Grid-level controller for electrical vehicle charging stations

Abstract

A method for grid-level control for a power distribution grid with electrical vehicle (EV) charging stations (CSs) includes receiving present and historical data relating to the power distribution grid and an energy and power demand forecast from a plurality of EV CSs connected to the power distribution grid, calculating energy and power demand forecasts for the power distribution grid based on the received present and historical data and the energy and power demand forecasts, determining optimal set points for capacitor banks and voltage regulators in the power distribution grid, sending the optimal set points for the capacitor banks and the voltage regulators in the power distribution grid, determining energy and power limits for each EV CS of the plurality of EV CSs, and sending, to each EV CS of the plurality of EV CSs, the energy and power limits for that EV CS of the plurality of EV CSs.

Description

GRID-LEVEL CONTROLLER LOR ELECTRICAL VEHICLE CHARGING STATIONSSTATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0001] This invention was made with Government support under Federal Grant no. W9132T-22-C-0022 awarded by the US Army Corp of Engineers. The Federal Government has certain rights to this invention.BACKGROUND

[0002] With the ever-increasing use of electric vehicles (EVs), supplying EV charging stations (CSs) with sufficient energy and power is grid operator challenge. Indeed, existing power distribution grids will need significant upgrades using current systems and methods of supplying energy and power with the ever-increasing use of EVs. Therefore, in order to prevent brown-out and / or black-out situations in existing power distribution grids, there is a need for grid-level control of power distribution grids with multiple distributed EV CSs.BRIEF SUMMARY

[0003] Methods and systems for grid-level control for a power distribution grid with electrical vehicle (EV) charging stations (CSs) are provided herein. Advantageously, by determining energy and power demand forecasts across the power distribution grid, the described systems and methods are able to provide optimal set points for devices within the grid (e.g., capacitor banks and / or voltage regulators) as well as energy and power limits for each EV CS connected to the grid, reducing the need for significant upgrades to the power distribution grid and preventing brown-out and / or black-out situations in the power distribution grid.

[0004] A method for grid-level control for a power distribution grid with electrical vehicle (EV) charging stations (CSs) includes receiving present and historical data relating to the power distribution grid, receiving an energy and power demand forecast from a plurality of EV CSs connected to the power distribution grid, calculating energy and power demand forecasts for the power distribution grid based on the received present and historical data and the energy and power demand forecasts from the plurality of EV CSs connected to the power distribution grid, determining optimal set points for capacitor banks and voltage regulators in the power distribution grid based on the energy and power demand forecasts for the power distribution grid and the energy and power demand forecasts from the plurality of EV CSs connected to the power distribution grid, sending the optimal set points for the capacitor banks and the voltageregulators in the power distribution grid, determining energy and power limits for each EV CS of the plurality of EV CSs connected to the power distribution grid based at least on the energy and power demand forecasts for the power distribution grid and the energy and power demand forecasts from the plurality of EV CSs connected to the power distribution grid, and sending, to each EV CS of the plurality of EV CSs connected to the power distribution grid, the energy and power limits for that EV CS of the plurality of EV CSs connected to the power distribution grid.

[0005] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 illustrates a representational diagram of an operating environment for gridlevel control of electric vehicle (EV) charging stations (CSs).

[0007] Figure 2 illustrates a grid-level operating environment with multiple EV CS controllers.

[0008] Figure 3 illustrates a method for grid-level control for a power distribution grid with multiple EV CSs.

[0009] Figure 4 illustrates components of a computing device that may be used in certain embodiments described herein.DETAILED DESCRIPTION

[0010] Methods and systems for grid-level control for a power distribution grid with electrical vehicle (EV) charging stations (CSs) are provided herein. Advantageously, by determining energy and power demand forecasts across the power distribution grid, the described systems and methods are able to provide optimal set points for devices within the grid (e.g., capacitor banks and / or voltage regulators) as well as energy and power limits for each EV CS connected to the grid, reducing the need for significant upgrades to the power distribution grid and preventing brown-out and / or black-out situations in the power distribution grid.

[0011] The described grid-level controller is communicatively coupled to multiple EV CSs for energy and power management of those multiple EV controllers. In some cases, the described grid-level controller refers to a controller that can control energy and power acrossan entire power distribution grid. In some cases, the described grid-level controller refers to a controller that can control energy and power across a subset of a power distribution grid, such as a military or university campus.

[0012] Figure 1 illustrates a representational diagram of an operating environment for gridlevel control of electric vehicle (EV) charging stations (CSs). Referring to Figure 1, an operating environment 100 for grid-level control of EV CSs includes a grid-level controller 102, a power substation 104, a voltage regulator 106, utility-scale renewable energy resources (e.g., solar panel farms and / or wind turbines) and / or energy storage systems 108, capacitor banks 110, a first EV CS 112 and associated EV CS controller 114, a second EV CS 116 and associated EV CS controller 118, and a building -integrated EV CS 120 and associated EV CS controller 122. The grid-level controller 102 is communicatively coupled to at least the power substation 104, the voltage regulator 106, the utility-scale renewable energy resources and / or energy storage systems 108, the capacitor banks 110, the EV CS controller 114, the EV CS controller 118, and the EV CS controller 122.

[0013] Figure 2 illustrates a grid-level operating environment with multiple EV CS controllers. Referring to Figure 2, a power distribution grid 200 includes a slow charging EV CS 202 and an associated EV controller 204, a fast charging EV CS 206 and an associated EV controller 208, and a building with an integrated EV CS 210 and an associated EV CS controller 212. The power distribution grid 200 further includes a grid-level controller 214, a capacitor bank 216, as well as a stepdown transformer 218 for the slow charging EV CS 202, a stepdown transformer 220 for the fast charging EV CS 206, and a stepdown transformer 222 for the building with the integrated EV CS 210. The power distribution grid 200 further includes voltage regulators 224, a solar farm 226 and accompanying power inverter system 228, and an energy storage system 230 and accompanying power inverter system 232. Each EV CS (e.g., EV CS 202, EV CS 206, and EV CS 210) includes a plurality of EV chargers.

[0014] The power distribution grid 200 can further include a communication network 240 to exchange data. The communication network 240 can include grid-level controller 214 to EV controller (e.g., 204, 208, 212) and EV controller to EV controller communications as well as grid-level controller 214 to distribution services (e.g., voltage regulators 224, a solar farm 226 and accompanying power inverter system 228, and an energy storage system 230 and accompanying power inverter system 232) communications. The communication network 240 can include wired and / or wireless communications. In some cases, the grid-level controller 214 is on a cloud network or located at a central computer. In some cases, the grid-level controller214 can utilize algorithms to optimize and send data settings over the communication network 240.

[0015] Figure 3 illustrates a method for grid-level control for a power distribution grid with multiple EV CSs. Referring to Figures 2 and 3, a method 300 for grid-level control for a power distribution grid 200 with EV CSs that is implemented by a grid-level controller 214 includes receiving (302) present and historical data relating to the power distribution grid 200, receiving (304) an energy and power demand forecast from a plurality of EV CSs (e.g., EV CS 202, EV CS 206, and EV CS 210) connected to the power distribution grid 200, calculating (306) energy and power demand forecasts for the power distribution grid 200 based on the received present and historical data and the energy and power demand forecasts from the plurality of EV CSs connected to the power distribution grid 200, determining (308) optimal set points for capacitor banks 216 and voltage regulators 224 in the power distribution grid 200 based on the energy and power demand forecasts for the power distribution grid 200 and the energy and power demand forecasts from the plurality of EV CSs connected to the power distribution grid 200, sending (310) the optimal set points for the capacitor banks and the voltage regulators in the power distribution grid, determining (312) energy and power limits for each EV CS of the plurality of EV CSs connected to the power distribution grid 200 based at least on the energy and power demand forecasts for the power distribution grid 200 and the energy and power demand forecasts from the plurality of EV CSs connected to the power distribution grid 200, and sending (314), to each EV CS of the plurality of EV CSs connected to the power distribution grid 200, the energy and power limits for that EV CS of the plurality of EV CSs connected to the power distribution grid 200. As used herein, energy and power demand forecasts generally refer to energy demand forecasts (e.g., without a power metric) as power can vary significantly from second to second and a forecast of power is generally not accurate and / or reliable. In some cases, energy and power demand forecasts do include a power metric. The particular forecasting methodology can vary depending on implementation.

[0016] In some cases, receiving (302) present and historical data relating to the power distribution grid 200 does not include data relating to the EV CSs. In some cases, the received (302) present and historical data relating to the power distribution grid 200 includes traditional power and energy demand sources such as, but not limited to, residential, commercial, and / or public power and energy demand sources and / or energy producing resources such as renewable energy resources and / or energy storage system levels. In some cases, the received (302) present and historical data relating to the power distribution grid 200 includes distribution grid level data, all capacitor bank data across a grid, regulator data, switch data, recloser configurationdata, load data, and generation of energy and power. In some cases, the received (304) energy and power demand forecast from a plurality of EV CSs connected to the power distribution grid 200 are forecasts for atime in arange of 15 minutes to 24 hours. In some cases, the method 300 further includes determining that the calculated energy and power demand forecasts for the power distribution grid 200 are larger than a capacity of the power distribution grid 200.In some cases, the capacity of the of the power distribution grid 200 includes an imbalance load profile or a short load profile.

[0017] As used herein, an imbalance load profile refers to a situation where the overall energy and power capabilities of the power distribution grid 200 are greater than or equal to the calculated energy and power demand forecasts for the power distribution grid 200, however, a portion of the power distribution grid 200 is not capable of meeting the energy and power requirements needed for that portion of the power distribution grid 200. For example, a portion of the power distribution grid 200 having transformer 218 that service the slow charging EV CS 202 may require 100 kilowatt hours (kWh) (as forecasted by the EV CS controller 204), a portion of the power distribution grid 200 having transformer 220 that service the fast charging EV CS 206 may require 200 kilowatt hours (kWh) (as forecasted by the EV CS controller 208), and a portion of the power distribution grid 200 having transformer 222 that service the building with the integrated EV CS 210 may require 150 kWh (as forecasted by the EV CS controller 212). If the power distribution grid is capable of providing 450 kWh or more, than the overall energy and power capabilities (e.g., 450 kWh or more) for the power distribution grid 200 are greater than or equal to the calculated energy and power demand forecasts (e.g., 100 kWh + 200 kWh + 150 kWh = 450 kWh) for the power distribution grid 200. However, if each of the transformers 218, 220, 222 of the power distribution grid 200 are only capable of providing 150 kWh, then the fast charging EV CS 206 cannot be supplied with 200 kWh. Thus, there is an imbalance in load-source balancing during the time period under consideration. For example, the energy required by each of the EV CSs cannot be met based on the energy forecasted for this time period. This is sometimes referred to as power imbalance and / or imbalance load profile, however, as used herein, energy is used as a metric as power can vary significantly from second to second and a forecast of power is not accurate and / or reliable.

[0018] As used herein, a short load profile refers to a situation where the overall energy and power capabilities of the power distribution grid 200 are less than the calculated energy and power demand forecasts for the power distribution grid 200. For example, a portion of the power distribution grid 200 having transformer 218 that service the slow charging EV CS202 may require 100 kilowatt hours (kWh) (as forecasted by the EV CS controller 204), a portion of the power distribution grid 200 having transformer 220 that service the fast charging EV CS 206 may require 200 kilowatt hours (kWh) (as forecasted by the EV CS controller 208), and a portion of the power distribution grid 200 having transformer 222 that service the building with the integrated EV CS 210 may require 150 kWh (as forecasted by the EV CS controller 212). If the power distribution grid is capable of providing 400 kWh or less, than the overall energy and power requirements (e.g., 400 kWh or less) for the power distribution grid 200 are less than the calculated energy and power demand forecasts (e.g., 100 kWh + 200 kWh + 150 kWh = 450 kWh) for the power distribution grid 200. Thus, there is a short load profde where the power distribution grid 200 is unable to support the energy and power demand forecasted by the EV CS(s) for a time period.

[0019] In some cases, when the capacity of the power distribution grid 200 includes the short load profile, the method 300 further includes entering a limit grid power mode. The limit grid power mode includes limiting one or more EV CSs of the plurality of EV CSs connected to the power distribution grid 200 to an energy and power profile that is lower than the energy and power demand forecast for those corresponding one or more EV CSs. For example, one or more of the EV CS 202, the EV CS 206 and the EV CS 210 may be sent (314) energy and power limits from the grid-level controller 214 that are less than their respective received (304) energy and power demand forecasts.

[0020] In some cases, when the capacity of the power distribution grid 200 includes the imbalance load profile (as an example EV CS 202 is overloaded and EV CS 206 is not), the method 300 further includes determining reallocation scenarios that meet or exceed the calculated energy and power demand forecasts for the power distribution grid 200. In some cases, the reallocation scenarios include reallocating EVs from one or more over loaded EV CSs to one or more EV CSs that have additional capacity.

[0021] In some cases, the method 300 further includes determining that none of the reallocation scenarios meet or exceed the calculated energy and power demand forecasts for the power distribution grid 200, and entering the limit grid power mode.

[0022] In some cases, the method 300 further includes determining that one or more reallocation scenarios meet or exceed the calculated energy and power demand forecasts for the power distribution grid 200, determining an optimal reallocation scenario of the one or more reallocation scenarios meet or exceed the calculated energy and power demand forecasts for the power distribution grid. In some cases, sending (314), to each EV CS of the plurality of EV CSs connected to the power distribution grid 200, the energy and power limitsfor that EV CS of the plurality of EV CSs connected to the power distribution grid 200 further includes executing the optimal reallocation scenario.

[0023] In some cases, the optimal reallocation scenario prioritizes minimal reallocation of EVs from the one or more over loaded EV CSs to the EV CSs that have additional capacity. For example, given a first reallocation scenario where two EVs need to be reallocated from the slow charging EV CS 202 to the fast charging EV CS 206 and a second reallocation scenario where a single EV needs to be reallocated from the slow charging EV CS 202 to the fast charging EV CS 206 while the other EV can be charged at a slower rate, the second reallocation scenario is chosen as the optimal reallocation scenario.

[0024] In some cases, the method 300 further includes providing an incentive to owners of the EVs reallocated from the one or more over loaded EV CSs to the one or more EV CSs that have additional capacity. For example, owners of the EVs may be offered a reduced and / or free charging rate to charge their respective EV in order to move from a charger at an overloaded EV CS to a charger at an EV CS with additional capacity.

[0025] In some cases, executing the optimal reallocation scenario includes sending a message to a controller of the one or more over loaded EV CSs or the EVs at the one or more over loaded EV CSs. The message can include instructions to move to the one or more EV CSs that have additional capacity that are closest in proximity to the one or more over loaded EV CSs and are scheduled to be available (e.g., do not already have EVs charging and / or scheduled to be charged during the time that the EVs moving from the over loaded chargers to the chargers with additional capacity will need to be charged).

[0026] In some cases, communication can be facilitated between the grid-level controller 214 and the EV CS controllers 204, 208, 212 by synchronization using CS id, and profde key. This information can include such information as power limits from the grid-level controller to the EV CS controllers, and estimated grid power demand from EV CS controllers to the gridlevel controller. The grid-level controller operated by the service provider can communicate with EV CS controllers using REST APIs or similar infrastructure. An HTTPS protocol can be used at both ends — the grid-level controller and the CS EV controllers — to ensure data security. The grid-level controller that manifests a REST API server can transmit the power profiles via messages to the clients running the EV CS controller. The client can then share information with the EV CS controller methods through a shared time-series database module. The process to send power profile from the EV CS controller to grid-level controller works in the reverse order.

[0027] In some cases, information exchange between equipment and the EV CS controllers and / or grid-level controller can be realized through one or more protocols (e.g., Modbus, DNP3, IEEE 2030.5). This is a bi-directional point-to-point communication for configuration, monitoring and control information of the EV CS equipment. Data points are communicated (e.g., every 1 second) and are stored in the time series database in the EV CS controller. Every time a datapoint is received, the EV CS controller system-time is assigned to the datapoints and stored in the database. An HTTP service is developed to allow EV CS controller to get and post data from and to the grid-level controller (with required gateways for protocol conversion) for example in XML format.

[0028] In some cases, sending (310) the optimal set points for the capacitor banks and the voltage regulators in the power distribution grid 200 may not be sufficient and may require curtailing and / or lowering power generation for renewable energy resources (e.g., solar panel farm 226) because the renewable energy resources are feeding too much power into the power distribution grid 200. In some cases, sending (314), to each EV CS of the plurality of EV CSs connected to the power distribution grid 200, the energy and power limits for that EV CS of the plurality of EV CSs connected to the power distribution grid 200 includes curtailing and / or lowering the charge rate of chargers in an EV CS and / or lowering the power output from renewables integrated with the charging stations and / or increasing the power output from the integrated battery storage (e.g., energy storage system 230) integrated with the EV CS. In some cases, the energy and power limits for that EV CS of the plurality of EV CSs connected to the power distribution grid 200 are sent (314) to that EV CS controller of the plurality of EV CS controllers connected to the power distribution grid 200.

[0029] In some cases, the described method 300 is performed and / or updated after a predetermined horizon time period. For example, the method 300 can be performed and / or updated every 1 minute, 5 minutes, 15 minutes, 30 minutes, 1 hour, 2 hours, and / or 3 hours or any time period within the range of 1 minute to 3 hours. In some cases, the method 300 can be performed and / or updated continuously. In some cases, energy and power limits that are sent (314) to each EV CS of the plurality of EV CSs connected to the power distribution grid 200 are for a predetermined forecast time period into the future. For example, the predetermined forecast time period can be for 1 hour, 2 hours, 3 hours, 6 hours, 10 hours, 12 hours, and / or 24 hours or any time period between the range of 1 hour and 24 hours. In some cases, the optimal set points that are sent (310) to the capacitor banks and the voltage regulators in the power distribution grid 200 are also for a predetermined forecast time periodinto the future. For example, the predetermined forecast time period can be for 1 hour, 2 hours, 3 hours, 6 hours, 10 hours, 12 hours, and / or 24 hours or any time period between the range of 1 hour and 24 hours.

[0030] Figure 4 illustrates components of a computing device that may be used in certain embodiments described herein. Referring to Figure 4, system 400 may represent a computing device such as, but not limited to, a system on a chip, a personal computer, a reader, a mobile device, a personal digital assistant, a wearable computer, a smart phone, a tablet, a laptop computer (notebook or netbook), a hybrid computer, a desktop computer, or a server. Accordingly, more or fewer elements described with respect to system 400 may be incorporated to implement a particular computing device. The system 400 may embody elements of a gridlevel controller.

[0031] System 400 includes a processing system 405 of one or more processors to transform or manipulate data according to the instructions of software 410, such as method 300 of Figure 3, stored on a storage system 415. Examples of processors of the processing system 405 include general purpose central processing units, application specific processors, and logic devices, as well as any other type of processing device, combinations, or variations thereof. System 400 can implement the grid-level controller 102 of Figure 1 and / or grid-level controller 214 of Figure 2.

[0032] Processing system 405 can include one or more of any suitable processing devices (“processors”), such as a microprocessor, central processing unit (CPU), graphics processing unit (GPU), field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), logic circuits, and state machines.

[0033] Storage system 415 may comprise any computer readable storage media readable by the processing system 405 and capable of storing software 410. Storage system 415 may include volatile and nonvolatile memories, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Examples of storage media of storage system 415 include random access memory, read only memory, magnetic disks, optical disks, CDs, DVDs, flash memory, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other suitable storage media. In no case is the storage medium a transitory propagated signal.

[0034] Storage system 415 may be implemented as a single storage device but may also be implemented across multiple storage devices or sub-systems co-located or distributed relative to each other. Storage system 415 may include additional elements, such as a controller,capable of communicating with processing system 405. In some cases, storage system 415 can store one or more databases of present and historical data relating to the power distribution grid.

[0035] Software 410 may be implemented in program instructions, such as method 300 of Figure 3 and / or any other method, process, and / or calculations described herein, and among other functions may, when executed by system 400 in general or processing system 405 in particular, direct system 400 or the one or more processors of processing system 405 to operate as described herein. In some cases, a neural network, such as a convolution neural network, is implemented as an algorithm running on the system 400. In some cases, a neural network, such as a convolution neural network, is implemented on one or more processors (e.g., processing system 405) executing instructions and / or implemented on hardware (e.g., FPGAs and / or ASICs), where some or all of the neural network operations performed in software, hardware, or a combination thereof.

[0036] The system can further include user interface system 420, which may include input / output (I / O) devices and components that enable communication between a user and the system 400. User interface system 420 can include input devices such as a mouse, track pad, keyboard, a touch device for receiving a touch gesture from a user, a motion input device for detecting non-touch gestures and other motions by a user, a microphone for detecting speech, and other types of input devices and their associated processing elements capable of receiving user input.

[0037] The user interface system 420 may also include user interface software and associated software (e.g., for graphics chips and input devices) executed by the operating system (OS) in support of the various user input and output devices. The associated software assists the OS in communicating user interface hardware events to application programs using defined mechanisms. The user interface system 420 including user interface software may support a graphical user interface, a natural user interface, or any other type of user interface.

[0038] Network interface 430 may include communications connections and devices that allow for communication with other computing systems and / or controllers, over one or more communication networks (not shown). Communication with other computing systems and / or controllers can include requesting and / or receiving information and / or data from, for example, data sources having present and historical data relating to the power distribution grid. Examples of connections and devices that together allow for inter-system communication may include network interface cards, antennas, power amplifiers, RF circuitry, transceivers, and other communication circuitry. The connections and devices may communicate over communicationmedia (such as metal, glass, air, or any other suitable communication media) to exchange communications with other computing systems or networks of systems.

[0039] Although the subject matter has been described in language specific to structural features and / or acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as examples of implementing the claims and other equivalent features and acts that would be recognized by one skilled in the art are intended to be within the scope of the claims.

Claims

CLAIMSWhat is claimed is:

1. A method for grid-level control for a power distribution grid with electrical vehicle (EV) charging stations (CSs) comprising:receiving, at a grid-level controller, present and historical data relating to the power distribution grid;receiving, at the grid-level controller, an energy and power demand forecast from a plurality of EV CSs connected to the power distribution grid;calculating, by the grid-level controller, energy and power demand forecasts for the power distribution grid based on the received present and historical data and the energy and power demand forecasts from the plurality of EV CSs connected to the power distribution grid;determining, by the grid-level controller, optimal set points for capacitor banks and voltage regulators in the power distribution grid based on the energy and power demand forecasts for the power distribution grid and the energy and power demand forecasts from the plurality of EV CSs connected to the power distribution grid;sending, by the grid-level controller, the optimal set points for the capacitor banks and the voltage regulators in the power distribution grid;determining, by the grid-level controller, energy and power limits for each EV CS of the plurality of EV CSs connected to the power distribution grid based at least on the energy and power demand forecasts for the power distribution grid and the energy and power demand forecasts from the plurality of EV CSs connected to the power distribution grid; and sending, to each EV CS of the plurality of EV CSs connected to the power distribution grid, the energy and power limits for that EV CS of the plurality of EV CSs connected to the power distribution grid.

2. The method of claim 1, further comprising determining that the calculated energy and power demand forecasts demand for the power distribution grid are larger than a capacity of the power distribution grid.

3. The method of claim 2, wherein the capacity of the power distribution grid comprises an imbalance load profde or a short load profile.

4. The method of claim 3, wherein the capacity of the power distribution grid comprises the short load profde, the method further comprising entering a limit grid power mode.

5. The method of claim 4, wherein the limit grid power mode comprises limiting one or more EV CSs of the plurality of EV CSs connected to the power distribution grid to a energy and power profde that is lower than the energy and power demand forecast for those corresponding one or more EV CSs.

6. The method of claim 3, wherein the capacity of the power distribution grid comprises the imbalance load profde, the method further comprising determining reallocation scenarios that meet or exceed the calculated energy and power demand forecasts for the power distribution grid.

7. The method of claim 6, wherein the reallocation scenarios comprise reallocating EVs from one or more over loaded EV CSs to one or more EV CSs that have additional capacity.

8. The method of any of claim 5-7, further comprising:determining that none of the reallocation scenarios meet or exceed the calculated energy and power demand forecasts for the power distribution grid; andentering the limit grid power mode.

9. The method of claim 6 or 7, further comprising:determining that one or more reallocation scenarios meet or exceed the calculated energy and power demand forecasts for the power distribution grid; anddetermining an optimal reallocation scenario of the one or more reallocation scenarios meet or exceed the calculated energy and power demand forecasts for the power distribution grid,wherein sending the energy and power limits for that EV CS of the plurality of EV CSs connected to the power distribution grid further comprises executing the optimal reallocation scenario.

10. The method of claim 9, wherein the optimal reallocation scenario prioritizes minimal reallocation of EVs from the one or more over loaded EV CSs to the EV CSs that have additional capacity.

11. The method of claim 9 or 10, further comprising providing an incentive to owners of the EVs reallocated from the one or more overloaded EV CSs to the one or more EV CSs that have additional capacity.

12. The method of any of claims 9-11, wherein executing the optimal reallocation scenario comprises sending a message to a controller of the one or more overloaded EV CSs or the EVs at the one or more over loaded EV CSs, the message comprising instructions to move to the one or more EV CSs that have additional capacity that are closest in proximity to the one or more overloaded EV CSs and are scheduled to be available.

13. A method for grid-level control for a power distribution grid with electrical vehicle (EV) charging stations (CSs) comprising:receiving present and historical data relating to the power distribution grid; receiving an energy and power demand forecast from a plurality of EV CSs connected to the power distribution grid;calculating energy and power demand forecasts for the power distribution grid based on the received present and historical data and the energy and power demand forecasts from the plurality of EV CSs connected to the power distribution grid;determining that the calculated energy and power demand forecasts for the power distribution grid are larger than a capacity of the power distribution grid;when an overall capacity of the power distribution grid is lower than calculated energy and power demand forecasts for the power distribution grid, limiting one or more EV CSs of the plurality of EV CSs connected to the power distribution grid to an energy and power profile that is lower than the energy and power demand forecast for those corresponding one or more EV CSs;when the capacity of the power distribution grid comprises an imbalance load profile:determining reallocation scenarios that meet or exceed the calculated energy and power demand forecasts for the power distribution grid;determining that one or more reallocation scenarios meet or exceed the calculated energy and power demand forecasts for the power distribution grid; and determining an optimal reallocation scenario of the one or more reallocation scenarios meet or exceed the calculated energy and power demand forecasts for the power distribution grid;determining optimal set points for capacitor banks and voltage regulators in the power distribution grid based on the energy and power demand forecasts for the power distribution grid and the energy and power demand forecasts from the plurality of EV CSs connected to the power distribution grid;sending the optimal set points for the capacitor banks and the voltage regulators in the power distribution grid;determining energy and power limits for each EV CS of the plurality of EV CSs connected to the power distribution grid based at least on the energy and power demand forecasts for the power distribution grid and the energy and power demand forecasts from the plurality of EV CSs connected to the power distribution grid; andsending, to each EV CS of the plurality of EV CSs connected to the power distribution grid, the energy and power limits for that EV CS of the plurality of EV CSs connected to the power distribution grid.

14. A system for grid-level control of an electrical vehicle (EV) charging station (CS) comprising:a processing system;one or more storage media; andinstructions stored on the one or more storage media that, when executed by the processing system, direct the processing system to at least perform the method of any of claims 1-13.

15. One or more computer readable storage media having instructions stored thereon that, when executed by a processing system, direct the processing system to at least perform the method of any of claims 1-13.

16. A system for grid-level control of an electrical vehicle (EV) charging station (CS) and a grid-level controller based on a cloud network or at a central computer comprising: a communication network between a plurality of EV CSs and the central computer to exchange data;one or more processing systems that use algorithms to optimize and send the data settings via the network; andinstructions stored on one or more storage media that, when executed by the processing systems, direct the processing systems to at least perform any method of claims 1-13.

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