METHOD AND SYSTEM FOR ALLOCATING ENERGY

• US 20140094983A1
• Filed: 09/20/2013
• Published: 04/03/2014
• Est. Priority Date: 09/28/2012
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Dykeman, Harold D., Gallay, Olivier, Sundstroem, Olle L., Szabo, Jacint
• Granted Patent: US 9,705,335 B2
• Time in Patent Office: Days
• US Class Current: 700/295
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00017 using optical fiber
• CPC Class Codes: H02J 13/00028 involving the use of Internet protocols
• CPC Class Codes: H02J 3/008 involving trading of energy or energy transmission rights
• CPC Class Codes: H02J 4/00 Circuit arrangements for mains or distribution networks not specified as ac or dc
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y02E 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 10/12 Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
• 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 40/124 using wired telecommunication networks or data transmission busses
• CPC Class Codes: Y04S 50/10 Energy trading, including energy flowing from end-user application to grid

Abstract

A method for allocating energy to a plurality of devices, wherein each device is configured to consume, store and/or supply energy, the method includes the steps of: assigning each device to a group of devices; assigning an aggregation node device to each group of devices; for a selection of devices transmitting local power cost functions of the devices and/or power usage profiles of the devices with respect to a predetermined time slot to the assigned aggregation node device; at the aggregation node device, generating aggregated data as a function of the received local power cost functions and/or power usage profiles; transmitting the aggregated data to a central processing device; at the central processing device, optimizing a global cost function for allocating power to the devices as a function of the aggregated data.

Claims

1. A method for allocating energy to a plurality of devices, wherein each device is configured to consume, store and/or supply energy, the method comprising:

assigning each device to a group of devices;

assigning an aggregation node device to each group of devices;

for a selection of devices, transmitting local power cost functions of the devices and/or power usage profiles of the devices with respect to a predetermined time slot to the assigned aggregation node device;

at the aggregation node device, generating aggregated data as a function of the received local power cost functions and/or power usage profiles;

transmitting the aggregated data to a central processing device; and

at the central processing device, optimizing a global cost function for allocating power to the devices as a function of the aggregated data.

2. The method of claim 1, further comprising:

generating optimization data for each device at the central processing device.

3. The method of claim 2, further comprising:

transmitting the optimization data to the devices via the aggregation node devices.

4. The method of claim 2, further comprising:

optimizing the local power cost functions as a function of the optimization data at the devices.

5. The method of claim 2, further comprising:

for at least one device of a group, optimizing the local power cost function as a function of the optimization data for the at least one device at the assigned aggregation node device.

6. The method of claim 4, wherein the step of optimizing the local power cost function at a device is independent from the local power cost function of the device and/or power usage profile of the device with respect to a prior time slot.

7. The method of claim 1, further comprising:

for at least one device of a group, providing the assigned aggregation node device with the local power cost function of the at least one device and/or the power usage profile of the at least one device with respect to the predetermined time slot.

8. The method of claim 1, further comprising:

changing an assignment of a device from a first group to a second group.

9. The method of claim 1, wherein optimizing comprises minimizing the global cost function for allocating power to the devices as a function of the aggregated data.

10. The method of claim 1, wherein aggregating comprises combining the local power cost functions of the devices and/or power usage profiles of the devices for obtaining a group cost function or a group power usage profile.

11. The method of claim 1, wherein the steps of:

transmitting local power cost functions of the devices and/or power usage profiles of the devices with respect to a predetermined time slot to the assigned aggregation node device;

generating aggregated data as a function of the received local power cost functions and/or power usage profiles;

transmitting the aggregated data to the central processing device;

optimizing the global cost function for allocating power to the devices as a function of the aggregated data;

generating optimization data for each device at the central processing device;

transmitting the optimization data to the devices via the aggregation node devices; and

optimizing the local power cost functions as a function of the optimization data are performed iteratively.

12-15. -15. (canceled)