PREDICTING AND OPTIMIZING ENERGY STORAGE LIFETIME PERFORMANCE WITH ADAPTIVE AUTOMATION CONTROL SOFTWARE

• US 20160036272A1
• Filed: 07/31/2015
• Published: 02/04/2016
• Est. Priority Date: 07/31/2014
• Status: Abandoned Application

• Current Assignee: Growing Energy Labs, Inc.
• Original Assignee: Growing Energy Labs, Inc.
• Inventors: Wartena, Ryan Craig, Ernst, Zachary Raymond
• Application Number:
  US14/814,510
• Publication Number:
  US 20160036272A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G05B 13/041 in which a variable is automatically adjusted to optimise the performance
• 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 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 2310/14 The load or loads being home appliances
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• 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: 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: Y04S 20/00 Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/242 Home appliances

Abstract

A transactive energy system design is linked to an energy automation control process. A design process provides a predictive analytics engine at its core. This design process includes three models: application modeling, health/asset modeling, and revenue modeling. An energy storage system health model is the combination of the application model with storage life characteristic data that comprises electrical efficiency, effective capacity, and capacity fade as a function of temperature, voltage range, and calendar life. These models enable a predictive analytics engine to inform energy automation control software how to operate. The inventive concept involves utilization of various core data communication methods. The predictive analysis uses the same algorithms and processes as those used in the actual eACS and energy operating system. The continuity from analytics to operations improves the accuracy of the economic models, which reduces risk to financial planning and system financing.

Claims

1. A method of modeling and subsequent operating of an adaptive energy operating system, the method comprising:

modeling energy application performance for an energy asset;

modeling energy asset health for the energy asset;

modeling cost efficiency of the energy asset;

creating a forward operating profile for the energy application; and

creating a forward availability profile for the energy asset.

2. A method as recited in claim 1 further comprising:

combining the forward operating profile and forward availability profile with energy asset characteristics data and historical data, thereby enabling predictive analysis.

3. A method as recited in claim 1 further comprising:

outputting an asset operating profile.

4. A method as recited in claim 1 further comprising:

creating a predictive analytics data package containing the forward operating profile and the forward availability profile; and

inputting said data package to the adaptive energy operating system.

5. A method as recited in claim 1 further comprising:

performing predictive analytics for operation and management of energy devices.

6. A method as recited in claim 1 further comprising:

simulating energy service applications, algorithms, and methods that are used in the adaptive energy operating system when performing the modeling.

7. A method as recited in claim 1 wherein modeling energy asset health further comprises:

examining degradation as a function of use.

8. A method as recited in claim 1 wherein modeling for cost efficiency further comprises:

utilizing a dynamic rate structure library to connect energy with economics.

9. A method as recited in claim 1 wherein modeling for cost efficiency further comprises:

predicting revenue the asset will likely generate over asset lifetime.

10. A method as recited in claim 1 further comprising:

utilizing storage life characteristics data in predictive analysis; and

utilizing efficiency, effective capacity, and capacity fade as a function of charge rate and discharge rates at a given temperature, voltage range, and calendar life of the asset.

11. A method as recited in claim 1 further comprising:

utilizing battery charge and discharge rates;

utilizing temperature, voltage range, and calendar life; and

generating energy storage characteristic functions including efficiency, effective capacity, and capacity fade of a battery.

12. A method as recited in claim 1 further comprising:

utilizing the difference between a prediction derived from modeling and actual operational performance of an asset, wherein a model can be updated.

13. A method as recited in claim 1 further comprising:

re-computing an application profile and forward operating profile, thereby updating the behavior of the asset in real-time.

14. A method as recited in claim 1 further comprising:

calculating an asset forward availability profile to fulfill an application forward operating profile; and

storing said forward availability profile and said forward operating profile in a predictive analytics data package.

15. A method of operating an adaptive energy operating system in communication with one or more energy assets, the method comprising:

receiving a forward availability profile for an asset and a forward operating profile for an application;

receiving a predictive analytics data package containing models;

collecting runtime operation profile data and runtime asset profile data;

comparing runtime operation profile data and runtime asset profile data with models;

transforming asset profile data into energy asset life characteristic data; and

updating forward availability profile and forward operating profile.

16. A method as recited in claim 15 wherein the models include an application performance model, an asset/health model, and a financial model.

17. A method as recited in claim 15 further comprising:

executing an application by a controlling asset.

18. A method as recited in claim 15 further comprising:

updating the predictive analytics data package.

19. An adaptive energy operating system comprising:

a predictive analytics engine;

one or more energy-related applications;

a server for creating and utilizing a forward operating profile and a forward availability profile; and

energy automation control software.

20. An adaptive energy operating system as recited in claim 19 further comprising:

one or more energy device drivers.