Method and Instrumentation for Sustainable Energy Load Flow Management System (SelfMaster(TM))

• US 20140337002A1
• Filed: 05/08/2013
• Published: 11/13/2014
• Est. Priority Date: 05/08/2013
• Status: Active Grant

• Current Assignee: Instant Access Networks LLC
• Original Assignee: Instant Access Networks LLC
• Inventors: Manto, Charles Leo
• Granted Patent: US 10,169,832 B2
• Time in Patent Office: Days
• US Class Current: 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
• 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 method of simulating the energy balance in an isolated electrical micro grid given changing environmental impacts on the energy production systems (such as wind speed, solar radiation, and ambient temperature (either predicted, actual or both) in relation to identification of the actual input-output characteristics of energy conversion elements;

energy storage units; and

load characteristics in real time, during their operation.

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

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

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

9. The method of claim 7, further comprising the step of using multiple sensors positioned to receive wind speed, solar radiation, sky cover, precipitation, and shading factors.

13. The method of claim 1 further comprising the 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.

2. A method for identification of the actual input-output characteristics of energy conversion elements;

energy storage units; and

load characteristics in real time, during their operation.

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

6. The method of claim 2 further comprising the step of the use of an expert system that learns the behavior of the components evaluating archived data

8. The method of claim 2 further comprising the steps of estimating the energy storage system such as battery charge level in real and then displaying this information and communicating it to other elements of a micro-grid.

10. The method of claim 2, further comprising the step of estimating future energy balance of the system

14. The method of claim 2 further comprising the 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.

11. The method of adaptive system control for optimal load and activity management based on weather and energy availability forecasts

12. The method of claim 11 further comprising the 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.