Site-level controller for electrical vehicle charging stations

Abstract

A method for localized control of an electrical vehicle (EV) charging station (CS) includes receiving present and historical data relating to the EV CS, calculating power and energy forecasts for the EV CS based on the received present and historical data, determining available modes of power and energy including power source options for the EV CS based on the calculated power and energy forecasts for the EV CS, selecting a preferred mode of power and energy for the EV CS from the available modes of power and energy, and executing the preferred mode of power and energy for the EV CS. Selecting the preferred mode of power and energy for the EV CS from the available modes of power and energy may be based on utility rates, environmental factors, individual EV load factors, CS load factors, time-of-use rates, power grid limits, renewable energy generation limits, and energy storage system reserves.

Description

SITE-LEVEL CONTROLLER FOR 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 power and energy is an ever-increasing challenge. Indeed, existing power grids will need significant upgrades using current systems and methods of supplying power and energy with the ever-increasing use of EVs. Existing power grids utilities also employ Time-of-Use (ToU) rates, which increase the price of power and energy during peak hours (e.g., usually afternoon and evening hours), and may even prevent EV CSs from receiving enough power and energy to supply the demand at the EV CSs themselves to avoid brown outs and / or black outs across the power grid.

[0003] Renewable energy resources, including solar energy, and / or accompanying energy storage systems (e.g., battery banks) have been proposed to be directly integrated to serve power and energy needs of the EV CSs to delay and / or remedy the need large-scale upgrades to existing power grids. However, these renewable energy resources come at significant capital expense for the owner of the EV CS to employ, and are usually not able to produce enough power and energy for the EV CS without power and energy from the power grid (e.g., which can lead to inadequate power and energy available for the EV CS if the power grid cannot meet the power and energy needs of the EV CS).

[0004] Therefore, in order to minimize the need for using power and energy from the power grid during high ToU rates as well as to ensure that the power and energy needs of the EV CS are met, efficient and improved short-term localized control of the EV CSs are needed.BRIEF SUMMARY

[0005] Methods and systems for localized control of an electrical vehicle (EV) charging station (CS) are provided herein. Advantageously, by determining short-term (e.g., 6 hours) power and energy forecasts for the EV CS, selection and execution of a preferred mode of power and energy utilization for the EV CS ensures that at high time-of-use (ToU) rates of thepower grid, energy usage is minimized and / or avoided altogether while simultaneously ensuring that the EV CS can meet its power and energy needs.

[0006] A method for localized control of an electrical vehicle (EV) charging station (CS) includes receiving present and historical data relating to the EV CS, calculating power and energy forecasts for the EV CS based on the received present and historical data, determining available modes of power and energy including power source options for the EV CS based on the calculated power and energy forecasts for the EV CS, selecting a preferred mode of power and energy for the EV CS from the available modes of power and energy, and executing the preferred mode of power and energy for the EV CS.

[0007] In some cases, determining the available modes of power and energy for the EV CS based on the calculated power and energy forecasts for the EV CS ensures that the available modes of power and energy for the EV CS include modes that are capable of meeting the calculated power and energy forecasts for the EV CS.

[0008] In some cases, the available modes of power include self-sustaining mode (e.g., utilization of a majority of power and energy from localized renewable energy and battery storage), reliable mode (e.g., utilization of a majority of power and energy from a grid connected to the EV CS), demand-flexible mode (e.g., utilization of power and energy from both the localized renewable energy and battery storage and the grid connected to the EV CS), and energy conservation mode (e.g., utilization of power and energy from the grid and / or localized renewable energy to increase energy storage system reserves).

[0009] In some cases, selecting the preferred mode of power and energy for the EV CS from the available modes of power and energy includes selecting self-sustaining mode when low individual EV load factors, high energy storage system reserves, and high time-of-use rates are forecasted, selecting reliable mode when high individual EV load factors, low energy storage system reserves, and low charging station load factors are forecasted, selecting demand-flexible mode when low time-of-use rates and high charging station load factors are forecasted, and selecting energy conservation mode when low energy storage system reserves and high individual EV load factors are forecasted.

[0010] In some cases, selecting the preferred mode of power and energy for the EV CS from the available modes of power and energy is based on utility rates, environmental factors, individual EV load factors, CS load factors, time-of-use rates, and / or energy storage system reserves.

[0011] 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 toidentify 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

[0012] Figure 1 illustrates a representational diagram of an operating environment for localized control of an electric vehicle (EV) charging station (CS).

[0013] Figure 2 illustrates a charging station controller framework that may be used in certain embodiments described herein.

[0014] Figure 3 illustrates a method for localized control of an EV CS.

[0015] Figure 4 illustrates a forecasting module used in an EV CS controller that may be used in certain embodiments described herein.

[0016] Figure 5 illustrates a conceptual diagram for mode selection of the EV CS.

[0017] Figure 6 illustrates a grid-level operating environment with multiple EV CS controllers.

[0018] Figure 7A illustrates an adaptive persistence model-based forecasting utilizing cumulative energy values.

[0019] Figure 7B illustrates EV charging profde forecasting utilizing a Random Forest method.

[0020] Figure 8 illustrates components of a computing device that may be used in certain embodiments described herein.DETAILED DESCRIPTION

[0021] Methods and systems for localized control of an electrical vehicle (EV) charging station (CS) are provided herein. Advantageously, by determining short-term (e.g., 6 hours) power and energy forecasts for the EV CS, selection and execution of a preferred mode of power and energy utilization for the EV CS ensures that at high time-of-use (ToU) rates of the power grid, energy usage is minimized and / or avoided altogether while simultaneously ensuring that the EV CS can meet its power and energy needs.

[0022] Traditional power and energy management controls are focused on solving a multiobjective problems that address energy management and serving loads with well defined profiles and are looking for a global optimal performance over longer durations. The systems and methods described herein can address shorter time-frames with specific performance metrics that ensures that the EV CS can meet its power and energy needs despite stochastic arrival / departure times and initial state of charge of the EVs, varying utility rate structures fromthe power grid, and power and energy constraints imposed by the power grid utility, while simultaneously optimizing the use of integrated renewable energy resources (e.g., solar panels) and energy storage systems (e.g., battery banks).

[0023] Figure 1 illustrates a representational diagram of an operating environment for localized control of an electric vehicle (EV) charging station (CS). Referring to Figure 1, the operating environment 100 includes an EV CS 102 and power grid 120 connected to the EV CS 102. The EV CS includes an EV CS controller 104, alternating current (AC) and / or direct current (DC) distribution 106, and a plurality of EV chargers 108. In some cases, the EV CS 102 further includes an energy storage system 110 (e.g., battery storage) and / or a renewable energy resource 112 (e.g., solar panels).

[0024] The AC and / or DC distribution 106 is coupled to the energy storage system 110, the renewable energy resource 112, and / or the power grid 120 for receiving power and / or energy for the plurality of EV chargers 108. The AC and / or DC distribution 106 is also coupled to the plurality of EV chargers 108 to supply the power and energy from the energy storage system 110, the renewable energy resource 112, and / or the power grid 120 to the EVs connected to the plurality of EV chargers 108. The EV CS controller 104 may be coupled to the AC and / or DC distribution 106 and / or switches coming into the AC and / or DC distribution 106 from the energy storage system 110, the renewable energy resource 112, and / or the power grid 120 to control where the power and energy supplied to the plurality of EV chargers 108 is sourced. In some cases, the EV CS 102 includes DC fast charging. In some cases, EV CS controller 104 may be implemented as described with respect to EV CS controller 200 of Figure 2.

[0025] Producing energy by integrating a renewable energy resource 112, such as solar panels and / or wind turbines, into the EV CS 102 provides the EV CS 102 the ability to operate independently of the power grid 120. Furthermore, storing of excess energy in an energy storage system 110 so that EVs can be charged even when the renewable energy resource 112 is not producing energy (e.g., the sun is not shining for solar panels and / or the wind is not moving for wind turbines) is contemplated herein. In some cases, the energy produced by renewable energy resource 112 can be used to directly provide power to EVs and / or to store the energy storage system 110 for later use to charge EVs. In some cases, the renewable energy resource 112 may not be able to provide enough power and energy for the EV CS 102 100% of the time; accordingly, the EV CS 102 still requires access to the power grid for some and / or all of the power and energy requirements at certain times.

[0026] Figure 2 illustrates a charging station controller framework that may be used in certain embodiments described herein. Referring to Figure 2, an EV CS controller 200 includesa mode performance metrics module 202, a mode selection module 204, a forecast module 206, and an optimization engine 208. The EV CS controller 200 can receive present and historical data relating to the EV CS. For example, the forecast module 206 can receive CS site data 210 and / or PV and EV power data 212. The mode performance metrics module 202 can receive utility tariff data 214 and / or asset measurements and constraints data 216. In some cases, the optimization engine 208 can receive the asset measurements and constraints data 216. The EV CS controller 200 uses this present and historical data relating to the EV CS to select and execute a preferred mode of power and energy for the EV CS, including dispatching 218 a schedule for EV chargers, energy storage systems, and / or inverters.

[0027] Table 1: Modes of operation and power and energy delivery details for the EV CS.

[0028] Referring to Table 1 and Figure 2, the mode performance metrics module 202 can determine the transition drivers from the present and historical data relating to the EV CS. The mode selection module 204 can select the mode of operation (e.g., demand-flexible, reliable, self-sustainable, and / or energy conservation) for the EV CS from the available modes of power and energy. The forecast module 206 can calculate power and energy forecasts for the EV CS based on the received present and historical data relating to the EV CS. The optimization engine 208 can execute the preferred mode of power and energy for the EV CS (e.g., control energy and power sources for the EV CS).

[0029] For example, the optimization engine 208 is designed to meet the performance objectives, metrics and / or criteria of the EV CS owner. To meet those objectives, metrics and / or criteria, an optimization calculation can be utilized by systematically selecting appropriate weightage factors. The optimization engine 208 performs energy resourcemanagement in each mode considering, for example, present asset power measurements, utility signals, and forecasted power profdes over a predetermined horizon time period (e.g., as explained in detail with respect to Figure 4). As an outcome of this energy resource management, equipment (e.g., DC fast chargers, energy storage systems, converters, inverters, etc.) are activated, deactivated, and / or controlled to produce a desired effect by the EV CS controller 200. In some cases, the optimization engine 208 can also compute power grid and energy storage system power and energy forecasts to aid in driving the mode selection module 204.

[0030] In some cases, the operation of each mode is represented as a moving horizon weighted objective in terms of states (e.g., see “Affected Primary States” in last column of Table 1) and weightage factors (wi, W2, ... wn) as seen in the equation:

[0031] the weightage factors are subject to state equality and inequality constraints, asset sizes and capabilities, and / or system constraints and limits. Selection of an operating mode can be a function of a set of weightage factors and transition drivers (tdi, t(h, ... tdk) as represented by the equation:

[0032] modex= function([wl,w2, ... wn], [tdl, td2, ... tdk]).

[0033] Figure 3 illustrates a method for localized control of an EV CS . Referring to Figure 3, a method 300 for localized control of an EV CS includes receiving (302) present and historical data relating to the EV CS, calculating (304) power and energy forecasts for the EV CS based on the received present and historical data, determining (306) available modes of power and energy including power source options for the EV CS based on the calculated power and energy forecasts for the EV CS, selecting (308) a preferred mode of power and energy for the EV CS from the available modes of power and energy, and executing (310) the preferred mode of power and energy for the EV CS.

[0034] In some cases, a forecast module (e.g., forecast module 206 of Figure 2) performs the calculating (304) step. In some cases, a mode performance metrics module (e.g., mode performance metrics module 202 of Figure 2) performs the determining (306) step. In some cases, a mode selection module (e.g., mode selection module 204 of Figure 2) performs the selecting (308) step. In some cases, an optimization engine (e.g., optimization engine 208 of Figure 2) performs the executing (310) step.

[0035] In some cases, determining (306) the available modes of power and energy for the EV CS based on the calculated power and energy forecasts for the EV CS includes generating transition drivers based on the calculated power and energy forecasts for the EV CS. In somecases, the transition drivers include utility rates, environmental factors, individual EV load factors, CS load factors (e.g., the ratio of the average EV load to the maximum EV load over a forecasted time period), time-of-use rates, power grid limits, renewable energy generation limits, and energy storage system reserves. In some cases, generating the transition drivers based on the calculated power and energy forecasts for the EV CS includes generating a rating for each transition driver based on the calculated power and energy forecasts for the EV CS.

[0036] In some cases, determining (306) the available modes of power and energy for the EV CS based on the calculated power and energy forecasts for the EV CS includes determining power and energy capacities for each potential mode of power and energy for the EV CS and comparing the determined power and energy capacities for each potential mode of power and energy for the EV CS to the calculated power and energy forecasts for the EV CS. In some cases, the available modes of power and energy for the EV CS include modes that are capable of meeting the calculated power and energy forecasts for the EV CS. In some cases, the available modes of power and energy for the EV CS do not include modes that are not capable of meeting the calculated power and energy forecasts for the EV CS. In some cases, the potential modes of power and energy for the EV CS include self-sustaining mode, reliable mode, demand-flexible mode, and energy conservation mode.

[0037] In some cases, self-sustaining mode includes utilization of a majority of power and energy from localized renewable energy and battery storage. In some cases, reliable mode includes utilization of a majority of power and energy from a grid connected to the EV CS. In some cases, demand-flexible mode includes utilization of power and energy from both the localized renewable energy and battery storage and the grid connected to the EV CS. In some cases, energy conservation mode includes increasing energy storage system reserves using power energy from grid and localized renewable energy resources. .

[0038] In some cases, selecting (308) the preferred mode of power and energy for the EV CS from the available modes of power and energy includes selecting the preferred mode of power and energy for the EV CS from the available modes of power and energy based on the generated transition drivers and / or the ratings for the generated transition drivers. In some cases, selecting (308) the preferred mode of power and energy for the EV CS from the available modes of power and energy includes selecting self-sustaining mode when low individual EV load factors, high energy storage system reserves, and high time-of-use rates are forecasted, selecting reliable mode when high individual EV load factors, low energy storage system reserves, and low charging station load factors are forecasted, selecting demand-flexible mode when low time-of-use rates and high charging station load factors are forecasted, and selectingenergy conservation mode when low energy storage system reserves and high individual EV load factors are forecasted. In some cases, selecting (308) the preferred mode of power and energy for the EV CS from the available modes of power and energy is based on utility rates, environmental factors, individual EV load factors, CS load factors, time-of-use rates, power grid limits, renewable energy generation limits, and energy storage system reserves.

[0039] In some cases, executing (310) the preferred mode of power and energy for the EV CS includes supplying power and energy from at least one of localized renewable energy, battery storage, and a grid connected to the EV CS. For example, signals and / or instructions may be sent to an AC and / or DC distribution system (e.g., AC and / or DC distribution 106 of Figure 1) and / or switches to cause the energy storage system, the renewable energy resource, and / or the power grid to provide (or not provide) the power and energy supplied to the plurality of EV chargers of the EV CS.

[0040] In some cases, when the EV charging load is more than the available power and energy in the selected and / or preferred mode of operation, power and energy requirements are met by lowering the charging rate of individual EV chargers through EV charge management. This extreme case could lead to the delay of charging EVs. However, the EVs that included delayed charging can be added to the EV CS load in subsequent operations, if necessary. In this (extreme) scenario, this can be added to an existing mode of operation (e.g., as described herein) as a control strategy.

[0041] Figure 4 illustrates a forecasting module used in an EV CS controller that may be used in certain embodiments described herein. Referring to Figure 4, a forecasting module 400 includes storage 402 for storing received present and historical data relating to the EV CS, such as present and / or historical renewable resources power and energy measurements 404 and present and / or historical EV load measurements 406. Other data, including weather forecasts, present and / or historical time-of-use rates, present and / or historical power grid limits, and / or present and / or historical energy storage system reserves may also be included in the received and stored present and historical data relating to the EV CS. This data, including historical data, may also be stored in CS site data 408, historical power profdes 410. The storage 402 can also pass this data along to the historical power profdes 410 for storage and / or compilation.

[0042] The data is fed into a trained model 414. The trained model 414 has been trained on data from the historical power profdes 410. Using the present CS site data 408, the trained model 414 generates power forecasts 416 and energy forecasts 418. In some cases, the trained model 414 is a neural network, such as a convolution neural network. In some cases, the trained model is updated periodically as needed (e.g., every week). The power forecasts 416 and energyforecasts 418 can also include a cumulative energy calculation from the energy profile computation 412. The cumulative energy calculation is performed by converting the historical power profiles 410 into cumulative energy values for the a time period (e.g., a day) according to the equation:

[0043] where t0represents the beginning of the time period, t0+ N denotes the time horizon under consideration with the value resetting at the end of the time period, and P(t\N) are the power samples in the time horizon.

[0044] In some cases, the power forecasts 416 and the energy forecasts 418 include moving horizon forecasts such that the power forecasts 416 and the energy forecasts 418 are generated / updated after a predetermined horizon time period. For example, the power forecasts 416 and the energy forecasts 418 can be generated / 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 power forecasts 416 and the energy forecasts 418 are generated / updated continuously. In some cases, the power forecasts 416 and the energy forecasts 418 are forecasts 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.

[0045] The power forecasts 416 and the energy forecasts 418 are then sent to the mode performance metrics module 420 and the optimization engine 422 (e.g., mode performance metrics module 202 and optimization engine 208 of Figure 2). CS site measurements 424 can also be sent to the optimization engine 422 to perform their respective functionality. In some cases, the CS site measurements 424 and the CS site data 408 include the same data.

[0046] Figure 5 illustrates a conceptual diagram for mode selection of the EV CS . Referring to Figure 5, the conceptual diagram 500 includes four available and / or potential modes of power and energy for an EV CS, including self-sustaining mode 502, reliable mode 504, demand flexible mode 506, and energy conservation mode 508. The available and / or potential mode of power and energy for an EV CS that is selected is capable of meeting the calculated power and energy forecasts for the EV CS. Using transition drivers and / or ratings for each transition driver, one of the modes is selected. The transition drivers can include utility rates, environmental factors, individual EV load factors including EV charge management, CS load factors, time-of-use rates, power grid limits, renewable energy generation limits, and energy storage system reserves.

[0047] The rating for each transition driver can correspond to a state for that transition driver. For example, a low rating for energy storage system reserves can correspond to a state of having (relatively) low amounts of energy storage system reserves. A high rating can correspond to a state of having (relatively) high amounts of energy storage system reserves.

[0048] In some cases, self-sustaining mode 502 includes utilization of a majority of power and energy from localized renewable energy and battery storage, reliable mode 504 includes utilization of a majority of power and energy from a grid connected to the EV CS, demand flexible mode 506 includes utilization of power and energy from both the localized renewable energy and battery storage and the grid connected to the EV CS, and energy conservation mode 508 includes increasing energy storage system reserves using power and energy from the power utility grid and localized renewable energy.

[0049] Referring to Figure 3 and Figure 5, in some cases, selecting (308) the preferred mode of power and energy for the EV CS from the available modes of power and energy includes selecting self-sustaining mode 502 when low individual EV load factors, high energy storage system reserves, and high time-of-use rates are forecasted, selecting reliable mode 504 when high individual EV load factors, low energy storage system reserves, and low charging station load factors are forecasted, selecting demand flexible mode 506 when low time-of-use rates and high charging station load factors are forecasted, and selecting energy conservation mode 508 when low energy storage system reserves and high individual EV load factors are forecasted.

[0050] The transition drivers can also be used to shift from one mode to another when certain transition drivers change. For example, when currently in self-sustaining mode 502 and transition drivers are (re)generated, the mode can be shifted to reliable mode 504 when low energy storage system reserves are forecasted and high energy storage system reserves are presently measured; and can shift to energy conservation mode 508 when low energy storage system reserves are forecasted and low energy storage system reserves are presently measured.

[0051] When in reliable mode 504 and transition drivers are (re)generated, the mode can be shifted to demand flexible mode 506 when low time-of-use rates and high CS load factors are forecasted and / or presently measured; can shift to energy conservation mode 508 when low energy storage system reserves are presently measured and / or forecasted; and can shift to self-sustaining mode 502 when high energy storage system reserves and high time-of-use rates are forecasted.

[0052] When in demand flexible mode 506 and transition drivers are (re)generated, the mode can be shifted to reliable mode 504 when low CS load factors are forecasted and / orpresently measured; can shift to energy conservation mode 508 when low energy storage system reserves are forecasted and / or presently measured.

[0053] When in energy conservation mode 508 and transition drivers are (re)generated, the mode can be shifted to self-sustaining mode 502 when high energy storage system reserves are presently measured and high time-of-use rates are forecasted and / or presently measured; can shift to reliable mode 504 when no shift in EV / CS demand are forecasted and / or presently measured and high energy storage system reserves are presently measured; and can shift to demand flexible mode 506 when low time-of-use rates are forecasted and / or presently measured, there is a shift in EV and / or CS demand presently measured (e.g., a currently scheduled EV charging session(s) is pushed to a later time), and high energy storage system reserves are presently measured.

[0054] Figure 6 illustrates a grid-level operating environment with multiple EV CS controllers. Referring to Figure 6, a power grid 600 includes a slow charging EV CS 602, a fast charging EV CS 604, and a building with an integrated EV CS 606. Each of the slow charging EV CS 602, the fast charging EV CS 604, and the building with an integrated EV CS 606 can include an EV CS controller 608 (e.g., EV CS controller 104 of Figure 1 and / or EV CS controller 200 of Figure 2). The power grid can include a grid-level controller 610. Each MODICS (modular DER Integrated Charging Station) can have its own site controller, share a site controller with a cluster of charging stations (e.g., subset of total charging stations at a lot / location), or share a single site controller for a lot / location. Communication between gridlevel controller (e.g., “campus controller”) and site-level controllers 608 can be via a controller-tO-controller gateway.

[0055] Referring to Figure 3 and Figure 6, the method 300 for localized control of an EV CS (e.g., 602, 604, 606) can further include receiving, from a grid-level controller 610, power and energy limits for the EV CS from a power grid 600 connected to the EV CS. In some cases, determining (306) the available modes of power and energy including power and energy source options for the EV CS is further based on the received power and energy limits for the EV CS from the power grid 600 connected to the EV CS. The method 300 can further include sending, to the grid-level controller 610, the calculated power and energy forecasts for the EV CS prior to receiving the power and energy limits for the EV CS. In this way, the grid-level controller 610 can gather the calculated power and energy forecasts for each EV CS (e.g., 602, 604, 606), determine the power and energy across the power grid 600 and for each EV CS, and then send the determined power and energy limits to each EV CS for their respectivecontrollers to choose an appropriate mode of power and energy that ensures the power and energy demands are met.

[0056] Figure 7A illustrates an adaptive persistence model-based forecasting utilizing cumulative energy values. Figure 7B illustrates EV charging profde forecasting utilizing a Random Forest method. Referring to Figures 7A and 7B, a cumulative energy calculation (e.g., cumulative energy calculation from the energy profde computation 412 of Figure 4) can be used to aid in generate power forecasts and energy forecasts (e.g., power forecasts 416 and energy forecasts 418 of Figure 4). Referring to Figure 7A, an adaptive persistence model-based energy forecasting 700 includes an MAE of 433.32, which is only 3.3% when compared to the peak value of 12939 kWh. Referring to Figure 7B, a Random Forest method-based power forecasting 710 includes MAE of 127.5 which is 6% of the peak value of 2000 kW. Any realtime discrepancy in the forecasts can be managed by the optimization engine through the utilization of the energy storage system as a buffer.

[0057] Figure 8 illustrates components of a computing device that may be used in certain embodiments described herein. Referring to Figure 8, system 800 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, or a desktop computer. Accordingly, more or fewer elements described with respect to system 800 may be incorporated to implement a particular computing device. The system 800 may embody elements of an EV CS controller.

[0058] System 800 includes a processing system 805 of one or more processors to transform or manipulate data according to the instructions of software 810, such as method 300 of Figure 3, stored on a storage system 815. Examples of processors of the processing system 805 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 800 can include EV CS controller 200 of Figure 2.

[0059] Processing system 805 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.

[0060] Storage system 815 may comprise any computer readable storage media readable by the processing system 805 and capable of storing software 810. Storage system 815 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 815 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.

[0061] Storage system 815 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 815 may include additional elements, such as a controller, capable of communicating with processing system 805.

[0062] Software 810 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 800 in general or processing system 805 in particular, direct system 800 or the one or more processors of processing system 805 to operate as described herein. In some cases, adaptive persistence model-based energy forecasting and / or Random Forest method-based power forecasting is implemented on software, such as software 810. In some cases, a neural network, such as a convolution neural network, is implemented as an algorithm running on the system 800. In some cases, a neural network, such as a convolution neural network, is implemented on one or more processors (e.g., processing system 805) 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.

[0063] The system can further include user interface system 820, which may include input / output (I / O) devices and components that enable communication between a user and the system 800. User interface system 820 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.

[0064] The user interface system 820 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 820 including user interface software may support a graphical user interface, a natural user interface, or any other type of user interface.

[0065] Network interface 830 may include communications connections and devices that allow for communication with other computing systems, over one or more communication networks (not shown). Communication with other computing systems can include requesting and / or receiving information and / or data from, for example, data sources having present and historical data relating to the EV CS. 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 communication media (such as metal, glass, air, or any other suitable communication media) to exchange communications with other computing systems or networks of systems.

[0066] 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 localized control of an electrical vehicle (EV) charging station (CS) comprising:receiving, at an EV CS controller, present and historical data relating to the EV CS; calculating, at the EV CS controller, power and energy forecasts for the EV CS based on the received present and historical data;determining, by the EV CS controller, available modes of power and energy including power and energy source options for the EV CS based on the calculated power and energy forecasts for the EV CS;selecting, by the EV CS controller, a preferred mode of power and energy for the EV CS from the available modes of power and energy; andexecuting, by the EV CS controller, the preferred mode of power and energy for the EV CS.

2. The method of claim 1, wherein determining the available modes of power and energy for the EV CS based on the calculated power and energy forecasts for the EV CS comprises generating transition drivers based on the calculated power and energy forecasts for the EV CS.

3. The method of claim 2, wherein the transition drivers include utility rates, environmental factors, individual EV load factors, CS load factors, time-of-use rates, power grid limits, renewable energy generation limits, and energy storage system reserves.

4. The method of claim 2 or 3, generating the transition drivers based on the calculated power and energy forecasts for the EV CS comprises generating a rating for each transition driver based on the calculated power and energy forecasts for the EV CS.

5. The method of any claim 2-4, wherein selecting the preferred mode of power and energy for the EV CS from the available modes of power and energy comprises selecting the preferred mode of power and energy for the EV CS from the available modes of power and energy based on the generated transition drivers.

6. The method of any of claim 1-5, wherein determining the available modes of power and energy for the EV CS based on the calculated power and energy forecasts for the EV CS comprises:determining power and energy capacities for each potential mode of power and energy for the EV CS; andcomparing the determined power and energy capacities for each potential mode of power and energy for the EV CS to the calculated power and energy forecasts for the EV CS, wherein the available modes of power and energy for the EV CS include modes that are capable of meeting the calculated power and energy forecasts for the EV CS.

7. The method of any of claims 1-6, wherein the available modes of power and energy for the EV CS and / or the potential modes of power and energy for the EV CS comprise self-sustaining mode, reliable mode, demand-flexible mode, and energy conservation mode.

8. The method of claim 7, wherein self-sustaining mode comprises utilization of a majority of power and energy from localized renewable energy and battery storage, wherein reliable mode comprises utilization of a majority of power and energy from a grid connected to the EV CS,wherein demand-flexible mode comprises utilization of power and energy from both the localized renewable energy and battery storage and the grid connected to the EV CS, wherein energy conservation mode comprises increasing energy storage system reserves using power and energy from the grid and localized renewable energy.

9. The method of claim 7 or 8, wherein selecting the preferred mode of power and energy for the EV CS from the available modes of power and energy comprises:selecting self-sustaining mode when low individual EV load factors, high energy storage system reserves, and high time-of-use rates are forecasted;selecting reliable mode when high individual EV load factors, low energy storage system reserves, and low charging station load factors are forecasted;selecting demand-flexible mode when low time-of-use rates and high charging station load factors are forecasted; andselecting energy conservation mode when low energy storage system reserves and high individual EV load factors are forecasted.

10. The method of claim 1, further comprising receiving, from a grid-level controller, power and energy limits for the EV CS from a grid connected to the EV CS,wherein determining the available modes of power and energy including power and energy source options for the EV CS is further based on the received power and energy limits for the EV CS from the grid connected to the EV CS.

11. The method of claim 10, further comprising sending, to the grid-level controller, the calculated power and energy forecasts for the EV CS prior to receiving the power and energy limits for the EV CS.

12. The method of claim 1, wherein selecting the preferred mode of power and energy for the EV CS from the available modes of power and energy is based on utility rates, environmental factors, individual EV load factors, CS load factors, time-of-use rates, power grid limits, renewable energy generation limits, and energy storage system reserves.

13. The method of claim 1, wherein executing the preferred mode of power and energy for the EV CS comprises supplying power and energy from at least one of localized renewable energy, battery storage, and a grid connected to the EV CS.

14. A method for localized control of an electrical vehicle (EV) charging station (CS) comprising:receiving present and historical data relating to the EV CS;calculating power and energy forecasts for the EV CS based on the received present and historical data;sending, to a grid -level controller, the calculated power and energy forecasts for the EV CS;receiving, from the grid-level controller, power and energy limits for the EV CS from a grid connected to the EV CS;determining available modes of power and energy including power and energy source options for the EV CS based on the calculated power and energy forecasts for the EV CS and the received power and energy limits for the EV CS from the grid connected to the EV CS by:generating transition drivers based on the calculated power and energy forecasts for the EV CS, wherein the transition drivers include utility rates, environmentalfactors, individual EV load factors, CS load factors, time-of-use rates, power grid limits, renewable energy generation limits, and energy storage system reserves;generating a rating for each transition driver based on the calculated power and energy forecasts for the EV CS;determining power and energy capacities for each potential mode of power and energy for the EV CS; andcomparing the determined power and energy capacities for each potential mode of power and energy for the EV CS to the calculated power and energy forecasts for the EV CS and the received power and energy limits for the EV CS from the grid connected to the EV CS, wherein the potential modes of power and energy for the EV CS comprise self-sustaining mode, reliable mode, demand-flexible mode, and energy conservation mode;selecting a preferred mode of power and energy for the EV CS from the available modes of power and energy based on the transition drivers by:selecting the self-sustaining mode when low individual EV load factors, high energy storage system reserves, and high time-of-use rates are forecasted;selecting the reliable mode when high individual EV load factors, low energy storage system reserves, and low charging station load factors are forecasted;selecting the demand-flexible mode when low time-of-use rates and high charging station load factors are forecasted; andselecting the energy conservation mode when low energy storage system reserves and high individual EV load factors are forecasted; andexecuting the preferred mode of power and energy for the EV CS by supplying power and energy from at least one of localized renewable energy, battery storage, and a grid connected to the EV CS.

15. A system for localized 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 any method of claims 1-14.

16. 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 any method of claims 1-14.

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