Computer implemented electrical energy hub management system and method

• US 9,671,843 B2
• Filed: 03/30/2012
• Issued: 06/06/2017
• Est. Priority Date: 03/31/2011
• Status: Active Grant

• Current Assignee: Rodan Energy Solutions Inc.
• Original Assignee: Energent Incorporated (Rodan Energy Solutions Inc.)
• Inventors: Ellis, Gordon D., Canizares, Claudio Adrian, Bhattacharya, Kankar, Chehreghani Bozchalui, Mohammad, Hassen, Hussin, Hashmi, Syed Ahsan
• Primary Examiner(s): Lo, Kenneth M
• Assistant Examiner(s): Pan, Yuhui R
• Application Number:
  US14/008,439
• Publication Number:
  US 20140018971A1
• Time in Patent Office: 1,894 Days
• Field of Search: 700297
• CPC Class Codes: G06F 1/26 Power supply means, e.g. regulation thereof for memories G11C
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 2300/24 of photovoltaic origin
• CPC Class Codes: H02J 2310/14 The load or loads being home appliances
• CPC Class Codes: H02J 2310/64 The condition being economic, e.g. tariff based load management
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: H02J 3/381 Dispersed generators
• CPC Class Codes: Y02B 70/30 Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02E 10/56 Power conversion systems, e.g. maximum power point trackers
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/242 Home appliances
• CPC Class Codes: Y04S 50/10 Energy trading, including energy flowing from end-user application to grid

Abstract

There is disclosed a system, computer program and method provided for enabling an energy hub for improved management and optimization of energy utilization (consumption, production and storage). In an embodiment, a computer-implemented energy hub management system comprises a micro energy hub configured to communicate with two or more energy components at a premises. An energy optimization engine has an energy component model for each energy component based on each energy component'"'"'s operating characteristics, the energy optimization engine adapted to receive at least one input from the two or more energy components and an input from an external data source on any external energy utilization restrictions for the micro energy hub. In response to at least one input from the two or more energy components and any external energy utilization restrictions on the micro energy hub, the energy optimization engine is adapted to issue one or more control signals to at least one of the energy components at the premises to optimize energy utilization based on one or more optimization criteria.

Claims

1. A computer-implemented energy hub management system, comprising:

a micro energy hub configured to communicate with two or more energy components at a premises, the energy components comprising one or more of energy consuming components, one or more of energy generating components, and one or more of energy storing components, at least some of the energy components controllable by the micro energy hub;

wherein the micro energy hub is configured to optimize the energy utilization from the customer'"'"'s point of view;

an energy optimization engine having an energy component model for each energy component based on each energy component'"'"'s operating characteristics, the energy optimization engine adapted to receive at least one input from the two or more energy components, user optimization criteria to minimize one or more of cost, consumption, and carbon footprint, and an input from an external data source on any external energy utilization restrictions for the micro energy hub; and

a macro energy hub embodied in a computing device and adapted to control two or more micro energy hubs, the macro energy hub adapted to aggregate energy management information from the two or more micro energy hubs;

wherein the macro energy hub is configured to optimize the energy utilization from a utility point of view;

wherein, in response to load and capacity information for an energy grid obtained from a central controller, and energy management information aggregated from the two or more micro energy hubs, the macro energy hub is adapted to dynamically manage and enforce specific rules for energy utilization by energy components connected to each micro energy hub by having two or more micro energy hubs issue one or more control signals to at least one of the energy components at the premises to optimize energy consumption, generation and storage;

wherein at least one of the specific rules determines whether customer driven or utility driven factors shall govern in particular circumstances, within a particular time period.

2. The computer-implemented energy hub management system of claim 1, wherein the macro energy hub is adapted to calculate a peak load based on the aggregated energy management information from the two or more micro energy hubs, and if necessary to maintain the peak load within available capacity of the energy grid, communicate additional external energy utilization restrictions to each micro energy hub.

3. The computer-implemented energy hub management system of claim 2, wherein the macro energy hub is adapted to communicate additional external energy utilization restrictions to each micro energy hub based on a rolling schedule.

4. The computer-implemented energy hub management system of claim 1, wherein the energy hub management system is further adapted to hierarchically optimize the macro energy hub and the two or more micro energy hubs to optimize energy utilization on the energy grid.

5. The computer-implemented energy hub management system of claim 4, wherein the one or more macro energy hubs are adapted to temporarily override a schedule for operation of energy components established by the micro energy hubs in order to prevent a brown-out or black-out on the energy grid.

6. The computer-implemented energy hub management system of claim 1, wherein the micro energy hub is adapted to access the energy optimization engine remotely via a cloud network.

7. The computer-implemented energy hub management system of claim 1, wherein the premises is a residence, a commercial premises, an institutional premises, an industrial premises, or an agricultural premises.

8. The computer-implemented energy hub management system of claim 7, wherein a plurality of micro energy hubs are grouped for control by a macro energy hub based on an analysis of aggregate energy information for the grouped micro energy hubs.

9. The computer-implemented energy hub management system of claim 1, wherein each energy component model is based on one or more of measured past behaviour of the operation of the energy component and predicted operational information supplied by a manufacturer or reseller of the energy component.

10. The computer-implemented energy hub management system of claim 1, wherein the micro energy hub is adapted to receive external and environmental data, including energy market prices and weather forecasts.

11. The computer-implemented energy hub management system of claim 1, wherein the micro energy hub is further adapted to communicate to a macro energy hub via an internet web-based interface.

12. The computer-implemented energy hub management system of claim 1, wherein the micro energy hub is operatively connected to one or more smart meters for obtaining energy usage information for the premises.

13. The computer-implemented energy hub management system of claim 1, wherein the energy optimization engine is adapted to optimize energy utilization at each micro energy hub, and to optimize peak load and capacity requirements for an external energy grid.

14. The computer-implemented energy hub management system of claim 1, wherein the energy optimization engine is adapted to operate the energy components based on an established schedule to optimize energy utilization.

15. A computer-implemented method for managing an energy hub, comprising:

configuring a micro energy hub to communicate with two or more energy components at a premises, the energy components comprising one or more of energy consuming components, one or more of energy generating components, and one or more of energy storing components, at least some of the energy components controllable by the micro energy hub;

wherein the micro energy hub is configured to optimize the energy utilization from the customer'"'"'s point of view;

providing an energy optimization engine having an energy component model for each energy component based on each energy component'"'"'s operating characteristics, the energy optimization engine adapted to receive at least one input from the two or more energy components, user optimization criteria to minimize one or more of cost, consumption, and carbon footprint, and an input from an external data source on any external energy utilization restrictions for the micro energy hub;

providing a macro energy hub embodied in a computing device and adapted to control two or more micro energy hubs, the macro energy hub adapted to aggregate energy management information from the two or more micro energy hubs;

wherein the macro energy hub is configured to optimize the energy utilization from a utility point of view; and

in response to receiving load and capacity information for an energy grid obtained from a central controller; and

energy management information aggregated from the two or more micro energy hubs, adapting the macro energy hub to dynamically manage and enforce specific rules for energy utilization by energy components connected to each micro energy hub by having two or more micro energy hubs issue one or more control signals from the energy optimization engine to at least one of the energy components at the premises to optimize energy consumption, generation and storage;

wherein at least one of the specific rules determines whether customer driven or utility driven factors shall govern in particular circumstances, within a particular time period.

16. The computer-implemented method of claim 15, further comprising adapting the macro energy hub to calculate a peak load based on the aggregated energy management information from the two or more micro energy hubs, and if necessary to maintain the peak load within available capacity of the energy grid, communicate additional external energy utilization restrictions to each micro energy hub.

17. The computer-implemented method of claim 16, further comprising adapting the macro energy hub to communicate additional external energy utilization restrictions to each micro energy hub based on a rolling schedule.

18. The computer-implemented method of claim 15, further comprising hierarchically optimizing the macro energy hub and the two or more micro energy hubs to optimize energy utilization on the energy grid.

19. The computer-implemented method of claim 18, further comprising adapting one or more macro energy hubs to temporarily override a schedule for operation of energy components established by the micro energy hubs in order to prevent a brown-out or black-out on the energy grid.

20. The computer-implemented method of claim 15, further comprising adapting the micro energy hub to access the energy optimization engine remotely via a cloud network.

21. The computer-implemented method of claim 15, wherein the premises is a residence, a commercial premises, an institutional premises, an industrial premises, or an agricultural premises.

22. The computer-implemented method of claim 21, further comprising grouping a plurality of micro energy hubs for control by a macro energy hub based on an analysis of aggregate energy information for the grouped micro energy hubs.

23. The computer-implemented method of claim 15, wherein each energy component model is based on one or more of measured past behaviour of the operation of the energy component and predicted operational information supplied by a manufacturer or reseller of the energy component.

24. The computer-implemented method of claim 15, wherein the micro energy hub is adapted to receive external and environmental data, including energy market prices and weather forecasts.

25. The computer-implemented method of claim 15, wherein the micro energy hub is further adapted to communicate to a macro energy hub via an internet web-based interface.

26. The computer-implemented method of claim 15, wherein the micro energy hub is operatively connected to one or more smart meters for obtaining energy usage information for the premises.

27. The computer-implemented method of claim 15, wherein the energy optimization engine is adapted to optimize energy utilization at each energy micro hub, and to optimize peak load and capacity requirements for an external energy grid.

28. The computer-implemented method of claim 15, wherein the energy optimization engine is adapted to operate the energy components based on an established schedule to optimize energy utilization.