SOFTWARE MODELING SYSTEMS FOR METERING AND TRANSLATING MEASUREMENTS

• US 20110010158A1
• Filed: 03/31/2010
• Published: 01/13/2011
• Est. Priority Date: 03/31/2009
• Status: Active Grant

• Current Assignee: Gridpoint (Twenty First Century Utilities LLC)
• Original Assignee: Gridpoint (Twenty First Century Utilities LLC)
• Inventors: Bridges, Seth W.
• Granted Patent: US 8,781,809 B2
• Time in Patent Office: Days
• US Class Current: 703/18
• CPC Class Codes: B60L 2240/70 Interactions with external data bases, e.g. traffic centres
• CPC Class Codes: B60L 53/18 Cables specially adapted for charging electric vehicles
• CPC Class Codes: B60L 53/305 Communication interfaces
• CPC Class Codes: B60L 53/63 in response to network capacity
• CPC Class Codes: B60L 53/64 Optimising energy costs, e.g. responding to electricity rates
• CPC Class Codes: B60L 53/65 involving identification of vehicles or their battery types
• CPC Class Codes: B60L 53/665 Methods related to measuring, billing or payment
• CPC Class Codes: B60L 53/68 Off-site monitoring or control, e.g. remote control
• CPC Class Codes: B60L 55/00 Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
• CPC Class Codes: H02J 2300/10 The dispersed energy generation being of fossil origin, e.g. diesel generators
• CPC Class Codes: H02J 3/381 Dispersed generators
• CPC Class Codes: H02J 7/00 Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 10/72 Electric energy management in electromobility
• CPC Class Codes: Y02T 90/12 Electric charging stations
• CPC Class Codes: Y02T 90/14 Plug-in electric vehicles
• CPC Class Codes: Y02T 90/16 Information or communication technologies improving the operation of electric vehicles
• CPC Class Codes: Y02T 90/167 Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
• CPC Class Codes: Y04S 10/126 : the energy generation units being or involving electric vehicles [EV] or hybrid vehicles [HEV], i.e. power aggregation of EV or HEV, vehicle to grid arrangements [V2G]
• CPC Class Codes: Y04S 30/12 : Remote or cooperative charging
• CPC Class Codes: Y04S 30/14 : Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing

Abstract

Systems and methods are provided for collecting and aggregating a plurality of power flow measurements from a plurality of devices in a power management system. The error bounds of the aggregated power flow measurement are then determined using at least one error model. Systems and methods are also provided for inferring AC power flows from DC power flows. A device having at least one DC power flow sensor is augmented with at least one AC power flow sensor AC and DC power flows through the device are measured using the sensors over a range of operating points. An inference model of AC power flow in the device as a function of DC power flow is then built, wherein the error of the model is bounded. DC power flow through the device and in similar devices can then be then measured and used to infer AC power flow for the device.

Claims

1. A method comprising:

receiving, over a network, a plurality of power flow measurements from each of a plurality of devices, each device being associated with a power flow, each of the devices being capable of measuring the respective device'"'"'s power flow within a measurement error;

aggregating, using at least one computing device, the plurality of power flow measurements, producing an aggregated power flow measurement;

determining, using at least one computing device, the error bounds of the aggregated power flow measurement using at least one error model.

2. The method of claim 1 wherein the aggregated power flow measurement is the sum of the individual measurements of each of the plurality of devices.

3. The method of claim 1 wherein one or more of the at least one error models is an error model for a subset of the plurality of devices comprising devices of an identical type.

4. The method of claim 3 wherein the error model for the subset of the plurality of devices of an identical type holds that all such devices have all measurements within +/−

N % of the true value.

5. The method of claim 1 wherein the at least one error model assumes that power flows at each respective device are independent and identically distributed

6. The method of claim 5 wherein a standard error of the aggregate power flow measurement is calculated by dividing the error bounds of each of the plurality of devices by the square root of the number of devices in the plurality of devices, and wherein the error bounds of the aggregated power flow measurement is calculated by multiplying the standard error by a fixed number of standard deviations corresponding to a desired level of confidence.

7. The method of claim 1 wherein the plurality of devices comprises at least two sets of devices, each set of devices comprising at least one device of the same type and being described by a device error model, wherein the error bounds are computed as a weighted average of the error models of the respective set of devices where the weight is the percentage of total power measured by the respective set of devices.

25. The system of claim 1 wherein the inference model is a linear regression that scales a constant, M, by the DC power measured by the respective sensor plus a fixed offset, B.

8. A system comprising:

a plurality of devices, each device being associated with a power flow, each of the devices being capable of measuring the respective device'"'"'s power flow within a measurement error;

an aggregate power measurement module comprising one or more processors programmed to execute software code retrieved from a computer readable storage medium storing software for;

receiving, over a network, a plurality of power flow measurements from each of the plurality of devices;

aggregating the plurality of power flow measurements, producing an aggregated power flow measurement;

determining the error bounds of the aggregated power flow measurement using at least one error model.

9. The system of claim 8 wherein the aggregated power flow measurement is the sum of the individual measurements of each of the plurality of devices.

10. The system of claim 8 wherein one or more of the at least one error models is an error model for a subset of the plurality of devices comprising devices of an identical type.

11. The system of claim 10 wherein the error model for the subset of the plurality of devices of an identical type holds that all such devices have all measurements within +/−

N % of the true value.

12. The system of claim 8 wherein the at least one error model assumes that power flows at each respective device are independent and identically distributed

13. The system of claim 12 wherein a standard error of the aggregate power flow measurement is calculated by dividing the error bounds of each of the plurality of devices by the square root of the number of devices in the plurality of devices, and wherein the error bounds of the aggregated power flow measurement is calculated by multiplying the standard error by a fixed number of standard deviations corresponding to a desired level of confidence.

14. The system of claim 8 wherein the plurality of devices comprises at least two sets of devices, each set of devices comprising at least one device of the same type and being described by a device error model, wherein the error bounds are computed as a weighted average of the error models of the respective set of devices where the weight is the percentage of total power measured by the respective set of devices.

15. A computer-readable medium having computer-executable instructions for a method comprising the steps of:

receiving, over a network, a plurality of power flow measurements from each of a plurality of devices, each device being associated with a power flow, each of the devices being capable of measuring the respective device'"'"'s power flow within a measurement error;

aggregating, using at least one computing device, the plurality of power flow measurements, producing an aggregated power flow measurement;

determining, using at least one computing device, the error bounds of the aggregated power flow measurement using at least one error model.

16. The computer-readable medium of claim 15 wherein the aggregated power flow measurement is the sum of the individual measurements of each of the plurality of devices.

17. The computer-readable medium of claim 15 wherein one or more of the at least one error models is an error model for a subset of the plurality of devices comprising devices of an identical type.

18. The computer-readable medium of claim 17 wherein the error model for the subset of the plurality of devices of an identical type holds that all such devices have all measurements within +/−

N % of the true value.

19. The computer-readable medium of claim 15 wherein the at least one error model assumes that power flows at each respective device are independent and identically distributed

20. The computer-readable medium of claim 19 wherein a standard error of the aggregate power flow measurement is calculated by dividing the error bounds of each of the plurality of devices by the square root of the number of devices in the plurality of devices, and wherein the error bounds of the aggregated power flow measurement is calculated by multiplying the standard error by a fixed number of standard deviations corresponding to a desired level of confidence.

21. The computer-readable medium of claim 15 wherein the plurality of devices comprises at least two sets of devices, each set of devices comprising at least one device of the same type and being described by a device error model, wherein the error bounds are computed as a weighted average of the error models of the respective set of devices where the weight is the percentage of total power measured by the respective set of devices.

22. A method comprising

augmenting a device having at least one DC power flow sensor with at least one AC power flow sensor;

measuring, using the at least one DC power flow sensor and the AC power flow sensor, AC and DC power flows through the device over a range of operating points;

building, using at least one computing device, an inference model of AC power flow in the device as a function of DC power flow, wherein the error of the model is bounded;

removing at least one AC power flow sensor from the device;

measuring, using the at least one DC power flow DC power flow for the device;

inferring, using at least one computing device, the AC power flow for the device using the inference model and measurements from the at least one DC power flow sensor.

23. The method of claim 22 wherein the inference model is a linear regression that scales a constant, M, by the DC power measured by the respective sensor plus a fixed offset, B.

24. A system comprising

a plurality of endpoint devices, each endpoint device having at least one sensor for measuring power flow the respective device, wherein one or more of the at least one of sensors are DC sensors;

an inference module comprising one or more processors programmed to execute software code retrieved from a computer readable storage medium storing software for;

receiving measurements, over a network, from each of the at least one sensors;

inferring an AC power flow of the plurality of endpoint devices using the measurements, wherein AC power flow for devices having DC sensors is inferred using an inference model developed by measuring the relationship of AC power flow to DC sensor measurements in devices similar to the respective device.

26. A computer-readable medium having computer-executable instructions for a method comprising the steps of:

augmenting a device having at least one DC power flow sensor with at least one AC power flow sensor;

measuring, using the at least one DC power flow sensor and the AC power flow sensor, AC and DC power flows through the device over a range of operating points;

building, using at least one computing device, an inference model of AC power flow in the device as a function of DC power flow, wherein the error of the model is bounded;

removing at least one AC power flow sensor from the device;

measuring, using the at least one DC power flow DC power flow for the device;

inferring, using at least one computing device, the AC power flow for the device using the inference model and measurements from the at least one DC power flow sensor.

27. The computer-readable medium of claim 26 wherein the inference model is a linear regression that scales a constant, M, by the DC power measured by the respective sensor plus a fixed offset, B.