Cogeneration networks

• US 10,132,271 B2
• Filed: 06/03/2016
• Issued: 11/20/2018
• Est. Priority Date: 03/23/2012
• Status: Active Grant

• Current Assignee: Concentric Power, Inc.
• Original Assignee: Concentric Power, Inc.
• Inventors: Curtis, Brian Mark
• Primary Examiner(s): Masinick, Michael D
• Application Number:
  US15/172,834
• Publication Number:
  US 20160356243A1
• Time in Patent Office: 900 Days
• CPC Class Codes: F01K 13/00 General layout or general methods of operation of complete plants
• CPC Class Codes: F01K 13/02 Controlling, e.g. stopping or starting
• CPC Class Codes: F02B 63/04 for electric generators
• CPC Class Codes: F02D 25/00 Controlling two or more co-operating engines
• CPC Class Codes: F02D 29/06 peculiar to engines driving electric generators
• CPC Class Codes: F02G 5/00 Profiting from waste heat of combustion engines, not otherwise provided for
• CPC Class Codes: F25B 2400/06 Several compression cycles arranged in parallel
• CPC Class Codes: F25B 27/00 Machines, plants or systems, using particular sources of energy F25B30/06 takes precedence
• CPC Class Codes: G05B 19/406 characterised by monitoring or safety G05B19/19 takes precedence
• CPC Class Codes: G05B 2219/31334 Database with devices, configuration, of plant
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02K 7/1815 structurally associated with reciprocating piston engines general aspects of generating sets, e.g. housing, F02B63/04
• CPC Class Codes: Y02E 20/14 Combined heat and power generation [CHP]
• CPC Class Codes: Y02P 80/15 On-site combined power, heat or cool generation or distribution, e.g. combined heat and power [CHP] supply

Abstract

The invention provides systems and methods for a network of cogenerations systems. In some cases, each system includes at least one cogeneration plant and at least one host facility, where the systems in the network are under the control of a common control system that optimizes a result for the network as a whole. In some cases, each cogeneration system in the network has an individual profile that is used by the control system for controlling that individual cogeneration system. In some cases, one or more of the cogeneration plants is assembled from a set of at least two modular transportable units where the modular units contain sensors that are configured to be ready for transmission of inputs to the common control system with little or no modification when the units are assembled into the final cogeneration plant and actuators or actuator systems that are configured to be ready for reception of outputs from the common control system with little or no modification when the units are assembled into the cogeneration plant.

Claims

1. A cogeneration network comprising

(i) a plurality of cogeneration systems comprising a first and second cogeneration system, wherein the first and second cogeneration systems are different, wherein each cogeneration system comprises a cogeneration plant operably connected to a host facility that receives a thermal product and/or mechanical work product and, optionally, electrical power from the cogeneration plant;

and

(ii) a common controller that is operably connected to the plurality of cogeneration systems wherein the common controller

(a) receives inputs from a plurality of sensors in or near each of the plurality of cogeneration systems;

(b) processes the inputs to determine a plurality of outputs, and

(c) transmits the outputs to a plurality of actuators in the plurality of cogeneration systems; and

wherein the common controller is configured to learn by periodically updating a profile for a first cogeneration system in the network, wherein the profile for the first cogeneration system comprises at least one performance curve for at least one of a unit comprising one or more prime mover/generators, one or more heat transfer systems, one or more thermal product carrier producers, or one or more cooling towers, and wherein the performance curves are not off-the-shelf curves provided by manufacturers of the components but comprise curves produced after the commissioning of the first cogeneration system, during operation of the system over time, and applying the profile to a second cogeneration system in the network.

2. The cogeneration network of claim 1 wherein the controller is configured to learn from an outcome of one or more previous outputs and adjust the determining of future outputs based on the learning, or on an override of an output or a plurality of overrides of outputs by an operator of the host facility, or a combination thereof.

3. The cogeneration network of claim 1 wherein the controller is configured to learn by changing one or more outputs directed at a system or subsystem of one or more components of at least one cogeneration system of the network, during operation of the system, to be different from the output of a current controller, evaluating the results to determine if the change in output produced a better or worse result than would have been expected from the current controller, and adjusting algorithms used by the processing system according to the results.

4. The cogeneration network of claim 3 wherein the algorithms are adjusted to apply to cogeneration systems in the network that were not subject to the changing of one or more outputs.

8. The network of claim 1 wherein the controller is configured so that the processing of step (ii)(b) comprises a forecast step, and wherein the forecast step forecasts a future value or range of values, or a probability of a future value or range of values, for a fuel price, an electricity export price, an electricity import price, an ambient environmental condition, an emissions limit, an incentive for the cogeneration plant, a price for a demand response action, a price for a thermal product, a price for water, an electrical demand from a host facility, or a thermal product demand from a host facility, wherein the future value or range of future values, or probability of a future value or range of future values, is a value or range of values or probability of a value or range of values for at least one of the cogeneration systems;

and

wherein the controller is configured so that the processing step of ii(b) may comprise determining a change in one or more set points for one or more the actuators based at least in part on one or more of the forecast values or range of values.

9. The network of claim 8 wherein the common controller further comprises a data storage unit for storing a value for a timing and/or result that occurs after the change in the one or more set points, and wherein the controller is configured to adjust the processing step based on one or more of the results of a previous output, such as a result stored in the data storage unit, to improve the function of the network in the future.

10. The network of claim 1 comprising at least 3 different cogeneration systems.

11. The network of claim 1 comprising at least 10 different cogeneration systems.

12. The network of claim 1 wherein the common controller is configured to learn in a process that comprises learning from an outcome of an output at the first cogeneration plant in the plurality of cogeneration plants and applying the learning to the determining step for an output for the second cogeneration plant in the plurality of cogeneration plants.

13. The network of claim 1 wherein the control system learns by artificial intelligence, machine learning, or evolutionary algorithms.

14. The network of claim 1 wherein the control system establishes a profile for each of the first and second cogeneration systems and groups the first and second cogeneration systems into a peer group based on their profiles.

15. The network of claim 1 wherein the common controller further receives inputs from indicators of market conditions for the first and the second cogeneration systems, wherein the market conditions comprise at least one of a price for a fuel for the first and/or second cogeneration systems;

a price for electrical energy exported from the first and/or second cogeneration systems;

a price for imported electrical energy to the first and/or second cogeneration systems;

a price for an incentive for the first and/or second cogeneration systems;

a price for a thermal product produced by the first and/or second cogeneration systems;

a price for water for the first and/or second cogeneration systems;

, or a variable maintenance price for at the first and/or second cogeneration systems.

16. The network of claim 1 wherein the common controller further receives inputs from indicators of market conditions for the first and the second cogeneration systems, wherein the market conditions comprise at least two of a price for a fuel for the first and/or second cogeneration systems;

a price for electrical energy exported from the first and/or second cogeneration systems;

a price for imported electrical energy to the first and/or second cogeneration systems;

a price for an incentive for the first and/or second cogeneration systems;

a price for a thermal product produced by the first and/or second cogeneration systems;

a price for water for the first and/or second cogeneration systems;

, or a variable maintenance price for at the first and/or second cogeneration systems.

17. The network of claim 1 wherein the common controller receives input indicating a desired future modulation in a condition of at least one of the host facilities of the first or second cogeneration systems.

5. A controller for a network comprising a plurality of cogeneration systems, wherein the controller is configured to

(i) receive inputs from a plurality of sensors in or near each of the plurality of cogeneration systems;

(ii) process the inputs to determine a plurality of outputs, and

(iii) transmit the outputs to a plurality of actuators in the plurality of cogeneration systems;

wherein the common controller is configured to learn, and wherein said learning comprises

(a) learning from an outcome of one or more previous outputs and adjusting the determining of future outputs based on the learning, or on an override of an output or a plurality of overrides of outputs by an operator of the host facility, or a combination thereof

(b) periodically updating a profile for a first cogeneration system in the network, wherein the profile for the first cogeneration system comprises at least one performance curve for at least one of a unit comprising one or more prime mover/generators, one or more heat transfer systems, one or more thermal product carrier producers, or one or more cooling towers, and wherein the performance curves are not off-the-shelf curves provided by manufacturers of the components but comprise curves produced after the commissioning of the first cogeneration system, during operation of the system over time, and applying the profile to a second cogeneration system in the network; and

(c) by changing one or more outputs directed at a system or subsystem of one or more components of at least one cogeneration system of the network, during operation of the system, to be different from the output of a current controller, evaluating the results to determine if the change in output produced a better or worse result than would have been expected from the current controller, and adjusting algorithms used by the processing system according to the results.

6. The controller of claim 5 wherein said learning comprises at least one of

(i) learning from an outcome of one or more previous outputs and adjusting the determining of future outputs based on the learning, or on an override of an output or a plurality of overrides of outputs by an operator of the host facility, or a combination thereof;

(ii) periodically updating a profile for a first cogeneration system in the network, wherein the profile for the first cogeneration system comprises at least one performance curve for at least one of a unit comprising one or more prime mover/generators, one or more heat transfer systems, one or more thermal product carrier producers, or one or more cooling towers, and wherein the performance curves are not off-the-shelf curves provided by manufacturers of the components but comprise curves produced after the commissioning of the first cogeneration system, during operation of the system over time, and applying the profile to a second cogeneration system in the network;

or

(iii) by changing one or more outputs directed at a system or subsystem of one or more components of at least one cogeneration system of the network, during operation of the system, to be different from the output of a current controller, evaluating the results to determine if the change in output produced a better or worse result than would have been expected from the current controller, and adjusting algorithms used by the processing system according to the results.

7. The controller of claim 5 wherein said learning comprises at least two of

(i) learning from an outcome of one or more previous outputs and adjusting the determining of future outputs based on the learning, or on an override of an output or a plurality of overrides of outputs by an operator of the host facility, or a combination thereof;

(ii) periodically updating a profile for a first cogeneration system in the network, wherein the profile for the first cogeneration system comprises at least one performance curve for at least one of a unit comprising one or more prime mover/generators, one or more heat transfer systems, one or more thermal product carrier producers, or one or more cooling towers, and wherein the performance curves are not off-the-shelf curves provided by manufacturers of the components but comprise curves produced after the commissioning of the first cogeneration system, during operation of the system over time, and applying the profile to a second cogeneration system in the network;

or

(iii) by changing one or more outputs directed at a system or subsystem of one or more components of at least one cogeneration system of the network, during operation of the system, to be different from the output of a current controller, evaluating the results to determine if the change in output produced a better or worse result than would have been expected from the current controller, and adjusting algorithms used by the processing system according to the results.