VIRTUALIZATION, OPTIMIZATION AND ADAPTATION OF DYNAMIC DEMAND RESPONSE IN A RENEWABLE ENERGY-BASED ELECTRICITY GRID INFRASTRUCTURE

• US 20130218548A1
• Filed: 12/30/2012
• Published: 08/22/2013
• Est. Priority Date: 02/16/2012
• Status: Active Grant

• Current Assignee: Spyros J. Lazaris
• Original Assignee: Spyros J. Lazaris
• Inventors: LAZARIS, SPYROS JAMES
• Granted Patent: US 9,188,109 B2
• Time in Patent Office: Days
• US Class Current: 703/18
• CPC Class Codes: F03G 6/00 Devices for producing mechanical power from solar energy solar boilers F24
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 19/02 electric
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06Q 30/0605 Supply or demand aggregation
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00002 characterised by monitoring
• CPC Class Codes: H02J 13/00034 the elements or equipment being or involving an electric power substation
• CPC Class Codes: H02J 2300/20 The dispersed energy generation being of renewable origin
• 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 3/381 Dispersed generators
• CPC Class Codes: H02J 3/46 Controlling of the sharing of output between the generators, converters, or transformers
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02E 10/46 Conversion of thermal power into mechanical power, e.g. Rankine, Stirling or solar thermal engines
• 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: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 10/123 : the energy generation units being or involving renewable energy sources
• CPC Class Codes: Y04S 10/30 : State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge
• CPC Class Codes: Y04S 10/50 : Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
• CPC Class Codes: Y04S 20/222 : Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 50/00 : Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
• CPC Class Codes: Y04S 50/10 : Energy trading, including energy flowing from end-user application to grid
• CPC Class Codes: Y04S 50/16 : Energy services, e.g. dispersed generation or demand or load or energy savings aggregation

Abstract

In a renewable energy-based electricity grid infrastructure, distributed data analytics enable modeling and delivery of an appropriate, time-sensitive, dynamic demand response from multiple renewable energy resource components to an intelligent power distribution network. Distributed data analytics also enable the electricity grid infrastructure to virtually, optimally and adaptively make decisions about power production, distribution, and consumption so that a demand response is a dynamic reaction across the electricity grid infrastructure in a distributed energy generation from multiple renewable energy resources responsive to various types of grid demand situations, such as customer demand, direct current-specific demand, and security issues, and so that power production is substantially balanced with power consumption.

Claims

1. A method of virtualizing dynamic demand response in a renewable energy-based electricity grid infrastructure, comprising:

identifying a power requirement over a specified power of time of one or more microgrids among a plurality of microgrids forming an intelligent power distribution network, each microgrid capable of being separately decoupled and separately generating and communicating data defining the power requirement;

aggregating the data defining the power requirement and additional input data for a plurality of mathematical modeling functions from across different portions of a plurality of remote interconnected computing networks in a shared, secure and privately-hosted computing environment forming a distributed computing infrastructure, wherein at least a portion of the additional input data for the plurality of mathematical modeling functions is requested from one or more external computing networks;

analyzing, across the different portions of the plurality of remote interconnected computing networks in the distributed computing infrastructure, the aggregated data defining the power requirement with the additional input data, within the plurality of mathematical modeling functions configured to continuously model a response to the power requirement with at least one of modeling commodity pricing of one or more renewable energy resources, modeling meteorological conditions, modeling power usage patterns of the one or more microgrids, modeling a power production capacity of multiple renewable energy resource components configured on a multi-resource offshore renewable energy installation, and modeling a load capacity of a power transmission system, and to generate one or more resulting data sets relative to the response to the power requirement;

adapting the one or more resulting data sets relative to the response to the power requirement to data representative of real-time conditions within an electricity grid infrastructure communicated by the intelligent power distribution network, the multi-resource offshore renewable energy installation, and the power transmission system to develop a dynamic demand response that includes a production, transmission and distribution of power optimized to real-time conditions within the renewable energy-based electricity grid infrastructure; and

delivering the dynamic demand response that includes the production, transmission, and distribution of power from the multiple renewable energy resource components configured on the multi-resource offshore renewable energy installation to the intelligent power distribution network,

the distributed computing infrastructure configured to securely communicate, manage, store and process the data defining the power requirement and additional input data for the plurality of mathematical modeling functions so that the mathematical modeling functions are performed across the different portions of the plurality of remote interconnected computing networks to utilize an efficient allocation of available computing resources to minimize weakness points in the renewable energy-based electricity grid infrastructure, to enable real-time scalability and flexibility of a dynamic demand response to the power requirement in response to the real-time conditions, and to facilitate a secure delivery of the dynamic demand response to the power requirement from the multiple renewable energy resource components configured on the multi-resource offshore renewable energy installation to the intelligent power distribution network.

2. The method of claim 1, wherein the delivering the dynamic demand response that includes the production, transmission, and distribution of power further comprises communicating with one or more of the multi-resource offshore renewable energy installation and the power transmission system to deliver the dynamic demand response to the one or more microgrids, the dynamic demand response comprising an instruction to the multi-resource offshore renewable energy installation to produce power for transmission to the intelligent power distribution network and an instruction to the transmission power system to transmit power from the multi-resource offshore renewable energy installation to the intelligent power distribution network.

3. The method of claim 1, wherein the delivering the dynamic demand response that includes the production, transmission, and distribution of power further comprises communicating with one or more of the multi-resource offshore renewable energy installation and the power transmission system to deliver the dynamic demand response to the one or more microgrids, wherein the dynamic demand response is an instruction to the multi-resource offshore renewable energy installation to produce power for transmission to each decoupled microgrid requiring power and an instruction to the power transmission system to separately transmit power to each decoupled microgrid requiring power.

4. The method of claim 1, wherein the identifying a power requirement over a specified power of time of one or more microgrids further comprises receiving data indicative of a request for at least one of a delivery of power to specific decoupled microgrids in the one or more microgrids, a delivery of power in direct current form only to a specific decoupled microgrid, and a delivery of power in at least one specified amount from one or more components in the multiple renewable energy resource components.

5. The method of claim 1, wherein the analyzing the aggregated data defining the power requirement with a plurality of mathematical modeling functions further comprises modeling weather conditions over the specified period of time at a deep-ocean location of the multi-resource offshore renewable energy installation site to predict most favorable weather conditions and least favorable weather conditions for optimal operation of a specific renewable energy resource component, modeling future trends in energy commodity prices over the specified period of time to predict most favorable commodity pricing and least favorable commodity pricing to develop a range of commodity pricing for optimal operation of a specific renewable energy resource component, and modeling the power production capacity of the multiple renewable energy resource components by assessing an operational availability of each specific renewable energy resource component, wherein the renewable energy resource components include at least two of a wind component comprising at least one wind turbine, a photovoltaic component comprising at least one photovoltaic module, a hydrokinetic component comprising at least one of a surface wave turbine, an oscillating column, and an undersea wave turbine, and a solar thermal energy component comprising at least one high-temperature solar thermal collector.

6. The method of claim 1, wherein the analyzing the aggregated data defining the power requirement with a plurality of mathematical modeling functions further comprises modeling the load capacity of the power transmission system by communicating with a transmission control system to monitor a power output circuit of each renewable energy resource component and an output of each voltage source converter in a system of voltage source converters so that a combined power output of the multi-resource offshore renewable energy installation is within the load capacity of a high voltage direct current transmission system to ensure a stable power transmission link between the multi-resource offshore renewable energy installation and the intelligent power distribution network.

7. The method of claim 1, wherein the analyzing the aggregated data defining the power requirement with a plurality of mathematical modeling functions is performed by at least one artificial neural network capable of heuristically modeling the aggregated data.

8. A method of optimizing dynamic demand response in a renewable energy-based electricity grid infrastructure, comprising:

analyzing, across a plurality of remote interconnected computing networks in a shared, secure and privately-hosted computing environment forming a distributed computing infrastructure, aggregated data defining a power requirement over a specific period of time and additional input data relative to a plurality of mathematical modeling functions, the data defining a power requirement communicated by one or more microgrids among a plurality of microgrids forming an intelligent power distribution network, each microgrid capable of being separately decoupled to generate and communicate the power requirement to an energy management system in a renewable energy-based electricity grid infrastructure, the plurality of mathematical modeling functions configured to continuously model a response to the power requirement with at least one of modeling commodity pricing of one or more renewable energy resources, modeling meteorological conditions, modeling power usage patterns of the one or more microgrids, modeling a power production capacity of multiple renewable energy resource components configured on a multi-resource offshore renewable energy installation, and modeling a load capacity of a power transmission system, and to generate one or more resulting data sets relative to the response to the power requirement, wherein the multiple renewable energy resource components include at least two of a wind component comprising at least one wind turbine, a photovoltaic component comprising at least one photovoltaic module, a hydrokinetic component comprising at least one of a surface wave turbine, an oscillating column, and an undersea wave turbine, and a solar thermal energy component comprising at least one high-temperature solar thermal collector;

adapting the one or more resulting data sets to develop, in real time, a dynamic demand response to the power requirement that includes generating and delivering instructions to an offshore multi-resource renewable energy installation and to a power transmission system coupling the multi-resource offshore renewable energy installation to the intelligent power distribution network; and

optimizing an efficient power production capacity and an efficient transmission load capacity to deliver the dynamic demand response from the multi-resource offshore renewable energy installation to the intelligent power distribution network by communicating with the multi-resource offshore renewable energy installation the power transmission system to ensure that distributed energy generation from the multiple renewable energy resource components is within a load capacity of the power transmission system.

9. The method of claim 8, wherein the distributed computing infrastructure is configured to securely communicate, manage, store and process the data defining the power requirement and additional input data relative to the plurality of mathematical modeling functions so that the mathematical modeling functions are performed across different portions of the plurality of remote interconnected computing networks to utilize an efficient allocation of available computing resources to minimize weakness points in a renewable energy-based electricity grid infrastructure, to enable real-time scalability and flexibility of a dynamic demand response to the power requirement in response to real-time conditions within the renewable energy-based electricity grid infrastructure, and to facilitate a secure delivery of the dynamic demand response to the power requirement from the multiple renewable energy resource components configured on the multi-resource offshore renewable energy installation to the intelligent power distribution network.

10. The method of claim 8, wherein the optimizing an efficient power production capacity and an efficient transmission load capacity in response to the instructions to deliver the dynamic demand response further comprises instructing the multi-resource offshore renewable energy resource installation to produce an optimal amount of power from the multiple renewable energy resource component and transmit the optimal amount of power to the intelligent power distribution network.

11. The method of claim 8, wherein the optimizing an efficient power production capacity and an efficient transmission load capacity in response to the instructions to deliver the dynamic demand response further comprises instructing the multi-resource offshore renewable energy resource installation to produce an optimal amount of power from the multiple renewable energy resource components and transmit the optimal amount of power to each decoupled microgrid requiring power.

12. The method of claim 8, wherein the analyzing aggregated data defining a power requirement over a specific period of time and additional input data is performed by at least one artificial neural network capable of heuristically analyzing the aggregated data and continuously modeling the response to the power requirement.

13. The method of claim 8, wherein the data defining the power requirement includes a request for at least one of a delivery of power to specific decoupled microgrids in the one or more microgrids, a delivery of power in direct current form only to a specific decoupled microgrid, and a delivery of power in at least one specified amount from one or more components in the multiple renewable energy resource components.

14. The method of claim 8, wherein the analyzing aggregated data defining a power requirement and additional input data relative to the plurality of mathematical modeling functions further comprises:

modeling weather conditions over the specified period of time at a deep-ocean location of the multi-resource offshore renewable energy installation site to predict most favorable weather conditions and least favorable weather conditions for optimal operation of a specific renewable energy resource component, modeling future trends in energy commodity prices over the specified period of time to predict most favorable commodity pricing and least favorable commodity pricing to develop a range of commodity pricing for optimal operation of a specific renewable energy resource component, and modeling the power production capacity of the multiple renewable energy resource components by assessing an operational availability of each specific renewable energy resource component, and

modeling the load capacity of the power transmission system by communicating with a transmission control system to monitor a power output circuit of each renewable energy resource component and an output of each voltage source converter in a system of voltage source converters so that a combined power output of the multi-resource offshore renewable energy installation is within the load capacity of a high voltage direct current transmission system.

15. An energy management system configured to perform dynamic demand response in a renewable energy-based electricity grid infrastructure, comprising:

a shared, secure and privately-hosted computing environment forming a distributed computing infrastructure configured to securely communicate, manage, store and process aggregated data defining a power requirement and additional input data relative to a plurality of mathematical modeling functions across different portions of a plurality of remote interconnected computing networks to utilize an efficient allocation of available computing resources, to minimize weakness points in a renewable energy-based electricity grid infrastructure and enable real-time scalability and flexibility of a dynamic demand response to the power requirement in response to real-time conditions within a renewable energy-based electricity grid infrastructure;

a plurality of data processing modules resident among the plurality of remote interconnected networks and configured to model a response to the power requirement, the plurality of data processing modules including a grid analytics module configured to perform continuous mathematical modeling of at least one of commodity pricing of one or more renewable energy resources, meteorological conditions, power usage patterns of the one or more microgrids, a power production capacity of multiple renewable energy resource components configured on a multi-resource offshore renewable energy installation, and a load capacity of a power transmission system to generate one or more resulting data sets relative to the response to the power requirement, and an adaptive response module to develop the dynamic demand response to the power requirement from the one or more resulting data sets relative to the response to the power requirement that includes a production, transmission and distribution of power optimized to the real-time conditions within an electricity grid infrastructure communicated by the intelligent power distribution network, the multi-resource offshore renewable energy installation, and the power transmission system; and

a plurality of instructions embodied in output data communicated by the plurality of data processing modules to optimize an efficient power production capacity and produce power and an efficient transmission load capacity to deliver the dynamic demand response from the multi-resource offshore renewable energy installation to the intelligent power distribution network by producing an optimal amount of power from the multiple renewable energy resource components configured on the multi-resource renewable energy installation and transmit the optimal amount of power to the intelligent power distribution network,

the shared, secure and privately-hosted computing environment configured to minimize weakness points in the renewable energy-based electricity grid infrastructure and enable real-time scalability and flexibility of the dynamic demand response to the power requirement in response to the real-time conditions.

16. The energy management system of claim 15, wherein the grid analytics module is further configured to perform continuous mathematical modeling of a power production capacity of multiple renewable energy resource components configured on a multi-resource offshore renewable energy installation by modeling weather conditions over the specified period of time at a deep-ocean location of the multi-resource offshore renewable energy installation site to predict most favorable weather conditions and least favorable weather conditions for optimal operation of a specific renewable energy resource component, modeling future trends in energy commodity prices over the specified period of time to predict most favorable commodity pricing and least favorable commodity pricing to develop a range of commodity pricing for optimal operation of a specific renewable energy resource component, and modeling the power production capacity of the multiple renewable energy resource components by assessing an operational availability of each specific renewable energy resource component.

17. The energy management system of claim 15, wherein the grid analytics module is further configured to perform continuous mathematical modeling of a load capacity of a power transmission system by communicating with at least one transmission control system to monitor a power output circuit of each renewable energy resource component and an output of each voltage source converter in a system of voltage source converters so that a combined power output of the multi-resource offshore renewable energy installation is within the load capacity of the high voltage direct current transmission system.

18. The energy management system of claim 15, wherein the data defining the power requirement includes a request for at least one of a delivery of power to specific decoupled microgrids in the one or more microgrids, a delivery of power in direct current form only to a specific decoupled microgrid, and a delivery of power in at least one specified amount from one or more components in the multiple renewable energy resource components.

19. The energy management system of claim 15, wherein the plurality of mathematical modeling functions are performed by at least one artificial neural network capable of heuristically modeling the aggregated data defining the power requirement and the additional input data in the plurality of data processing modules.

20. The energy management system of claim 15, wherein the renewable energy resource components include at least two of a wind component comprising at least one wind turbine, a photovoltaic component comprising at least one photovoltaic module, a hydrokinetic component comprising at least one of a surface wave turbine, an oscillating column, and an undersea wave turbine, and a solar thermal energy component comprising at least one high-temperature solar thermal collector.