. The increased possibilities of controllable node and circuit configurations in transmission power grids can make centralized control of these grids challenging. However, the systems and methods described herein distribute control across multiple transmission area controllers, such as transmission area controllers 112. For example, a transmission power grid may be divided into multiple transmission areas 102, and a transmission area controller 112 associated with an assigned transmission area within those multiple transmission areas 102 is used to control and manage that assigned transmission area. Such a configuration of transmission power grid control can lead to reduced latency, greater use of real-time edge information, improved modeling accuracy and management, and improved control. Furthermore, in conventional transmission power grids, data from measuring devices such as IEDs may not be effectively used. For example, all IED data may not be used for higher-level grid decision-making. In contrast, in systems and methods for transmission management and control, data from IEDs is utilized for decision-making and control targeting specific transmission areas or for higher-level grids.

[0055] Furthermore, it is envisioned that the systems and methods described herein for operating, managing, and controlling transmission power grids can provide real-time control of regional assets, such as major substation equipment. The methods and systems can receive feedback on regional operations to improve transmission area modeling and predictive action. Moreover, operating the transmission power grid as multiple transmission areas reduces the amount of data and associated communications that utilities need to process, and also reduces computational bandwidth. In addition, the systems and methods can proactively adapt to the integration of emerging Renewable Energy Networks (RENs) and Distributed Energy Resources (DERs) with transmission power grids, as well as extreme weather events.

[0056] The system and method described herein facilitate the visualization and management of power transmission networks as a collection of different region types, which can be dynamically formed based on the power grid network topology. The plurality of transmission regions 102 can be intelligently formed, and each region has associated programmable region actions. During normal operating mode, the transmission region controller 112 can operate the assigned transmission regions via adaptive control actions. The joint grid management system 134 can also intervene in normal operating mode, for example, during abnormal conditions, to issue intervention controls to the transmission regions 102. In some aspects, the transmission region controller 112 can also operate the assigned transmission regions via predictive control actions based on region prediction data and machine learning, the predictive control actions being control actions for future time intervals.

[0057] Figure 2 A transmission area controller 112 according to some embodiments is shown. The transmission area controller 112 is associated with a transmission area 102. In some aspects, the transmission area controller 112 is associated with and configured to operate an assigned transmission area among a plurality of transmission areas 102. Furthermore, in some aspects, each of the plurality of transmission areas 102 may be assigned the same controller as the transmission area controller 112.

[0058] The transmission area controller 112 may include one or more of the following: a machine learning engine 150, an area autonomous management agent 152, an area adaptive control agent 154, and an area predictive control agent 156. The machine learning engine 150, the area autonomous management agent 152, the area adaptive control agent 154, and the area predictive control agent 156 can communicate with each other. It is also envisioned that, with reference to... Figure 2 The described operation or function can be performed by a single agent, engine, or module.

[0059] The machine learning engine 150 may contain one or more machine learning models. The machine learning engine 150 may communicate with and receive training data from the training database 122.

[0060] In one example, machine learning engine 150 may include an autonomous control machine learning model. The autonomous control machine learning model may receive, for example, transmission area operation data and area policies from transmission area measurement device 108 as input. The autonomous control machine learning model may receive, for example, area orchestration indices for transmission area 102 from area orchestration index database 132 or area autonomous management agent as input. The autonomous control machine learning model may determine or identify one or more adaptive control actions (e.g., for transmission area control device 110) as output. The autonomous control machine learning model may be trained on at least one of historical area operation parameters, historical best control measures, or historical area adaptive policies for transmission area 102. In some methods, when the transmission area is one of multiple transmission areas, the autonomous control machine learning model may also be trained on historical operation data and historical area orchestration indices from another transmission area (e.g., a transmission area of ​​the same type as transmission area 102) among the multiple transmission areas. In this way, the autonomous control machine learning model can learn from the operation of similar types of areas.

[0061] In another example, the machine learning engine 150 may include a predictive control machine learning model. The predictive control machine learning model may receive, for example, regional forecasting and / or planning data from the regional forecasting / planning database 128 as input. The predictive control machine learning model may also receive, for example, regional policies from the regional policy database 125 as input. The predictive control machine learning model may determine or identify one or more predictive control actions (e.g., for the transmission area control device 110) for the transmission area 102 as output.

[0062] Regional Autonomous Management Agent 152 can be configured to determine the transmission area orchestration index of transmission area 102. The regional orchestration index can be a measure of the performance of transmission area 102 relative to baseline performance or relative to a reference index. Regional Autonomous Management Agent 152 can determine the regional orchestration index, for example, based on at least one of area operation data, area strategy, or area forecasting data from transmission area measurement device 108. Regional Autonomous Management Agent 152 can also communicate with one or more databases, such as area strategy database 125, area forecasting / planning database, and regional orchestration index database 132. For example, Regional Autonomous Management Agent 152 can receive data from area strategy database 125 and area forecasting / planning database. In some examples, Regional Autonomous Management Agent 152 can transmit data to regional orchestration index database 132.

[0063] The zone adaptive control agent 154 can determine one or more adaptive control actions for transmission zone 102, for example, based on at least one of transmission zone operation data or zone policies. Zone policies can define the operating parameters, rules, and / or constraints for zone operations. Zone policies can also define rules for the delineation or division of transmission zones within the power grid. For example, a zone policy can define rules regarding how transmission zones are formed and how zone operation data is used and analyzed within those zones. In another example, a zone policy can define how the zone controller should react during abnormal operations, such as when problems or violations occur within the zone. Furthermore, the transmission zone controller 112 for a specific transmission zone can coordinate with other transmission zone controllers.

[0064] A regional strategy can define how transmission area controller 112 should act, handle problems, or analyze data related to its interactions with other transmission area controllers. For example, if a particular transmission area controller 112 cannot handle a specific situation, another transmission area controller or a joint grid management system in the power grid can take over control to handle the situation as defined in the regional strategy. In another example, if transmission area controller 112 violates operating rules as defined in the regional strategy, the regional strategy can define penalties for the area controller. In some implementations, operating rules may involve compensation. In one example, the operating rules for a transmission area may stipulate that if the voltage drops below a predetermined rate under certain circumstances, the penalty for violating the operating rules may be that the area pays some compensation to its customers. In some implementations, the regional strategy can specify operating rules related to generation, trading, and / or pricing associated with the area. In one example, the maximum and minimum prices at which people should purchase electricity from a particular area can be defined in the regional strategy. In another example, the operating rules may stipulate that a region should generate a predetermined amount of electricity output (e.g., in MW). However, if the region fails to generate the predetermined amount of electricity output, the region may have to pay for not reaching the predetermined amount of electricity generated as defined in the operating rules.

[0065] In some respects, one or more machine learning models (such as autonomous control machine learning models) of machine learning engine 150 may be integrated with a portion of area adaptive control agent 154. Area adaptive control agent 154 may communicate with and receive data (such as policies) from area policy database 125. Area adaptive control agent 154 may communicate with one or more transmission area control devices in transmission area control device 110. In this way, area adaptive control agent 154 may be configured to deliver or transmit commands to one or more transmission area control devices in transmission area control device 110.

[0066] The regional predictive control agent 156 can determine predictive control actions for transmission area 102, for example, based on at least one of regional policies and regional predictive data. Using the regional predictive control agent 156, the transmission area controller 112 can predict the future behavior of the transmission power grid to determine predictive control actions to achieve the intended or target performance of the transmission power grid. For example, predictive control actions may include any control actions for transmission area control device 110 during a future time period. In some aspects, one or more machine learning models of the machine learning engine 150 (such as predictive control machine learning models) may be integrated with or be part of the regional predictive control agent 156. The regional predictive control agent 156 can communicate with and receive data (such as policies) from the regional policy database 125. Furthermore, the regional predictive control agent 156 can communicate with and receive data (such as predictive data) from the regional predictive / planning database 128.

[0067] Figure 3 Methods for operating a transmission area controller to generate adaptive or predictive control actions for transmission areas in a power grid, according to some embodiments, are illustrated. In some examples, the transmission area controller is a reference... Figure 1 and Figure 6 The described power transmission area controller 112, and the power transmission area is a reference Figure 1 The described power transmission area is 102.

[0068] In step 160, the transmission area controller receives transmission area operation data from the assigned transmission area. For example, the transmission area controller 112 may receive transmission area operation data from one or more transmission area measuring devices 108 in the transmission area 102.

[0069] In some methods, the transmission area controller may also receive a region adaptation policy for transmission area 102. The region adaptation policy may include or define standard rules for using region control resources to mitigate operational or non-operational violations in transmission area 102. The transmission area controller may receive the region adaptation policy, for example, from a region policy database 125 or from a combined grid management system 134.

[0070] In step 162, the transmission area controller determines the transmission area scheduling index for the assigned transmission area based on transmission area operation data. For example, transmission area controller 112 may determine the transmission area scheduling index.

[0071] As mentioned above, the transmission area scheduling index can be a measure of the performance of an assigned transmission area relative to baseline performance or a reference index.

[0072] In some examples, the zone orchestration index indicates at least one of operational violations or non-operational violations in the assigned zone. For example, a transmission zone controller can identify operational violations or non-operational violations by comparing transmission zone operational data with thresholds or reference data defined in a zone adaptive strategy. Operational violations may indicate whether one or more of voltage, power, load, reactive power, power factor, congestion stability, frequency, inertia, or grid strength are within the corresponding threshold limits or reference ranges. For example, operational violations may include one or more of the following: voltage and / or frequency values ​​outside threshold limits, active / reactive power outside threshold limits, transmission line limits exceeding dynamic line ratings, generator overload, transformer overload, or low power factor. Non-operational violations may involve at least one of the following: power grid maintenance, hardware, software, or communication, operator, or network security issues. The transmission zone controller may also be configured to isolate nodes or devices in the assigned transmission zone when a non-operational violation is identified.

[0073] In some examples, the region orchestration index can be a numerical value representing a bitstream of encoding operation violations or non-operation violations; and the bits in said bitstream are arranged in order, with critical violations placed on the left side of the bitstream and non-critical violations placed on the right side of the bitstream based on severity.

[0074] In some methods, in step 164, the transmission area controller may transfer the transmission area scheduling index to another transmission area. For example, transmission area controller 112 may transfer the transmission area scheduling index to a transmission area controller associated with another transmission area.

[0075] In some methods, in step 166, the transmission area controller may transmit the transmission area scheduling index to the unified grid management system. For example, transmission area controller 112 may transmit the transmission area scheduling index to the unified grid management system 134.

[0076] In step 168, the transmission area controller determines adaptive control actions for control devices in the assigned area based on the transmission area orchestration index. For example, transmission area controller 112 may determine adaptive control actions for one or more transmission area control devices 110 in transmission area 102. In some examples, the transmission area controller may also determine adaptive control actions for control devices in another transmission area among the plurality of transmission areas, for example, to compromise or compete with the other transmission area.

[0077] In some methods, adaptive control actions can control at least one of the following: the voltage, power factor, load, and frequency of an asset, such as a major substation device in the assigned transmission area or another of the plurality of transmission areas. Furthermore, adaptive control actions can control at least one of the following: a battery-based energy storage system (BESS), distributed energy resource (DER), or renewable energy network (REN) in the assigned area or another of the plurality of transmission areas.

[0078] In some methods, adaptive control actions can influence competition or compromise between an assigned transmission area and another transmission area among the plurality of transmission areas.

[0079] In some methods, the transmission area controller may also receive transmission area operation data or transmission area orchestration index from another transmission area among the plurality of transmission areas. The transmission area controller may then determine adaptive control actions based at least in part on the transmission area operation data or orchestration index from the other transmission area among the plurality of transmission areas. For example, the transmission area controller may control competition and / or compromise between the plurality of transmission areas.

[0080] In step 170, the transmission area controller transmits commands to the control devices based on adaptive control actions. For example, the transmission area controller 112 may transmit commands to one or more transmission area control devices 110 in transmission area 102. In some examples, the transmission area controller may also transmit commands to a control device in another transmission area among the plurality of transmission areas. The commands may be configured to cause the control devices to perform adaptive control actions in real time. In some examples, the commands are configured to perform pre-scheduled control based on a transmission area orchestration index. For example, pre-scheduled control may be defined in an area adaptive strategy.

[0081] In some methods, the transmission area controller can transmit commands to the control unit when an operational violation is detected. As discussed above, the transmission area orchestration index can indicate operational violations in the assigned area. Therefore, the transmission area controller can implement control actions to resolve operational violations.

[0082] In some methods, the transmission area controller may also transmit alarms or electronic messages to the user interface associated with the assigned area. For example, when a non-operational violation is detected in the assigned area, the transmission area controller may send an alarm or electronic message to notify the user of the non-operational violation, instead of sending a command to the control device, or in addition to sending a command to the control device.

[0083] In step 172, the transmission area controller may receive a regional policy. For example, the transmission area controller 112 may receive a regional policy from the combined grid management system 134 or from the regional policy database 125.

[0084] In step 174, the transmission area controller may determine predictive control actions for control devices in the assigned area based on the area policy and transmission area operation data. For example, transmission area controller 112 may determine predictive control actions for one or more transmission area control devices 110 in transmission area 102.

[0085] In some approaches, the transmission area controller may use a predictive control machine learning model to determine predictive control actions, which is trained on at least one of historical area operation parameters, historical best control measures or historical area prediction strategies and area operation prediction data.

[0086] In step 176, the transmission area controller may transmit commands to control devices based on predictive control actions. For example, transmission area controller 112 may transmit commands to one or more transmission area control devices in transmission area control device 110.

[0087] refer to Figure 4 and Figure 5 Examples of different types of transmission areas that may exist in a power grid system (such as power grid system 100) are shown. The different types of transmission areas may include at least one of the following: Renewable Energy Network (REN) area 180A, Generation (GEN) area 180B, Transmission area 180C, Flexible area 180D, Microgrid (MG) or Distributed Energy (DER) area 180E or Industrial area 180F.

[0088] REN Area 180A may include one or more substations with incoming and / or outgoing lines integrated into the REN. In REN Area 180A, the transmission area controller may provide one or more of the following: inertial, fast frequency response (FFR), ancillary services (AS), voltage ride-through (VRT), or frequency ride-through (FRT) support.

[0089] GEN 180B may contain one or more substations with incoming generation (GEN) and one or more outgoing lines. Within GEN 180B, the transmission area controller can provide one or more of inertial, FFR, stability, or black-start support. In one example, when a complete or partial power outage occurs in GEN 180B due to an extreme problem such as a cyber event, the transmission area controller can provide black-start support by controlling the restart of equipment within the area. Part of such a black-start operation performed by the transmission area controller may involve in-pocket restarting of GEN 180B to bring the area back to normal operation.

[0090] Transmission zone 180C may include one or more substations with one or more incoming lines and one or more outgoing lines.

[0091] Flexible Zone 180D may include one or more substations with incoming generation (GEN) and outgoing lines to one or more distribution systems and / or one or more loads. In Flexible Zone 180D, the transmission area controller can provide AS support and penalty tracking.

[0092] A MG / DER area 180E may comprise one or more substations with one or more incoming lines and one or more outgoing lines to a microgrid (MG) or distributed energy source (DER). A microgrid can act as an independent small grid, with its own generation and distribution, and can be connected into the main grid but also operate independently. Distributed energy sources may be part of a microgrid and may include diesel stations, solar, and wind power. In a MG / DER area 180E, when a problem exists in the main grid, the transmission area controller can isolate the area and operate it as an independent grid. The transmission area controller can also provide support to another transmission area in the grid that has problems. For example, a transmission area controller assigned to a MG / DER area 180E can manage when a microgrid kicks in to support a nearby area.

[0093] Industrial Zone 180F may contain one or more substations with one or more incoming lines from a generation unit (GEN) and one or more outgoing lines to industrial loads. Within Industrial Zone 180F, a transmission area controller can provide one or more of ancillary services (AS), demand response (DR), and load management and control (LMC) support. Industrial Zone 180F may supply power to the main grid and may also be a large load on the main grid. Depending on the scenario, Industrial Zone 180F may need to limit its load or supply excess power to the main grid. Therefore, the transmission area controller assigned to Industrial Zone 180F can instruct when to limit power within Industrial Zone 180F or when to supply excess power to nearby areas.

[0094] In each type of area, a transmission area controller (such as transmission area controller 112) can monitor, manage constraints on, and / or control one or more of the following: voltage, frequency, inertia, congestion, reactive power load, and power factor. The transmission area controller (such as transmission area controller 112) can also manage, monitor, and provide auxiliary control for one or more of the following: voltage, frequency, inertia, congestion, reactive power load, and power factor in downstream areas. Furthermore, the transmission area controller can control coordination with multiple upstream, downstream, and remote areas within the transmission power grid system.

[0095] In some methods, for example, the combined grid management system 134 may delineate or form multiple transmission areas within a transmission power grid. For instance, the combined grid management system 134 may delineate the multiple transmission areas based on a power grid architecture that may be determined based on a single-line diagram (SLD) / nanowatt (NW) model of the transmission power grid.

[0096] Distribution operations, management, and control

[0097] In some embodiments, the systems and methods provided herein can be used to operate, manage, or control distribution power networks. The increased possibilities for controllable assets and circuit configurations within distribution power networks can make centralized control of these networks challenging. The methods described herein utilize a region-based distribution area controller network to divide the distribution power network into multiple distribution areas. Furthermore, the methods described herein employ distributed intelligence for area automation and control to enable Advanced Distribution Management System (ADMS) solutions. The combination of ADMS operation and a region-based distribution area controller network can help improve or optimize the operation of the distribution power network and reduce unpredictability. Additionally, using a region-based distribution area controller network reduces latency, increases the use of real-time information, improves modeling accuracy, and provides improved distribution power network control.

[0098] The systems and methods described herein for operating, managing, and controlling distribution power networks can be used to provide real-time control over regional assets. Feedback from distribution area controllers can also improve distribution area modeling and predictive action. Using a region-based distribution area controller network can also reduce regional outages and individual circuit interruptions within the region. Furthermore, operating the distribution power network as multiple distribution areas reduces the amount of data and associated communications that utilities need to process, and also reduces computing bandwidth. In addition, the systems and methods described can effectively manage the increasing integration of renewable energy networks (RENs) and distributed energy resources (DERs) with the distribution grid, as well as extreme weather events.

[0099] Within a distribution area, a distribution area controller (DAC) can perform voltage / volt-ampere reactive power (VVO) optimization (VVO), voltage reduction energy saving (CVR), fault location, isolation and service restoration (FLIRS), switching, ramping, power control, power quality control, voltage control, and power factor control, and can reduce or minimize supply interruptions. DACs may also have machine learning capabilities to learn about area operations and control actions. DACs can also integrate the control of electric vehicles (EVs), passive loads, distributed energy resources (DERs), microgrids, battery energy storage systems (BESS), and load shedding with traditional asset control. Furthermore, a region-based DAC network can coordinate responses between areas to manage area recovery and distribution grid demand. Additionally, a region-based DAC network can provide the ability to resiliently isolate one or more distribution areas and provide resilience to adjacent areas.

[0100] Figure 6 A distribution area controller 120 is shown according to some embodiments. The distribution area controller 120 is associated with or assigned to a distribution area 104. In some aspects, the distribution area controller 120 is associated with and configured to operate an assigned distribution area among a plurality of distribution areas 104. Furthermore, in some aspects, each of the plurality of distribution areas 104 may be assigned the same controller as the distribution area controller 120.

[0101] The distribution area controller 120 may include one or more of the following: a machine learning engine 186, an area monitoring agent 182, an area adaptive control agent 184, and an area predictive control agent 188. The machine learning engine 186, area monitoring agent 182, area adaptive control agent 184, and area predictive control agent 188 can communicate with each other. It is also envisioned that, with reference to... Figure 3 The described operation or function can be performed by a single agent, engine, or module.

[0102] The machine learning engine 186 may contain one or more machine learning models. The machine learning engine 186 may communicate with and receive training data from the training database 122.

[0103] In one example, the machine learning engine 186 may include a baseline machine learning model. The baseline machine learning model may receive at least one of the following as input: load data of the distribution area, control device settings, distributed energy generation (DER) profile data, or intermittency profile data. The baseline machine learning model may then determine or identify one or more baseline operating parameters or control device settings of the distribution area as output. The baseline machine learning model may be trained on at least one of the following: historical load data of the distribution area, simulated load data of the distribution area, historical settings of one or more distribution area control devices, historical distributed energy generation (DER) profile data of the distribution area, simulated distributed energy generation (TER) profile data of the distribution area, simulated intermittency profile data of the distribution area, or historical intermittency profile data of the distribution area.

[0104] In another example, machine learning engine 186 may include an adaptive control machine learning model. The adaptive control machine learning model may receive, for example, distribution area operation data and area policies from distribution area measurement device 116 as input. The adaptive control machine learning model may also receive, for example, area orchestration indexes for distribution area 104 from area orchestration index database 132 or from area monitoring agents as input. The adaptive control machine learning model may determine or identify one or more adaptive control actions (e.g., for distribution area control device 118) as output. The adaptive control machine learning model may be trained using at least one of the following: historical area orchestration indices of distribution area 104, historical distribution area operation parameters, simulated distribution area operation data, historical distribution control measures, simulated distribution control measures, simulated distribution area adaptive policies, or historical distribution area adaptive policies. The adaptive control machine learning model may also be trained using at least one of the following: historical distribution area operation parameters, simulated distribution area operation data, simulated distribution adaptive control measures, or historical distribution adaptive control measures. In some methods, when the distribution area is one of multiple distribution areas, the adaptive control machine learning model can also be trained on historical area orchestration indices and historical operating data from another distribution area (e.g., a distribution area of ​​the same type as distribution area 104) among the multiple distribution areas. In this way, the adaptive control machine learning model can learn from the operations of similar types of areas.

[0105] In another example, machine learning engine 186 may include a predictive control machine learning model. The predictive control machine learning model may receive, for example, regional prediction and / or planning data from regional prediction / planning database 128 as input. The predictive control machine learning model may also receive, for example, regional policies from regional policy database 125 as input. The predictive control machine learning model may determine or identify one or more predictive control actions (e.g., for distribution area control device 118) for distribution area 104 as output. The predictive control machine learning model may be trained using at least one of the following: historical distribution area operating parameters of distribution area 104, simulated distribution area operating data, historical distribution control measures, simulated distribution control measures, simulated distribution prediction data, or historical distribution prediction data.

[0106] Area monitoring agent 182 can be configured to determine the distribution area orchestration index of distribution area 104. The area orchestration index can be a measure of the performance of distribution area 104 relative to baseline performance or relative to a reference index. Area monitoring agent 182 can determine the area orchestration index, for example, based on at least one of distribution area operation data, area strategy, or area forecast data from distribution area measurement device 116. Area monitoring agent 182 can also communicate with one or more databases, such as area strategy database 125, area forecast / planning database, and area orchestration index database 132. For example, area monitoring agent 182 can receive data from area strategy database 125 and area forecast / planning database. In some examples, area monitoring agent 182 can transmit data to area orchestration index database 132 or to another distribution area controller.

[0107] In some examples, when the distribution area 104 includes sub-areas 250 and clusters 258, the area monitoring agent 182 can also communicate with and receive data from the sub-area measurement device 254 and the cluster control device 264.

[0108] In some examples, the area monitoring agent 182 may also include a low-voltage (LV) module configured to receive operational data from low-voltage circuits in the distribution area 104. The LV module may receive distribution area operational data from a distribution area measurement device 116, which may include, for example, an advanced metering infrastructure (AMI) or a meter data management system. The distribution area operational data received or monitored by the LV module may include at least one of the following: electric vehicle (EV) load, rooftop photovoltaic (PV) power generation, power quality, total load, critical load, or load imbalance.

[0109] The zone adaptive control agent 184 can determine one or more adaptive control actions for the distribution zone 104, for example, based on at least one of distribution zone operation data or zone policies. Zone policies can define operating parameters, rules, and / or constraints for zone operations. In some aspects, one or more machine learning models (such as adaptive control machine learning models) of the machine learning engine 186 can be integrated with a portion of the zone adaptive control agent 184. The zone adaptive control agent 184 can communicate with and receive data (such as policies) from the zone policy database 125. The zone adaptive control agent 154 can communicate with one or more distribution zone control devices in the distribution zone control unit 118. In this way, the zone adaptive control agent 184 can be configured to deliver or transmit commands to one or more distribution zone control devices in the distribution zone control unit 118.

[0110] The regional predictive control agent 188 can determine predictive control actions for distribution area 104, for example, based on at least one of regional policies and regional predictive data. Using the regional predictive control agent 188, the distribution area controller 120 can predict the future behavior of the distribution power grid to determine predictive control actions, thereby achieving the intended or target performance of the transmission power grid. For example, predictive control actions may include any control actions for distribution area control device 118 during a future time period. In some aspects, one or more machine learning models of the machine learning engine 186 (such as predictive control machine learning models) may be integrated with or be part of the regional predictive control agent 188. The regional predictive control agent 188 can communicate with and receive data (such as policies) from the regional policy database 125. Furthermore, the regional predictive control agent 188 can communicate with and receive data (such as predictive data) from the regional predictive / planning database 128.

[0111] In some examples, the distribution area controller 120 may also include a zone configuration module configured to adjust one or more thresholds, limits, or setpoints for feeders, assets, and / or installations in distribution area 104. The zone configuration module may adjust one or more thresholds, limits, or setpoints based on distribution area operating data. For example, the zone configuration module may adjust the thresholds, operating limits, or setpoints of one or more of the main equipment 114, the distribution area control device 118, or the distribution area measurement device 116. In some examples, the zone configuration module may adjust one or more of the following: power distribution energy, battery energy storage system, or volt / VAR optimization parameters.

[0112] In some examples, the distribution area controller 120 may also include a area feedback module configured to receive distribution area operation data after one or more control actions are performed in the distribution area 104.

[0113] Figure 7 Methods for operating a distribution area controller according to some embodiments to generate adaptive or predictive control actions for distribution areas in a power grid are illustrated. In some examples, the distribution area controller is a reference... Figure 1 and Figure 6 The described power distribution area controller 120, and the power distribution area is a reference Figure 1 The described power distribution area is 104.

[0114] In step 190, the distribution area controller receives distribution area operation data from the assigned distribution area. For example, the distribution area controller 120 may receive distribution area operation data from one or more distribution area measuring devices 116 in distribution area 104.

[0115] In step 192, the distribution area controller determines the distribution area arrangement index of the assigned distribution area based on the distribution area operation data. For example, the distribution area controller 120 may determine the distribution area arrangement index of distribution area 104 based on the distribution area operation data.

[0116] In some examples, the distribution area orchestration index can be a measure of the baseline operating data of the assigned distribution area, referencing the operation of the assigned distribution area. The baseline operating data may include at least one of the following: the baseline load of the assigned distribution area, the baseline settings of one or more distribution area control devices in the distribution area control unit, the baseline distributed energy (DER) generation characteristic curve of the assigned distribution area, or the baseline intermittency characteristic curve of the assigned distribution area.

[0117] In some examples, the distribution area orchestration index can indicate at least one of an operational violation or a non-operational violation in an assigned distribution area. The distribution area controller 120 can be configured to identify operational violations based on distribution area operation data and distribution area policies.

[0118] In some examples, the distribution area controller may also be configured to determine at least one of a load / generation growth index or a distributed energy (DER) intermittency level for an assigned distribution area. The load / generation growth index indicates the extent to which load / generation has increased in the assigned distribution area. The DER intermittency level indicates the difference between actual and predicted DER generation over a specific time interval in the assigned distribution area. The distribution area controller may then determine the settings of one or more distribution area control devices in the assigned distribution area based on at least one of the load / generation growth index or the DER intermittency level. Furthermore, the distribution area controller may determine a distribution area orchestration index based on at least one of load / generation growth and the DER intermittency level.

[0119] In step 194, the distribution area controller transfers the distribution area orchestration index to another distribution area. For example, distribution area controller 120 can transfer the distribution area orchestration index to another distribution area, such as to a distribution area controller 120 assigned to another distribution area.

[0120] In step 196, the distribution area controller transmits the distribution area orchestration index to the advanced distribution management system (ADMS). For example, the distribution area controller 120 can transmit the distribution area orchestration index to ADMS 144.

[0121] In some methods, the distribution area controller can be configured to transmit intervention control commands to the distribution area controller. A distribution area orchestration index is used for the assigned area, and a area orchestration index is used for another area among the plurality of distribution areas. In some examples, the intervention control command can be configured to resolve at least one of operational or non-operational violations among the plurality of distribution areas. The intervention control command can compromise between distribution areas to resolve violations in the distribution power grid.

[0122] In step 198, the distribution area controller determines adaptive control actions for control devices within the assigned distribution area based on the distribution area orchestration index. For example, distribution area controller 120 may determine adaptive control actions for control devices in distribution area 104.

[0123] In step 200, the distribution area controller transmits commands to the control devices based on adaptive control actions. For example, the distribution area controller 120 may transmit commands to one or more distribution area control devices 118. The commands may be configured to cause one or more distribution area control devices 118 to perform adaptive control actions. In some examples, the commands may be configured to control at least one of an electric vehicle load, a passive load, or an active load, wherein the passive load is an industrial, commercial, or residential load that can be switched on or off and cannot be controlled for partial use.

[0124] In step 202, the distribution area controller receives forecast data for the assigned distribution area. For example, the distribution area controller 120 may receive forecast data for distribution area 104 from the area forecast / planning database 128.

[0125] In some methods, the distribution area controller may also receive a prediction strategy. The distribution area controller may receive the prediction strategy, for example, from ADMS144 or from the area strategy database 125.

[0126] In step 204, the distribution area controller determines the predicted control actions for the control devices in the assigned distribution area based on the area policy, prediction data, and distribution area operation data. For example, the distribution area controller 120 may determine the predicted control actions for one or more distribution area control devices 118 in the assigned distribution area.

[0127] In some approaches, the distribution area controller can also determine predictive control actions based on area prediction strategies.

[0128] In step 206, the distribution area controller transmits commands to the control devices based on predictive control actions. For example, the distribution area controller 120 may transmit commands to one or more distribution area control devices in the distribution area control device 118.

[0129] Figure 8 Various methods are illustrated for determining the configuration of distribution area control devices 118 within distribution area 104. In some methods, the distribution area controller may be configured to evaluate the planning of an assigned distribution area and determine the corresponding control device configuration to implement such planning. Distribution area controller 120 may be configured to execute... Figure 8 One or more of the steps described in detail in the document.

[0130] In step 210, the distribution area controller can identify and configure distribution areas based on predefined rules. For example, predefined rules can be transmitted from the Advanced Distribution Management System (ADMS) to the distribution area controller.

[0131] In step 212, the distribution area controller can determine the baseline load of the distribution area based on historical operating data and baseline distributed energy (DER) generation / intermittency characteristic curves. The baseline DER generation / intermittency characteristic curves define the expected generation of one or more energy sources (such as solar or wind energy). Solar or wind energy resources can be considered to operate intermittently. The baseline DER generation / intermittency characteristic curves define how such resources are expected to perform over a period of time.

[0132] In step 214, the distribution area controller can determine at least one of the real-time aggregated load or aggregated growth of the distribution area.

[0133] In step 216, the distribution area controller may determine at least one of a load growth index or a generation growth index for the distribution area. The load growth index may be a value reflecting the real-time aggregated load in the distribution area relative to the baseline load. The generation growth index may be a value measuring the demand for generation relative to expected generation.

[0134] In step 218, the distribution area controller may adjust the settings of one or more distribution area control devices in the distribution area based on the load growth index and / or the power generation growth index.

[0135] In another example, the distribution area controller is configured to receive a fault recovery plan for the assigned distribution area. The distribution area controller can then determine the settings of one or more distribution area control units within the distribution area control unit based on the fault recovery plan.

[0136] In step 220, the distribution area controller receives a fault recovery plan for potential faults in the distribution area. The fault recovery plan can be any predefined post-fault response process or measure designed to detect, locate, isolate, and / or restore faults in the power grid. A fault can be any condition that prevents circuit elements from functioning as required. Examples of faults may include short circuits, open circuits, device malfunctions, or overloads.

[0137] In step 222, the distribution area controller determines the fault settings for potential faults. The fault settings may include settings from one or more distribution area control devices that enable fault recovery planning or restoration of power grid operation.

[0138] In step 224, when a real or actual fault occurs, the distribution area controller adjusts the control devices in the distribution area according to the fault settings. For example, the distribution area controller may send commands to one or more distribution area control devices to implement the fault settings.

[0139] In another example, the distribution area controller is configured to receive an outage restoration plan assigned to the distribution area. The distribution area controller can then determine the settings of one or more distribution area control units in the distribution area control unit based on the outage restoration plan.

[0140] In step 226, the distribution area controller receives the outage recovery plan for the distribution area. The outage recovery plan can be any predefined outage response process or measure designed to detect outages and / or restore power to the power grid.

[0141] In step 228, the distribution area controller determines the outage settings for the outage recovery plan. The outage settings may include settings of one or more distribution area control devices that can implement the outage recovery plan or restore power to the power grid.

[0142] In step 230, when an actual power outage occurs, the distribution area controller adjusts the control devices in the distribution area according to the power outage settings. For example, the distribution area controller may send commands to one or more distribution area control devices to implement the power outage settings.

[0143] In another example, the distribution area controller is configured to receive a load management plan for the assigned distribution area. The distribution area controller can then determine the settings of one or more distribution area control units in the distribution area control unit based on the load management plan.

[0144] In step 232, the distribution area controller receives the load management plan for the distribution area. The load management plan can be any predefined process or measure for load changes in the power grid, designed to detect load changes and / or operate power grid equipment in response to load changes.

[0145] In step 234, the distribution area controller determines the load management settings for the load management plan. The load management settings may include settings for one or more distribution area control devices that enable outage recovery planning or power restoration in the power grid.

[0146] In step 236, when a load change occurs, the distribution area controller adjusts the control devices in the distribution area according to the load management settings. For example, the distribution area controller may send commands to one or more distribution area control devices to implement the load management settings.

[0147] In another example, the distribution area controller is configured to receive new distributed energy resources (DER) commitments or registrations for an assigned distribution area. The distribution area controller can then determine the settings of one or more distribution area control units in the distribution area control apparatus based on the new distributed energy resources (DER) commitments or registrations.

[0148] In step 238, the distribution area controller receives distributed energy resources (DER) commitments or registrations for the distribution area. A distributed energy resources (DER) commitment may include generation and storage devices connected to or to be connected to the power grid. DER commitments or registrations may include various energy types, such as solar, wind, and battery storage.

[0149] In step 240, the distribution area controller determines updated settings to handle distributed energy resources (DER) commitments or registrations. The updated settings may include settings for one or more distribution area controllers designed to manage or otherwise operate the power grid in response to DER commitments or registrations.

[0150] In step 242, when a new commitment or registration occurs, the distribution area controller adjusts the control devices in the distribution area according to the updated settings. For example, the distribution area controller may send commands to one or more distribution area control devices to implement the updated settings.

[0151] In another example, the distribution area controller is configured to receive voltage / VAR currents on the feeders and bus under steady-state operation. The distribution area controller can then determine the voltage / VAR currents on the feeders and bus after a planned reconfiguration, and determine a target voltage / VAR characteristic curve based on these currents.

[0152] In step 244, the distribution area controller receives voltage / VAR flow on the feeder or bus in the distribution area under steady state and after reconfiguration or after fault location, isolation and service restoration (FLIRS).

[0153] In step 246, the distribution area controller adjusts the voltage / VAR current based on changes in load taps, renewable energy sources, capacitor banks, and distributed energy sources (DERs) within the distribution area. Changes in load taps, renewable energy sources, capacitor banks, and distributed energy sources (DERs) can occur due to reconfiguration of the distribution area or due to fault location, isolation, and service restoration (FLIRS).

[0154] Distribution zone computing

[0155] In some embodiments, the systems and methods provided herein can divide a distribution area into sub-areas for operation, management, or control. Distribution areas in a distribution power grid can be divided or classified into sub-areas. For example, a sub-area may represent a radial feeder in a distribution power grid. Sub-areas can be further divided or classified into clusters. In one example, a cluster may represent a low-voltage (LV) network in a distribution power grid. A distribution area controller assigned to a distribution area can then determine various situational awareness parameters for the distribution area on a per-sub-area or per-cluster basis. Situational awareness parameters may indicate levels of load flexibility, generation flexibility, or power quality flexibility in a sub-area or cluster. The situational awareness parameters can then be used to determine an orchestration index for each sub-area or cluster, enabling the sub-areas or clusters to be ordered. For example, the order may indicate a priority level for at least one of adaptive control actions, emergency situations, or future time interval planning.

[0156] Figure 9 A power distribution area 104 is shown according to some embodiments. Figure 9 In this process, the power distribution area 104 is divided into multiple sub-areas 250. At least one of the multiple sub-areas 250 can be further divided into multiple clusters 258.

[0157] Distribution area 104 may include main equipment 114, one or more distribution area measuring devices 116, one or more distribution area control devices 118, and distribution area controller 120. (Reference) Figure 1 Describe in detail the components of power distribution area 104.

[0158] One or more of the plurality of sub-regions 250 may include one or more assets 252, one or more sub-region measuring devices among the sub-region measuring devices 254, and one or more sub-region control devices among the sub-region control devices 256. A sub-region 250 may be a feeder within a power distribution area 104. For example, if there are four feeders in the power distribution system, each feeder can be considered a sub-region. All devices or assets 252 connected to the feeders can be part of a sub-region. Dividing a power distribution area into sub-regions simplifies the management of data within the power distribution area.

[0159] The one or more assets 252 may include one or more main equipment 114 of the power distribution area 104. That is, the main equipment 114 may be assigned to or divided into sub-areas 250 based on the demarcation of sub-areas 250 in the power distribution area 104.

[0160] Sub-area measurement device 254 can be configured to measure at least one sub-area operating parameter of sub-area 250. Sub-area operating parameters may include, for example, voltage, power factor, active / reactive power, load per node, battery-based energy storage system (BESS) capacity, distributed energy generation (DER), renewable energy generation (REN), frequency, electric vehicle (EV) load, microgrid generation, microgrid load, feeder voltage, feeder current, feeder load imbalance, power quality data, etc. Sub-area measurement device 254 may include one or more distribution area measurement devices 116 in distribution area 104. That is, distribution area measurement devices 116 may be assigned to or divided into sub-areas 250 based on the demarcation of sub-areas 250 within distribution area 104.

[0161] Sub-area control device 256 may be configured to adjust at least one sub-area operating parameter of sub-area 250. Sub-area control device 256 may include one or more distribution area control devices 118 in distribution area 104. That is, distribution area control device 118 may be assigned to or divided into sub-area 250 based on the demarcation of sub-area 250 in distribution area 104.

[0162] One or more of the plurality of clusters 258 may include one or more assets 260, one or more cluster measuring devices among cluster measuring devices 262, and one or more cluster control devices among cluster control devices 264. Cluster 258 may be one or more load points connected to a feeder in sub-area 250. For example, a feeder may have multiple load points drawing power from the feeder. One or more load points may be considered as cluster 258, and each load point may have means for providing electrical data associated with cluster 258. Such means may be cluster measuring devices 262, which will be further described below. In some aspects, each of the plurality of clusters 258 may be a low-voltage (LV) network in a distribution power grid system connected via one or more distribution transformers.

[0163] The one or more assets 260 may include one or more main equipment 114 of the power distribution area 104. That is, the main equipment 114 may be assigned to or assigned to cluster 258 based on the demarcation of cluster 258 in the power distribution area 104.

[0164] Cluster measurement device 262 can be configured to measure at least one sub-area operating parameter of cluster 258. Cluster operating parameters may include, for example, voltage, power factor, active / reactive power, load per node, battery-based energy storage system (BESS) capacity, distributed energy generation (DER), renewable energy generation (REN), frequency, electric vehicle (EV) load, microgrid generation, microgrid load, feeder voltage, feeder current, feeder load imbalance, power quality data, etc. Cluster measurement device 262 may include one or more distribution area measurement devices 116 in distribution area 104. That is, distribution area measurement devices 116 may be assigned to or divided into cluster 258 based on the demarcation of cluster 258 within distribution area 104.

[0165] The cluster control unit 264 can be configured to adjust at least one cluster operating parameter of cluster 258. The cluster control unit 264 may include one or more distribution area control units 118 in distribution area 104. That is, distribution area control units 118 can be assigned to or allocated to cluster 258 based on the demarcation of cluster 258 in distribution area 104.

[0166] Figure 10 The diagram illustrates a distribution area 270 in a power grid, based on some examples. Distribution area 270 can be compared with reference to... Figure 1 and Figure 9 Distribution area 104, shown and described, is configured in the same manner. Distribution area 270 contains sub-area 272. Sub-area 272 also contains cluster 274. Figure 10In this context, subregion 272 is the feeder, and cluster 274 contains low-voltage load points connected to the feeder.

[0167] Figure 11 Methods are illustrated for operating a distribution area controller according to some embodiments to divide a distribution area into sub-areas and clusters and to sort the sub-areas and clusters in order to prioritize adaptive control actions within the distribution area. In some examples, the distribution area controller is a reference... Figure 1 and Figure 6 The described power distribution area controller 120, and the power distribution area is a reference Figure 1 , Figure 6 and Figure 9 The described power distribution area is 104.

[0168] In step 280, the distribution area controller may divide the assigned distribution area into multiple sub-areas. For example, the distribution area controller may divide the assigned distribution area into the multiple sub-areas based on a policy defined in ADMS144 or the area policy database 125. For example, the policy may specify rules or logic for dividing the sub-areas based on at least one of the assets, network topology, or operational status of the assigned distribution area.

[0169] In step 282, the distribution area controller may receive sub-area operation data from each of the plurality of sub-areas. The distribution area controller may receive sub-area operation data from one or more of the sub-area measuring devices 254.

[0170] In some embodiments, the distribution area controller may determine at least one sub-area characteristic of an assigned sub-area based on sub-area operational data. The sub-area characteristic may include at least one of the following: critical load, non-critical load, electric vehicle load, vehicle-to-grid generation, load under demand response (DR) schemes, producer-consumer generation (PV / DER), voltage / current imbalance, total harmonic distortion, microgrid-enabled generation, or microgrid-enabled load. The distribution area controller may also compare the at least one sub-area characteristic with baseline operational characteristics. Baseline operational characteristics reflect the operation of the assigned sub-area under normal operating conditions. Normal operating conditions can be operating conditions without violations.

[0171] In some embodiments, in step 284, the distribution area controller may further divide at least one sub-area into multiple clusters. For example, the distribution area controller may divide the sub-area into the multiple clusters based on a policy defined in ADMS144 or the area policy database 125. For example, the policy may specify rules or logic for dividing the clusters based on at least one of the assets, network topology, or operational status in the sub-area.

[0172] In some methods, the assigned distribution area may contain multiple feeders. Cluster partitioning rules can instruct the Advanced Distribution Management System (ADMS) to partition each feeder into a sub-area and, via the distribution transformers within the sub-area, partition each load connection point into a cluster.

[0173] In some embodiments, in step 286, the distribution area controller may receive cluster operation data for each of the plurality of clusters. For example, the distribution area controller may receive cluster operation data from the cluster measurement device 262.

[0174] In some embodiments, the distribution area controller may determine at least one cluster characteristic based on cluster operation data of an assigned cluster. Cluster characteristics may include one or more of the following: critical loads, non-critical loads, electric vehicle loads, vehicle-to-grid generation, loads under demand response (DR) schemes, producer-consumer generation (PV / DER), voltage / current imbalance, total harmonic distortion, microgrid-enabled generation, or microgrid-enabled loads. The distribution area controller may also compare the at least one cluster characteristic with baseline operating characteristics that reflect the operation of the assigned cluster under normal operating conditions. The distribution area controller may also determine load situation awareness parameters (load situation awareness parameters indicating the load characteristics in the corresponding cluster) based on the at least one cluster characteristic.

[0175] In step 288, the distribution area controller can also determine the distribution area orchestration index of the assigned distribution area based on the sub-area operation data and the cluster operation data.

[0176] In some methods, the distribution area scheduling index can be determined based on at least one of the load flexibility index, generation flexibility index, or power quality index.

[0177] The distribution area controller can determine a load flexibility index based on load situation awareness parameters. The load flexibility index can be a value representing the level of load that can be adjusted. To determine the load flexibility index, the distribution area controller can determine the load situation awareness parameters for each of the plurality of clusters. The distribution area controller can determine the load situation awareness parameters based on at least one cluster characteristic. The load situation awareness parameters can indicate the load characteristics in the corresponding cluster. In some examples, the load situation awareness parameters include at least one of the following: loads available for curtailment, loads available for demand response execution, electric vehicle loads, and MG-enabled loads, the load dispatch flexibility level, the percentage of critical loads that can be serviced, the level of non-critical loads available for curtailment, or the load level registered under a DR scheme.

[0178] The distribution area controller can determine a generation flexibility index based on generation situational awareness parameters. The generation flexibility index can be a value representing the level at which generation can be adjusted within a given cluster. The distribution area controller can determine the generation situational awareness parameters based on at least one cluster characteristic. The generation situational awareness parameters can indicate the generation characteristics within the given cluster. In some examples, the generation situational awareness parameters may include at least one of the following: incoming generation, vehicle-to-grid generation, microgrid-enabled generation, PV-enabled generation, distributed energy-enabled generation, generation dispatch flexibility level, available DER generation level, microgrid generation level available for grid supply, or available ancillary service level.

[0179] The distribution area controller can determine the power quality index for each of the plurality of clusters based on power quality situational awareness parameters. The power quality flexibility index is a value representing the level of load adjustment capability. The distribution area controller can determine the power quality situational awareness parameters based on at least one cluster characteristic. The power quality situational awareness parameters can indicate the power quality characteristics in the corresponding cluster. In some examples, the power quality situational awareness parameters may include at least one of the following: voltage / current imbalance, total harmonic distortion (THD), voltage / frequency fluctuation, SAIDI / CAIDI / SAIFI, level of power quality impact, level of voltage sag / swell event, level of SAIDI / CAIDI, level of SAIFI / CAIFI, level of harmonic content, power quality impact assessment, level of no voltage sag / swell event, level of SAIDI / CAIDI, level of SAIFI / CAIFI, or harmonic content.

[0180] In step 290, the distribution area controller can determine the adaptive control action for the assigned distribution area. For example, the distribution area controller can determine the adaptive control action for at least one of the sub-area control devices 256 or cluster control devices 264 based on the distribution area orchestration index.

[0181] In some methods, the distribution area controller can use an adaptive control machine learning model to determine adaptive control actions. The adaptive control machine learning model can be trained on at least one of the following: historical sub-area operating parameters, simulated sub-area operating parameters, historical cluster operating parameters, simulated cluster operating parameters, historical distribution control measures, simulated distribution control measures, simulated distribution area adaptive strategies, or historical distribution area adaptive strategies. (Adaptive control machine learning model reference) Figure 6 It is described in further detail.

[0182] In step 292, the distribution area controller may transmit commands to at least one of the sub-area control devices or cluster control devices based on adaptive control actions.

[0183] In some embodiments, the distribution area controller may also determine predictive control actions for assigned distribution areas. The distribution area controller may receive predictive data from sub-areas or clusters. For example, the distribution area controller may receive predictive data from a region prediction / planning database 128. The distribution area controller may also receive region policies. For example, the distribution area controller may receive region policies from a region policy database. The distribution area controller may then determine predictive control actions based on at least one of sub-area operation data, cluster operation data, predictive data, and region policies.

[0184] In some methods, the distribution area controller can use a predictive control machine learning model to determine predictive control actions. The predictive control machine learning model can be trained on at least one of the following: historical sub-area operating parameters, simulated sub-area operating parameters, historical cluster operating parameters, simulated cluster operating parameters, historical distribution control measures, simulated distribution control measures, historical sub-area prediction data, simulated sub-area prediction data, simulated cluster prediction data, or historical cluster prediction data. In some methods, the distribution area controller can use an adaptive control machine learning model to determine adaptive control actions. The adaptive control machine learning model can be trained on at least one of the following: historical sub-area operating parameters, simulated sub-area operating parameters, historical cluster operating parameters, simulated cluster operating parameters, historical distribution control measures, simulated distribution control measures, simulated distribution area adaptive strategies, or historical distribution area adaptive strategies. (Predictive Control Machine Learning Model Reference) Figure 6 It is described in further detail.

[0185] Figure 12 Methods for operating a distribution area controller according to some embodiments are illustrated to determine the priority of adaptive control actions and predictive control actions within a distribution area. In some examples, the distribution area controller is a reference... Figure 1 and Figure 6 The described power distribution area controller 120, and the power distribution area is a reference. Figure 1 , Figure 6 and Figure 9 The described power distribution area is 104. Assume... Figure 12 One or more steps of the method can be combined Figure 11 The method is executed.

[0186] Steps 300-312 illustrate a method for determining the priority of adaptive control actions in a power distribution area.

[0187] In step 300, the distribution area controller can divide an assigned distribution area among multiple distribution areas into multiple sub-regions. These sub-regions can be modeled as digital twins by the distribution area controller. The digital twin can model the sub-regions (or, in some embodiments, regions) and can learn from the data of the sub-regions to understand and / or predict the operational characteristics of the sub-regions and how they will behave in a particular scenario. The digital twin model of a sub-region can make predictions based on historical data, operational data, and any other available data related to a particular sub-region. For example, the digital twin model can be used to determine how a sub-region responds to a current drop.

[0188] Digital twins can model one or more specific assets within a sub-region. In a power grid distribution system, a digital twin can model assets within a sub-region as software assets. For example, a sub-region might contain transformers as physical assets. The digital twin model associated with that sub-region can then include a 3D model of the transformer, which analyzes historical data to help operators understand how the transformer operates, its load, and its responses. Operators can access the digital twin model of the sub-region to aid in decision-making regarding how the asset will operate or perform in certain scenarios.

[0189] In step 302, the distribution area controller may further divide at least one of the plurality of sub-regions into multiple clusters. These multiple clusters may also be modeled as digital twins by the distribution area controller. The digital twin model of the clusters is similar to the digital twin models described above for the sub-regions.

[0190] In step 304, the distribution area controller may determine one or more of the following parameters for the current time period based on the sub-area operation data: load situation awareness parameters, generation situation awareness parameters, or power quality situation awareness parameters.

[0191] In step 306, the distribution area controller may determine one or more of the following indices for the current time period based on the sub-area operation data: load flexibility index, generation flexibility index, or power flexibility index.

[0192] In step 308, the distribution area controller may determine the distribution area arrangement index of the at least one sub-region based on the load flexibility index, the generation flexibility index, and the power quality flexibility index.

[0193] In step 310, the distribution area controller may determine the order of the plurality of sub-areas based on an area orchestration index. The order may indicate the priority level of at least one of adaptive control actions, emergency situations, or future time interval planning. In some examples, an emergency situation includes at least one of a fault or weather-related situation.

[0194] In step 312, the distribution area controller may determine the priority of adaptive control actions for the assigned distribution areas based on the sorting. The distribution area controller may also transmit one or more commands to the distribution area control devices within the assigned distribution areas, wherein the commands are configured to perform adaptive control actions.

[0195] Steps 314-326 illustrate a method for determining the priority of predictive control actions in a power distribution area.

[0196] In step 314, the distribution area controller can divide the assigned distribution area among multiple distribution areas into multiple sub-regions. The multiple sub-regions can be modeled as digital twins by the distribution area controller.

[0197] In step 316, the distribution area controller can divide at least one of the multiple sub-regions into multiple clusters. The multiple clusters can be modeled as digital twins by the distribution area controller.

[0198] In step 318, the distribution area controller may determine one or more of the following parameters for a future time period based on sub-area operation data: load situation awareness parameters, generation situation awareness parameters, or power quality situation awareness parameters.

[0199] In step 320, the distribution area controller may determine one or more of the following indices for a future time period based on sub-area operation data: load flexibility index, generation flexibility index, or power quality flexibility index.

[0200] In step 322, the distribution area controller may determine the distribution area orchestration index of the at least one sub-region based on the load flexibility index, the generation flexibility index, and the power quality flexibility index.

[0201] In step 324, the distribution area controller can determine the order of the multiple sub-areas based on the area orchestration index.

[0202] In step 326, the distribution area controller may determine the priority of the predictive control actions assigned to the distribution area based on the sorting.

[0203] Distribution intelligent fault location, isolation, and service restoration (FLISR)

[0204] In some embodiments, the systems and methods provided herein can divide a distribution area into sub-areas and clusters to perform intelligent fault location, isolation, and service restoration within the distribution area. A distribution area controller assigned to the distribution area can then determine a power restoration feasibility index for the distribution area on a sub-area or cluster-by-cluster basis. The power restoration feasibility index guides the prioritization of power restoration control actions within the distribution area.

[0205] Figure 13 Methods for operating a distribution area controller according to some embodiments are illustrated for determining and performing restoration control actions in a distribution area. In some examples, the distribution area controller is a reference... Figure 1 and Figure 6 The described power distribution area controller 120, and the power distribution area is a reference. Figure 1 , Figure 6 and Figure 9 The described power distribution area is 104.

[0206] In step 330, the distribution area controller may receive sub-area operation data assigned to the distribution area. For example, the distribution area controller 120 may receive sub-area operation data from one or more sub-area measuring devices in the sub-area measuring devices 254.

[0207] In some embodiments, the distribution area controller may determine at least one sub-area characteristic of an assigned sub-area based on sub-area operational data. The sub-area characteristic may include at least one of the following: critical load, non-critical load, electric vehicle load, vehicle-to-grid generation, load under demand response (DR) schemes, producer-consumer generation (PV / DER), voltage / current imbalance, total harmonic distortion, microgrid-enabled generation, or microgrid-enabled load.

[0208] In step 332, the distribution area controller may receive cluster operation data assigned to the distribution area. For example, the distribution area controller 120 may receive cluster operation data from one or more cluster measurement devices in the cluster measurement devices 262.

[0209] In some embodiments, the distribution area controller may determine at least one cluster characteristic of an assigned sub-area based on sub-area operational data. The cluster characteristic may include at least one of the following: critical load, non-critical load, electric vehicle load, vehicle-to-grid generation, load under demand response (DR) scheme, producer-consumer generation (PV / DER), voltage / current imbalance, total harmonic distortion, microgrid-enabled generation, or microgrid-enabled load.

[0210] In step 334, the distribution area controller may determine the power restoration feasibility index of at least one sub-area in the assigned distribution area based on at least one of the sub-area operation data or cluster operation data.

[0211] In some embodiments, the distribution area controller may determine the power restoration feasibility index based on at least one of the load recovery index, generation flexibility index, or resilience index.

[0212] The distribution area controller can determine the load recovery index of at least one of the plurality of clusters based on load situation awareness parameters. The load recovery index can be a value representing the level at which load can be adjusted. The distribution area controller can determine the load situation awareness parameters of at least one of the plurality of clusters based on the characteristics of the at least one cluster. The load situation awareness parameters can indicate load characteristics. In some examples, the load situation awareness parameters may include at least one of the following: load that can be reduced, load that can be used for demand response execution, electric vehicle load, MG-enabled load, load dispatch flexibility level, percentage of critical load that can be serviced, level of non-critical load that can be reduced, or load level registered under a DR scheme.

[0213] The distribution area controller can determine the generation flexibility index of at least one of the plurality of clusters based on generation situation awareness parameters. The generation flexibility index is a value representing the level at which generation can be adjusted. The distribution area controller can determine the generation situation awareness parameters based on the characteristics of the at least one cluster. The generation situation awareness parameters can indicate generation characteristics. In some examples, the generation situation awareness parameters may include at least one of the following: incoming generation, vehicle-to-grid generation, microgrid-enabled generation, PV-enabled generation, distributed energy-enabled generation, generation dispatch flexibility level, available DER generation level, microgrid generation level available for grid supply, or available ancillary service level.

[0214] The distribution area controller can determine a resilience index for at least one of the plurality of clusters based on power recovery situational awareness parameters. The resilience index can be a value representing a power reliability level. The distribution area controller can determine the power recovery situational awareness parameters based on the characteristics of the at least one cluster. The power recovery situational awareness parameters can indicate power recovery characteristics. In some examples, the power recovery situational awareness parameters may include at least one of the following: voltage or current imbalance, voltage violation, frequency violation, dielectric load of assets, total harmonic distortion of assets, generation load imbalance, System Average Interruption Duration Index (SAIDI), Customer Average Interruption Duration Index (CAIDI), System Average Interruption Frequency Index (SAIFI) or Customer Average Interruption Frequency Index (CAIFI), number of assets under power outage, recovery time, number of overloaded assets, recovery time, generation / load balance, or amount of unserviceable load.

[0215] In step 336, the distribution area controller may determine the power restoration control action based on the power restoration feasibility index. In some examples, the power restoration control action may adjust the settings of one or more sub-area control units in sub-area control unit 256 for power grid system restoration. In some examples, the power restoration control action may adjust the settings of one or more cluster control units in cluster control unit 264 for power grid system restoration.

[0216] In some embodiments, the distribution area controller may determine recovery control actions based on a recovery machine learning model. The recovery machine learning model may be trained on at least one of the following: historical sub-area operating parameters, historical cluster operating parameters, historical resilience index, historical recovery control measures, historical fault locations, historical fault types, historical fault severity levels, historical recovery times, or simulated faults per location data.

[0217] In step 338, the distribution area controller may transmit commands to one or more sub-area controllers or to one or more cluster controllers based on power restoration control actions. In some examples, the distribution area controller 120 may transmit commands to sub-area controller 256 or to cluster controller 264. In some methods, at least one of the plurality of sub-areas may include an associated sub-area controller. The sub-area controller may be communicatively coupled to the distribution area controller and configured to implement restoration control actions.

[0218] In some embodiments, the distribution area controller may also determine the predicted restoration control action for an assigned distribution area based on a predicted power restoration feasibility index. The distribution area controller may anticipate or predict a fault or event that will occur in the distribution area. The predicted restoration control action may be an action taken to avoid such a predicted fault or event. If it is impossible to avoid the fault or event, the predicted restoration control action may be an action to remedy, mitigate, or otherwise respond to the predicted fault or event in the distribution area. In determining the predicted restoration control action, the distribution area controller may ensure that restoration is possible without posing a physical challenge to the distribution grid. For example, the predicted restoration control action may ensure the restoration of power in the distribution area, or, in some cases, the restoration of maximum power. The controller may use a power restoration feasibility index for a specific distribution area to determine the predicted restoration control action. For example, for a fault at a specific location in the distribution area, the distribution area controller will determine the power restoration feasibility index. The power restoration feasibility index may indicate that the feasibility of restoring power may be less or more if a fault occurs at that specific location. When the power restoration feasibility index indicates that restoration is not feasible, operators may consider adding additional generation or control requirements in the distribution area, or they may create plans to reboot some power to that specific location.

[0219] In some embodiments, the distribution area controller determines predictive recovery control actions based on a predictive recovery machine learning model. The predictive recovery machine learning model may be trained on at least one of the following: historical sub-area operating parameters, simulated sub-area operating parameters, historical cluster operating parameters, simulated cluster operating parameters, historical predictive resilience index, historical recovery control measures, historical fault locations, historical fault types, historical fault severity levels, historical recovery times, or simulated fault data by location.

[0220] Figure 14 Methods for operating a distribution area controller according to some embodiments are illustrated to determine the priority of adaptive control actions and predictive control actions within a distribution area. In some examples, the distribution area controller is a reference... Figure 1 and Figure 6 The described power distribution area controller 120, and the power distribution area is a reference Figure 1 , Figure 6 and Figure 9 The described power distribution area is 104.

[0221] Steps 350-362 illustrate a method for determining the priority of adaptive control actions in a power distribution area.

[0222] In step 350, the distribution area controller can divide the assigned distribution area among multiple distribution areas into multiple sub-regions. The sub-regions can be modeled as digital twins.

[0223] In step 352, the distribution area controller can divide at least one of the plurality of sub-regions into multiple clusters. The clusters can be modeled as digital twins.

[0224] In step 354, the distribution area controller may determine the situational awareness parameters for the current time period based on sub-area operation data and / or cluster operation data. The situational awareness parameters may include at least one of load situational awareness parameters, generation situational awareness parameters, or power restoration situational awareness parameters. The distribution area controller may determine the situational awareness parameters for at least one sub-area within the sub-area and / or at least one cluster within the cluster.

[0225] In step 356, the distribution area controller may determine one or more indices for the current time period based on sub-area operation data and / or cluster operation data. The indices may include at least one of a load flexibility index, a generation flexibility index, or a power restoration flexibility index. The distribution area controller may determine the indices for at least one sub-area within the sub-area and / or at least one cluster within the cluster.

[0226] In step 358, the distribution area controller may determine the power restoration feasibility index of the at least one sub-area based on the load flexibility index, generation flexibility index, and power quality flexibility index.

[0227] In step 360, the distribution area controller may determine the order of the plurality of sub-areas based on a power restoration feasibility index. The order may indicate the sequence of restoration within the assigned distribution areas.

[0228] In step 362, the distribution area controller may determine the priority of the adaptive recovery control actions assigned to the distribution area based on the sorting.

[0229] Steps 364-376 illustrate a method for determining the priority of predictive control actions in a power distribution area.

[0230] In step 364, the distribution area controller can divide the assigned distribution area among multiple distribution areas into multiple sub-areas. The distribution area controller can model the sub-areas as digital twins.

[0231] In step 366, the distribution area controller can divide at least one of the plurality of sub-regions into multiple clusters. The distribution area controller can model the clusters as digital twins.

[0232] In step 368, the distribution area controller may determine predicted situational awareness parameters for a future time period based on sub-area operational data and / or cluster operational data. The predicted situational awareness parameters may include at least one of load situational awareness parameters, generation situational awareness parameters, or power restoration situational awareness parameters. The distribution area controller may determine predicted situational awareness parameters for at least one sub-area within the sub-area and / or at least one cluster within the cluster.

[0233] In step 370, the distribution area controller may determine a forecast index for a future time period based on sub-area operational data: a forecast load flexibility index, a forecast generation flexibility index, or a forecast power restoration flexibility index. The distribution area controller may determine the forecast index for at least one sub-area and / or at least one cluster within a sub-area.

[0234] In step 372, the distribution area controller may determine the predicted power recovery feasibility index of the at least one sub-area based on the predicted load flexibility index, the predicted generation flexibility index, and the predicted power quality flexibility index.

[0235] In step 374, the distribution area controller may determine the order of the plurality of sub-areas based on a predicted power restoration feasibility index. The order may indicate the sequence of restoration within the assigned distribution areas.

[0236] In step 376, the distribution area controller may determine the priority of the predictive recovery control actions assigned to the distribution area based on the sorting.

[0237] Joint grid management system

[0238] In some embodiments, the systems and methods provided herein may use a joint grid management system to provide an aggregated data representation of all physical assets in the power grid, offering a unified view of the power grid across markets, planners, operators, and prosumers. Such aggregated data representations provide increased visibility and data sharing across the power grid. The aggregated data representations may also leverage power grid edge intelligence to provide increased data fidelity at the local level, facilitating increased visibility and control over decisions made at decentralized locations. The joint grid management system may include digital twins, such as cloud-based digital twins, to improve visibility into increased data sharing and collaboration among all participants or users across the power grid.

[0239] In some respects, a unified grid management system can customize the data presented to users of the power grid. In this way, the unified grid management system can provide data exchange and control for transmission system operators (TSOs), distribution system operators (DSOs), and distribution network operators (DNOs). Traditional methods of power grid data acquisition and modeling may use an "all-in-one" approach, where all data is visible and accessible, rather than customized for specific power grid users or participants. Because the complexity of power grids increases with the accelerating vision of decarbonization and electrification, the "all-in-one" approach can be technically and financially daunting. A unified grid management system can exchange and control data on the power grid based on participants or users. Providing customized data fidelity (e.g., based on location within the power grid and specific users or participants) improves the digitization, automation, and orchestration of the power grid for different planners, operators, and participants connected to it. Customizing data exchange and control in this way can help improve data visibility within the power grid (e.g., with the increase of variable renewable energy (VRE) and distributed energy (DER)).

[0240] Figure 15 A joint grid management system 134 is illustrated according to some embodiments. The joint grid management system 134 may include a joint grid modeling agent 136, a regional autonomous control and management agent 138, a regional predictive control and management agent 139, a machine learning engine 140, and a data construction engine 142.

[0241] The joint power grid modeling agent 136 can be configured to model a power grid having multiple regions with a uniform structure. The uniform structure can be a region topology, region network similarity (e.g., representing regions in a common format so that the power grid network has a uniform structure), region representation, or a region model that is identical for each of the multiple regions. The joint power grid modeling agent 136 can model a transmission grid as multiple transmission regions with a uniform structure. The joint power grid modeling agent 136 can also model a distribution grid as multiple distribution regions with a uniform structure. In some examples, the multiple distribution regions can be modeled as loads in the transmission grid. The joint power grid modeling agent 136 can communicate with a training database 122.

[0242] Regional autonomous control and management agent 138 can be configured to determine or identify one or more adaptive control actions in power grid system 100. For example, regional autonomous control and management agent 138 can determine adaptive control actions based on at least one of regional forecast data and operational data (e.g., regional orchestration index) of transmission area 102. In some aspects, one or more machine learning models of machine learning engine 140 can be integrated with regional autonomous control and management agent 138. For example, adaptive control machine learning models can determine or identify adaptive control actions for transmission area 102. Adaptive control machine learning models can be trained on at least one of historical forecast data or historical regional operational data of the plurality of area controllers. Regional autonomous control and management agent 138 can communicate with and receive data (such as forecast data) from planning database 128. Regional autonomous control and management agent 138 can also communicate with one or more of transmission area controllers 112. In this way, regional autonomous control and management agent 138 can be configured to pass or transmit adaptive control actions to one or more transmission area controllers in transmission area controller 112.

[0243] The regional predictive control and management agent 139 can be configured to determine or identify one or more predictive control actions for transmission areas 102 in the power grid system 100. For example, the regional predictive control and management agent 139 can determine predictive control actions based on at least one of the additions to the power grid or operational data of transmission areas 102 (e.g., regional orchestration indices). In some aspects, one or more machine learning models of the machine learning engine 140 can be integrated with the regional predictive control and management agent 139. For example, the predictive control machine learning model can determine or identify predictive control actions for transmission areas 102. The predictive control machine learning model can be trained on at least one of the historical regional operational data of the plurality of regional controllers or historical additions to the power grid. The regional predictive control and management agent 139 can also communicate with one or more transmission area controllers in the transmission area controllers 112. In this way, the regional predictive control and management agent 139 can be configured to pass or transmit predictive control actions to one or more transmission area controllers in the transmission area controllers 112. The data construction engine 142 can communicate with the user interface 378. The data construction engine 142 can customize the level of data presented via the user interface 378 based on specific users or applications. In this way, the data construction engine 142 can control the level of data fidelity presented to users via the unified grid management system 134. The data construction engine 142 places incoming data from multiple distribution areas in an internally common or consistent format, making the distribution grid model uniform. The data construction engine 142 also customizes the presentation of data on the distribution grid to a level of detail specific to the specific user of the distribution grid (e.g., a higher level is magnified to the level of interest or specific asset in the distribution system). Therefore, the data construction engine 142 can automatically represent or store data in a common format and customize the visualization or presentation of data on the distribution grid to a specific level.

[0244] Furthermore, in some embodiments, the combined grid management system 134 can communicate with one or more distribution area controllers in the distribution area controller 120 or with one or more transmission area controllers in the transmission area controller 112, such as... Figure 1 As shown in the diagram. Data or information received from the area controller can be stored in a database associated with the combined grid management system 134. The data or information received from the area controller can be organized, mapped, stored, or learned by the combined grid management system 134 in a uniform manner. For example, data or information from the area controller can be organized, mapped, or labeled in a standardized manner, so that the combined grid management system 134 can identify the data or information as belonging to a specific type of area based on the way it is organized, mapped, and labeled. The combined grid management system 134 can then apply or use the information or data associated with the area accordingly.

[0245] Figure 16This refers to a method, based on some embodiments, of operating a joint grid management system to determine adaptive strategies for transmission areas and to transmit intervention commands for those transmission areas. In some examples, the joint grid management system may be a reference... Figure 1 and Figure 15 The described integrated power grid management system 134. Assume, Figure 16 The method can be performed by one or more components of the combined grid management system 134.

[0246] In step 380, the combined grid management system may receive area operation data from at least one of a plurality of transmission areas. The area operation data may include, for example, an area orchestration index determined by a transmission area controller associated with said at least one transmission area.

[0247] In step 382, ​​the joint grid management system may receive regional forecast data associated with the at least one transmission area. In some examples, the joint grid management system may receive regional forecast data from a regional forecast / planning database. In some examples, the joint grid management system may receive regional forecast data from a transmission area controller associated with the at least one transmission area.

[0248] In step 384, the joint grid management system can determine a regional adaptive strategy for the at least one transmission area based on regional operational data and regional forecast data. The regional adaptive strategy can define one or more rules for the transmission area controller in normal operating mode.

[0249] In step 386, the joint grid management system may pass a region adaptation strategy to a transmission area controller associated with the at least one transmission area. The joint grid management system may also pass a region adaptation strategy to a transmission area controller associated with another transmission area among the plurality of transmission areas.

[0250] In step 388, the joint grid management system may determine intervention commands for transmission areas based on a regional adaptive strategy. In some examples, the intervention command may be passed to one or more transmission control devices associated with the at least one transmission area. For example, the intervention command may intervene in the operation of a transmission area controller associated with the at least one transmission area. In one example, analytics from the joint grid management system may be used to design a regional strategy for the at least one transmission area such that if an emergency control action is taken in the area, the joint grid management system may issue an intervention command to the transmission area controller to handle the emergency. Such an emergency may be a fault in the transmission area where local protection cannot control the fault or is no longer operational. In such scenarios where a power fault exists in the transmission area but restoration has not occurred, the joint grid management system may issue an intervention command to reroute power from another source (such as an adjacent transmission area) to restore power.

[0251] In step 390, the joint grid management system can transmit an intervention command to the transmission area controller. Transmitting the intervention command to the transmission area controller can intervene in the normal operating mode.

[0252] Figure 17A This describes a method for operating a combined grid management system according to some embodiments. In some examples, the combined grid management system may be a reference... Figure 1 and Figure 15 The described integrated power grid management system 134. Assume, Figure 16 The method can be performed by one or more components of the combined grid management system 134.

[0253] In step 396, the joint grid management system can determine the architecture of the power grid. The power grid may include a transmission grid and a distribution grid. The architecture of the power grid can be determined, for example, based on a single-line diagram / nanowatt (SLD / NW) model of the power grid.

[0254] In step 398, the joint grid management system may form multiple transmission areas within the power grid. In some examples, the joint grid management system may form these multiple transmission areas based on assets (e.g., primary and / or auxiliary assets) and interconnections within the power grid. The joint grid management system may demarcate or form transmission areas based on one or more assets and one or more policies or rules (which define how transmission areas are to be formed within the power grid). It is envisioned that the joint grid management system may dynamically form areas, allowing changes in the boundaries between transmission areas to accommodate the addition or removal of assets from the power grid.

[0255] In step 400, the joint grid management system can identify the primary and secondary assets in each of the multiple transmission areas.

[0256] In step 404, the unified grid management system may also identify multiple distribution areas within the power grid. The unified grid management system may represent or model these distribution areas as loads within one or more of the multiple transmission areas. In some embodiments, the unified grid management system may also identify, form, or demarcate transmission areas within the power grid.

[0257] In step 406, the joint power grid management system can combine the multiple transmission areas to generate a real-time power grid model.

[0258] In step 408, the joint grid management system can control the power grid based on a real-time power grid model. For example, the joint grid management system can issue one or more intervention commands to the transmission area controllers in the transmission area to intervene in the normal operating mode.

[0259] In step 410, the combined grid management system may also generate a user-specific combined grid model. The user-specific combined grid model may be customized for a specific user of the power grid (e.g., in terms of data presentation). The user-specific combined grid model may include one or more of the following: operational combined grid model, market combined grid model, user combined grid model, or asset performance management combined grid model.

[0260] Figure 17B This is another method of operating a combined grid management system according to some embodiments. In some examples, the combined grid management system may be a reference... Figure 1 and Figure 15 The described integrated power grid management system 134. Assume, Figure 16 The method can be performed by one or more components of the combined grid management system 134.

[0261] In step 396, the joint grid management system 134 determines the power grid architecture. The joint grid management system 134 may determine the power grid architecture, for example, via a single-line diagram (SLD) and / or nanowatt (NW) model of the power grid.

[0262] In step 402, the combined grid management system 134 also receives planning data for the power grid. The planning data may include planned or future contracts, Nava (NW) plans, or other requirements of the power grid. The planning data may be correlated with the power grid status at a future time. In some examples, users may input planning data into the combined grid management system 134 via a user interface (e.g., user interface 378). In other examples, the combined grid management system 134 may receive planning data from a regional forecasting / planning database 128.

[0263] In step 398, the combined grid management system 134 forms multiple transmission areas within the power grid. In some examples, the combined grid management system 134 forms the multiple transmission areas based on one or more of the power grid architecture or planning data. The combined grid management system 134 may, for example, form or determine the boundaries of transmission areas within the power grid based on one or more strategies. The strategies may define rules for forming or demarcating transmission areas within the power grid, for example, based on the power grid architecture or planning data. The planning data may indicate new components that will be added to the power grid in the future and may affect the power grid architecture. Therefore, transmission areas may be rearranged based on the planning data. In one example, the strategy may indicate or include rules specifying the formation of areas based on industries, microgrids (e.g., solar or wind energy assets), power supply sources, or dominant load clusters present in the power grid architecture. The strategy may specify forming industries, microgrids, power supply sources, or dominant load clusters as areas. In another example, a substation (including incoming and outgoing lines from the substation) can be an area. The rules in the strategy can be based on the dominant factors of the power grid, such as load-rich areas and microgrids, as indicated by the power grid architecture, to form regions.

[0264] In step 400, the combined grid management system 134 can also identify primary and secondary assets within the established plurality of transmission areas. As discussed further below, a primary asset can be any electrical distribution equipment. For example, a primary asset may include at least one of a transformer, circuit breaker, or generator. Secondary assets can be intelligent electronic devices (IEDs) that control the primary assets. For example, a secondary asset can be a relay, gating unit, or gateway. The combined grid management system 134 can identify primary and secondary assets in the power grid, for example, based on power grid architecture and / or planning data.

[0265] In step 403, the combined grid management system 134 may receive short-term and / or long-term forecasts of the power grid. In some examples, a user may input forecast data (containing short-term and / or long-term forecasts) into the combined grid management system 134 via a user interface (e.g., user interface 378). In other examples, the combined grid management system 134 may receive forecast data, including short-term and / or long-term forecasts, from a regional forecasting / planning database 128. Figure 17C In the embodiment shown, step 403 is omitted.

[0266] In step 404, the combined grid management system 134 identifies multiple distribution areas in the power grid. The combined grid management system 134 models or represents one or more of the multiple distribution areas as loads in the transmission system, that is, models or represents loads coupled to the multiple transmission areas formed in step 398.

[0267] In step 406, the combined power grid management system 134 combines the multiple transmission areas to generate a real-time power grid model. The real-time power grid model can capture both data about transmission areas and data about distribution areas (e.g., when a distribution area is modeled as a load within a transmission area).

[0268] In step 408, the combined grid management system 134 can control the power grid based on a real-time power grid model. For example, the combined grid management system 134 can transmit or issue one or more intervention commands or actions to the transmission area controller 112 and / or the distribution area controller 120 to control the operation of the power grid.

[0270] In step 410, the combined grid management system 134 generates a user-specific combined grid model 410. The user-specific combined grid model 410, or a portion thereof, may be presented to the user, for example, via user interface 378. The user-specific combined grid model may include one or more of an operational combined grid model, a market combined grid model, a user combined grid model, or an asset performance management combined grid model.

[0271] The operational integrated grid model can display data associated with the management of regional asset control in the multiple transmission and / or distribution areas, and / or facilitate the management of regional asset control in the multiple transmission and / or distribution areas. In some aspects, the operational integrated grid model can also display and / or facilitate the management of operational expenditures on a regional basis. The operational integrated grid model may include one or more load control modules (LCMs) for the multiple transmission and / or distribution areas. The operational integrated grid model can also allow users to view or manage the transmission system reliability (TSR) and resource management (RM) of the multiple transmission and / or distribution areas.

[0272] The market-linked grid model can display data related to the management of virtual power plants (VPPs) in the power grid and / or facilitate the management of VPPs in the power grid. The market-linked grid model may also include or be coupled to balancing mechanisms and / or bidding analysis systems. Furthermore, the market-linked grid model may include or be coupled to one or more customer management systems (CMS) to facilitate power grid customers in managing their load demand and other interactions with the power grid.

[0273] The user-joint grid model can display data related to the management of transmission system operations (TSOs) and / or distribution system operations (DSOs) in the multiple transmission areas and / or the multiple distribution areas, and / or facilitate the management of these operations. Furthermore, the user-joint grid model can display data related to the management of one or more renewable energy networks (RENs), distributed energy sources (DERs), microgrids (MGs), and / or aggregators (Aggs), and / or facilitate their management.

[0274] The Asset Performance Management Joint Grid Model displays data related to the regional asset management of the multiple distribution areas and / or one or more of the multiple distribution areas, and / or facilitates the regional asset management of the multiple distribution areas and / or one or more of the multiple distribution areas. Furthermore, the Asset Performance Management Joint Grid Model displays data related to the fleet management and / or operation and maintenance (O&M) of assets in the multiple distribution areas and / or one or more of the multiple distribution areas, and / or facilitates the fleet management and / or operation and maintenance (O&M) of assets in the multiple distribution areas and / or one or more of the multiple distribution areas.

[0275] Zone asset management

[0276] In some embodiments, the systems and methods provided herein can be used to manage assets within a region of a power grid system, for example, via an asset management controller. An asset management controller can manage one or more assets within a distribution area and / or a transmission area. A region asset management controller can utilize a region to manage both primary and secondary assets. Traditional asset management approaches typically involve two separate management systems—one for primary assets and one for secondary assets. In traditional approaches, the system for primary assets and the system for secondary assets may not be able to communicate or interact with each other. The methods described herein use a single asset management system or controller for both primary and secondary assets. Using a single asset management system or controller provides a system with improved flexibility and modularity, allowing utility companies to deploy the system on an application-by-application basis. A single asset management system provides simplified asset management, which can be more efficient and reduce operating costs, for example, by avoiding the need to use and add additional human resources to configure assets, assess their health, and collect data. A single asset management system provides a holistic view of asset usage, optimization, and lifecycle management.

[0277] In some respects, the regional asset management architecture described herein may enable one or more of the following: asset queue management, primary asset management, secondary asset management, asset lifecycle management, advanced regional analytics, regional performance forecasting, and regional autonomous control. The asset management controller can adjust the depth of detail at the regional level. Primary asset management may include providing one or more of the following: health, operational, and control actions or recommendations for primary assets. Secondary asset management may include providing autonomous device operation, upgrades, and management for secondary assets. Asset lifecycle management may include providing end-to-end asset replacement strategies, plans, recommendations, and associated actions for assets. Advanced regional analytics may include intelligence on an asset-by-asset and / or region-by-region basis in the form of digital twin systems. Regional performance forecasting may include asset failure prediction, asset risk assessment, asset forecasting, and operation and maintenance (O&M) planning (for short, medium, and long term).

[0278] Figure 18 This is an exemplary method for operating a power grid system. For example, this method may be performed by an asset management controller. In this method, the asset management controller measures, monitors, and merges primary asset data and secondary asset data. In some examples, this method, or a portion thereof, may be performed by an asset management controller 145 of the power grid system 100. The asset management controller may be associated with one or more of multiple regions. For example, the asset management controller may be associated with one or more transmission regions in transmission region 102 and / or one or more distribution regions in the plurality of distribution regions 104.

[0279] In block 430, the asset management controller receives primary asset data for at least one primary asset associated with at least one of the plurality of regions. The primary asset can be any electrical distribution equipment. For example, a primary asset may include at least one of a transformer, circuit breaker, or generator. Primary asset data can be any parameter related to the operation or performance of the primary asset. Primary asset data may indicate the health or well-being of the asset. Non-limiting examples of primary asset data include transformer frequency, load, and power factor, such as those measured by an auxiliary asset (e.g., an IED).

[0280] In block 432, the asset management controller receives auxiliary asset data for at least one auxiliary asset associated with at least one of the plurality of zones. The auxiliary asset can be an intelligent electronic device (IED) that controls the primary asset or any other asset that protects, monitors, controls, or measures data associated with the primary asset. The auxiliary asset can be a relay, gating unit, or gateway. The auxiliary asset data can be any parameter related to the operation or performance of the auxiliary asset. Non-limiting examples of auxiliary asset data include IED status, security protocols, and communication details. The auxiliary asset data can be any data related to the IED, and in some examples, may be provided by the IED manufacturer.

[0281] In block 434, the asset management controller determines at least one region analysis parameter via at least one machine learning model. The region analysis parameter may include at least one of process analysis parameters, health analysis parameters, performance analysis parameters, or security analysis parameters.

[0282] Process analysis parameters may include information indicating at least one of the following: hardware performance, health, or lifecycle of the auxiliary asset. Process analysis parameters may include at least one of the following: device compute time, device performance during an event, hardware diagnostics, firmware logs, device-related watchdog events, hardware basic input / output system (BIOS) logs, or device time accuracy.

[0283] In some examples, the machine learning model may include a process machine learning model trained using at least one of historical primary asset data, historical secondary asset data, or historical process analysis parameters.

[0284] Health analysis parameters may indicate at least one of the following: health risk, performance, condition status, criticality, or reliability of a primary asset. Health analysis parameters may be determined based on data measured from one or more sensors associated with the primary asset. Performance analysis parameters may indicate at least one of the following: design constraints or loads of the primary asset based on the thermal characteristics of the at least one primary asset. Performance analysis parameters may be determined based on at least one of the following: historical load conditions, historical load patterns, current load conditions, or current load patterns of the primary asset.

[0285] In some examples, the machine learning model may include a health machine learning model trained using at least one of historical primary asset data, historical secondary asset data, or historical health analysis parameters.

[0286] Performance analysis parameters may indicate at least one of the design constraints or loads of the primary asset based on its thermal characteristics. These parameters are determined based on at least one of the primary asset's historical load conditions, historical load patterns, current load conditions, or current load patterns.

[0287] In some examples, the machine learning model may include a performance machine learning model trained using at least one of historical primary asset data, historical secondary asset data, or historical performance analysis parameters.

[0288] Security analysis parameters can indicate communication and network security (CCS) risks associated with ancillary assets. CCS risks can be identified from security logs, communication logs, event sequences, or business analysis associated with the device (e.g., ancillary asset). In some examples, CCS risks may involve bandwidth levels, latency levels, network security issues, pending security patch upgrades, communication-related misconfigurations, security-related misconfigurations, or required updates.

[0289] In some examples, the machine learning model may include a cybersecurity machine learning model trained using at least one of historical primary asset data, historical secondary asset data, or historical security analysis parameters.

[0290] In block 436, the asset management controller identifies at least one asset management control action for a control device associated with the at least one region based on the at least one region analysis parameter.

[0291] In some examples, asset management control actions are based on health analysis parameters and performance analysis parameters. Asset management control actions may be taken by a transmission area controller (e.g., by transmission area control unit 110) or a distribution area controller (e.g., by distribution area control unit 118) in conjunction with area asset management sequencing. The set of asset management control actions may include load guidance for primary assets. This load guidance can control the load level on the at least one primary asset to mitigate risk or redeploy sources from another area within the multiple areas of the power grid system. In one example, if a transformer (e.g., a primary asset) is not in good health and should be operating at 100% load, an asset management control action may adjust the transformer's load below 100% based on health analysis parameters or performance analysis parameters indicating poor transformer health. In another example, if health analysis parameters or performance analysis parameters indicate that an auxiliary asset (e.g., an IED) is not communicating correctly, an asset management control action may manage IED communication.

[0292] In some embodiments, the asset management controller may determine a regional situation awareness index for the at least one region based on the at least one regional analysis parameter. The regional situation awareness index may indicate at least one of multiple high-risk assets, asset criticality, and asset type. Asset management control actions may then be determined based on the regional situation awareness index. The asset management controller may also prioritize the at least one region among the multiple regions based on the regional situation awareness index. In this way, the asset management controller can use the regional situation awareness index to prioritize asset management control actions. The regional situation awareness index may be based on multiple primary assets and multiple secondary assets in the at least one region.

[0293] In block 438, the asset management controller transmits a command to the distribution area controller associated with the at least one area based on the at least one asset management control action. The command may be transmitted to ancillary assets associated with the at least one of the plurality of areas. In some examples, the command is configured to implement the at least one asset management control action on a primary asset associated with the ancillary asset.

[0294] Imagine, in Figure 18 In this method, the asset management controller can also be configured to sort at least one primary asset or at least one secondary asset on a region-by-region basis.

[0295] In some embodiments, the asset management controller may determine the lifecycle risk of the at least one auxiliary asset based on process analysis parameters. Furthermore, the asset management controller may determine the communication and cybersecurity (CCS) risk of the at least one auxiliary asset based on security analysis parameters. The asset management controller may then determine the ranking of the at least one auxiliary asset based on the lifecycle risk and CCS risk. The ranking of auxiliary assets may indicate the relative short-term, medium-term, or long-term health of the auxiliary assets. The ranking of auxiliary assets may also identify one or more high-risk auxiliary assets or auxiliary assets requiring action (such as maintenance actions, failure risk assessment, changes in operating conditions, replacement, disconnection, or load reduction).

[0296] In some embodiments, the asset management controller may determine the asset risk of the at least one auxiliary asset based on health analysis parameters. The asset management controller may also determine the asset load risk of the at least one primary asset based on performance analysis parameters. The asset management controller may then determine the ranking of the at least one primary asset based on lifecycle risk and CCS risk. The ranking of primary assets may indicate the relative short-term, medium-term, or long-term health of the primary assets. The ranking may also identify one or more high-risk primary assets or primary assets requiring action (such as repair, maintenance, failure risk assessment, changes in operating conditions, replacement, disconnection, or load reduction).

[0297] Figure 19 This is an exemplary method for operating a power grid system. For example, this method may be performed by an asset management controller. In this method, the asset management controller measures, monitors, and merges primary asset data and secondary asset data. In some examples, this method, or a portion thereof, may be performed by power grid system 100 or a portion thereof (such as asset management controller 145). The asset management controller may be associated with one or more of multiple regions. For example, the asset management controller may be associated with one or more transmission regions in transmission region 102 and / or one or more distribution regions in the plurality of distribution regions 104.

[0298] In block 440, the method includes determining primary asset data for one or more primary assets. In some methods, asset management controller 145 may determine or receive secondary asset data. Primary asset data may be determined via a power automation and control system (PACS) or via another measurement and control (M&C) system associated with the power grid system. In some examples, primary asset data may be determined via one or more transmission area measurement devices in transmission area measurement device 108 and / or via one or more distribution area measurement devices in distribution area measurement device 116.

[0299] In block 442, the method includes determining ancillary asset data for one or more ancillary assets. In some methods, asset management controller 145 may determine or receive the ancillary asset data. The ancillary asset data may be determined via a power automation and control system (PACS) or via another measurement and control (M&C) system associated with the power grid system. In some examples, the ancillary asset data may be determined via one or more transmission area measurement devices in transmission area measurement device 108 and / or via one or more distribution area measurement devices in distribution area measurement device 116.

[0300] In block 444, the method includes determining one or more area analysis parameters. Area analysis parameters may be determined based on primary asset and / or ancillary asset data. One or more machine learning algorithms may be used to determine or calculate area analysis parameters. In some methods, the asset management controller 145 may determine or calculate area analysis parameters. Area analysis parameters may include one or more of process analysis parameters, health analysis parameters, performance analysis parameters, or CCS analysis parameters. Each parameter in the area analysis parameters refers to... Figure 18 Further details are being discussed.

[0301] In block 446, the method includes determining the lifecycle risk of one or more ancillary assets from process analysis parameters. In some methods, the asset management controller 145 may determine the lifecycle risk.

[0302] In block 450, the method includes determining the CCS risk of one or more ancillary assets from CCS analysis parameters. In some methods, the asset management controller 145 may determine the CCS risk.

[0303] In block 454, the method includes determining the ordering of auxiliary assets based on their lifecycle risk and CCS risk. As discussed above, the ordering of auxiliary assets can indicate their relative short-term, medium-term, or long-term health. The ordering can also identify one or more high-risk auxiliary assets or those requiring action (such as maintenance, failure risk assessment, changes in operating condition, replacement, disconnection, or load reduction). The ordering can provide the ordering of auxiliary assets in a queue, for example, on a region-by-region basis. Therefore, the ordering can be used to perform region queue management.

[0304] In block 456, the method includes managing one or more auxiliary assets based on the sorting. Management of auxiliary assets may include actions to adjust the operation of one or more auxiliary assets. In some examples, management of auxiliary assets may include changes in operating conditions or reductions in the load on the auxiliary assets. For example, management of auxiliary assets may include inputs (such as control commands) to transmission area controller 112 or distribution area controller 120. Asset management controller 145 may transmit commands or other inputs to transmission area controller 112 to adjust the operation of one or more devices (such as transmission area control device 110) in transmission area 102. Asset management controller 145 may also transmit commands or other inputs to distribution area controller 120 to adjust the operation of one or more devices (such as distribution area control device 118) in distribution area 104. In other examples, management of auxiliary assets may include repairing, replacing, or disconnecting auxiliary assets.

[0305] In block 448, the method includes determining the asset health risk of one or more major assets from health analysis parameters of the respective major assets. In some methods, the asset management controller 145 may determine the asset health risk.

[0306] In block 452, the method includes determining the asset load risk of one or more primary assets from performance analysis parameters of the respective primary assets. In some methods, the asset management controller 145 may determine the asset load risk.

[0307] In block 458, the method includes determining the priority of primary assets based on their asset health risk and asset load risk. As discussed above, the priority of primary assets can indicate their relative short-term, medium-term, or long-term health. The priority can also identify one or more high-risk primary assets or those requiring action (such as repair, maintenance, failure risk assessment, changes in operating conditions, replacement, disconnection, or load reduction). The priority can provide a ranking of primary assets in a queue, for example, on a region-by-region basis. Therefore, the priority can be used to perform region queue management.

[0308] In block 460, the method includes managing one or more primary assets based on the sorting. Management of primary assets may include actions to adjust the operation of one or more primary assets. In some examples, management of secondary assets may include changes in operating conditions or reductions in load on primary assets. For example, management of secondary assets may include inputs (such as control commands) to transmission area controller 112 or distribution area controller 120. In some aspects, asset management controller 145 may provide asset load guidance inputs to transmission area controller 112 or distribution area controller 120 to adjust load levels on assets to avoid immediate risks. Asset management controller 145 may pass commands or other inputs to transmission area controller 112 to adjust the operation of one or more devices (such as transmission area control device 110) in transmission area 102. Asset management controller 145 may also pass commands or other inputs to distribution area controller 120 to adjust the operation of one or more devices (such as distribution area control device 118) in distribution area 104. In other examples still present, management of primary assets may include repairing, replacing, or disconnecting secondary assets.

[0309] The terms and expressions used herein have the ordinary technical meanings that would be given to such terms and expressions by one of ordinary skill in the art as set forth above, except where different specific meanings have been set forth herein. The word “or” as used herein should be interpreted as having a separating structure rather than a connecting structure, unless otherwise specifically indicated. The terms “coupled,” “fixed,” “attached to,” and the like refer both to direct coupling, fixing, or attachment, and to indirect coupling, fixing, or attachment through one or more intermediate components or features, unless otherwise specified herein.

[0310] The singular forms “a” (“a”, “an”) and “the” contain plural references unless the context clearly indicates otherwise.

[0311] The approximate language used throughout the specification and claims is applied to modify any quantitative representation that may be altered without changing its associated essential function. Therefore, values ​​modified by one or more terms such as “approximately,” “about,” and “substantially” should not be limited to specified precise values. In at least some cases, approximate language may correspond to the accuracy of the instrument used to measure the value, or the accuracy of the method or machine used to construct or manufacture the component and / or system. For example, approximate language may refer to a margin of 10%.

[0312] Further aspects of this disclosure are provided by the subject matter of the following provisions:

[0313] A power grid system includes: a plurality of transmission areas, each of the plurality of transmission areas comprising: one or more area measurement devices configured to measure at least one area operation parameter of an assigned transmission area among the plurality of transmission areas; and an area controller associated with the assigned transmission area and communicating with the one or more area measurement devices, the area controller being configured to: receive area operation data of the assigned transmission area from the one or more area measurement devices; determine an area orchestration index based on the area operation data; determine an adaptive control action for a control device in the assigned transmission area or another transmission area among the plurality of transmission areas based on the area orchestration index; and transmit commands to the control device based on the adaptive control action.

[0314] Any power grid system covered by the foregoing provisions, wherein the command is configured to cause the control device to perform the adaptive control action; and wherein the adaptive control action comprises at least one of the following: voltage control, power factor control, load control, battery-based energy storage system (BESS) control, distributed energy source (DER) control, renewable energy network (REN) energy control, or frequency control.

[0315] Any power grid system covered by the foregoing clauses, wherein the command is configured to cause the control device to perform the adaptive control action in real time.

[0316] Any power grid system as described in the foregoing clauses, wherein the control device is a device associated with a substation in the designated transmission area or another transmission area among the plurality of transmission areas.

[0317] Any power grid system as described in the foregoing clauses, wherein the control device is configured to use associated auxiliary equipment to adjust the operating parameters of the main equipment in the substation.

[0318] In any of the foregoing provisions, the regional orchestration index is a measure of the performance of the assigned transmission area relative to a baseline performance or reference index.

[0319] Any of the foregoing provisions of the power grid system, wherein the area controller is further configured to transmit the area orchestration index to the central control system of the power grid system comprising the plurality of transmission areas.

[0320] In any of the foregoing provisions of the power grid system, the area controller is further configured to transfer the area orchestration index of the assigned transmission area to another transmission area among the plurality of transmission areas, and to receive an external area orchestration index from at least one of the plurality of transmission areas.

[0321] Any power grid system under the foregoing provisions, wherein the regional orchestration index indicates at least one of an operational violation or a non-operational violation in the designated transmission area.

[0322] In any of the foregoing provisions of the power grid system, wherein the area controller is further configured to receive an area adaptive policy from a joint grid management system communicating with the area controller, the area adaptive policy comprising standard rules for using area control resources to mitigate at least one of operational or non-operational violations in the assigned transmission area.

[0323] In any of the foregoing provisions of the power grid system, the area controller is further configured to identify the operational violation or the non-operational violation based on the area operation data and the area adaptive policy.

[0324] Any power grid system as described in the foregoing clauses, wherein the area controller is configured to identify the operational violation or the non-operational violation by comparing area operational data with one or more thresholds and / or reference data defined in the area adaptive strategy.

[0325] In any of the foregoing provisions of the power grid system, when the operational violation is detected in the designated transmission area, the area controller transmits the command to the control device.

[0326] In any of the foregoing provisions of the power grid system, the area controller is further configured to transmit an alarm or electronic message to the user interface associated with the assigned transmission area when the non-operational violation is detected in the assigned transmission area.

[0327] Any power grid system covered by the foregoing provisions, wherein the command is configured to perform pre-scheduled control based on the regional orchestration index; and wherein the pre-scheduled control is defined in the regional adaptive strategy.

[0328] In any of the foregoing provisions of the power grid system, the regional orchestration index is a numerical value representing the bit stream encoding the operational violation or the non-operational violation; and the bits in the bit stream are arranged in order, wherein critical violations are placed on the left side of the bit stream based on severity, and non-critical violations are placed on the right side of the bit stream.

[0329] In any of the foregoing provisions of the power grid system, the operational violation indicates whether one or more of the following are within the corresponding threshold limits or reference ranges: voltage, power, load, reactive power, power factor, congestion, stability, frequency, inertia, or grid strength.

[0330] Any power grid system covered by the foregoing provisions, wherein the operational violation includes one or more of the following: voltage and / or frequency values ​​outside of threshold limits, active / reactive power outside of threshold limits, transmission line limits exceeding dynamic line ratings, generator overload, transformer overload, or low power factor.

[0331] Any power grid system covered by the foregoing provisions, wherein the non-operational violation relates to at least one of power grid maintenance, hardware, software or communications, operator or network security issues, and wherein the area controller is further configured to isolate nodes or devices in the assigned transmission area upon identification of the non-operational violation.

[0332] Any of the foregoing provisions of the power grid system, wherein the one or more area measurement devices include at least one of sensors or intelligent electronic devices (IEDs) associated with the designated transmission area.

[0333] Any power grid system covered by the foregoing clauses, wherein the at least one area operating parameter includes one or more of voltage, frequency, inertia, congestion, unexpected events, reactive power, load, or power factor.

[0334] In any of the foregoing provisions of the power grid system, the adaptive control action is determined based on an autonomous control machine learning model, which is trained on at least one of historical region operating parameters, historical best control measures, or historical region adaptive strategies.

[0335] In any of the foregoing provisions of the power grid system, the area controller is further configured to receive area operation data or area orchestration index from another of the plurality of transmission areas; and the adaptive control action is determined at least in part based on the area operation data or area orchestration index from the other of the plurality of transmission areas.

[0336] Any power grid system under the foregoing provisions, wherein adaptive control actions control competition or compromise between the assigned area and another transmission area among the plurality of transmission areas.

[0337] In any of the foregoing provisions of the power grid system, the autonomous control machine learning model is also trained on at least one of the historical regional orchestration index and historical operation data from another of the plurality of transmission regions.

[0338] In any of the foregoing provisions of the power grid system, the area controller is further configured to: receive a area forecasting strategy from a joint grid model in communication with the area controller; and determine predictive control actions for the current, next, and / or future time intervals based on the area operation data and the area forecasting strategy.

[0339] In any of the foregoing provisions of the power grid system, the predictive control action is determined based on a predictive control machine learning model, which is trained on at least one of historical regional operating parameters, historical optimal control measures or historical regional prediction strategies and regional operating prediction data.

[0340] The power grid system of any of the foregoing provisions, wherein the plurality of transmission areas comprises a plurality of different types of transmission areas, the different types of transmission areas being at least in part based on regional assets.

[0341] Any of the foregoing provisions in the power grid system, wherein the plurality of different types of transmission areas include at least one of a transmission area, a renewable energy network (REN) area, a generation (GEN) area, an industrial area, or a flexible area.

[0342] A method includes, at each of a plurality of area controllers associated with a plurality of transmission areas: receiving area operation data of an assigned transmission area from one or more area measurement devices associated with an assigned transmission area among the plurality of transmission areas; determining an area orchestration index based on the area operation data; determining an adaptive control action for a control device in the assigned transmission area or another transmission area among the plurality of transmission areas based on the area orchestration index; and transmitting a command to the control device based on the adaptive control action.

[0343] Any of the methods described in the foregoing clauses, wherein the command is configured to cause the control device to perform the adaptive control action.

[0344] Any of the methods described in the foregoing clauses, wherein the control device is an apparatus associated with a substation within the designated transmission area.

[0345] Any of the methods described in the foregoing clauses, wherein the control device is configured to adjust the operating parameters of at least one of the following devices in the substation: main transformer, switch, current transformer, voltage transformer, circuit breaker, voltage regulator, flexible AC transmission system (FACTS) device, power electronic controller, HVDC link controller, renewable energy or grid forming inverter.

[0346] Any of the methods described in the foregoing clauses, wherein the regional orchestration index is a measure of the performance of the assigned transmission area relative to a baseline or reference performance.

[0347] Any of the methods described in the foregoing clauses also includes: transferring the regional orchestration index to a combined grid model of a power grid that includes the plurality of transmission regions.

[0348] Any of the methods described in the foregoing provisions also includes: transferring the regional arrangement index of the assigned transmission area to another transmission area among the plurality of transmission areas.

[0349] Any of the methods described in the foregoing clauses further includes: receiving a regional adaptive strategy from a joint grid model communicating with the regional controller, the regional adaptive strategy comprising standard rules for using regional control resources to mitigate at least one of operational or non-operational violations in the assigned transmission area.

[0350] Any of the methods described in the foregoing clauses also includes: identifying regional operation violations based on the regional operation data and the regional adaptive strategy.

[0351] Any of the methods described in the foregoing clauses, wherein the one or more area measurement devices include at least one of a sensor or intelligent electronic device (IED) associated with the designated transmission area.

[0352] Any of the methods described in the foregoing clauses, wherein the at least one area operating parameter includes one or more of voltage, frequency, inertia, congestion, unexpected events, reactive power, load, or power factor.

[0353] Any of the methods described in the foregoing clauses also includes: adjusting the non-operating parameters of the assigned transmission area based on the regional orchestration index.

[0354] Any of the methods described in the foregoing clauses, wherein the adaptive control action is determined based on an autonomous control machine learning model, which is trained on at least one of historical region operating parameters, historical best control measures, or historical region adaptive strategies.

[0355] The method of any of the foregoing provisions further includes: receiving area operation data or area orchestration index from another of the plurality of transmission areas; wherein the adaptive control action is determined at least in part based on the area operation data or area orchestration index from the other of the plurality of transmission areas.

[0356] The method of any of the foregoing provisions, wherein the autonomous control machine learning model is also trained on at least one of historical regional orchestration indices and historical operational data from another of the plurality of transmission regions.

[0357] The methods described in any of the foregoing provisions further include: receiving a regional forecasting strategy from a joint power grid model communicating with the regional controller; and determining predictive control actions for the current, next, and / or future time intervals based on the regional operation data and the regional forecasting strategy.

[0358] Any of the methods described in the foregoing clauses, wherein the predictive control action is determined based on a predictive control machine learning model, which is trained on at least one of historical regional operating parameters, historical best control measures or historical regional prediction strategies and regional operating prediction data.

[0359] Any of the methods described in the foregoing provisions, wherein the plurality of transmission areas comprises a plurality of different types of transmission areas, the different types of transmission areas being at least partially based on regional assets.

[0360] Any of the methods described in the foregoing clauses, wherein the plurality of different types of transmission areas include at least one of a transmission area, a renewable energy network (REN) area, a generation (GEN) area, an industrial area, or a flexible area.

Claims

1. A power grid system, comprising: Multiple transmission areas, each of the multiple transmission areas comprising: One or more area measurement devices are configured to measure at least one area operation parameter of an assigned transmission area among the plurality of transmission areas; A zone controller, associated with the assigned transmission zone and communicating with the one or more zone measurement devices, is configured to: Receive area operation data of the assigned transmission area from the one or more area measurement devices; The regional orchestration index is determined based on the aforementioned regional operational data; Based on the regional orchestration index, an adaptive control action is determined for the control device in the assigned transmission area or another transmission area among the multiple transmission areas; as well as The adaptive control action transmits commands to the control device.

2. The power grid system as described in claim 1, wherein, The command is configured to cause the control device to perform the adaptive control action; and wherein the adaptive control action includes at least one of the following: voltage control, power factor control, load control, battery-based energy storage system (BESS) control, distributed energy source (DER) control, renewable energy network (REN) energy control, or frequency control.

3. The power grid system as described in claim 2, wherein, The command is configured to cause the control device to perform the adaptive control action in real time.

4. The power grid system as described in claim 1, wherein, The control device is a device associated with a substation in the assigned transmission area or another transmission area among the plurality of transmission areas.

5. The power grid system as described in claim 4, wherein, The control device is configured to use associated auxiliary equipment to adjust the operating parameters of the main equipment in the substation.

6. The power grid system as described in claim 1, wherein, The regional orchestration index is a measure of the performance of the assigned transmission area relative to baseline performance or a reference index.

7. The power grid system as described in claim 1, wherein, The area controller is also configured to transmit the area orchestration index to the central control system of the power grid system that includes the plurality of transmission areas.

8. The power grid system as described in claim 1, wherein, The area controller is also configured to pass the area orchestration index of the assigned transmission area to another transmission area among the plurality of transmission areas, and to receive an external area orchestration index from at least one of the plurality of transmission areas.

9. The power grid system as described in claim 1, wherein, The regional orchestration index indicates at least one of the operational violations or non-operational violations in the assigned transmission area.

10. The power grid system as described in claim 9, wherein, The area controller is also configured to receive an area adaptive policy from a joint grid management system that communicates with the area controller. The area adaptive policy includes standard rules for using area control resources to mitigate at least one of operational or non-operational violations in the assigned transmission area.

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