Patent Grant
12197240
This specification relates to a system for controlling the use of “behind-the-meter” power.
The price for power distributed through regional and national electric power grids is composed of Generation, Administration, and Transmission & Distribution (“T&D”) costs. T&D costs are a significant portion of the overall price paid by consumers for electricity. T&D costs include capital costs (land, equipment, substations, wire, etc.), electrical transmission losses, and operation and maintenance costs. Electrical power is typically generated at local stations (e.g., coal, natural gas, nuclear, and renewable sources) in the Medium Voltage class of 2.4 kVAC to 69 kVAC before being converted in an AC-AC step up transformer to High Voltage at 115 kVAC or above. T&D costs are accrued at the point the generated power leaves the local station and is converted to High Voltage electricity for transmission onto the grid.
Local station operators are paid a variable market price for the amount of power leaving the local station and entering the grid. However, grid stability requires that a balance exist between the amount of power entering the grid and the amount of power used from the grid. Grid stability and congestion is the responsibility of the grid operator and grid operators take steps, including curtailment, to reduce power supply from local stations when necessary. Frequently, the market price paid for generated power will be decreased in order to disincentivize local stations from generating power. In some cases, the market price will go negative, resulting in a cost to local station operators who continue to supply power onto a grid. Grid operators may sometimes explicitly direct a local station operator to reduce or stop the amount of power the local station is supplying to the grid.
Power market fluctuations, power system conditions such as power factor fluctuation or local station startup and testing, and operational directives resulting in reduced or discontinued generation all can have disparate effects on renewal energy generators and can occur multiple times in a day and last for indeterminate periods of time. Curtailment, in particular, is particularly problematic.
According to the National Renewable Energy Laboratory's Technical Report TP-6A20-60983 (March 2014):
In an example, a method is described. The method may involve receiving, at a first control system, information indicative of a plurality of power-generation economic signals, and, based on the received information, identifying, by the first control system, at least one of: (i) a change indicative of a power-generation economic signal that exceeds a predefined threshold change, (ii) a power-generation economic signal that is below a predefined lower threshold limit, or (iii) a power-generation economic signal that is above a predefined upper threshold limit. The method may involve, based on the identification, performing at least one of: (i) adjusting a rate of power use by a flexible datacenter and (ii) providing an indication of computation resource availability to a second control system. The flexible datacenter may comprise a behind-the-meter power input system, a power distribution system, and a plurality of computing systems configured to receive power from the behind-the-meter power input system via the power distribution system.
In another example, a system is described. The system may comprise a flexible datacenter comprising: a behind-the-meter power input system, a power distribution system, and a plurality of computing systems configured to receive power from the behind-the-meter power input system via the power distribution system. The system may further comprise a first control system configured to: receive information indicative of a plurality of power-generation economic signals, and based on the received information, identify at least one of: (i) a change indicative of a power-generation economic signal that exceeds a predefined threshold change, (ii) a power-generation economic signal that is below a predefined lower threshold limit, or (iii) a power-generation economic signal that is above a predefined upper threshold limit. The first control system may be configured to, based on the identification, perform at least one of: (i) adjusting a rate of power use by a flexible datacenter, and (ii) providing an indication of computation resource availability to a second control system.
In a further example, a non-transitory computer readable medium having stored thereon instructions, that when executed by one or more processors, cause a control system to perform functions. The functions may comprise receiving information indicative of a plurality of power-generation economic signals and, based on the received information, identifying at least one of: (i) a change indicative of a power-generation economic signal that exceeds a predefined threshold change, (ii) a power-generation economic signal that is below a predefined lower threshold limit, or (iii) a power-generation economic signal that is above a predefined upper threshold limit. The functions may further comprise, based on the identification, performing at least one of: (i) adjusting a rate of power use by a flexible datacenter, and (ii) providing an indication of computation resource availability to a second control system. The flexible datacenter may comprise a behind-the-meter power input system, a power distribution system, and a plurality of computing systems configured to receive power from the behind-the-meter power input system via the power distribution system.
Other aspects of the present invention will be apparent from the following description and claims.
One or more embodiments of the present invention are described in detail with reference to the accompanying figures. For consistency, like elements in the various figures are denoted by like reference numerals. In the following detailed description of the present invention, specific details are set forth in order to provide a thorough understanding of the present invention. In other instances, well-known features to one having ordinary skill in the art are not described to avoid obscuring the description of the present invention.
The embodiments provided herein relate to providing an electrical load “behind the meter” at local stations such that generated power can be directed to the behind-the-meter load instead of onto the grid, typically for intermittent periods of time. “Behind-the-meter” power includes power that is received from a power generation system (for instance, but not limited to, a wind or solar power generation system) prior to the power undergoing step-up transformation to High Voltage class AC power for transmission to the grid. Behind-the-meter power may therefore include power drawn directly from an intermittent grid-scale power generation system (e.g. a wind farm or a solar array) and not from the grid.
The embodiments herein provide an advantage when, for example, the power system conditions exhibit excess local power generation at a local station level, excess local power generation that a grid cannot receive, local power generation that is subject to economic curtailment, local power generation that is subject to reliability curtailment, local power generation that is subject to power factor correction, low local power generation, start up local power generation situations, transient local power generation situations, conditions where the cost for power is economically viable (e.g., low cost for power), or testing local power generation situations where there is an economic advantage to using local behind-the-meter power generation. This is not least because the excess power can be utilized by the behind-the-meter electrical load rather than going to waste. In addition, by providing an electrical load behind-the-meter rather than connected to the grid, electrical transmission losses resulting from transmission of power through the grid can be reduced. In addition, any degradation in the power generation systems which may result from curtailment may be reduced.
Preferably, controlled computing systems that consume electrical power through computational operations can provide a behind-the-meter electrical load that can be granularly ramped up and down quickly under the supervision of control systems that monitor power system conditions and direct the power state and/or computational activity of the computing systems. In one embodiment, the computing systems preferably receive all their power for computational operations from a behind-the-meter power source. In another embodiment, the computing systems may additionally include a connection to grid power for supervisory and communication systems or other ancillary needs. In yet another embodiment, the computing systems can be configured to switch between behind-the-meter power and grid power under the direction of a control system.
Among other benefits, a computing system load with controlled granular ramping allows a local station to avoid negative power market pricing and to respond quickly to grid directives.
Various computing systems can provide granular behind-the-meter ramping. Preferably the computing systems perform computational tasks that are immune to, or not substantially hindered by, frequent interruptions or slow-downs in processing as the computing systems ramp up and down. In one embodiment, control systems can activate or de-activate one or more computing systems in an array of similar or identical computing systems sited behind the meter. For example, one or more blockchain miners, or groups of blockchain miners, in an array may be turned on or off. In another embodiment, control systems can direct time-insensitive computational tasks to computational hardware, such as CPUs and GPUs, sited behind the meter, while other hardware is sited in front of the meter and possibly remote from the behind-the-meter hardware. Any parallel computing processes, such as Monte Carlo simulations, batch processing of financial transactions, graphics rendering, and oil and gas field simulation models are all good candidates for such interruptible computational operations.
To enable cost efficient use of available power and computational resources, a control system may be configured to manage computational availability based on power-generation economics. Particularly, the control system may receive information indicative of power-generation economic signals. A power-generation economic signal may provide information that can be used when managing computational resources. The information included within each power-generation economic signal may depend on the source providing the information. For example, a power-generation economic signal may be based on a cost of power received from a power grid. As the power grid supplies a datacenter (e.g., a flexible datacenter) with power, the cost of the power can change over time based on various factors, such as the time of day, the demand for the power, and the availability of the power. The control system or another computing system may be configured to measure power-generation economic signals.
A power-generation economic signal may similarly be based on a cost of power received from a B-T-M power source. In some embodiments, a control system may use one or more power-generation economic signals that are based on a comparison of a price for power grid power relative to a price for behind-the-meter power. The comparison may enable the control system to determine when utilizing behind-the-meter to power computational resources is advantageous.
Power-generation economic signals may also be based on a purchase price associated with supplying power to computing systems within a flexible datacenter. Particularly, the control system may control or assist with the control of the computing systems within the flexible datacenter, including modulating the power supplied to the computing systems. As such, the control system may utilize power-generation economic signals that provide information about the purchase price for power associated with supplying the flexible datacenter to determine whether to ramp up or ramp down power supplied to flexible datacenter.
In addition, the control system may also use information within power-generation economic signals to determine whether to switch the power source that the flexible datacenter is receiving power from. For example, the control system may switch the flexible datacenter from a first B-T-M power source to a second B-T-M power source based on changes in purchase prices for power indicated within power-generation economic signals. Thus, when managing computational resources (e.g., a flexible datacenter), the control system may utilize information provided within power-generation economic signals.
As indicated above, a control system may obtain power-generation economic signals to manage available computational resources. Using information within one or more power-generation economic signals, the control system may identify a change in a power-generation economic signal that exceeds a predefined threshold change. A change in a power-generation economic signal that undergoes the predefined threshold change may trigger the control system to perform an action in response, such as adjusting a rate of power use by one or more flexible datacenters or providing an indication of computational resource availability in light of the threshold change. For example, a sudden drop in the purchase price of B-T-M power may result in the control system ramping up the power supplied to one or more flexible datacenters or ramping up the number of computing systems within a flexible datacenter receiving power. Other threshold changes represented in power-generation economic signals might cause the control system to perform other actions in response, such as ramping down power supplied to a flexible datacenter or providing an indication of the change to consumers using the available computational resources.
The control system may also use information within one or more power-generation economic signals to identify a power-generation economic signal that is below a predefined lower threshold limit or above a predefined upper threshold limit. The lower and upper threshold limits may be predefined to enable the control system to monitor when to make changes to the availability of computational resources. For example, when the cost of power received from the power grid drops below a predefined lower threshold price, the control system may increase the reliance or consumption of power from the power grid by available computational resources to capitalize on the price decrease. Similarly, the control system may decrease the reliance or consumption of power from the power grid by computational resources when the cost of power from the power grid exceeds the upper threshold price.
A similar set up with upper and lower threshold prices may be set up for managing power consumption from B-T-M power sources as well. In some examples, the control system may switch the computing systems of one or more flexible datacenters between grid power and a B-T-M power source based on changes in prices represented within power-generation economic signals. Additionally, the control system may also switch computing systems of one or more flexible datacenters between different B-T-M power sources based on price changes and power availability indicated within power-generation economic signals.
The control system may use information identified within power-generation economic signals to perform actions, such as adjusting a rate of power used by computing resources (e.g., a flexible datacenter) and providing an indication of computation resource availability to other computing devices (e.g., another control system). The power-generation economic signals can help the control system dynamically increase or decrease the rate of power used by available computation resources. For example, when B-T-M power is available at a purchase price that is less than the price a customer is willing to spend on a task, the control system may cause computing resources within one or more flexible datacenters to use the B-T-M power to perform the task for the customer. The control system may use the variety of information provided within power-generation economic signals to efficiently power and manage available computing resources, including determining when to switch between available power sources, when to ramp up or ramp down the power supplied, and when to adjust the quantity of computing systems performing a task, etc. This way, the control system may accommodate customers' price expectations through measuring and comparing the availability and prices associated with receiving power from different power sources.
Within examples, the control system may represent a computing system capable of receiving and processing power-generation economic signals. In some embodiments, the control system may correspond to a datacenter control system that is collocated with a flexible datacenter. As such, the datacenter control system may be configured to receive power from a power grid and one or more B-T-M power sources. In other embodiments, the control system may be a remote master control system located remote from a flexible datacenter. As such, the remote master control system may communicate with a datacenter control system that is collocated with the flexible datacenter. The communication may enable the remote master control system to remotely adjust the rate of power used by the flexible datacenter or to provide indications of power supplies available or computation resource availability to the datacenter control center.
In some cases, the local station may generate more power than can be consumed by the computing systems or distributed to the grid, or the computing systems may need to continue computational operations for a limited period of time beyond when a ramp-down condition is met. Accordingly, in one or more embodiments of the present invention, methods and systems for dynamic power delivery to a flexible datacenter that uses behind-the-meter power sources that includes both generated power and stored behind-the-meter power, each without transmission and distribution costs. A flexible datacenter may be configured to modulate power delivery to at least a portion of the computing systems based on monitored power system conditions or an operational directive. For example, the flexible datacenter may ramp-up to a full capacity status, ramp-down to an off capacity status, or dynamically reduce power consumption, act a load balancer, or adjust the power factor. Each of these activities may be performed using any or all of: behind-the-meter generated power, behind-the-meter stored power, and/or grid power. Advantageously, the flexible datacenter may perform computational operations, such as blockchain hashing operations or simulations, with little to no energy costs, using clean and renewable energy that would otherwise be wasted.
CPU 105 may be a general purpose computational device typically configured to execute software instructions. CPU 105 may include an interface 108 to host bridge 110, an interface 118 to system memory 120, and an interface 123 to one or more IO devices, such as, for example, one or more GPUs 125. GPU 125 may serve as a specialized computational device typically configured to perform graphics functions related to frame buffer manipulation. However, one of ordinary skill in the art will recognize that GPU 125 may be used to perform non-graphics related functions that are computationally intensive. In certain embodiments, GPU 125 may interface 123 directly with CPU 125 (and interface 118 with system memory 120 through CPU 105). In other embodiments, GPU 125 may interface 121 with host bridge 110 (and interface 116 or 118 with system memory 120 through host bridge 110 or CPU 105 depending on the application or design). In still other embodiments, GPU 125 may interface 133 with IO bridge 115 (and interface 116 or 118 with system memory 120 through host bridge 110 or CPU 105 depending on the application or design). The functionality of GPU 125 may be integrated, in whole or in part, with CPU 105.
Host bridge 110 may be an interface device configured to interface between the one or more computational devices and IO bridge 115 and, in some embodiments, system memory 120. Host bridge 110 may include an interface 108 to CPU 105, an interface 113 to IO bridge 115, for embodiments where CPU 105 does not include an interface 118 to system memory 120, an interface 116 to system memory 120, and for embodiments where CPU 105 does not include an integrated GPU 125 or an interface 123 to GPU 125, an interface 121 to GPU 125. The functionality of host bridge 110 may be integrated, in whole or in part, with CPU 105. IO bridge 115 may be an interface device configured to interface between the one or more computational devices and various IO devices (e.g., 140, 145) and IO expansion, or add-on, devices (not independently illustrated). IO bridge 115 may include an interface 113 to host bridge 110, one or more interfaces 133 to one or more IO expansion devices 135, an interface 138 to keyboard 140, an interface 143 to mouse 145, an interface 148 to one or more local storage devices 150, and an interface 153 to one or more network interface devices 155. The functionality of IO bridge 115 may be integrated, in whole or in part, with CPU 105 or host bridge 110. Each local storage device 150, if any, may be a solid-state memory device, a solid-state memory device array, a hard disk drive, a hard disk drive array, or any other non-transitory computer readable medium. Network interface device 155 may provide one or more network interfaces including any network protocol suitable to facilitate networked communications.
Computing system 100 may include one or more network-attached storage devices 160 in addition to, or instead of, one or more local storage devices 150. Each network-attached storage device 160, if any, may be a solid-state memory device, a solid-state memory device array, a hard disk drive, a hard disk drive array, or any other non-transitory computer readable medium. Network-attached storage device 160 may or may not be collocated with computing system 100 and may be accessible to computing system 100 via one or more network interfaces provided by one or more network interface devices 155.
One of ordinary skill in the art will recognize that computing system 100 may be a conventional computing system or an application-specific computing system. In certain embodiments, an application-specific computing system may include one or more ASICs (not shown) that are configured to perform one or more functions, such as hashing, in a more efficient manner. The one or more ASICs (not shown) may interface directly with CPU 105, host bridge 110, or GPU 125 or interface through IO bridge 115. Alternatively, in other embodiments, an application-specific computing system may be reduced to only those components necessary to perform a desired function in an effort to reduce one or more of chip count, printed circuit board footprint, thermal design power, and power consumption. The one or more ASICs (not shown) may be used instead of one or more of CPU 105, host bridge 110, IO bridge 115, or GPU 125. In such systems, the one or more ASICs may incorporate sufficient functionality to perform certain network and computational functions in a minimal footprint with substantially fewer component devices.
As such, one of ordinary skill in the art will recognize that CPU 105, host bridge 110, IO bridge 115, GPU 125, or ASIC (not shown) or a subset, superset, or combination of functions or features thereof, may be integrated, distributed, or excluded, in whole or in part, based on an application, design, or form factor in accordance with one or more embodiments of the present invention. Thus, the description of computing system 100 is merely exemplary and not intended to limit the type, kind, or configuration of component devices that constitute a computing system 100 suitable for performing computing operations in accordance with one or more embodiments of the present invention.
One of ordinary skill in the art will recognize that computing system 100 may be a stand-alone, laptop, desktop, server, blade, or rack mountable system and may vary based on an application or design.
In certain embodiments, mobile container 205 may be a storage trailer disposed on wheels and configured for rapid deployment. In other embodiments, mobile container 205 may be a storage container (not shown) configured for placement on the ground and potentially stacked in a vertical or horizontal manner (not shown). In still other embodiments, mobile container 205 may be an inflatable container, a floating container, or any other type or kind of container suitable for housing a mobile datacenter 200. And in still other embodiments, flexible datacenter 200 might not include a mobile container. For example, flexible datacenter 200 may be situated within a building or another type of stationary environment.
Flexible datacenter 200 may be rapidly deployed on site near a source of unutilized behind-the-meter power generation. Behind-the-meter power input system 210 may be configured to input power to flexible datacenter 200. Behind-the-meter power input system 210 may include a first input (not independently illustrated) configured to receive three-phase behind-the-meter alternating current (“AC”) voltage. In certain embodiments, behind-the-meter power input system 210 may include a supervisory AC-to-AC step-down transformer (not shown) configured to step down three-phase behind-the-meter AC voltage to single-phase supervisory nominal AC voltage or a second input (not independently illustrated) configured to receive single-phase supervisory nominal AC voltage from the local station (not shown) or a metered source (not shown). Behind-the-meter power input system 210 may provide single-phase supervisory nominal AC voltage to datacenter control system 220, which may remain powered at almost all times to control the operation of flexible datacenter 200. The first input (not independently illustrated) or a third input (not independently illustrated) of behind-the-meter power input system 210 may direct three-phase behind-the-meter AC voltage to an operational AC-to-AC step-down transformer (not shown) configured to controllably step down three-phase behind-the-meter AC voltage to three-phase nominal AC voltage. Datacenter control system 220 may controllably enable or disable generation or provision of three-phase nominal AC voltage by the operational AC-to-AC step-down transformer (not shown).
Behind-the-meter power input system 210 may provide three phases of three-phase nominal AC voltage to power distribution system 215. Power distribution system 215 may controllably provide a single phase of three-phase nominal AC voltage to each computing system 100 or group 240 of computing systems 100 disposed within flexible datacenter 200. Datacenter control system 220 may controllably select which phase of three-phase nominal AC voltage that power distribution system 215 provides to each computing system 100 or group 240 of computing systems 100. In this way, datacenter control system 220 may modulate power delivery by either ramping-up flexible datacenter 200 to fully operational status, ramping-down flexible datacenter 200 to offline status (where only datacenter control system 220 remains powered), reducing power consumption by withdrawing power delivery from, or reducing power to, one or more computing systems 100 or groups 240 of computing systems 100, or modulating a power factor correction factor for the local station by controllably adjusting which phases of three-phase nominal AC voltage are used by one or more computing systems 100 or groups 240 of computing systems 100. In some embodiments, flexible datacenter 200 may receive DC power to power computing systems 100.
Flexible datacenter 200 may include a climate control system (e.g., 250, 260, 270, 280, 290) configured to maintain the plurality of computing systems 100 within their operational temperature range. In certain embodiments, the climate control system may include an air intake 250, an evaporative cooling system 270, a fan 280, and an air outtake 260. In other embodiments, the climate control system may include an air intake 250, an air conditioner or refrigerant cooling system 290, and an air outtake 260. In still other embodiments, the climate control system may include a computer room air conditioner system (not shown), a computer room air handler system (not shown), or an immersive cooling system (not shown). One of ordinary skill in the art will recognize that any suitable heat extraction system (not shown) configured to maintain the operation of the plurality of computing systems 100 within their operational temperature range may be used in accordance with one or more embodiments of the present invention.
Flexible datacenter 200 may include a battery system (not shown) configured to convert three-phase nominal AC voltage to nominal DC voltage and store power in a plurality of storage cells. The battery system (not shown) may include a DC-to-AC inverter configured to convert nominal DC voltage to three-phase nominal AC voltage for flexible datacenter 200 use. Alternatively, the battery system (not shown) may include a DC-to-AC inverter configured to convert nominal DC voltage to single-phase nominal AC voltage to power datacenter control system 220.
One of ordinary skill in the art will recognize that a voltage level of three-phase behind-the-meter AC voltage may vary based on an application or design and the type or kind of local power generation. As such, a type, kind, or configuration of the operational AC-to-AC step down transformer (not shown) may vary based on the application or design. In addition, the frequency and voltage level of three-phase nominal AC voltage, single-phase nominal AC voltage, and nominal DC voltage may vary based on the application or design in accordance with one or more embodiments of the present invention.
In the figure, for purposes of illustration only, eighteen racks 240 are divided into a first group of six racks 310, a second group of six racks 320, and a third group of six racks 330, where each rack contains eighteen computing systems 100. The power distribution system (215 of
Local station control system 410 may be a computing system (e.g., 100 of
Datacenter control system 220 may monitor unutilized behind-the-meter power availability at the local station (not independently illustrated) and determine when a datacenter ramp-up condition is met. Unutilized behind-the-meter power availability may include one or more of excess local power generation, excess local power generation that the grid cannot accept, local power generation that is subject to economic curtailment, local power generation that is subject to reliability curtailment, local power generation that is subject to power factor correction, conditions where the cost for power is economically viable (e.g., low cost for power), situations where local power generation is prohibitively low, start up situations, transient situations, or testing situations where there is an economic advantage to using locally generated behind-the-meter power generation, specifically power available at little to no cost and with no associated transmission or distribution losses or costs.
The datacenter ramp-up condition may be met if there is sufficient behind-the-meter power availability and there is no operational directive from local station control system 410, remote master control system 420, or grid operator 440 to go offline or reduce power. As such, datacenter control system 220 may enable 435 behind-the-meter power input system 210 to provide three-phase nominal AC voltage to the power distribution system (215 of
Remote master control system 420 may specify to datacenter control system 220 what sufficient behind-the-meter power availability constitutes, or datacenter control system 220 may be programmed with a predetermined preference or criteria on which to make the determination independently. For example, in certain circumstances, sufficient behind-the-meter power availability may be less than that required to fully power the entire flexible datacenter 200. In such circumstances, datacenter control system 220 may provide power to only a subset of computing systems (100 of
While flexible datacenter 200 is online and operational, a datacenter ramp-down condition may be met when there is insufficient, or anticipated to be insufficient, behind-the-meter power availability or there is an operational directive from local station control system 410, remote master control system 420, or grid operator 440. Datacenter control system 220 may monitor and determine when there is insufficient, or anticipated to be insufficient, behind-the-meter power availability. As noted above, sufficiency may be specified by remote master control system 420 or datacenter control system 220 may be programmed with a predetermined preference or criteria on which to make the determination independently. An operational directive may be based on current dispatchability, forward looking forecasts for when unutilized behind-the-meter power is, or is expected to be, available, economic considerations, reliability considerations, operational considerations, or the discretion of the local station 410, remote master control 420, or grid operator 440. For example, local station control system 410, remote master control system 420, or grid operator 440 may issue an operational directive to flexible datacenter 200 to go offline and power down. When the datacenter ramp-down condition is met, datacenter control system 220 may disable power delivery to the plurality of computing systems (100 of
While flexible datacenter 200 is online and operational, changed conditions or an operational directive may cause datacenter control system 220 to modulate power consumption by flexible datacenter 200. Datacenter control system 220 may determine, or local station control system 410, remote master control system 420, or grid operator 440 may communicate, that a change in local conditions may result in less power generation, availability, or economic feasibility, than would be necessary to fully power flexible datacenter 200. In such situations, datacenter control system 220 may take steps to reduce or stop power consumption by flexible datacenter 200 (other than that required to maintain operation of datacenter control system 220). Alternatively, local station control system 410, remote master control system 420, or grid operator 440, may issue an operational directive to reduce power consumption for any reason, the cause of which may be unknown. In response, datacenter control system 220 may dynamically reduce or withdraw power delivery to one or more computing systems (100 of
One of ordinary skill in the art will recognize that datacenter control system 220 may be configured to have a number of different configurations, such as a number or type or kind of computing systems (100 of
Remote master control system 420 may provide supervisory control over fleet 500 of flexible datacenters 200 in a similar manner to that shown and described with respect to
The output side of AC-to-AC step-up transformer 640 that connects to grid 660 may be metered and is typically subject to transmission and distribution costs. In contrast, power consumed on the input side of AC-to-AC step-up transformer 640 may be considered behind-the-meter and is typically not subject to transmission and distribution costs. As such, one or more flexible datacenters 200 may be powered by three-phase wind-generated AC voltage 620. Specifically, in wind farm 600 applications, the three-phase behind-the-meter AC voltage used to power flexible datacenter 200 may be three-phase wind-generated AC voltage 620. As such, flexible datacenter 200 may reside behind-the-meter, avoid transmission and distribution costs, and may be dynamically powered when unutilized behind-the-meter power is available.
Unutilized behind-the-meter power availability may occur when there is excess local power generation. In high wind conditions, wind farm 600 may generate more power than, for example, AC-to-AC step-up transformer 640 is rated for. In such situations, wind farm 600 may have to take steps to protect its equipment from damage, which may include taking one or more turbines 610 offline or shunting their voltage to dummy loads or ground.
Advantageously, one or more flexible datacenters 200 may be used to consume power on the input side of AC-to-AC step-up transformer 640, thereby allowing wind farm 600 to operate equipment within operating ranges while flexible datacenter 200 receives behind-the-meter power without transmission or distribution costs. The local station control system (not independently illustrated) of local station 690 may issue an operational directive to the one or more flexible datacenters 200 or to the remote master control system (420 of
Another example of unutilized behind-the-meter power availability is when grid 660 cannot, for whatever reason, take the power being produced by wind farm 600. In such situations, wind farm 600 may have to take one or more turbines 610 offline or shunt their voltage to dummy loads or ground. Advantageously, one or more flexible datacenters 200 may be used to consume power on the input side of AC-to-AC step-up transformer 640, thereby allowing wind farm 600 to either produce power to grid 660 at a lower level or shut down transformer 640 entirely while flexible datacenter 200 receives behind-the-meter power without transmission or distribution costs. The local station control system (not independently illustrated) of local station 690 or the grid operator (not independently illustrated) of grid 660 may issue an operational directive to the one or more flexible datacenters 200 or to the remote master control system (420 of
Another example of unutilized behind-the-meter power availability is when wind farm 600 is selling power to grid 660 at a negative price that is offset by a production tax credit. In certain circumstances, the value of the production tax credit may exceed the price wind farm 600 would have to pay to grid 660 to offload their generated power. Advantageously, one or more flexible datacenters 200 may be used to consume power behind-the-meter, thereby allowing wind farm 600 to produce and obtain the production tax credit, but sell less power to grid 660 at the negative price. The local station control system (not independently illustrated) of local station 690 may issue an operational directive to the one or more flexible datacenters 200 or to the remote master control system (420 of
Another example of unutilized behind-the-meter power availability is when wind farm 600 is selling power to grid 660 at a negative price because grid 660 is oversupplied or is instructed to stand down and stop producing altogether. The grid operator (not independently illustrated) may select certain power generation stations to go offline and stop producing power to grid 660. Advantageously, one or more flexible datacenters 200 may be used to consume power behind-the-meter, thereby allowing wind farm 600 to stop producing power to grid 660, but making productive use of the power generated behind-the-meter without transmission or distribution costs. The local station control system (not independently illustrated) of the local station 690 or the grid operator (not independently illustrated) of grid 660 may issue an operational directive to the one or more flexible datacenters 200 or to the remote master control system (420 of
Another example of unutilized behind-the-meter power availability is when wind farm 600 is producing power to grid 660 that is unstable, out of phase, or at the wrong frequency, or grid 660 is already unstable, out of phase, or at the wrong frequency for whatever reason. The grid operator (not independently illustrated) may select certain power generation stations to go offline and stop producing power to grid 660. Advantageously, one or more flexible datacenters 200 may be used to consume power behind-the-meter, thereby allowing wind farm 600 to stop producing power to grid 660, but make productive use of the power generated behind-the-meter without transmission or distribution costs. The local station control system (not independently illustrated) of local station 690 may issue an operational directive to the one or more flexible datacenters 200 or to the remote master control system (420 of
Further examples of unutilized behind-the-meter power availability is when wind farm 600 experiences low wind conditions that make it not economically feasible to power up certain components, such as, for example, the local station (not independently illustrated), but there may be sufficient behind-the-meter power availability to power one or more flexible datacenters 200. Similarly, unutilized behind-the-meter power availability may occur when wind farm 600 is starting up, or testing, one or more turbines 610. Turbines 610 are frequently offline for installation, maintenance, and service and must be tested prior to coming online as part of the array. One or more flexible datacenters 200 may be powered by one or more turbines 610 that are offline from farm 600. The above-noted examples of when unutilized behind-the-meter power is available are merely exemplary and are not intended to limit the scope of what one of ordinary skill in the art would recognize as unutilized behind-the-meter power availability. Unutilized behind-the-meter power availability may occur anytime there is power available and accessible behind-the-meter that is not subject to transmission and distribution costs and there is an economic advantage to using it.
One of ordinary skill in the art will recognize that wind farm 600 and wind turbine 610 may vary based on an application or design in accordance with one or more embodiments of the present invention.
The output side of AC-to-AC step-up transformer 760 that connects to grid 790 may be metered and is typically subject to transmission and distribution costs. In contrast, power consumed on the input side of AC-to-AC step-up transformer 760 may be considered behind-the-meter and is typically not subject to transmission and distribution costs. As such, one or more flexible datacenters 200 may be powered by three-phase solar-generated AC voltage 750. Specifically, in solar farm 700 applications, the three-phase behind-the-meter AC voltage used to power flexible datacenter 200 may be three-phase solar-generated AC voltage 750. As such, flexible datacenter 200 may reside behind-the-meter, avoid transmission and distribution costs, and may be dynamically powered when unutilized behind-the-meter power is available.
Unutilized behind-the-meter power availability may occur when there is excess local power generation. In high incident sunlight situations, solar farm 700 may generate more power than, for example, AC-to-AC step-up transformer 760 is rated for. In such situations, solar farm 700 may have to take steps to protect its equipment from damage, which may include taking one or more panels 710 offline or shunting their voltage to dummy loads or ground. Advantageously, one or more flexible datacenters 200 may be used to consume power on the input side of AC-to-AC step-up transformer 760, thereby allowing solar farm 700 to operate equipment within operating ranges while flexible datacenter 200 receives behind-the-meter power without transmission or distribution costs. The local station control system (not independently illustrated) of local station 775 may issue an operational directive to the one or more flexible datacenters 200 or to the remote master control system (420 of
Another example of unutilized behind-the-meter power availability is when grid 790 cannot, for whatever reason, take the power being produced by solar farm 700. In such situations, solar farm 700 may have to take one or more panels 710 offline or shunt their voltage to dummy loads or ground. Advantageously, one or more flexible datacenters 200 may be used to consume power on the input side of AC-to-AC step-up transformer 760, thereby allowing solar farm 700 to either produce power to grid 790 at a lower level or shut down transformer 760 entirely while flexible datacenter 200 receives behind-the-meter power without transmission or distribution costs. The local station control system (not independently illustrated) of local station 775 or the grid operator (not independently illustrated) of grid 790 may issue an operational directive to the one or more flexible datacenters 200 or to the remote master control system (420 of
Another example of unutilized behind-the-meter power availability is when solar farm 700 is selling power to grid 790 at a negative price that is offset by a production tax credit. In certain circumstances, the value of the production tax credit may exceed the price solar farm 700 would have to pay to grid 790 to offload their generated power. Advantageously, one or more flexible datacenters 200 may be used to consume power behind-the-meter, thereby allowing solar farm 700 to produce and obtain the production tax credit, but sell less power to grid 790 at the negative price. The local station control system (not independently illustrated) of local station 775 may issue an operational directive to the one or more flexible datacenters 200 or to the remote master control system (420 of
Another example of unutilized behind-the-meter power availability is when solar farm 700 is selling power to grid 790 at a negative price because grid 790 is oversupplied or is instructed to stand down and stop producing altogether. The grid operator (not independently illustrated) may select certain power generation stations to go offline and stop producing power to grid 790. Advantageously, one or more flexible datacenters 200 may be used to consume power behind-the-meter, thereby allowing solar farm 700 to stop producing power to grid 790, but making productive use of the power generated behind-the-meter without transmission or distribution costs. The local station control system (not independently illustrated) of the local station 775 or the grid operator (not independently illustrated) of grid 790 may issue an operational directive to the one or more flexible datacenters 200 or to the remote master control system (420 of
Another example of unutilized behind-the-meter power availability is when solar farm 700 is producing power to grid 790 that is unstable, out of phase, or at the wrong frequency, or grid 790 is already unstable, out of phase, or at the wrong frequency for whatever reason. The grid operator (not independently illustrated) may select certain power generation stations to go offline and stop producing power to grid 790. Advantageously, one or more flexible datacenters 200 may be used to consume power behind-the-meter, thereby allowing solar farm 700 to stop producing power to grid 790, but make productive use of the power generated behind-the-meter without transmission or distribution costs. The local station control system (not independently illustrated) of local station 775 may issue an operational directive to the one or more flexible datacenters 200 or to the remote master control system (420 of
Further examples of unutilized behind-the-meter power availability is when solar farm 700 experiences intermittent cloud cover such that it is not economically feasible to power up certain components, such as, for example local station 775, but there may be sufficient behind-the-meter power availability to power one or more flexible datacenters 200.—Similarly, unutilized behind-the-meter power availability may occur when solar farm 700 is starting up, or testing, one or more panels 710. Panels 710 are frequently offline for installation, maintenance, and service and must be tested prior to coming online as part of the array. One or more flexible datacenters 200 may be powered by one or more panels 710 that are offline from farm 700. The above-noted examples of when unutilized behind-the-meter power is available are merely exemplary and are not intended to limit the scope of what one of ordinary skill in the art would recognize as unutilized behind-the-meter power availability. Behind-the-meter power availability may occur anytime there is power available and accessible behind-the-meter that is not subject to transmission and distribution costs and there is an economic advantage to using it.
One of ordinary skill in the art will recognize that solar farm 700 and solar panel 710 may vary based on an application or design in accordance with one or more embodiments of the present invention.
In step 920, the datacenter control system (220 of
While operational, the datacenter control system (220 of
As such,
In step 970, the datacenter control system (220 of
One of ordinary skill in the art will recognize that a datacenter control system (220 of
System 1000 represents an example system capable of determining and providing an indication of computational resource availability based on power-generation economic signals. Power-generation economic signals may include information that can assist with the management of computing resources. A control system (e.g., datacenter control system (220 of
A power-generation economic signal may represent information useful for managing computational resources. The information may depend on the source providing the power-generation economic signal. As such, a set of power-generation economic signals may indicate the purchase price for power from various sources (e.g., the power grid, B-T-M power sources), the availability of power from the power sources, and customers' price expectations for tasks performed by available computing resources (e.g., flexible datacenter 200). Within examples, different techniques may be used to determine the power-generation economic signals, including sensors or computing processing techniques.
In some embodiments, a power-generation economic signal may indicate power availability from a power source. Power availability may indicate a quantity of power available and a demand for the power from the power source. For example, a power-generation economic signal may indicate the power availability from the power grid and another power-generation economic signal may indicate power availability from a B-T-M power source (e.g., wind farm 600 of
A power-generation economic signal may also indicate a purchase price for power associated with supplying power to flexible datacenter 200 or another computing system. The purchase price for power may represent the price associated with using B-T-M power to power computing systems of flexible datacenter 200. A power-generation economic signal may similarly be based on a cost of power received from the power grid or based on a cost associated with selling power from an amount of behind-the-meter power to a power grid. A power-generation economic signal may also be based on a comparison of a price for power grid power relative to a price for behind-the-meter power. The information specifying and comparing different prices for power from the different sources can be used when managing flexible datacenter 200, including determining when to switch power sources and when to ramp up or ramp down computing systems within flexible datacenter 200. Additionally, a control system may use the cost information to determine and provide an indication of computational resource availability to one or more computing devices, such as another control system or to indicate cost to a customers using flexible datacenter 200.
In some examples, cost for power from a power source may depend on multiple factors. For instance, power-generation economics may provide information that enables a control system to determine when utilizing flexible datacenter 200 to consume power from one or more B-T-M power sources to avoid paying fees to offload the power on the grid. In addition, although described separately, a power-generation economic signal may represent both the availability and cost for power from a power source. For example, a power-generation economic signal may convey the cost and availability for power supplied by a B-T-M power source, such as wind farm 600 of
In some examples, a power-generation economic signal may represent a price that a customer is willing to pay to use available computational resources. Particularly, the control system may use prices set forth by a customer when managing available computational resources since the customers' preferences could impact management of computational resources in different ways. For example, the control system may adjust the number of computing systems (or number of flexible datacenters) used, the rate of power use by the computing systems, and the power sources used based on customer preferences. The control system may devote more resources at a higher cost to a customer willing to cover the costs for a high priority computing task.
Within examples, power-generation economic signals may convey other information that can be used to manage computational resources. The above examples of information represented within a power-generation economic signal are included for illustration purposes, but should not be construed as limiting. Additionally, the information represented in each power-generation economic signal may depend on the source providing the power-generation economic signal. In particular, a power-generation economic signal can include a variety of information that a control system may use to adjust power use of one or more flexible datacenters 200 or to convey computational resources availability.
Within system 1000, the datacenter control system (220 of
In some examples, the control system may receive a set of power-generation economic signals from multiple sources enabling a comparison of the information within the different signals to determine a strategy for managing available computational resources. Determining the strategy may involve using a weighted combination of multiple factors included within the computational resources. For example, the control system may perform an analysis to determine a cost-efficient way to use power from the available power sources to power flexible datacenter 200 at a price that satisfies customer expectations.
In some examples, the control system may analyze power-generation economic signals to identify changes in power-generation economic signals that exceed predefined threshold changes. A predefined threshold change may depend on the information represented in the particular power-generation economic signal. For example, a change in a power-generation economic signal may indicate a threshold change in purchase price or cost for power from a particular power source. This may trigger the control system to modify power use from the power source and provide an updated indication of available computational resources based on the change in the price for power.
A control system may also use one or more power-generation economic signals to identify when the availability of power from a power source exceeds a predefined threshold change. For example, a B-T-M power source may experience a threshold decrease or increase in power production due to environmental conditions (e.g., cloud cover, lack of wind) that can influence the use of power from that B-T-M power source.
A control system within system 1000 (e.g., the datacenter control system 220, the remote master control system 420) may also identify when a power-generation economic signal is below a predefined lower threshold or is above a predefined upper threshold. Predefined lower and upper thresholds may serve to alert the control system of substantial changes in the price for power from a source or availability of power. For example, the control system may identify when the cost of power from a power source falls below a predefined lower price threshold. The control system may direct flexible datacenter 200 to use power from the power source providing power for the lowest price. Similarly, the control system may also use power-generation economic signals to identify when power from a source exceeds a predefined upper price threshold. The control system may switch flexible datacenter 200 from using power from a particular power source when the price for power exceeds the upper price threshold.
When analyzing costs for power, the control system may factor the cost associated with offloading power from a B-T-M power source to the grid. In some situations, offloading power to the grid may cost money, which might be indicated within one or more power-generation economic signals. For instance, grid operator 440 or local station control system 410 may provide an indication to a control system that indicates an increase in the cost associated with offloading power from the B-T-M power source to the grid. As a result, the control system may adjust power use by one or more flexible datacenters 200 to use at least some of the power from the B-T-M power source.
At block 1102, method 1100 involves receiving information indicative of a plurality of power-generation economic signals. For example, a first control system may receive the information indicative of the power-generation economic signals. The first control system may be a datacenter associated with a flexible datacenter. For instance, the first control system may include a datacenter control system collocated with the flexible datacenter. As such, the first control system may be configured to receive power from a power grid.
In some embodiments, the first control system may be a remote master control system located remote from the flexible datacenter. As such, the remote master control system may communicate with a datacenter control system associated with the flexible datacenter.
In some embodiments, at least one or more power-generation economic signals of the plurality of power-generation economic signals are based on a cost of power received from a power grid.
In some embodiments, at least one or more power-generation economic signals of the plurality of power-generation economic signals are based on a purchase price for power associated with the flexible datacenter.
In some embodiments, at least one or more power-generation economic signals of the plurality of power-generation economic signals are based on a cost associated with selling power from an amount of behind-the-meter power to a power grid.
In some embodiments, at least one or more power-generation economic signals of the plurality of power-generation economic signals are based on a comparison of a first price for power grid power relative to a second price for behind-the-meter power.
At block 1104, method 1100 involves, based on the received information, identifying, by the first control system, at least one of: (i) a change indicative of a power-generation economic signal that exceeds a predefined threshold change, (ii) a power-generation economic signal that is below a predefined lower threshold limit, or (iii) a power-generation economic signal that is above a predefined upper threshold limit.
At block 1106, method 1100 involves, based on the identification, performing at least one of: (i) adjusting a rate of power use by a flexible datacenter and (ii) providing an indication of computation resource availability to a second control system. The flexible datacenter may include a behind-the-meter power input system, a power distribution system, and a plurality of computing systems configured to receive power from the behind-the-meter power input system via the power distribution system. The plurality of computing systems may also be configured to receive power from a power grid via the power distribution system. As a result, the plurality of computing systems may be capable of switching between receiving behind-the-meter power and grid power.
The second control system may include a datacenter control system collocated with the flexible datacenter. As such, the second control system may be configured to receive power from a power grid. For instance, power from the power grid may be used to keep the control powered up when the flexible datacenter ramps down. In other embodiments, the second control system may be a remote master control system located remote from the flexible datacenter. In some embodiments, the first control system and the second control system are part of the same control system.
Advantages of one or more embodiments of the present invention may include one or more of the following:
In one or more embodiments of the present invention, a method and system for dynamic power delivery to a flexible datacenter using unutilized energy sources based on power-generation economic signals may adjust a rate of power used by the flexible datacenter. An indication of computational resource availability may also be provided based on power-generation economic signals.
In one or more embodiments of the present invention, a method and system for dynamic power delivery to a flexible datacenter using unutilized energy sources based on power-generation economic signals may decrease costs associated with powering the flexible datacenter. The power-generation economic signals may enable switching between power sources offering power for the lowest price.
In one or more embodiments of the present invention, a method and system for dynamic power delivery to a flexible datacenter using unutilized energy sources allows for the rapid deployment of mobile datacenters to local stations. The mobile datacenters may be deployed on site, near the source of power generation, and receive unutilized behind-the-meter power when it is available.
In one or more embodiments of the present invention, a method and system for dynamic power delivery to a flexible datacenter using unutilized energy sources allows for the power delivery to the datacenter to be modulated based on conditions or an operational directive received from the local station or the grid operator.
In one or more embodiments of the present invention, a method and system for dynamic power delivery to a flexible datacenter using energy sources may dynamically adjust power consumption by ramping-up, ramping-down, or adjusting the power consumption of one or more computing systems within the flexible datacenter.
In one or more embodiments of the present invention, a method and system for dynamic power delivery to a flexible datacenter using unutilized energy sources may be powered by unutilized behind-the-meter power that is free from transmission and distribution costs. As such, the flexible datacenter may perform computational operations, such as hashing function operations, with little to no energy cost.
In one or more embodiments of the present invention, a method and system for dynamic power delivery to a flexible datacenter using unutilized energy sources provides a number of benefits to the hosting local station. The local station may use the flexible datacenter to adjust a load, provide a power factor correction, to offload power, or operate in a manner that invokes a production tax credit.
In some scenarios, a local station control system might be able to act more quickly than a remote master control system in directing a flexible datacenter to modulate its power consumption. In these and other scenarios, actions by the local station control system would not require communications (e.g., directives, or power availability information) to be routed through the remote master control system, and thus, such communications would not be blocked or delayed by the remote master control system.
Conversely, the remote master control system can act on information that is not available to the local station control system, such as performance data or other data related to the flexible datacenter and the computing systems thereof, as discussed above. For at least this reason, it could be advantageous in some scenarios to have the remote master control system direct the flexible datacenter in addition to or instead of the local station control system. (One of the reasons for why the local station control system might not have access to this type of information is that the flexible datacenter and the remote master control system are operated by or otherwise associated with the same entity, whereas the local station control system is operated by a different entity.) Thus, in one or more embodiments of the present invention, a method and system for distributed power control allows for a datacenter control system of a flexible datacenter to be in communication with a remote master control system, which in turn allows the remote master control system to issue directives to the flexible datacenter based on various conditions associated with a behind-the-meter power source. Thus, the method and system for distributed power control allows for power consumption by the flexible datacenter to be modulated based on ramp-down and/or ramp-up directives received from the remote master control system.
One or more embodiments of the present invention provides a green solution to two prominent problems: the exponential increase in power required for growing blockchain operations and the unutilized and typically wasted energy generated from renewable energy sources.
One or more embodiments of the present invention allows for the rapid deployment of mobile datacenters to local stations. The mobile datacenters may be deployed on site, near the source of power generation, and receive unutilized behind-the-meter power when it is available.
One or more embodiments of the present invention allows for the power delivery to the datacenter to be modulated based on conditions or an operational directive received from the local station or the grid operator.
One or more embodiments of the present invention may dynamically adjust power consumption by ramping-up, ramping-down, or adjusting the power consumption of one or more computing systems within the flexible datacenter.
One or more embodiments of the present invention may be powered by behind-the-meter power that is free from transmission and distribution costs. As such, the flexible datacenter may perform computational operations, such as hashing function operations, with little to no energy cost.
One or more embodiments of the present invention provides a number of benefits to the hosting local station. The local station may use the flexible datacenter to adjust a load, provide a power factor correction, to offload power, or operate in a manner that invokes a production tax credit and/or generates incremental revenue.
One or more embodiments of the present invention allows for continued shunting of behind-the-meter power into a storage solution when a flexible datacenter cannot fully utilize excess generated behind-the-meter power.
One or more embodiments of the present invention allows for continued use of stored behind-the-meter power when a flexible datacenter can be operational but there is not an excess of generated behind-the-meter power.
It will also be recognized by the skilled worker that, in addition to improved efficiencies in controlling power delivery from intermittent generation sources, such as wind farms and solar panel arrays, to regulated power grids, the invention provides more economically efficient control and stability of such power grids in the implementation of the technical features as set forth herein.
While the present invention has been described with respect to the above-noted embodiments, those skilled in the art, having the benefit of this disclosure, will recognize that other embodiments may be devised that are within the scope of the invention as disclosed herein. Accordingly, the scope of the invention should be limited only by the appended claims.
This application is a Continuation of copending U.S. patent application Ser. No. 16/132,098, filed Sep. 14, 2018. The disclosure set forth in the referenced application is incorporated herein by reference in its entirety.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4106097 | Fox et al. | Aug 1978 | A |
| 4245319 | Hedges | Jan 1981 | A |
| 4247786 | Hedges | Jan 1981 | A |
| 4371779 | Maynard et al. | Feb 1983 | A |
| 4551812 | Gurr et al. | Nov 1985 | A |
| 5142672 | Johnson et al. | Aug 1992 | A |
| 5367669 | Holland et al. | Nov 1994 | A |
| 5544046 | Niwa | Aug 1996 | A |
| 5761083 | Brown, Jr. et al. | Jun 1998 | A |
| 5913046 | Barth et al. | Jun 1999 | A |
| 6115698 | Tuck et al. | Sep 2000 | A |
| 6244516 | Langervik et al. | Jun 2001 | B1 |
| 6288456 | Cratty | Sep 2001 | B1 |
| 6437692 | Petite et al. | Aug 2002 | B1 |
| 6473744 | Tuck et al. | Oct 2002 | B1 |
| 6519509 | Nierlich et al. | Feb 2003 | B1 |
| 6618709 | Sneeringer | Sep 2003 | B1 |
| 6633823 | Bartone et al. | Oct 2003 | B2 |
| 6643567 | Kolk et al. | Nov 2003 | B2 |
| 6681154 | Nierlich et al. | Jan 2004 | B2 |
| 6745105 | Fairlie et al. | Jun 2004 | B1 |
| 6748932 | Sorter et al. | Jun 2004 | B1 |
| 6772031 | Strand | Aug 2004 | B1 |
| 6775595 | Yabutani et al. | Aug 2004 | B1 |
| 6785592 | Smith | Aug 2004 | B1 |
| 6868293 | Schurr et al. | Mar 2005 | B1 |
| 6912450 | Fairlie et al. | Jun 2005 | B2 |
| 7010363 | Donnelly et al. | Mar 2006 | B2 |
| 7027992 | Zaccaria et al. | Apr 2006 | B2 |
| 7035179 | Chen et al. | Apr 2006 | B2 |
| 7053767 | Petite et al. | May 2006 | B2 |
| 7054271 | Brownrigg et al. | May 2006 | B2 |
| 7062360 | Fairlie et al. | Jun 2006 | B2 |
| 7076339 | Yabutani et al. | Jul 2006 | B2 |
| 7088014 | Nierlich et al. | Aug 2006 | B2 |
| 7127328 | Ransom | Oct 2006 | B2 |
| 7130832 | Bannai et al. | Oct 2006 | B2 |
| 7135956 | Bartone et al. | Nov 2006 | B2 |
| 7143300 | Potter et al. | Nov 2006 | B2 |
| 7149605 | Chassin et al. | Dec 2006 | B2 |
| 7181316 | Fairlie et al. | Feb 2007 | B2 |
| 7188003 | Ransom et al. | Mar 2007 | B2 |
| 7206670 | Pimputkar et al. | Apr 2007 | B2 |
| 7278273 | Whitted et al. | Oct 2007 | B1 |
| 7305282 | Chen | Dec 2007 | B2 |
| 7333880 | Brewster et al. | Feb 2008 | B2 |
| 7369968 | Johnson et al. | May 2008 | B2 |
| 7376851 | Kim | May 2008 | B2 |
| 7420293 | Donnelly et al. | Sep 2008 | B2 |
| 7460930 | Howell et al. | Dec 2008 | B1 |
| 7468661 | Petite et al. | Dec 2008 | B2 |
| 7519453 | Fairlie et al. | Apr 2009 | B2 |
| 7561977 | Horst et al. | Jul 2009 | B2 |
| 7565224 | Fairlie et al. | Jul 2009 | B2 |
| 7647516 | Ranganathan et al. | Jan 2010 | B2 |
| 7702931 | Goodrum et al. | Apr 2010 | B2 |
| 7779276 | Bolan et al. | Aug 2010 | B2 |
| 7783907 | Dubinsky | Aug 2010 | B2 |
| 7861102 | Ranganathan et al. | Dec 2010 | B1 |
| 7921315 | Langgood et al. | Apr 2011 | B2 |
| 7970561 | Pfeiffer | Jun 2011 | B2 |
| 8001403 | Hamilton et al. | Aug 2011 | B2 |
| 8006108 | Brey et al. | Aug 2011 | B2 |
| 8214843 | Boss et al. | Jul 2012 | B2 |
| 8260913 | Knapp et al. | Sep 2012 | B2 |
| 8327123 | Juffa et al. | Dec 2012 | B2 |
| 8374928 | Gopisetty et al. | Feb 2013 | B2 |
| 8447993 | Greene et al. | May 2013 | B2 |
| 8571820 | Pfeiffer | Oct 2013 | B2 |
| 8595094 | Forbes, Jr. | Nov 2013 | B1 |
| 8595515 | Weber et al. | Nov 2013 | B1 |
| 8601287 | Weber et al. | Dec 2013 | B1 |
| 8627123 | Jain et al. | Jan 2014 | B2 |
| 8639392 | Chassin | Jan 2014 | B2 |
| 8700929 | Weber et al. | Apr 2014 | B1 |
| 8706914 | Duchesneau | Apr 2014 | B2 |
| 8706915 | Duchesneau | Apr 2014 | B2 |
| 8719223 | Knapp et al. | May 2014 | B2 |
| 8789061 | Pavel et al. | Jul 2014 | B2 |
| 8799690 | Dawson et al. | Aug 2014 | B2 |
| 8839551 | Swann | Sep 2014 | B2 |
| 9003211 | Pfeiffer | Apr 2015 | B2 |
| 9003216 | Sankar et al. | Apr 2015 | B2 |
| 9026814 | Aasheim et al. | May 2015 | B2 |
| 9027024 | Mick et al. | May 2015 | B2 |
| 9143392 | Duchesneau | Sep 2015 | B2 |
| 9207993 | Jain | Dec 2015 | B2 |
| 9218035 | Li et al. | Dec 2015 | B2 |
| 9252598 | Belady et al. | Feb 2016 | B2 |
| 9282022 | Matthews et al. | Mar 2016 | B2 |
| 9416904 | Belady et al. | Aug 2016 | B2 |
| 9477279 | Piszczek et al. | Oct 2016 | B1 |
| 9542231 | Khan et al. | Jan 2017 | B2 |
| 9552234 | Boldyrev et al. | Jan 2017 | B2 |
| 9557792 | Potlapally et al. | Jan 2017 | B1 |
| 9618991 | Clidaras et al. | Apr 2017 | B1 |
| 9645596 | Lee et al. | May 2017 | B1 |
| 9800052 | Li et al. | Oct 2017 | B2 |
| 9890905 | Plummer et al. | Feb 2018 | B2 |
| 9915927 | Kobayashi | Mar 2018 | B2 |
| 9946815 | Weber et al. | Apr 2018 | B1 |
| 9994118 | Williams et al. | Jun 2018 | B2 |
| 10033210 | Peterson et al. | Jul 2018 | B2 |
| 10250039 | Wenzel et al. | Apr 2019 | B2 |
| 10334758 | Ramirez et al. | Jun 2019 | B1 |
| 10340696 | Paine et al. | Jul 2019 | B2 |
| 10360077 | Mahindru et al. | Jul 2019 | B2 |
| 10367335 | Kawashima et al. | Jul 2019 | B2 |
| 10367353 | Mcnamara et al. | Jul 2019 | B1 |
| 10367535 | Corse et al. | Jul 2019 | B2 |
| 10444818 | Mcnamara et al. | Oct 2019 | B1 |
| 10452127 | Mcnamara et al. | Oct 2019 | B1 |
| 10452532 | Mcvay et al. | Oct 2019 | B2 |
| 10497072 | Hooshmand et al. | Dec 2019 | B2 |
| 10545560 | Mahindru et al. | Jan 2020 | B2 |
| 10608433 | Mcnamara et al. | Mar 2020 | B1 |
| 10618427 | Mcnamara et al. | Apr 2020 | B1 |
| 10637353 | Ohyama et al. | Apr 2020 | B2 |
| 10709076 | Pham | Jul 2020 | B2 |
| 10795428 | Walsh | Oct 2020 | B2 |
| 10819599 | Mahindru et al. | Oct 2020 | B2 |
| 10822992 | Spears | Nov 2020 | B2 |
| 10838482 | Mahindru et al. | Nov 2020 | B2 |
| 10857899 | Mcnamara et al. | Dec 2020 | B1 |
| 10862307 | Cavness et al. | Dec 2020 | B2 |
| 10862309 | Cavness et al. | Dec 2020 | B2 |
| 10873211 | Mcnamara et al. | Dec 2020 | B2 |
| 10931117 | Shoemaker | Feb 2021 | B2 |
| 11009909 | Kuwabara et al. | May 2021 | B2 |
| 11016456 | Henson et al. | May 2021 | B2 |
| 11016458 | Mcnamara et al. | May 2021 | B2 |
| 11016553 | Mcnamara et al. | May 2021 | B2 |
| 11025060 | Mcnamara et al. | Jun 2021 | B2 |
| 11031787 | Mcnamara et al. | Jun 2021 | B2 |
| 11031813 | Mcnamara et al. | Jun 2021 | B2 |
| 11042948 | Mcnamara et al. | Jun 2021 | B1 |
| 11128165 | Mcnamara et al. | Sep 2021 | B2 |
| 11163280 | Henson et al. | Nov 2021 | B2 |
| 11169592 | Mahindru et al. | Nov 2021 | B2 |
| 11194150 | Baba | Dec 2021 | B2 |
| 11256320 | Mcnamara et al. | Feb 2022 | B2 |
| 11275427 | Mcnamara et al. | Mar 2022 | B2 |
| 11397999 | Mcnamara et al. | Jul 2022 | B2 |
| 11418037 | Cavness et al. | Aug 2022 | B2 |
| 11431195 | Mcnamara et al. | Aug 2022 | B2 |
| 11451059 | Cavness et al. | Sep 2022 | B2 |
| 11537183 | Lewis et al. | Dec 2022 | B2 |
| 20020072868 | Bartone et al. | Jun 2002 | A1 |
| 20020158749 | Ikeda et al. | Oct 2002 | A1 |
| 20020196124 | Howard et al. | Dec 2002 | A1 |
| 20030023885 | Potter et al. | Jan 2003 | A1 |
| 20030037150 | Nakagawa | Feb 2003 | A1 |
| 20030074464 | Bohrer et al. | Apr 2003 | A1 |
| 20030171851 | Brickfield et al. | Sep 2003 | A1 |
| 20040027004 | Bayoumi et al. | Feb 2004 | A1 |
| 20040117330 | Ehlers et al. | Jun 2004 | A1 |
| 20050005528 | Brault et al. | Jan 2005 | A1 |
| 20050034128 | Nagashima et al. | Feb 2005 | A1 |
| 20050102068 | Pimputkar et al. | May 2005 | A1 |
| 20050102539 | Hepner et al. | May 2005 | A1 |
| 20050154499 | Aldridge et al. | Jul 2005 | A1 |
| 20050165512 | Peljto | Jul 2005 | A1 |
| 20050203761 | Barr et al. | Sep 2005 | A1 |
| 20060031180 | Tamarkin et al. | Feb 2006 | A1 |
| 20060059772 | Brault et al. | Mar 2006 | A1 |
| 20060161765 | Cromer et al. | Jul 2006 | A1 |
| 20060253675 | Johannes Bloks | Nov 2006 | A1 |
| 20070067657 | Ranganathan et al. | Mar 2007 | A1 |
| 20070228837 | Nielsen et al. | Oct 2007 | A1 |
| 20080000151 | Houweling et al. | Jan 2008 | A1 |
| 20080013596 | Dunne et al. | Jan 2008 | A1 |
| 20080030078 | Whitted et al. | Feb 2008 | A1 |
| 20080082844 | Ghiasi et al. | Apr 2008 | A1 |
| 20080094797 | Coglitore et al. | Apr 2008 | A1 |
| 20080238195 | Shaver et al. | Oct 2008 | A1 |
| 20090012523 | Ruuttu et al. | Jan 2009 | A1 |
| 20090055665 | Maglione et al. | Feb 2009 | A1 |
| 20090070611 | Bower, III et al. | Mar 2009 | A1 |
| 20090078401 | Cichanowicz | Mar 2009 | A1 |
| 20090089595 | Brey et al. | Apr 2009 | A1 |
| 20090144566 | Bletsch et al. | Jun 2009 | A1 |
| 20090216910 | Duchesneau | Aug 2009 | A1 |
| 20090235097 | Hamilton et al. | Sep 2009 | A1 |
| 20090254660 | Hanson et al. | Oct 2009 | A1 |
| 20100058350 | Boss et al. | Mar 2010 | A1 |
| 20100088261 | Montalvo | Apr 2010 | A1 |
| 20100211810 | Zacho | Aug 2010 | A1 |
| 20100235004 | Thind | Sep 2010 | A1 |
| 20100280675 | Tate, Jr. et al. | Nov 2010 | A1 |
| 20100313203 | Dawson et al. | Dec 2010 | A1 |
| 20100328849 | Ewing et al. | Dec 2010 | A1 |
| 20100333113 | Johnson et al. | Dec 2010 | A1 |
| 20110072289 | Kato | Mar 2011 | A1 |
| 20110115223 | Stahlkopf et al. | May 2011 | A1 |
| 20110238342 | Pfeiffer | Sep 2011 | A1 |
| 20110239015 | Boyd et al. | Sep 2011 | A1 |
| 20110282527 | Inbarajan et al. | Nov 2011 | A1 |
| 20110282982 | Jain | Nov 2011 | A1 |
| 20110316337 | Pelio et al. | Dec 2011 | A1 |
| 20120000121 | Swann | Jan 2012 | A1 |
| 20120032665 | Shaver, II et al. | Feb 2012 | A1 |
| 20120072745 | Ahluwalia et al. | Mar 2012 | A1 |
| 20120078430 | Fan et al. | Mar 2012 | A1 |
| 20120109705 | Belady et al. | May 2012 | A1 |
| 20120150679 | Lazaris | Jun 2012 | A1 |
| 20120290865 | Kansal et al. | Nov 2012 | A1 |
| 20120300524 | Fornage et al. | Nov 2012 | A1 |
| 20120306271 | Kuriyama | Dec 2012 | A1 |
| 20120321309 | Barry et al. | Dec 2012 | A1 |
| 20120324245 | Sinha et al. | Dec 2012 | A1 |
| 20120324259 | Aasheim et al. | Dec 2012 | A1 |
| 20120326511 | Johnson | Dec 2012 | A1 |
| 20130006401 | Shan | Jan 2013 | A1 |
| 20130007515 | Shaw et al. | Jan 2013 | A1 |
| 20130054987 | Pfeiffer et al. | Feb 2013 | A1 |
| 20130063991 | Xiao et al. | Mar 2013 | A1 |
| 20130086404 | Sankar et al. | Apr 2013 | A1 |
| 20130111494 | Hyser et al. | May 2013 | A1 |
| 20130117621 | Saraiya et al. | May 2013 | A1 |
| 20130187464 | Smith et al. | Jul 2013 | A1 |
| 20130213038 | Lazaris | Aug 2013 | A1 |
| 20130218356 | Lee et al. | Aug 2013 | A1 |
| 20130227139 | Suffling | Aug 2013 | A1 |
| 20130304903 | Mick et al. | Nov 2013 | A1 |
| 20130328395 | Krizman et al. | Dec 2013 | A1 |
| 20140020292 | Mcnamara et al. | Jan 2014 | A1 |
| 20140070756 | Kearns et al. | Mar 2014 | A1 |
| 20140075222 | Jackson | Mar 2014 | A1 |
| 20140114829 | Forbes, Jr. | Apr 2014 | A1 |
| 20140137468 | Ching | May 2014 | A1 |
| 20140149761 | Allen-Ware et al. | May 2014 | A1 |
| 20140150336 | Houweling | Jun 2014 | A1 |
| 20140180886 | Forbes, Jr. | Jun 2014 | A1 |
| 20140222225 | Rouse et al. | Aug 2014 | A1 |
| 20140365402 | Belady et al. | Dec 2014 | A1 |
| 20140365795 | Nielsen et al. | Dec 2014 | A1 |
| 20140379156 | Kamel et al. | Dec 2014 | A1 |
| 20150006940 | Kim et al. | Jan 2015 | A1 |
| 20150012113 | Celebi | Jan 2015 | A1 |
| 20150058845 | Song et al. | Feb 2015 | A1 |
| 20150106811 | Holler et al. | Apr 2015 | A1 |
| 20150121113 | Ramamurthy et al. | Apr 2015 | A1 |
| 20150155712 | Mondal | Jun 2015 | A1 |
| 20150212122 | Sobotka et al. | Jul 2015 | A1 |
| 20150229227 | Aeloiza et al. | Aug 2015 | A1 |
| 20150277410 | Gupta et al. | Oct 2015 | A1 |
| 20150278968 | Steven et al. | Oct 2015 | A1 |
| 20150278969 | Benoy et al. | Oct 2015 | A1 |
| 20150280492 | Narita | Oct 2015 | A1 |
| 20150288183 | Villanueva, Jr. et al. | Oct 2015 | A1 |
| 20150363132 | Uehara | Dec 2015 | A1 |
| 20150371328 | Gabel et al. | Dec 2015 | A1 |
| 20150372538 | Siegler et al. | Dec 2015 | A1 |
| 20160006066 | Robertson | Jan 2016 | A1 |
| 20160011617 | Liu et al. | Jan 2016 | A1 |
| 20160013652 | Li et al. | Jan 2016 | A1 |
| 20160043552 | Villanueva, Jr. et al. | Feb 2016 | A1 |
| 20160054774 | Song et al. | Feb 2016 | A1 |
| 20160087909 | Chatterjee et al. | Mar 2016 | A1 |
| 20160091948 | Mitchell et al. | Mar 2016 | A1 |
| 20160109916 | Li et al. | Apr 2016 | A1 |
| 20160126783 | Cheng et al. | May 2016 | A1 |
| 20160170469 | Sehgal et al. | Jun 2016 | A1 |
| 20160172900 | Welch, Jr. | Jun 2016 | A1 |
| 20160187906 | Bodas et al. | Jun 2016 | A1 |
| 20160198656 | Mcnamara et al. | Jul 2016 | A1 |
| 20160202744 | Castro-Leon | Jul 2016 | A1 |
| 20160212954 | Argento | Jul 2016 | A1 |
| 20160248631 | Duchesneau | Aug 2016 | A1 |
| 20160261226 | Hamilton et al. | Sep 2016 | A1 |
| 20160324077 | Frantzen et al. | Nov 2016 | A1 |
| 20160329708 | Day | Nov 2016 | A1 |
| 20160377306 | Drees et al. | Dec 2016 | A1 |
| 20170023969 | Shows et al. | Jan 2017 | A1 |
| 20170104332 | Wenzel et al. | Apr 2017 | A1 |
| 20170104336 | Elbsat et al. | Apr 2017 | A1 |
| 20170104337 | Drees | Apr 2017 | A1 |
| 20170104342 | Eibsat et al. | Apr 2017 | A1 |
| 20170104343 | Eibsat et al. | Apr 2017 | A1 |
| 20170185132 | Bodas et al. | Jun 2017 | A1 |
| 20170192483 | Boss et al. | Jul 2017 | A1 |
| 20170194791 | Budde | Jul 2017 | A1 |
| 20170201098 | Carpenter | Jul 2017 | A1 |
| 20170214070 | Wang et al. | Jul 2017 | A1 |
| 20170237261 | Maug et al. | Aug 2017 | A1 |
| 20170261949 | Hoffmann et al. | Sep 2017 | A1 |
| 20170300359 | Kollur et al. | Oct 2017 | A1 |
| 20170366412 | Piga | Dec 2017 | A1 |
| 20170373500 | Shafi et al. | Dec 2017 | A1 |
| 20180026478 | Peloso | Jan 2018 | A1 |
| 20180052431 | Shaikh et al. | Feb 2018 | A1 |
| 20180101220 | Mahindru et al. | Apr 2018 | A1 |
| 20180105051 | Zheng et al. | Apr 2018 | A1 |
| 20180116070 | Broadbent et al. | Apr 2018 | A1 |
| 20180144414 | Lee et al. | May 2018 | A1 |
| 20180166881 | Suryanarayana et al. | Jun 2018 | A1 |
| 20180175666 | Ayer et al. | Jun 2018 | A1 |
| 20180202825 | You et al. | Jul 2018 | A1 |
| 20180240112 | Castinado et al. | Aug 2018 | A1 |
| 20180267839 | Maisuria et al. | Sep 2018 | A1 |
| 20180294649 | Bright et al. | Oct 2018 | A1 |
| 20180356770 | Eibsat et al. | Dec 2018 | A1 |
| 20180366978 | Matan et al. | Dec 2018 | A1 |
| 20180367320 | Montalvo | Dec 2018 | A1 |
| 20190052094 | Pmsvvsv et al. | Feb 2019 | A1 |
| 20190082618 | Lopez | Mar 2019 | A1 |
| 20190168630 | Mrlik et al. | Jun 2019 | A1 |
| 20190173283 | Epel et al. | Jun 2019 | A1 |
| 20190258307 | Shaikh et al. | Aug 2019 | A1 |
| 20190261589 | Pham | Aug 2019 | A1 |
| 20190280521 | Lundstrom et al. | Sep 2019 | A1 |
| 20190318327 | Sowell et al. | Oct 2019 | A1 |
| 20190324820 | Krishnan et al. | Oct 2019 | A1 |
| 20190339756 | Lewis et al. | Nov 2019 | A1 |
| 20200019230 | Rong et al. | Jan 2020 | A1 |
| 20200040272 | Cavness et al. | Feb 2020 | A1 |
| 20200051184 | Barbour | Feb 2020 | A1 |
| 20200073466 | Walsh | Mar 2020 | A1 |
| 20200089307 | Mcnamara et al. | Mar 2020 | A1 |
| 20200136387 | Mcnamara et al. | Apr 2020 | A1 |
| 20200136388 | Mcnamara et al. | Apr 2020 | A1 |
| 20200167197 | Bahramshahry et al. | May 2020 | A1 |
| 20200177100 | Wang et al. | Jun 2020 | A1 |
| 20200318843 | Wenzel et al. | Oct 2020 | A1 |
| 20200321776 | Shaver et al. | Oct 2020 | A1 |
| 20200379537 | Henson et al. | Dec 2020 | A1 |
| 20210021135 | Eibsat et al. | Jan 2021 | A1 |
| 20210035242 | Mcnamara et al. | Feb 2021 | A1 |
| 20210036547 | Mcnamara et al. | Feb 2021 | A1 |
| 20210101499 | Mcnamara et al. | Apr 2021 | A1 |
| 20210124322 | Mcnamara et al. | Apr 2021 | A1 |
| 20210126456 | Mcnamara et al. | Apr 2021 | A1 |
| 20210175710 | Campbell et al. | Jun 2021 | A1 |
| 20210287309 | Gebhardt et al. | Sep 2021 | A1 |
| 20210294405 | Mcnamara et al. | Sep 2021 | A1 |
| 20210296893 | Mcnamara et al. | Sep 2021 | A1 |
| 20210296928 | Mcnamara et al. | Sep 2021 | A1 |
| 20210298195 | Barbour | Sep 2021 | A1 |
| 20210312574 | Mcnamara et al. | Oct 2021 | A1 |
| 20220033517 | Hendry et al. | Feb 2022 | A1 |
| 20220039333 | Avila | Feb 2022 | A1 |
| 20220197363 | Mcnamara et al. | Jun 2022 | A1 |
| 20220294219 | Mcnamara et al. | Sep 2022 | A1 |
| 20220366517 | Mcnamara et al. | Nov 2022 | A1 |
| 20220407350 | Mcnamara et al. | Dec 2022 | A1 |
| 20230208138 | Mcnamara et al. | Jun 2023 | A1 |
| 20230275432 | Mcnamara et al. | Aug 2023 | A1 |
| Number | Date | Country |
|---|---|---|
| 1806374 | Jul 2006 | CN |
| 101799681 | Aug 2010 | CN |
| 101803148 | Aug 2010 | CN |
| 101895150 | Nov 2010 | CN |
| 102185382 | Sep 2011 | CN |
| 102591921 | Jul 2012 | CN |
| 103163904 | Jun 2013 | CN |
| 103440028 | Dec 2013 | CN |
| 103748757 | Apr 2014 | CN |
| 104144183 | Nov 2014 | CN |
| 104508932 | Apr 2015 | CN |
| 204243874 | Apr 2015 | CN |
| 104715340 | Jun 2015 | CN |
| 104731294 | Jun 2015 | CN |
| 104969434 | Oct 2015 | CN |
| 105406580 | Mar 2016 | CN |
| 106226718 | Dec 2016 | CN |
| 106464523 | Feb 2017 | CN |
| 106716299 | May 2017 | CN |
| 107967536 | Apr 2018 | CN |
| 3850462 | Jul 2021 | EP |
| 3850465 | Jun 2022 | EP |
| 2011123873 | Jun 2011 | JP |
| 2013524317 | Jun 2013 | JP |
| 2017530449 | Oct 2017 | JP |
| 20090012523 | Feb 2009 | KR |
| WO-2008039773 | Apr 2008 | WO |
| WO-2010050249 | May 2010 | WO |
| WO-2014005156 | Jan 2014 | WO |
| WO-2015039122 | Mar 2015 | WO |
| WO-2015199629 | Dec 2015 | WO |
| WO-2017163126 | Sep 2017 | WO |
| WO-2018068042 | Apr 2018 | WO |
| WO2019060180 | Mar 2019 | WO |
| WO-2019116375 | Jun 2019 | WO |
| WO-2019139632 | Jul 2019 | WO |
| WO-2019139633 | Jul 2019 | WO |
| WO-2020056308 | Mar 2020 | WO |
| WO-2020056319 | Mar 2020 | WO |
| WO-2020056322 | Mar 2020 | WO |
| WO-2020227811 | Nov 2020 | WO |
| WO-2022031836 | Feb 2022 | WO |
| Entry |
|---|
| Sharma et al., “Microgrids: A New Approach To Supply-Side Design For Data Centers”, 2009 ASME (Year: 2009). |
| Huang, Qiang, et al. “Power consumption of virtual machine live migration in clouds.” 2011 third international conference on communications and mobile computing. IEEE, 2011. (Year: 2011). |
| Wang, Rui, et al. “Datacenters as controllable load resources in the electricity market.” 2013 IEEE 33rd International Conference on Distributed Computing Systems. IEEE, 2013. (Year: 2013). |
| Zeeman, Julia. “Emerging Business Models for Local Distribution Companies in Ontario.” (2016) (Year: 2016). |
| Advisory Action mailed on Nov. 13, 2020 for U.S. Appl. No. 16/529,360, filed Aug. 1, 2019, 182 pages. |
| Advisory Action mailed on Oct. 22, 2020 for U.S. Appl. No. 16/528,348, filed Jul. 31, 2019, 3 pages. |
| Bakar et al., “Microgrid and Load Shedding Scheme During Islanded Mode: a Review,” Elsevier, May 26, 2020, vol. 71, pp. 161-169. https://www.sciencedirect.com/science/article/pii/S1364032116311030. |
| Bird et al., “Wind and Solar Energy Curtailment: Experience and Practices in the United States,” National Renewable Energy Lab (NREL), Technical Report NREL/TP-6A20-60983, Mar. 2014, 58 pages. |
| Choi et al., “Optimal Load Shedding for Maximizing Satisfaction in an Islanded Microgrid,” Energies, 2017, vol. 10, p. 45. doi: 10.3390/en10010045. |
| EPEX Spot, “How They Occur, What They Mean,” 2018, 2 pages. Retrieved from Internet: [URL:https://www.epexspot.com/en/company- info/basics_of_the_power_market/negative_prices]. |
| Final Office Action mailed Jul. 23, 2020 on for U.S. Appl. No. 16/132,062, filed Sep. 14, 2018, 26 pages. |
| Final Office Action mailed on May 19, 2020 for U.S. Appl. No. 16/809,111, filed Mar. 4, 2020, 36 pages. |
| Final Office Action mailed on Jun. 3, 2020 for U.S. Appl. No. 16/528,348, filed Jul. 31, 2019, 33 pages. |
| Final Office Action mailed on May 28, 2020 for U.S. Appl. No. 16/132,098, filed Sep. 14, 2018, 24 pages. |
| Final Office Action mailed on Oct. 1, 2019 for U.S. Appl. No. 16/175,246, filed Oct. 30, 2018, 18 pages. |
| Final Office Action mailed on Apr. 17, 2020 for U.S. Appl. No. 16/529,402, filed Aug. 1, 2019, 59 pages. |
| Final Office Action mailed on Jul. 29, 2020 for U.S. Appl. No. 16/132,092, filed Sep. 14, 2018, 5 pages. |
| Gao et al., “Dynamic Load Shedding for an Islanded Microgrid With Limited Generation Resources,” IET Generation, Transmission & Distribution, Sep. 2016, vol. 10(12), pp. 2953-2961. doi: 10.1049/iet-gtd.2015.1452. |
| Ghamkhari et al., “Optimal Integration of Renewable Energy Resources in Data Centers with Behind-the-Meter Renewable Generator,” Department of Electrical and Computer Engineering Texas Tech University, 2012, pp. 3340-3444. |
| Hayes, Adam S., “A Cost of Production Model for Bitcoin,” Department of Economics, The New School for Social Research, Mar. 2015, 5 pages. |
| International Search Report and Written Opinion of PCT Application No. PCT/US2018/017955, mailed on Apr. 30, 2018, 22 pages .<iframe class=“ginger-extension-definitionpopup” src=“chrome- extension://kdfieneakcjfaiglcfcgkidlkmlijnh/content/popups/definitionPopup/index.html?title=filed &description=record%20in%20a%20public%20office%20or%20in%20a%20court%20of%20law” style=“left: 396px; top: - 116px; z-index: 100001; display: none;”></iframe>. |
| International Search Report and Written Opinion of PCT Application No. PCT/US2018/017950, mailed on May 31, 2018, 15 pages. |
| International Search Report and Written Opinion of PCT Application No. PCT/US2020/044536, mailed on Aug. 26, 2020, 24 pages. |
| International Search Report and Written Opinion of PCT Application No. PCT/US2020/044539, mailed on Aug. 26, 2020, 7 pages. |
| International Search Report and Written Opinion of PCT Application No. PCT/US2021/019875, mailed on Apr. 29, 2021, 12 pages. |
| International Search Report and Written Opinion of PCT Application No. PCT/US20/57686, mailed on Feb. 22, 2021, 67 pages. |
| ISO, “Distributed Energy Resources Roadmap for New York's Wholesale Electricity Markets,” Distributed Energy Resource Roadmap, Jan. 2017, pp. 1-39. [retrieved on 2020-12-15], Retrieved from the Internet:< url :< a=“” href=“https://www.nyiso.com/documents/20142/1391862/Distributed_Energy_Resources_Road map.pdf/ec0b3b64-4de2-73e0-ffef- 49a4b8b1”>https://www.nyiso.com/documents/20142/1391862/Distributed_Energy_Resources_ Roadmap.pdf/ec0b3b64-4de2-73e0-ffef-49a4b8b1 b3ca .< /url:>. |
| John, “Stem and CPower to Combine Behind-the-Meter Batteries and Demand Response,” Energy Storage, Aug. 8, 2017, 5 pages. |
| Kewl, “Start-Up From the Heart of Berlin Has Pioneered Decentralized Mobile Mining by Combining Blockchain With Regenerative Energy” Nov. 2017, 3 pages pdf http://www.crypto-news.net/start-up-from-the-heart-of-berlin-has-pioneered-decentralized- mobile-mining-by-combining-blockchain-with-regenerative-energy/ (Year: 2017). |
| Lim et al., “Distributed Load-shedding System for Agent-based Autonomous Microgrid Operations,” Energies, 2014, vol. 7(1), pp. 385-401. doi: 10.3390/en7010385. |
| Liu et al., “Improved Average Consensus Algorithm Based Distributed Cost Optimization for Loading Shedding of Autonomous Microgrids,” International Journal of Electrical Power & Energy Systems, Dec. 2015, vol. 73, pp. 89-96. doi: 10.1016/j.ijepes.2015.04.006. |
| Mcnamara et al., U.S. Appl. No. 16/175,246, mailed on Oct. 30, 2018, 64 pages. |
| Mousavizadeh et al., “A Linear Two-stage Method for Resiliency Analysis in Distribution Systems Considering Renewable Energy and Demand Response Resources,” Elsevier, 2017, pp. 443-460. doi: 10.1016/j.apenergy.2017.11.067. |
| Non-Final Office Action mailed on Dec. 5, 2019 for U.S. Appl. No. 16/529,360, filed Aug. 1, 2019, 72 pages. |
| Non-Final Office Action mailed on Dec. 10, 2019 for U.S. Appl. No. 16/596,190, filed Oct. 8, 2019, 72 pages. |
| Non-Final Office Action mailed on Jun. 12, 2020 for U.S. Appl. No. 16/803,109, filed Dec. 27, 2020, 31 pages. |
| Non-Final Office Action mailed on Nov. 14, 2019 for U.S. Appl. No. 16/132,098, filed Sep. 14, 2018, 25 pages. |
| Non-Final Office Action mailed on Feb. 20, 2020 for U.S. Appl. No. 16/702,894, filed Dec. 4, 2019, 30 pages. |
| Non-Final Office Action mailed on Nov. 21, 2019 for U.S. Appl. No. 16/529,402, filed Aug. 1, 2019, 57 pages. |
| Non-Final Office Action mailed Feb. 4, 2021 on for U.S. Appl. No. 16/284,610, filed Feb. 25, 2019, 9 pages. |
| Non-Final Office Action mailed Apr. 1, 2021 on for U.S. Appl. No. 16/482,495, filed Jul. 31, 2019, 59 pages. |
| Non-Final Office Action mailed Apr. 2, 2020 on for U.S. Appl. No. 16/132,011, filed Sep. 14, 2018, 5 pages. |
| Non-Final Office Action mailed Mar. 8, 2021 on for U.S. Appl. No. 16/525,142, filed Jul. 29, 2019, 71 pages. |
| Non-Final Office Action mailed Dec. 11, 2019 on for U.S. Appl. No. 16/132,062, filed Sep. 14, 2018, 17 pages. |
| Non-Final Office Action mailed Feb. 12, 2021 on for U.S. Appl. No. 16/528,348, filed Jul. 31, 2019, 54 pages. |
| Non-Final Office Action mailed May 14, 2020 on for U.S. Appl. No. 16/834,987, filed Mar. 30, 2020, 30 pages. |
| Non-Final Office Action mailed Mar. 25, 2021 on for U.S. Appl. No. 16/573,577, filed Sep. 17, 2019, 65 pages. |
| Non-Final Office Action mailed on Dec. 10, 2019 for U.S. Appl. No. 16/528,348, filed Jul. 31, 2019, 33 pages. |
| Non-Final Office Action mailed on May 11, 2021 for U.S. Appl. No. 16/529,360, filed Aug. 1, 2019, 64 pages. |
| Non-Final Office Action mailed on May 28, 2021 for U.S. Appl. No. 16/658,983, filed Oct. 21, 2019, 21 pages. |
| Non-Final Office Action mailed on Mar. 30, 2020 for U.S. Appl. No. 16/132,092, filed Sep. 14, 2018, 46 pages. |
| Notice of Allowance mailed May 12, 2021 on for U.S. Appl. No. 16/132,062, filed Sep. 14, 2018, 2 pages. |
| Notice of Allowance mailed Oct. 13, 2020 on for U.S. Appl. No. 16/132,098, filed Sep. 14, 2018, 5 pages. |
| Notice of Allowance mailed Jun. 12, 2020 on for U.S. Appl. No. 16/834,987, filed Mar. 30, 2020, 9 pages. |
| Notice of Allowance mailed Feb. 8, 2021 on for U.S. Appl. No. 16/132,062, filed Sep. 14, 2018, 21 pages. |
| Notice of Allowance mailed Feb. 8, 2021 on for U.S. Appl. No. 16/803,109, filed Feb. 27, 2020, 29 pages. |
| Notice of Allowance mailed Feb. 8, 2021 on for U.S. Appl. No. 16/834,987, filed Mar. 30, 2020, 180 pages. |
| Notice of Allowance mailed Jan. 13, 2021 on for U.S. Appl. No. 16/175,246, filed Oct. 30, 2018, 5 pages. |
| Notice of Allowance mailed Sep. 17, 2020 on for U.S. Appl. No. 16/175,246, filed Oct. 30, 2018, 5 pages. |
| Notice of Allowance mailed Nov. 19, 2020 on for U.S. Appl. No. 16/132,062, filed Sep. 14, 2018, 7 pages. |
| Notice of Allowance mailed Apr. 20, 2021 on for U.S. Appl. No. 16/482,495, filed Jul. 31, 2019, 5 pages. |
| Notice of Allowance mailed Jan. 25, 2021 on for U.S. Appl. No. 16/132,098, filed Sep. 14, 2018, 5 pages. |
| Notice of Allowance mailed Jan. 25, 2021 on for U.S. Appl. No. 16/702,894, filed Dec. 4, 2019, 24 pages. |
| Notice of Allowance mailed Jan. 27, 2021 on for U.S. Appl. No. 16/132,092, filed Sep. 14, 2018, 8 pages. |
| Notice of Allowance mailed May 27, 2021 on for U.S. Appl. No. 16/284,610, filed Feb. 25, 2019, 5 pages. |
| Notice of Allowance mailed Jul. 29, 2020 on for U.S. Appl. No. 16/132,011, filed Sep. 14, 2018, 5 pages. |
| Notice of Allowance mailed Oct. 29, 2020 on for U.S. Appl. No. 16/132,092, filed Sep. 14, 2018, 8 pages. |
| Notice of Allowance mailed on Apr. 2, 2019, for U.S. Appl. No. 16/175,335, filed Oct. 30, 2018, 12 pages. |
| Notice of Allowance mailed on Mar. 2, 2020, for U.S. Appl. No. 16/596,190, filed Oct. 8, 2019, 15 pages. |
| Notice of Allowance mailed on Apr. 6, 2020, for U.S. Appl. No. 16/175,246, filed Oct. 30, 2018, 12 pages. |
| Notice of Allowance mailed on Aug. 15, 2019, for U.S. Appl. No. 16/175,146, filed Oct. 30, 2018, 17 pages. |
| Notice of Allowance mailed on Jan. 27, 2020, for U.S. Appl. No. 16/702,931, filed Dec. 4, 2019, 23 pages. |
| Notice of Allowance mailed on Jul. 29, 2019, for U.S. Appl. No. 16/245,532, filed Jan. 11, 2019, 13 pages. |
| Notice of Allowance mailed on Jun. 9, 2021 for U.S. Appl. No. 16/528,348, filed Jul. 31, 2019, 11 pages. |
| Pashajavid et al., “A Multimode Supervisory Control Scheme for Coupling Remote Droop-Regulated Microgrids,” IEEE Transactions on Smart Grid, May 26, 2020, vol. 9(5), pp. 5381-5392. https://ieeexplore.ieee.org/abstract/document/7888570/. |
| Pashajavid et al., “Frequency Support for Remote Microgrid Systems With Intermittent Distributed Energy Resources-A Two-level Hierarchical Strategy,” IEEE Systems Journal, May 26, 2020, vol. 12(3), pp. 2760-2771. https://ieeexplore.ieee.org/abstract/document/7862156/. |
| Rahimi, Farrokh, “Using a Transactive Energy Framework,” IEEE Electrification Magazine, Dec. 2016, pp. 23-29. |
| Rudez and Mihalic, “Predictive Underfrequency Load Shedding Scheme for Islanded Power Systems With Renewable Generation,” Electric Power Systems Research, May 2015, vol. 126, pp. 21-28. doi: 10.1016/j.epsr.2015.04.017. |
| Soluna., “Powering the Block Chain,” Aug. 2018, version 1.1, 29 pages. |
| Wilson, Joseph Nathanael, “A Utility-Scale Deployment Project of Behind-the-Meter Energy Storage for Use in Ancillary Services, Energy Resiliency, Grid Infrastructure Investment Deferment, and Demand-Response Integration,” Portland State University, 2016, 154 pages. |
| Xu et al., “Distributed Load Shedding for Microgrid With Compensation Support via Wireless Network,” IET Generation, Transmission & Distribution, May 2018, vol. 12(9), pp. 2006-2018. doi: 10.1049/iet-gtd.2017.1029. |
| Zhou et al., “Two-Stage Load Shedding for Secondary Control in Hierarchical Operation of Islanded Microgrids,” IEEE Transactions on Smart Grid, May 2019, vol. 10(3), pp. 3103-3111. doi: 10.1109/TSG.2018.2817738. |
| European Patent Application No. 18900411.2, Extended European Search Report dated Dec. 13, 2021. |
| European Patent Application No. 19858812.1, Extended European Search Report dated May 2, 2022. |
| European Patent Application No. 19861222.8, Extended European Search Report dated May 2, 2022. |
| European Patent Application No. 19861223.6, Extended European Search Report dated Apr. 19, 2022. |
| European Patent Application No. EP18900411.2, Partial Supplementary European Search Report dated Sep. 9, 2021. |
| Final Office Action mailed Jan. 6, 2022 on for U.S. Appl. No. 16/529,360, filed Aug. 1, 2019, 40 pages. |
| Final Office Action mailed Aug. 9, 2021 on for U.S. Appl. No. 16/529,402, filed Aug. 1, 2019, 43 pages. |
| Final Office Action mailed Aug. 9, 2021 on for U.S. Appl. No. 16/573,577, filed Sep. 17, 2019, 16 pages. |
| Final Office Action mailed Jul. 9, 2021 on for U.S. Appl. No. 16/525,142, filed Jul. 29, 2019, 18 pages. |
| Ghamkhari et al., “Energy and Performance Management of Green Data Centers: A Profit Maximization Approach,” IEEE Transactions on Smart Grid, Jun. 2013, vol. 4 (2), pp. 1017-1025. |
| Ghatikar et al., “Demand Response Opportunities and Enabling Technologies for DataCenters: Findings from Field Studies,” Lawrence Berkeley National Laboratory, Aug. 2012, 57 pages. |
| Huang et al., “Data Center Energy Cost Optimization in Smart Grid: a Review,” Journal of Zhejiang University (Engineering Science), 2016, vol. 50 (12), pp. 2386-2399. |
| International Search Report and Written Opinion of PCT Application No. PCT/US2021/045972, mailed on Nov. 15, 2021, 16 pages. |
| Kiani et al., “Profit Maximization for Geographical Dispersed Green Data Centers,” Arxiv.org, Cornell University Library, 201 Olin Library Cornell University Ithaca, Apr. 2015, pp. 1-5. |
| Li et al., “iSwitch: Coordinating and Optimizing Renewable Energy Powered Server Clusters,” 2012 39th Annual International Symposium on Computer Architecture, Jun. 2012, pp. 512-523. |
| Non-Final Office Action mailed Dec. 24, 2021 on for U.S. Appl. No. 17/128,830, filed Dec. 21, 2020, 4 pages. |
| Notice of Allowance mailed Jul. 26, 2021 on for U.S. Appl. No. 16/284,610, filed Feb. 25, 2019, 2 pages. |
| Notice of Allowance mailed Oct. 8, 2021 on for U.S. Appl. No. 16/528,348, filed Jul. 31, 2019, 3 pages. |
| Notice of Allowance mailed on Feb. 2, 2022, for U.S. Appl. No. 16/525,142, filed Jul. 29, 2019, 5 pages. |
| Notice of Allowance mailed on Feb. 3, 2022, for U.S. Appl. No. 16/573,577, filed Sep. 17, 2019, 8 pages. |
| Notice of Allowance mailed on Jan. 5, 2022, for U.S. Appl. No. 16/658,983, filed Oct. 21, 2019, 14 pages. |
| Notice of Allowance mailed on Apr. 18, 2022, for U.S. Appl. No. 17/128,830, filed Dec. 21, 2020, 7 pages. |
| Notice of Allowance mailed on Jan. 24, 2022, for U.S. Appl. No. 16/525,142, filed Jul. 29, 2019, 9 pages. |
| Notice of Allowance mailed on Sep. 24, 2021 for U.S. Appl. No. 16/528,348, filed Jul. 31, 2019, 06 pages. |
| Notice of Allowance mailed on Jan. 26, 2022, for U.S. Appl. No. 17/328,275, filed May 24, 2021, 10 pages. |
| Wang et al., “SHIP: Scalable Hierarchical Power Control for Large-scale Data Centers,” 2009 18th International Conference onParallel Architectures and Compilation Techniques, Sep. 2009, pp. 91-100. |
| Ercot, Nodal Protocols (Oct. 18, 2019)—Applicant particularly notes the following pp. 2-4, 2-5, 2-15, 2-17, 2-24 to 26, 2-28, 2-29, 2- 38, 2-41, 2-51, 2-52, 2-58, 2-62 to 63, 2-67, 2-69, 3-77 to 80, 3-176 to 3-186, 3-208 to 213, 3- 214 to 216, 4-1 to 4, 4-10, 4-20, 4-25 to 27, 4-59 to 62, 4-64 to 67, 6-100 to 116, 8-1 to 58. |
| Ercot, Business Procedures, Load Resource Qualification, Non-Controllable Load Resource Qualification and Testing Procedure (Apr. 1, 2011). |
| Ercot, Business Procedures, Load Resource Qualification, Initial Qualification and Periodic Testing, Controllable Load Qualification Test Procedure for Ancillary Services (Jun. 1, 2014). |
| Ercot, Large Flexible Load Resource Participation in the Ercot Region, presentation to Large Flexible Load Task Force (Apr. 26, 2022). |
| Ercot, Load Resource Participation in the Ercot Region, presentation (Sep. 27, 2022). |
| Ercot, Emergency Response Service Technical Requirements & Scope of Work, Oct. 1, 2018 through Jan. 31, 2019. |
| Ercot, ERS QSE Training 101, Updated Apr. 6, 2022. |
| Ercot, Controllable Load Resource (CLR) Participation in the Ercot Market (Dec. 20, 2007). |
| European Patent Application No. 19878191.6, Extended European Search Report dated Jul. 4, 2022. |
| European Patent Application No. 19858739.6, Extended European Search Report dated May 31, 2022. |
| European Patent Application No. 19877576.9, Extended European Search Report dated Jun. 3, 2022. |
| European Patent Application No. 20738289.6, Extended European Search Report dated Aug. 8, 2022. |
| European Patent Application No. 22157111.0, Extended European Search Report dated Aug. 17, 2022. |
| Non-Final Office Action mailed Sep. 22, 2022 on for U.S. Appl. No. 16/961,386, filed Jul. 10, 2020, 52 pages. |
| Non-Final Office Action mailed Aug. 25, 2022 on for U.S. Appl. No. 16/529,360, filed Aug. 1, 2019, 91 pages. |
| Non-Final Office Action mailed Sep. 29, 2022 on for U.S. Appl. No. 17/353,285, filed Jun. 21, 2021, 16 pages. |
| Notice of Allowance mailed Aug. 10, 2022 on for U.S. Appl. No. 17/328,337, filed May 24, 2021, 9 pages. |
| Notice of Allowance mailed May 31, 2022 on for U.S. Appl. No. 16/529,402, filed Aug. 1, 2019 13 pages. |
| Notice of Allowance mailed on Aug. 3, 2022, for U.S. Appl. No. 17/340,886, filed Jun. 7, 2021, 09 pages. |
| Wierman et al., “Opportunities and Challenges for Data Center Demand Response,” International Green Computing Conference, IEEE, Nov. 2014, pp. 1-10. |
| Abhyankar et al., “Using PETSc to Develop Scalable Applications for Next-Generation Power Grid,” High Performance Computing, Networking and Analytics for the Power Grid, Nov. 2011 pp. 67-74 https://doi.org/10.1145/2096123.2096138. |
| Advisory Action mailed on Mar. 22, 2023 for U.S. Appl. No. 17/101,784, filed Nov. 23, 2020, 3 pages. |
| Chen et al., “Power Trading Model for Distributed Power Generation Systems Based on Consortium Blockchains,” Proceedings of the 12th Asia-Pacific Symposium on Internetware, Jul. 2021, pp. 91-98. https://doi.org/10.1145/3457913.3457929. |
| Examination Report mailed Jan. 17, 2023 for EP Application No. EP19858812.1 filed on Mar. 25, 2021. |
| Examination Report mailed Dec. 9, 2022 for EP Application No. EP2019086122.3 filed on Sep. 13, 2019. |
| Final Office Action mailed Apr. 11, 2023 on for U.S. Appl. No. 16/529,360, filed Aug. 1, 2019, 46 pages. |
| Final Office Action mailed on Dec. 1, 2022 for U.S. Appl. No. 17/101,784 filed on on Mar. 16, 2013, 14 pages. |
| Hung et al., “Application of Improved Differential Evolution Algorithm for Economic and Emission Dispatch of Thermal Power Generation Plants,” Proceedings of the 3rd International Conference on Machine Learning and Soft Computing, Jan. 2019, pp. 93-98. https://doi.org/10.1145/3310986.3311003. |
| Kim et al., “Automated di/dt Stressmark Generation for Microprocessor Power Delivery Networks,” IEEE/ACM International Symposium on Low Power Electronics and Design, Aug. 2011, pp. 253-258. |
| Ko., “Ultra-Low Power Challenges for the next Generation ASIC,” International Symposium on Low Power Electronics and Design, Jul. 2012, pp. 365-366. https://doi.org/10.1145/2333660.2333743. |
| Li et al., “Research on Evaluation Method of Integrated Energy Service Level of Power Generation Enterprises,” Informatics, Environment, Energy and Applications, Jun. 2021, pp. 57-62. https://doi.org/10.1145/3458359.3458372. |
| Lin et al., “Automated Classification of Power Plants by Generation Type,” E-Energy '20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems, Jun. 2020, pp. 86-96. https://doi.org/10.1145/3396851.3397708. |
| Marcano et al., “Soil Power ?: Can Microbial Fuel Cells Power Non-Trivial Sensors?,” LP-IoT'21, Proceedings of the 1st ACM Workshop on No. Power and Low Power Internet-of-Things, Jan. 2022, pp. 8-13. https://doi.org/10.1145/3477085.3478989. |
| Miyazaki et al., “Electric-Energy Generation Using Variable-Capacitive Resonator for Power-Free LSI: Efficiency Analysis and Fundamental Experiment,” International Symposium on Low Power Electronics and Design, Aug. 2003, pp. 193-198, Doi: 10.1109/LPE.2003.1231861. |
| Nawaz et al., “Assisting the Power Generation Sector Toward Sustainability—an Lot Based System for Power Theft Detection With Theft Location Identification,” Proceedings of the 2nd International Conference on Computing Advancements, Aug. 2022, pp. 309-315, https://doi.org/10.1145/3542954.3542999. |
| Non-Final Office Action mailed Aug. 6, 2012 for U.S. Appl. No. 12/587,564, filed Oct. 8, 2009, 24 pages. |
| Non-Final Office Action mailed Nov. 7, 2022 on for U.S. Appl. No. 17/692,636, filed Mar. 11, 2022, 9 pages. |
| Non-Final Office Action mailed Dec. 8, 2022 for U.S. Appl. No. 17/340,643, filed Jun. 7, 2021, 5 pages. |
| Non-Final Office Action mailed Mar. 17, 2023 for U.S. Appl. No. 17/868,381, filed Jul. 19, 2022, 49 pages. |
| Non-Final Office Action mailed Sep. 21, 2018 for U.S. Appl. No. 15/289,272, filed Oct. 10, 2016, 27 pages. |
| Non-Final Office Action mailed Apr. 25, 2023 for U.S. Appl. No. 17/340,643, filed Jun. 7, 2021, 4 pages. |
| Non-Final Office Action mailed Mar. 29, 2023 for U.S. Appl. No. 18/066,616, filed Dec. 15, 2022, 120 pages. |
| Non-Final Office Action mailed on Apr. 11, 2023 for U.S. Appl. No. 17/353,285, filed Jun. 21, 2021, 4 pages. |
| Non-Final Office Action mailed on Dec. 20, 2022, for U.S. Appl. No. 17/513,558, filed Oct. 28, 2021, 16 pages. |
| Notice of Allowance mailed Dec. 2, 2022 for U.S. Appl. No. 17/328,337, filed May 24, 2021, 5 pages. |
| Notice of Allowance mailed Dec. 2, 2022 on for U.S. Appl. No. 17/673,318, filed Feb. 16, 2022, 17 pages. |
| Notice of Allowance mailed Nov. 2, 2022 on for U.S. Appl. No. 17/340,886, filed Jun. 7, 2021, 9 pages. |
| Notice of Allowance mailed Nov. 23, 2022 on for U.S. Appl. No. 17/340,664, filed Jun. 7, 2021, 2 pages. |
| Notice of Allowance mailed on Apr. 4, 2023, for U.S. Appl. No. 17/673,318, filed Feb. 16, 2022, 2 pages. |
| Notice of Allowance mailed on Mar. 8, 2023, for U.S. Appl. No. 16/961,386, filed Jul. 10, 2020, 2 pages. |
| Notice of Allowance mailed on Nov. 9, 2022, for U.S. Appl. No. 17/340,664, filed Dec. 16, 2013, 4 pages. |
| Notice of Allowance mailed on Jan. 13, 2023, for U.S. Appl. No. 16/961,386 , filed on Jul. 10, 2020, 13 pages. |
| Notice of Allowance mailed on Feb. 21, 2023, for U.S. Appl. No. 17/340,664, filed Jun. 7, 2021, 2 pages. |
| Notice of Allowance mailed on Mar. 21, 2023 for U.S. Appl. No. 17/692,636, filed Mar. 11, 2022, 09 pages. |
| Notice of Allowance mailed on Feb. 23, 2023, for U.S. Appl. No. 17/353,285, filed Jun. 21, 2021, 5 pages. |
| Notice of Allowance mailed on Apr. 24, 2023, for U.S. Appl. No. 16/961,386, filed Jul. 10, 2020, 10 pages. |
| Notice of Allowance mailed on Apr. 24, 2023 for U.S. Appl. No. 17/692,636, filed Mar. 11, 2022, 08 pages. |
| Notice of Allowance mailed on Apr. 24, 2023 for U.S. Appl. No. 17/750,883, filed May 23, 2022, 10 pages. |
| Response to Non-Final Office Action mailed Feb. 6, 2021 for U.S. Appl. No. 12/587,564, filed Oct. 8, 2009, 14 pages. |
| Response to Non-Final Office Action mailed Dec. 21, 2018 for U.S. Appl. No. 15/289,272, filed Oct. 10, 2016, 10 pages. |
| Sakurai., “Next-Generation Power-Aware Design,” ISLPED, Aug. 2008, pp. 383-384. |
| Sankaragomathi et al., “Optimal Power and Noise for Analog and Digital Sections of a Low Power Radio Receiver,” International Symposium on Low Power Electronics & Design, Aug. 2008, pp. 271-276. https://doi.org/10.1145/1393921.1393993. |
| Sethuraman et al., “Multicasting Based Topology Generation and Core Mapping for a Power Efficient Networks-On-Chip,” International Symposium on Low Power Electronics & Design, Aug. 2007, pp. 399-402. https://doi.org/10.1145/1283780.1283868. |
| Singh et al., “Targeted Random Test Generation for Power-Aware Multicore Designs,” ACM Transactions on Design Automation of Electronic Systems, Jun. 2012, vol. 17(3), pp. 1-19. https://doi.org/10.1145/2209291.2209298. |
| Tao et al., “Simulation Model of Photo-Voltaic Grid-Connected Power Generation,” Artificial Intelligence and Advanced Manufacture, Mar. 2022, pp. 2921-2926. https://doi.org/10.1145/3495018.3501208. |
| Villani et al., “RF Power Transmission: Energy Harvesting for Self-Sustaining Miniaturized Sensor Nodes,” Embedded Networked Sensor Systems, Sensys, Nov. 2021, pp. 592-593. https://doi.org/10.1145/3485730.3493365. |
| Yang et al., “Investment Value Analysis of Household Rooftop PV Power Generation Project under Carbon Trading Mode,” Information Management and Management Science, Aug. 2019, pp. 160-165. https://doi.org/10.1145/3357292.3357330. |
| Yaramasu V., et al., “High-Power Wind Energy Conversion Systems: State-of-the-Art and Emerging Technologies”, Proceedings of the IEEE, May 2015, vol. 103 (5), 49 pages. |
| Zhang et al., “Calculation of the Carrying Capacity of Distrubuted Power Generation in Distribution Network Under Carbon Peaking and Carbon Neutrality Goals,” Electrical, Power and Computer Engineering, Apr. 2022, pp. 1-5. https://doi.org/10.1145/3529299.3529308. |
| Zhang et al., “Wind Power Generation Prediction Based on LSTM,” International Conference on Mathematics and Artificial Intelligence, Apr. 2019, pp. 85-89. https://doi.org/10.1145/3325730.3325735. |
| Zhou et al., “An Online Power Generation Dispatching Method to Improve the Small Signal Stability of Power System with Fluctuated Wind Power,” Electrical Power and Computer Engineering, Apr. 2022, pp. 1-12. https://doi.org/10.1145/3529299.3529302. |
| U.S. Appl. No. 62/556,880, filed Sep. 11, 2017 (claims, spec, and appendix). |
| Appalachian Power Company, SCC Docket Search, PUE-2014-00026, Sep. 5, 2023, Ex. (1025), 4 Pages. |
| Appalachian Power Company, State Corporation Commission, Commonwealth of Virginia, Nov. 26, 2014, Case No. PUE-2014-00026, Ex. (1019), 51 Pages. |
| Beltran, H., et al., “Evaluation of Storage Energy Requirements for Constant Production in PV Power Plants,” IEEE Transactions on Industrial Electronics, 2012, vol. 60 (3), pp. 1225-1234. |
| Cho C., et al., “Active synchronizing control of a microgrid”, IEEE Transactions on Power Electronics, 2011, vol. 26(12), 13 pages. |
| Cholla Petitioner's Power of Attorney for U.S. Appl. No. 10/608,433 dated Mar. 3, 20201, 1 page. |
| Cholla United States District Court for the District of Delaware for U.S. Appl. No. 10/608,433, 1 page. |
| Decision in Petition for Inter Partes Review dated Mar. 13, 2024 for U.S. Pat. No. 10,608,433, IPR2023-01407, 36 Pages. |
| Declaration of Andres E. Carvallo mailed Sep. 12, 2023 for U.S. Pat. No. 10,608,433, IPR2023-01407, (Ex. 1003), 103 Pages. |
| Emergency Demand Response Program Manual, New York ISO, Manual 7, Nov. 2022, Ex. (1010), 50 Pages. |
| European Patent Application No. 20847753.9, Extended European Search Report dated Jul. 20, 2023. |
| European Patent Application No. 20847907.1, Extended European Search Report dated Jul. 18, 2023. |
| European Patent Application No. 20880611.7, Extended European Search Report dated Nov. 3, 2023. |
| European Patent Application No. 21856804.6, Extended European Search Report dated Mar. 7, 2024. |
| Final Office Action mailed Feb. 15, 2024 for U.S. Appl. No. 17/340,643, filed Jun. 7, 2021, 25 pages. |
| Final Office Action mailed Jun. 20, 2023 for U.S. Appl. No. 17/340,643, filed Jun. 7, 2021. |
| Final Office Action mailed on Jun. 2, 2023 for U.S. Appl. No. 17/513,558, filed Oct. 28, 2021, 15 pages. |
| Final Office Action mailed on Dec. 6, 2023 for U.S. Appl. No. 17/896,376, filed Aug. 26, 2022, 14 pages. |
| Final Office Action mailed on Jan. 11, 2024 for U.S. Appl. No. 17/101,784, filed Nov. 23, 2020, 13 pages. |
| Final Office Action mailed on Oct. 13, 2023 for U.S. Appl. No. 18/066,616, filed Dec. 15, 2023, 10 pages. |
| Final Office Action mailed on Aug. 4, 2023 for U.S. Appl. No. 17/868,381, filed Jul. 19, 2022, 45 pages. |
| Gangloff M., “Appalachian Power'sproposed fee targets usersof solar panels,” OBM and Cholla Energy, Sep. 8, 2014, Ex. (1017), 3 Pages. |
| Hua Y., et al., “Building fuel powered supercomputing data center at low cost”, Proceedings of the 29th ACM on International Conference on Supercomputing—describes DG systems and associated datacenters, 2015, 10 pages. |
| International Search Report and Written Opinion of PCT Application No. PCT/US2023/22767, mailed on Aug. 4, 2023, 16 pages. |
| Letter from W. Hix, Indiana Michigan Power, OBM and Cholla Energy, 2014, Ex. (1035), 7 Pages. |
| Li C., et al., “Enabling distributed generation powered sustainable high-performance data center”, 19th International Symposium on High Performance Computer Architecture IEEE, 2013, 12 pages. |
| Madrigal R., Overview of Reliability Demand Response Resource, OBM and Cholla Energy, May 8, 2014, Ex. (1014), 74 Pages. |
| Main I., “Virginia energy policy made interesting,” Who's afraid of a Carbon Rule?, Power for the People VA, May 2014, Ex. (1018), 10 Pages. |
| Main I., “Virginia regulators approve Appalachian Power's solar tax”, Dec. 1, 2014, Power for the People VA, Ex. (1016), 5 Pages. |
| Meagher K., et al., “The Enterprise Data Center as a Microgrid”, Uptime Institute Symposium describes data centers as a microgrid that capable of operating in islanding mode and grid connected mode, 2010, 8 pages. |
| Non-Final Office Action mailed Apr. 3, 2024 for U.S. Appl. No. 18/139,134, filed Apr. 25, 2023, 13 Pages. |
| Non-Final Office Action mailed Nov. 9, 2023 for U.S. Appl. No. 17/340,643, filed Jun. 7, 2021, 21 pages. |
| Non-Final Office Action mailed May 11, 2023 for U.S. Appl. No. 18/114,503, filed Feb. 27, 2023. |
| Non-Final Office Action mailed Mar. 13, 2024 for U.S. Appl. No. 18/066,616, filed Dec. 15, 2022, 12 pages. |
| Non-Final Office Action mailed Oct. 13, 2023 for U.S. Appl. No. 18/106,102, filed Feb. 6, 2023, 18 pages. |
| Non-Final Office Action mailed Jan. 18, 2024 for U.S. Appl. No. 18/139,010, filed Apr. 25, 2023, 12 pages. |
| Non-Final Office Action mailed Jun. 21, 2023 for U.S. Appl. No. 17/896,376, filed Aug. 26, 2022. |
| Non-Final Office Action mailed Nov. 21, 2023 for U.S. Appl. No. 17/513,558, filed Oct. 28, 2023, 16 pages. |
| Non-Final Office Action mailed Jun. 22, 2023 for U.S. Appl. No. 17/101,784, filed Nov. 23, 2020. |
| Non-Final Office Action mailed Feb. 28, 2024 for U.S. Appl. No. 17/402,175, filed Aug. 13, 2021, 11 pages. |
| Non-Final Office Action mailed Mar. 29, 2024 for U.S. Appl. No. 18/367,673, filed Sep. 3, 2023, 9 Pages. |
| Non-Final Office Action mailed Nov. 30, 2023 for U.S. Appl. No. 18/143,277, filed May 4, 2023, 26 pages. |
| Non-Final Office Action mailed Oct. 5, 2023 for U.S. Appl. No. 17/479,750, filed Sep. 20, 2021, 4 pages. |
| Non-Final Office Action mailed Oct. 5, 2023 for U.S. Appl. No. 18/106,098, filed Feb. 6, 2023, 5 pages. |
| Non-Final Office Action mailed on Apr. 30, 2024 for U.S. Appl. No. 18/139,010, filed Apr. 25, 2023, 5 pages. |
| Notice of Allowance mailed on Nov. 3, 2023 for U.S. Appl. No. 18/106,093, filed Feb. 6, 2023, 4 pages. |
| Notice of Allowance mailed on May 7, 2024, for U.S. Appl. No. 18/114,503, filed Feb. 27, 2023, 5 pages. |
| Notice of Allowance mailed on Dec. 11, 2023 for U.S. Appl. No. 17/868,381, filed Jul. 19, 2022, 10 pages. |
| Notice of Allowance mailed on Sep. 14, 2023 for U.S. Appl. No. 16/529,360, filed Aug. 1, 2019, 8 pages. |
| Notice of Allowance mailed on May 15, 2024, for U.S. Appl. No. 18/143,277, filed May 4, 2023, 11 pages. |
| Notice of Allowance mailed on Nov. 22, 2023 for U.S. Appl. No. 18/106,093, filed Feb. 6, 2023, 8 pages. |
| Notice of Allowance mailed on Jan. 23, 2024 for U.S. Appl. No. 18/114,503, filed Feb. 27, 2023, 5 pages. |
| Notice of Allowance mailed on Oct. 25, 2023 for U.S. Appl. No. 16/529,360, filed Aug. 1, 2019, 8 pages. |
| Notice of Allowance mailed on Sep. 29, 2023 for U.S. Appl. No. 18/114,503, filed Feb. 27, 2023, 5 pages. |
| Notice of Allowance mailed on Aug. 4, 2023 for U.S. Appl. No. 18/106,093, filed Feb. 6, 2023, 8 pages. |
| Notice of Allowance mailed on Mar. 5, 2024 for U.S. Appl. No. 18/106,102, filed May 2, 2023, 8pages. |
| Notice of Allowance mailed on Mar. 5, 2024 for U.S. Appl. No. 18/143,280, filed May 4, 2023, 10 pages. |
| Office of Electricity, “Demand Response,” OBM and Cholla Energy, Ex. (1013), 3 Pages. |
| Patent Owner Preliminary Response to Petition, dated Dec. 20, 2023, Inter Partes Review for U.S. Pat. No. 10,608,433, IPR2023-01407, 71 Pages. |
| Petition for Inter Partes Review mailed on Sep. 13, 2023 for U.S. Pat. No. 10,608,433, IPR2023-01407, 92 Pages. |
| Pitt D., et al., “Analyzing the Costs and Benefits of Distributed Solar Generation in Virginia,” OBM and Cholla Energy, 2014, Ex. (1022), 70 Pages. |
| Pitt D., et al., “Assessing the Value of Distributed Solar Energy Generation,” Curr Sustainable Renewable Energy, 2015, vol. 2, Ex. (1021) pp. 105-113. |
| Pitt D., et al., “Optimizing the Grid Integration of Distributed Solar Energy,” Final Presidential Research Quest Fund Grant Report Virginia Commonwealth University, OBM and Cholla Energy, Apr. 2018, Ex. (1023), 78 Pages. |
| PJM, Working to Perfect the Flow of Energy, OBM and Cholla Energy, Manual 11: Energy & Ancillary Services Market Operations, May 11, 2017, 223 Pages. |
| Politics Bureaucracy and Justice, West Texas A&M University, OBM and Cholla Energy, 2016, vol. 5(1), Ex. 1024, 41 Pages. |
| Power of Attorney for Petitioner for U.S. Pat. No. 10,608,433 OBM, Inc, 3 pages. |
| Powers J., “Implementation Overview for PDR,” Obm and Cholla Energy, Aug. 26, 2014, Ex. (1012), 11 Pages. |
| Reisinger W T., Public Utilities Law, OBM and Cholla Energy, 2014, Ex. (1020), 33 Pages. |
| Requirement for Restriction/Election mailed on Dec. 12, 2023 for U.S. Appl. No. 17/402,175, filed Aug. 13, 2021, 5 pages. |
| Resource Testing Guidelines dated May 18, 2023, Operating Procedure, California ISO, Procedure No. 5330, Ex. (1009), 32 Pages. |
| The Wayback Machine. SCC Case Information, Public Comments/Notices, 2015, 5, Ex. (1032), 1 Page. |
| The Wayback Machine. SCC Case Information, Public Comments/Notices, 2015, 5, Ex. (1033), 5 Pages. |
| Trowler D., et al., “Bi-Directional Inverter and Energy Storage System,” Texas Instruments Analog Design Contest, University of Arkansas, May 2008, pp. 1-29. |
| Virginia S.C .C . Tariff No. 2425 Appalachian Power Company, OBM and Cholla Energy, Jan. 25, 2015, Ex. (1027), 64 Pages. |
| Virginia S.C .C . Tariff No. 2425 Appalachian Power Company, OBM and Cholla Energy, Jan. 25, 2015, Ex. (1028), 53 Pages. |
| Virginia S.C .C . Tariff No. 2425 Appalachian Power Company, OBM and Cholla Energy, Jan. 25, 2015, Ex. (1029), 49 Pages. |
| Virginia S.C .C . Tariff No. 2425 Appalachian Power Company, OBM and Cholla Energy, Jan. 25, 2015, Ex. (1030), 72 Pages. |
| Virginia S.C .C . Tariff No. 2425 Appalachian Power Company, OBM and Cholla Energy, Jan. 25, 2015, Ex. (1031), 81 Pages. |
| Virginia S.C .C . Tariff No. 2425 Appalachian Power Company, PUE-2014-00026, Jan. 14, 2014, Ex. (1026), 69 Pages. |
| Virginia S.C.C. Tarrif No. 2425 Appalachian Power Company dated Jan. 14, 2014, PUE- 2014-00026, Ex. (1005), 389 Pages. |
| Whited M., et al., “The Problem with Fixed Charges for Electricity,” Prepared for Consumers Union, Feb. 9, 2016, Ex. (1034), 58 Pages. |
| Number | Date | Country | |
|---|---|---|---|
| 20210288496 A1 | Sep 2021 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16132098 | Sep 2018 | US |
| Child | 17331440 | US |
