Method and instrumentation for sustainable energy load flow management system performing as resilient adaptive microgrid system

• US 10,169,832 B2
• Filed: 05/08/2013
• Issued: 01/01/2019
• Est. Priority Date: 05/08/2013
• Status: Active Grant

• Current Assignee: Instant Access Networks LLC
• Original Assignee: Instant Access Networks LLC
• Inventors: Soysal, Oguz, Soysal, Hilkat, Manto, Charles Leo
• Primary Examiner(s): Rivas, Omar Fernandez
• Assistant Examiner(s): Khan, Iftekhar
• Application Number:
  US13/889,867
• Publication Number:
  US 20140337002A1
• Time in Patent Office: 2,064 Days
• Field of Search: 703 18
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00002 characterised by monitoring
• CPC Class Codes: H02J 13/00004 characterised by the power network being locally controlled
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 2300/22 The renewable source being solar energy
• CPC Class Codes: H02J 2300/24 of photovoltaic origin
• CPC Class Codes: H02J 2300/28 The renewable source being wind energy wind motors F03D
• CPC Class Codes: H02J 2310/10 The network having a local or delimited stationary reach
• 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: H02J 3/28 Arrangements for balancing of the load in a network by storage of energy
• CPC Class Codes: H02J 3/30 using dynamo-electric machines coupled to flywheels
• CPC Class Codes: H02J 3/32 using batteries with converting means
• 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: 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 10/56 Power conversion systems, e.g. maximum power point trackers
• CPC Class Codes: Y02E 10/76 Power conversion electric or electronic aspects
• CPC Class Codes: Y02E 40/70 : Smart grids as climate change mitigation technology in the energy generation sector
• CPC Class Codes: Y04S 10/123 : the energy generation units being or involving renewable energy sources
• CPC Class Codes: Y04S 20/12 : Energy storage units, uninterruptible power supply [UPS] systems or standby or emergency generators, e.g. in the last power distribution stages
• CPC Class Codes: Y04S 20/222 : Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/242 : Home appliances

Abstract

Disclosed is a method and instrumentation for predictive and adaptive controllers devised to ensure uninterrupted operation of standalone electrical supply systems powered by sustainable energy sources. The device hereby referred to as SelfMaster™ is an expert system that manages the energy conversion, storage, and consumption in an isolated electric grid based on data collected during past and current operation of the system and predicted future states of the primary energy sources, storage level, and demand. The sustainable primary energy sources managed by SelfMaster™ may include, but not limited to, wind force, solar radiation, and biofuels. The energy storage system is a combination of batteries, hydrogen, biofuel, and hot water tanks. Electric demand consists of critical, non-critical, and deferrable loads identified according to the activities supported by the supply system.

Claims

1. A system and method of managing microgrids using Observer, Resource Estimator, Simulator, Scheduler, and Controller routines each considered as a separate virtual device created in the computer software as separate functions, comprising the steps of:

using a processor of a computing device to check collected processed data to determine the status of the micro-grid;

using the processor to determine if an electric battery bank is full, and if so then to command channelling excess energy to be stored in non-electrical form;

using the processor to determine if a secondary energy storage system is full, and if so then to command diverting power into dummy loads;

if the battery bank is not full, then using the processor to determine the likely available energy given weather data collected and weather forecasted in an observer routine, the step using a resource estimator routine to determine an amount of likely available energy required for an adequate level of electric storage, and then to;

i) run a load scheduler which limits use of deferrable loads by sending signals from a controlling computer to turn off deferrable loads according to priorities until a simulation indicates an adequate level of electric storage;

ii) then, if the level of electric storage is not indicated to be adequate, command on the use of the secondary energy storage system;

then,

iii) provide alarms and alerts to the system management system showing the use of the energy storage system;

then,

iv) check the rate of the energy storage system and continue to use the auxiliary energy system until the rate changes in order to meet the adequate level of electric storage;

v) compare an amount of energy reserve in the secondary storage system and the observer routine to determine projected power coming into the system, and using the resource estimator routine to determine the amount of time the auxiliary storage system will provide the needed amount of energy and provide alerts and alarms to the system management system;

using the processor to determine if the auxiliary energy storage system continues to discharge, and if so then, determine priorities of critical loads and begin to reduce the critical loads, maintaining the power management and alert system as the most critical load;

then

continue to send alarms and alerts to the management system;

using the processor to determine if the auxiliary management system continues to discharge at an unacceptable rate, and if so then begin the final safe and orderly shut down of the system while maintaining a minimum of power and system management of the system;

wherein each of the above steps utilizes at least one particular machine, said at least one particular machine comprising a computer and related industrial controls necessary to adjust power sources, storage systems and power using applications.

2. The method of claim 1 further comprising a step of collecting data to form a knowledge base for identification of actual component characteristics.

3. The method of claim 1 further comprising a step of the use of an expert system that learns the behavior of the components evaluating archived data.

4. The method of claim 1 further comprising steps of estimating the future states of the stochastic wind and solar resources, including temperature and precipitation, upon weather forecast data.

5. The method of claim 4, further comprising a step of using multiple sensors positioned to receive wind speed, solar radiation, sky cover, precipitation, and shading factors.

6. The method of claim 1 further comprising steps of assessing the amount of energy needed for critical loads in an energy system and considering the energy storage system itself as the most critical load to be maintained in order to protect the other critical loads.

7. The method of claim 1 further comprising a step of coordinating with a sponsored services routine whereby loads are provided to applications and users from accounts of those willing to pay for those loads.

8. The method of claim 1 further comprising a step of provisioning the microgrid with electromagnetic pulse protection.

9. The method of claim 1, wherein the non-electric form comprises at least one selected from the group consisting of:

hydrogen, methane, other gaseous or liquid fuels, biofuel production, thermal, kinetic, or potential energy.

10. The method of claim 1, wherein the non-electric form comprises hot water.

11. The method of claim 1, wherein the non-electric form comprises a flywheel.

12. The method of claim 1, wherein the non-electric form comprises compressed air or pumped water.