DETAILED ACTION
This action is in response to arguments filed 3/2/2026. Claims 1-31 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 8/20/2025 have been fully considered.
A) Applicant's arguments with respect to the 102 rejection of claims 1, 3, 5, 7, 9, 11 and 13 that De Riddle et al does not teach the claim limitation of “managing a peak load of the system based on at least the selected aggregated information” have been fully considered but they are not persuasive.
Regarding A) De Riddle teaches in paragraph 0240 i.e. This embodiment comprises a smart grid equipped with a two-way communication system. Each participant exchanges information with others through the communication infrastructure in order to converge to the optimal power consumption schedule and optimize its own local objectives without sharing private information with other users. This strategy will be illustrated on a district level with quite a lot of heat-pumps, so that coordination between the end-users becomes necessary in order to avoid overload of the distribution network and paragraph 0246 i.e. A first class often encountered in literature are so-called direct control methods ([6], [7]). Here consumers and producers aggregate all information and a central unit computes the optimal schedule, taking into account all local and global constraints. Next individual control actions are sent to each device).
While applicant states De Riddle does teaches away from aggregation at a central computer as being impractical. Just because De Riddle proposes a different aggregation solution for managing peak load does not mean that De Riddle does not teach the claim limitation of “managing a peak load of the system based on at least the selected aggregated information” as the claim states “broadcasting selected aggregated information with or without noise to a system operator or agents for each group” which is what De Riddle solution is for sending aggregated information the an agent for each group instead of the system operator.
B) Applicant's arguments with respect to the 102 rejection of claims 1, 3, 5, 7, 9, 11 and 13 that De Riddle et al does not teach the claim limitation of “locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods” have been fully considered but they are not persuasive.
Regarding B) De Ridder teach the new claim limitation of “locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods” in paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented.
C) Applicant's arguments with respect to the 103 rejection of claims 1, 3, 5, 7, 9, 11 and 13 that GENC in view of Shaffer do not teach the claim limitation of “locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods” have been fully considered but they are not persuasive.
GENC et al teaches “aggregating load” in paragraph 0022 i.e. Operating data may be transmitted and received between the control system 130 and the plurality of loads on the power grid 140. For example, information about set points, zone temperatures, air flow, and the like, may be communicated to the control system 130. Also, data may be transmitted and received between the control system 130 and the substations 112 and 122. Based on the data that is communicated to the control system 130, the control system 130 may control the amount of operating power provided to the loads. In an example in which the loads are TCLs or the like, the loads may provide values such as temperature related values to the control system 130 in order to enable the control system 130 to forecast the flexibility of the power grid 140.
Shaffer teaches locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)). Therefore
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 5, 7, 9, 11, 13, 15-21 and 29-31 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by De Ridder et al (US 2016/0105023).
With respect to claim 1 De Ridder teaches a privacy preserving method for peak load management of groups in a power and/or energy system, the method comprising:
locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented);
broadcasting selected aggregated information with or without noise to a system operator or agents for each group; and, managing a peak load of the system based on at least the selected aggregated information, whereby the managing preserves privacy and attains improved, near- optimal or optimal solutions (See De Ridder paragraph 0240 i.e. This embodiment comprises a smart grid equipped with a two-way communication system. Each participant exchanges information with others through the communication infrastructure in order to converge to the optimal power consumption schedule and optimize its own local objectives without sharing private information with other users. This strategy will be illustrated on a district level with quite a lot of heat-pumps, so that coordination between the end-users becomes necessary in order to avoid overload of the distribution network and paragraph 0246 i.e. A first class often encountered in literature are so-called direct control methods ([6], [7]). Here consumers and producers aggregate all information and a central unit computes the optimal schedule, taking into account all local and global constraints. Next individual control actions are sent to each device).
With respect to claim 3 De Ridder teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising:
a group agent configured to locally optimize or improve activities, aggregate load for a group, and determine selected aggregated information based on sums of energy consumption profiles for selected time periods (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented); and,
a system operator configured to selectively manage a peak load of the system based on at least the aggregated information to preserve privacy and attain improved, near-optimal or optimal solutions (See De Ridder paragraph 0240 i.e. This embodiment comprises a smart grid equipped with a two-way communication system. Each participant exchanges information with others through the communication infrastructure in order to converge to the optimal power consumption schedule and optimize its own local objectives without sharing private information with other users. This strategy will be illustrated on a district level with quite a lot of heat-pumps, so that coordination between the end-users becomes necessary in order to avoid overload of the distribution network and paragraph 0246 i.e. A first class often encountered in literature are so-called direct control methods ([6], [7]). Here consumers and producers aggregate all information and a central unit computes the optimal schedule, taking into account all local and global constraints. Next individual control actions are sent to each device).
With respect to claim 5 De Ridder teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising at least one processor and at least one memory having stored thereon code or instructions that, when executed by the processor, cause the system to:
locally optimize or improve activities and aggregate load in each group agent based on sums of energy consumption profiles for selected time periods (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented);
broadcast selected aggregated information with or without noise to a system operator or agents for each group; and, manage a peak load of the system based on at least the selected aggregated information, whereby the managing preserves privacy and attains improved, near- optimal or optimal solutions (See De Ridder paragraph 0240 i.e. This embodiment comprises a smart grid equipped with a two-way communication system. Each participant exchanges information with others through the communication infrastructure in order to converge to the optimal power consumption schedule and optimize its own local objectives without sharing private information with other users. This strategy will be illustrated on a district level with quite a lot of heat-pumps, so that coordination between the end-users becomes necessary in order to avoid overload of the distribution network and paragraph 0246 i.e. A first class often encountered in literature are so-called direct control methods ([6], [7]). Here consumers and producers aggregate all information and a central unit computes the optimal schedule, taking into account all local and global constraints. Next individual control actions are sent to each device).
With respect to claim 7 De Ridder teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising:
a group agent comprising at least one processor and at least one memory having stored thereon code or instructions that, when executed by the processor cause the system to: to based on sums of energy consumption profiles for selected time periods (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented),
locally optimize or improve activities, aggregate load for a group, and transmit selected aggregated information to a system operator configured to selectively manage a peak load of the system based on at least the aggregated information to preserve privacy and attain improved, near-optimal or optimal solutions (See De Ridder paragraph 0240 i.e. This embodiment comprises a smart grid equipped with a two-way communication system. Each participant exchanges information with others through the communication infrastructure in order to converge to the optimal power consumption schedule and optimize its own local objectives without sharing private information with other users. This strategy will be illustrated on a district level with quite a lot of heat-pumps, so that coordination between the end-users becomes necessary in order to avoid overload of the distribution network and paragraph 0246 i.e. A first class often encountered in literature are so-called direct control methods ([6], [7]). Here consumers and producers aggregate all information and a central unit computes the optimal schedule, taking into account all local and global constraints. Next individual control actions are sent to each device)
With respect to claim 9 De Ridder teaches a privacy preserving method for peak load management of groups in a power and/or energy system, the method comprising:
a group agent locally optimizing or improving activities based on sums of energy consumption profiles for selected time periods (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented);
aggregating load for a group; and, transmitting to a system operator selected aggregated information, the system operator being configured to selectively manage a peak load of the system based on at least the aggregated information to preserve privacy and attain improved, near-optimal or optimal solutions (See De Ridder paragraph 0240 i.e. This embodiment comprises a smart grid equipped with a two-way communication system. Each participant exchanges information with others through the communication infrastructure in order to converge to the optimal power consumption schedule and optimize its own local objectives without sharing private information with other users. This strategy will be illustrated on a district level with quite a lot of heat-pumps, so that coordination between the end-users becomes necessary in order to avoid overload of the distribution network and paragraph 0246 i.e. A first class often encountered in literature are so-called direct control methods ([6], [7]). Here consumers and producers aggregate all information and a central unit computes the optimal schedule, taking into account all local and global constraints. Next individual control actions are sent to each device).
With respect to claim 11 De Ridder teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising: a system operator comprising at least one processor and at least one memory having stored thereon code or instructions that, when executed by the processor, cause the system to receives iteratively selected aggregated information based on sums of energy consumption profiles for selected time periods representing aggregate load from at least one group agent (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented),
selectively manages a peak load of the system based on at least the aggregated information to preserve privacy and attains improved, near-optimal or optimal solutions (See De Ridder paragraph 0240 i.e. This embodiment comprises a smart grid equipped with a two-way communication system. Each participant exchanges information with others through the communication infrastructure in order to converge to the optimal power consumption schedule and optimize its own local objectives without sharing private information with other users. This strategy will be illustrated on a district level with quite a lot of heat-pumps, so that coordination between the end-users becomes necessary in order to avoid overload of the distribution network and paragraph 0246 i.e. A first class often encountered in literature are so-called direct control methods ([6], [7]). Here consumers and producers aggregate all information and a central unit computes the optimal schedule, taking into account all local and global constraints. Next individual control actions are sent to each device).
With respect to claim 13 De Ridder teaches a privacy preserving method for peak load management of groups in a power and/or energy system, the method comprising: a system operator receiving iteratively selected aggregated information based on sums of energy consumption profiles for selected time periods representing aggregate load from at least one group agent (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented); and selectively managing a peak load of the system based on at least the aggregated information to preserve privacy and attain improved, near-optimal or optimal solutions (See De Ridder paragraph 0240 i.e. This embodiment comprises a smart grid equipped with a two-way communication system. Each participant exchanges information with others through the communication infrastructure in order to converge to the optimal power consumption schedule and optimize its own local objectives without sharing private information with other users. This strategy will be illustrated on a district level with quite a lot of heat-pumps, so that coordination between the end-users becomes necessary in order to avoid overload of the distribution network and paragraph 0246 i.e. A first class often encountered in literature are so-called direct control methods ([6], [7]). Here consumers and producers aggregate all information and a central unit computes the optimal schedule, taking into account all local and global constraints. Next individual control actions are sent to each device).
With respect to claim 15 De Ridder teaches the method as set forth in claim 1 wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented).
With respect to claim 16 De Ridder teaches the system as set forth in claim 3 wherein the system is further configured to aggregate load in each group agent based on only sums of energy consumption profiles for selected time periods and linear calculations (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented).
With respect to claim 17 De Ridder teaches the system as set forth in claim 5 wherein the system is further configured to aggregate load in each group agent based on only sums of energy consumption profiles for selected time periods and linear calculations (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented).
With respect to claim 18 De Ridder teaches the system as set forth in claim 7 wherein the system is further configured to aggregate load in each group agent based on only sums of energy consumption profiles for selected time periods and linear calculations (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented).
With respect to claim 19 De Ridder teaches the method as set forth in claim 9 wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented).
With respect to claim 20 De Ridder teaches the system as set forth in claim 11 wherein the system is further configured to aggregate load in each group agent based on only sums of energy consumption profiles for selected time periods and linear calculations (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented).
With respect to claim 21 De Ridder teaches the method as set forth in claim 13 wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented).
With respect to claim 29 De Ridder teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising at least one processor and at least one memory having stored thereon code or instructions that, when executed by the processor, cause the system to:
locally optimize or improve activities and aggregate load in each group agent based on sums of energy consumption profiles for selected time periods (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented);
broadcast selected aggregated information with or without noise to a system operator or agents for each group (See De Ridder paragraph 0240 i.e. This embodiment comprises a smart grid equipped with a two-way communication system. Each participant exchanges information with others through the communication infrastructure in order to converge to the optimal power consumption schedule and optimize its own local objectives without sharing private information with other users. This strategy will be illustrated on a district level with quite a lot of heat-pumps, so that coordination between the end-users becomes necessary in order to avoid overload of the distribution network and paragraph 0246 i.e. A first class often encountered in literature are so-called direct control methods ([6], [7]). Here consumers and producers aggregate all information and a central unit computes the optimal schedule, taking into account all local and global constraints. Next individual control actions are sent to each device);
manage a peak load of the system based on at least the selected aggregated information to preserve privacy and attain improved, near-optimal or optimal solutions by linear calculation of peak load (See De Ridder paragraph 0240 i.e. This embodiment comprises a smart grid equipped with a two-way communication system. Each participant exchanges information with others through the communication infrastructure in order to converge to the optimal power consumption schedule and optimize its own local objectives without sharing private information with other users. This strategy will be illustrated on a district level with quite a lot of heat-pumps, so that coordination between the end-users becomes necessary in order to avoid overload of the distribution network and paragraph 0246 i.e. A first class often encountered in literature are so-called direct control methods ([6], [7]). Here consumers and producers aggregate all information and a central unit computes the optimal schedule, taking into account all local and global constraints. Next individual control actions are sent to each device); and,
employ a decomposition to solve for the solutions by using a Lagrange function (see De Ridder paragraph 0025-0026 i.e. In embodiments of the present invention, the steering signal may be a Lagrange multiplier. The steering signal in embodiments of the present invention is not overruling a local controller, but is an incentive to adapt behavior. The allows the distributed control system to find its own solution. Suppose, for example a local grid has an small energy shortage. The network can easily continue working if some devices lower their consumption and/or some others increase their production. What these embodiments do is not to overrule the devices. Instead, the centre can alter the Lagrange multiplier to promote a change in behaviour. Local controllers are free to decrease consumption or to ignore this incentive. If the problem is not solved, the centre can increase the value of the Lagrange multiplier until the desired reaction is reached).
With respect to claim 30 De Ridder teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising:
a group agent comprising at least one processor and at least one memory having stored thereon code or instructions that, when executed by the processor, cause the system to, based on sums of energy consumption profiles for selected time periods (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented),
locally optimize or improve activities, aggregate load for a group, transmit selected aggregated information to a system operator configured to selectively manage a peak load of the system based on at least the aggregated information to preserve privacy (See De Ridder paragraph 0240 i.e. This embodiment comprises a smart grid equipped with a two-way communication system. Each participant exchanges information with others through the communication infrastructure in order to converge to the optimal power consumption schedule and optimize its own local objectives without sharing private information with other users. This strategy will be illustrated on a district level with quite a lot of heat-pumps, so that coordination between the end-users becomes necessary in order to avoid overload of the distribution network and paragraph 0246 i.e. A first class often encountered in literature are so-called direct control methods ([6], [7]). Here consumers and producers aggregate all information and a central unit computes the optimal schedule, taking into account all local and global constraints. Next individual control actions are sent to each device) and
attain improved, near-optimal or optimal solutions by linear calculation of peak load and employ a decomposition to solve for the solutions by using a Lagrange function (see De Ridder paragraph 0025-0026 i.e. In embodiments of the present invention, the steering signal may be a Lagrange multiplier. The steering signal in embodiments of the present invention is not overruling a local controller, but is an incentive to adapt behavior. The allows the distributed control system to find its own solution. Suppose, for example a local grid has an small energy shortage. The network can easily continue working if some devices lower their consumption and/or some others increase their production. What these embodiments do is not to overrule the devices. Instead, the centre can alter the Lagrange multiplier to promote a change in behaviour. Local controllers are free to decrease consumption or to ignore this incentive. If the problem is not solved, the centre can increase the value of the Lagrange multiplier until the desired reaction is reached).
With respect to claim 31 De Ridder teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising:
a system operator comprising at least one processor and at least one memory having stored thereon code or instructions that, when executed by the processor, cause the system to receive iteratively selected aggregated information, based on sums of energy consumption profiles for selected time periods, representing aggregate load from at least one group agent (See De Ridder paragraphs 0200-0201 i.e. the system is adapted to use a Lagrange relaxation to merge common constraints in a cost function. Also in accordance with embodiments of the present invention the system is adapted to solve a dual problem that can be formulated (e.g. as a dual decomposition) in the following way: every local unit has local intelligence and digital processing power to run a solver which solves the local unit's own power use optimization problem taking into account the Lagrange Multiplier that it has received. Depending on the value of these Lagrange multipliers, (power) schedules are sent to the coordination centre from the local units. The coordination centre is adapted to receive these schedules and to update the Lagrange Multipliers and to resend these updated values to the local units. This latter communications are iterated until all constraints are met. According to the above embodiments, the coordination centre sends a control signal to every agent involved that determines or changes its behavior based thereon. Each local unit takes this control signal into account, together with its own objectives and constraints, and (potentially) the demands of its retailer, etc. . . . and makes a schedule for the next time period, e.g. a few hours. If every unit agrees on the schedules and all constraints are met, then a first control step is implemented),
selectively manage a peak load of the system based on at least the aggregated information to preserve privacy and attains improved, near-optimal or optimal solutions (See De Ridder paragraph 0240 i.e. This embodiment comprises a smart grid equipped with a two-way communication system. Each participant exchanges information with others through the communication infrastructure in order to converge to the optimal power consumption schedule and optimize its own local objectives without sharing private information with other users. This strategy will be illustrated on a district level with quite a lot of heat-pumps, so that coordination between the end-users becomes necessary in order to avoid overload of the distribution network and paragraph 0246 i.e. A first class often encountered in literature are so-called direct control methods ([6], [7]). Here consumers and producers aggregate all information and a central unit computes the optimal schedule, taking into account all local and global constraints. Next individual control actions are sent to each device) by linear calculation of peak load and employ a decomposition to solve for the solutions by using a Lagrange function (see De Ridder paragraph 0025-0026 i.e. In embodiments of the present invention, the steering signal may be a Lagrange multiplier. The steering signal in embodiments of the present invention is not overruling a local controller, but is an incentive to adapt behavior. The allows the distributed control system to find its own solution. Suppose, for example a local grid has an small energy shortage. The network can easily continue working if some devices lower their consumption and/or some others increase their production. What these embodiments do is not to overrule the devices. Instead, the centre can alter the Lagrange multiplier to promote a change in behaviour. Local controllers are free to decrease consumption or to ignore this incentive. If the problem is not solved, the centre can increase the value of the Lagrange multiplier until the desired reaction is reached).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 5, 7, 9, 11, 13, 15-21 and 29-31 are rejected under 35 U.S.C. 103 as being unpatentable over GENC et al (US 2018/0287382) in view of Shaffer et al (US 2012/0277921).
With respect to claim 1 GENC teaches a privacy preserving method for peak load management of groups in a power and/or energy system, the method comprising:
locally optimizing or improving activities and aggregating load in each group agent (see GENC paragraph 0019-0020 i.e. the loads may be managed based on power provided from various power systems such as a power plant 110 and renewable energy sources 120. Although not shown in FIG. 1, various transmission lines may be implemented between the power plant 110, the renewable energy sources 120, sub-stations 112 and 122, and the power grid 140. FIG. 1 also includes at least one control system 130 which is used to manage the power provided to the power grid 140 based on the individual loads and the availability of resources provided by the various power systems);
broadcasting selected aggregated information with or without noise to a system operator or agents for each group (see GENC paragraph 0022 i.e. Operating data may be transmitted and received between the control system 130 and the plurality of loads on the power grid 140. For example, information about set points, zone temperatures, air flow, and the like, may be communicated to the control system 130. Also, data may be transmitted and received between the control system 130 and the substations 112 and 122. Based on the data that is communicated to the control system 130, the control system 130 may control the amount of operating power provided to the loads. In an example in which the loads are TCLs or the like, the loads may provide values such as temperature related values to the control system 130 in order to enable the control system 130 to forecast the flexibility of the power grid 140); and,
managing a peak load of the system based on at least the selected aggregated information, whereby the managing preserves privacy and attains improved, near- optimal or optimal solutions (see GENC paragraph 0021 according to various embodiments, load flexibility may be forecasted using software (e.g., an application, a program, a service, etc.) executed by or otherwise accessed by the control system 130. The load flexibility software may forecast or otherwise predict a future flexibility associated with the plurality of loads on the power grid 140).
GENC does not disclose locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
Shaffer teaches locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 3 GENC teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising:
a group agent configured to locally optimize or improve activities, aggregate load for a group, and determine selected aggregated information (see GENC paragraph 0022 i.e. Operating data may be transmitted and received between the control system 130 and the plurality of loads on the power grid 140. For example, information about set points, zone temperatures, air flow, and the like, may be communicated to the control system 130. Also, data may be transmitted and received between the control system 130 and the substations 112 and 122. Based on the data that is communicated to the control system 130, the control system 130 may control the amount of operating power provided to the loads. In an example in which the loads are TCLs or the like, the loads may provide values such as temperature related values to the control system 130 in order to enable the control system 130 to forecast the flexibility of the power grid 140); and,
a system operator configured to selectively manage a peak load of the system based on at least the aggregated information to preserve privacy and attain improved, near-optimal or optimal solutions (see GENC paragraph 0021 according to various embodiments, load flexibility may be forecasted using software (e.g., an application, a program, a service, etc.) executed by or otherwise accessed by the control system 130. The load flexibility software may forecast or otherwise predict a future flexibility associated with the plurality of loads on the power grid 140).
GENC does not disclose locally optimizing or improving activities and aggregating load in each group and determine selected aggregated information based on sums of energy consumption profiles for selected time periods.
Shaffer teaches locally optimizing or improving activities and aggregating load in each group and determine selected aggregated information based on sums of energy consumption profiles for selected time periods (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 5 GENC teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising at least one processor and at least one memory having stored thereon code or instructions that, when executed by the processor cause the system to:
locally optimize or improve activities and aggregate load in each group agent (see GENC paragraph 0019-0020 i.e. he loads may be managed based on power provided from various power systems such as a power plant 110 and renewable energy sources 120. Although not shown in FIG. 1, various transmission lines may be implemented between the power plant 110, the renewable energy sources 120, sub-stations 112 and 122, and the power grid 140. FIG. 1 also includes at least one control system 130 which is used to manage the power provided to the power grid 140 based on the individual loads and the availability of resources provided by the various power systems);
broadcast selected aggregated information with or without noise to a system operator or agents for each group (see GENC paragraph 0022 i.e. Operating data may be transmitted and received between the control system 130 and the plurality of loads on the power grid 140. For example, information about set points, zone temperatures, air flow, and the like, may be communicated to the control system 130. Also, data may be transmitted and received between the control system 130 and the substations 112 and 122. Based on the data that is communicated to the control system 130, the control system 130 may control the amount of operating power provided to the loads. In an example in which the loads are TCLs or the like, the loads may provide values such as temperature related values to the control system 130 in order to enable the control system 130 to forecast the flexibility of the power grid 140); and,
manage a peak load of the system based on at least the selected aggregated information, whereby the managing preserves privacy and attains improved, near- optimal or optimal solutions (see GENC paragraph 0021 according to various embodiments, load flexibility may be forecasted using software (e.g., an application, a program, a service, etc.) executed by or otherwise accessed by the control system 130. The load flexibility software may forecast or otherwise predict a future flexibility associated with the plurality of loads on the power grid 140).
GENC does not disclose locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
Shaffer teaches locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 7 GENC teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising:
a group agent comprising at least one processor and at least one memory having stored thereon code or instructions that, when executed by the processor cause the system to: to locally optimize or improve activities, aggregate load for a group, and transmit selected aggregated information to a system operator configured to selectively manage a peak load of the system based on at least the aggregated information to preserve privacy and attain improved, near-optimal or optimal solutions (see GENC paragraph 0021 according to various embodiments, load flexibility may be forecasted using software (e.g., an application, a program, a service, etc.) executed by or otherwise accessed by the control system 130. The load flexibility software may forecast or otherwise predict a future flexibility associated with the plurality of loads on the power grid 140).
GENC does not disclose based on sums of energy consumption profiles for selected time periods, locally optimizing or improving activities and aggregating load in each group.
Shaffer teaches based on sums of energy consumption profiles for selected time periods, locally optimizing or improving activities and aggregating load in each group (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 9 GENC teaches a privacy preserving method for peak load management of groups in a power and/or energy system, the method comprising:
a group agent locally optimizing or improving activities (see GENC paragraph 0019-0020 i.e. he loads may be managed based on power provided from various power systems such as a power plant 110 and renewable energy sources 120. Although not shown in FIG. 1, various transmission lines may be implemented between the power plant 110, the renewable energy sources 120, sub-stations 112 and 122, and the power grid 140. FIG. 1 also includes at least one control system 130 which is used to manage the power provided to the power grid 140 based on the individual loads and the availability of resources provided by the various power systems);
aggregating load for a group; and, transmitting to a system operator selected aggregated information, the system operator being configured to selectively manage a peak load of the system based on at least the aggregated information to preserve privacy and attain improved, near-optimal or optimal solutions (see GENC paragraph 0022 i.e. Operating data may be transmitted and received between the control system 130 and the plurality of loads on the power grid 140. For example, information about set points, zone temperatures, air flow, and the like, may be communicated to the control system 130. Also, data may be transmitted and received between the control system 130 and the substations 112 and 122. Based on the data that is communicated to the control system 130, the control system 130 may control the amount of operating power provided to the loads. In an example in which the loads are TCLs or the like, the loads may provide values such as temperature related values to the control system 130 in order to enable the control system 130 to forecast the flexibility of the power grid 140).
GENC does not disclose locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
Shaffer teaches locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 11 GENC teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising:
a system operator comprising at least one processor and at least one memory having stored thereon code or instructions that, when executed by the processor cause the system to receive iteratively selected aggregated information representing aggregate load from at least one group agent, selectively manages a peak load of the system based on at least the aggregated information to preserve privacy and attains improved, near-optimal or optimal solutions (see GENC paragraph 0021 according to various embodiments, load flexibility may be forecasted using software (e.g., an application, a program, a service, etc.) executed by or otherwise accessed by the control system 130. The load flexibility software may forecast or otherwise predict a future flexibility associated with the plurality of loads on the power grid 140).
GENC does not disclose receives iteratively selected aggregated information based on sums of energy consumption profiles for selected time periods.
Shaffer teaches receives iteratively selected aggregated information based on sums of energy consumption profiles for selected time periods (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 13 GENC teaches a privacy preserving method for peak load management of groups in a power and/or energy system, the method comprising:
a system operator receiving iteratively selected aggregated information representing aggregate load from at least one group agent; (see GENC paragraph 0022 i.e. Operating data may be transmitted and received between the control system 130 and the plurality of loads on the power grid 140. For example, information about set points, zone temperatures, air flow, and the like, may be communicated to the control system 130. Also, data may be transmitted and received between the control system 130 and the substations 112 and 122. Based on the data that is communicated to the control system 130, the control system 130 may control the amount of operating power provided to the loads. In an example in which the loads are TCLs or the like, the loads may provide values such as temperature related values to the control system 130 in order to enable the control system 130 to forecast the flexibility of the power grid 140) and
selectively managing a peak load of the system based on at least the aggregated information to preserve privacy and attain improved, near-optimal or optimal solutions (see GENC paragraph 0021 according to various embodiments, load flexibility may be forecasted using software (e.g., an application, a program, a service, etc.) executed by or otherwise accessed by the control system 130. The load flexibility software may forecast or otherwise predict a future flexibility associated with the plurality of loads on the power grid 140).
GENC does not disclose locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
Shaffer teaches locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 15 GENC teaches the method as set forth in claim 1 but does not disclose wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations.
Shaffer teaches wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 16 GENC teaches the method as set forth in claim 3 but does not disclose wherein the system is further configured to aggregate load in each group agent based on only sums of energy consumption profiles for selected time periods and linear calculations.
Shaffer teaches wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 17 GENC teaches the method as set forth in claim 5 but does not disclose wherein the system is further configured to aggregate load in each group agent based on only sums of energy consumption profiles for selected time periods and linear calculations.
Shaffer teaches wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 18 GENC teaches the method as set forth in claim 7 but does not disclose wherein the system is further configured to aggregate load in each group agent based on only sums of energy consumption profiles for selected time periods and linear calculations.
Shaffer teaches wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 19 GENC teaches the method as set forth in claim 9 but does not disclose wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations.
Shaffer teaches wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 20 GENC teaches the method as set forth in claim 11 but does not disclose wherein the system is further configured to aggregate load in each group agent based on only sums of energy consumption profiles for selected time periods and linear calculations.
Shaffer teaches wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 21 GENC teaches the method as set forth in claim 13 but does not disclose wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations.
Shaffer teaches wherein the aggregating load in each group agent is based on only sums of energy consumption profiles for selected time periods and linear calculations (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 29 GENC teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising at least one processor and at least one memory having stored thereon code or instructions that, when executed by the processor, cause the system to:
locally optimize or improve activities and aggregate load in each group agent based on sums of energy consumption profiles for selected time periods (see GENC paragraph 0019-0020 i.e. the loads may be managed based on power provided from various power systems such as a power plant 110 and renewable energy sources 120. Although not shown in FIG. 1, various transmission lines may be implemented between the power plant 110, the renewable energy sources 120, sub-stations 112 and 122, and the power grid 140. FIG. 1 also includes at least one control system 130 which is used to manage the power provided to the power grid 140 based on the individual loads and the availability of resources provided by the various power systems);
broadcast selected aggregated information with or without noise to a system operator or agents for each group (see GENC paragraph 0022 i.e. Operating data may be transmitted and received between the control system 130 and the plurality of loads on the power grid 140. For example, information about set points, zone temperatures, air flow, and the like, may be communicated to the control system 130. Also, data may be transmitted and received between the control system 130 and the substations 112 and 122. Based on the data that is communicated to the control system 130, the control system 130 may control the amount of operating power provided to the loads. In an example in which the loads are TCLs or the like, the loads may provide values such as temperature related values to the control system 130 in order to enable the control system 130 to forecast the flexibility of the power grid 140); and,
manage a peak load of the system based on at least the selected aggregated information to preserve privacy and attain improved, near-optimal or optimal solutions by linear calculation of peak load (see GENC paragraph 0021 according to various embodiments, load flexibility may be forecasted using software (e.g., an application, a program, a service, etc.) executed by or otherwise accessed by the control system 130. The load flexibility software may forecast or otherwise predict a future flexibility associated with the plurality of loads on the power grid 140) and
employ a decomposition to solve for the solutions by using a Lagrange function (see GENC paragraphs 0056-0060 i.e. Next, the cost function can be augmented with the dynamic constraints via Lagrange multiplier denoted by A(t). Note that we are not concerned with the final state x(nt) be exactly equal to v(nt), the state, co-state, and stationary condition equations can be used to solve for optimal u* (relaxation not optimal input) using the following equations (dropping the subscript n from x, u and y to simplify notation for solving the differential equation system).
GENC does not disclose locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
Shaffer teaches locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 33 GENC teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising:
a group agent comprising at least one processor and at least one memory having stored thereon code or instructions that, when executed by the processor (see GENC paragraph 0022 i.e. Operating data may be transmitted and received between the control system 130 and the plurality of loads on the power grid 140. For example, information about set points, zone temperatures, air flow, and the like, may be communicated to the control system 130. Also, data may be transmitted and received between the control system 130 and the substations 112 and 122. Based on the data that is communicated to the control system 130, the control system 130 may control the amount of operating power provided to the loads. In an example in which the loads are TCLs or the like, the loads may provide values such as temperature related values to the control system 130 in order to enable the control system 130 to forecast the flexibility of the power grid 140), cause the system to ,
locally optimize or improve activities, aggregate load for a group, transmit selected aggregated information to a system operator configured to selectively manage a peak load of the system based on at least the aggregated information to preserve privacy and attain improved, near-optimal or optimal solutions (see GENC paragraph 0021 according to various embodiments, load flexibility may be forecasted using software (e.g., an application, a program, a service, etc.) executed by or otherwise accessed by the control system 130. The load flexibility software may forecast or otherwise predict a future flexibility associated with the plurality of loads on the power grid 140) by linear calculation of peak load and employ a decomposition to solve for the solutions by using a Lagrange function (see GENC paragraphs 0056-0060 i.e. Next, the cost function can be augmented with the dynamic constraints via Lagrange multiplier denoted by A(t). Note that we are not concerned with the final state x(nt) be exactly equal to v(nt), the state, co-state, and stationary condition equations can be used to solve for optimal u* (relaxation not optimal input) using the following equations (dropping the subscript n from x, u and y to simplify notation for solving the differential equation system).
GENC does not disclose locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
Shaffer teaches locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
With respect to claim 31 GENC teaches a privacy preserving system for peak load management of groups in a power and/or energy system, the system comprising:
a system operator comprising at least one processor and at least one memory having stored thereon code or instructions that, when executed by the processor, cause the system to receive iteratively selected aggregated information representing aggregate load from at least one group agent, selectively manage a peak load of the system based on at least the aggregated information to preserve privacy and attains improved, near-optimal or optimal solutions (see GENC paragraph 0021 according to various embodiments, load flexibility may be forecasted using software (e.g., an application, a program, a service, etc.) executed by or otherwise accessed by the control system 130. The load flexibility software may forecast or otherwise predict a future flexibility associated with the plurality of loads on the power grid 140),
by linear calculation of peak load and employ a decomposition to solve for the solutions by using a Lagrange function (see GENC paragraphs 0056-0060 i.e. Next, the cost function can be augmented with the dynamic constraints via Lagrange multiplier denoted by A(t). Note that we are not concerned with the final state x(nt) be exactly equal to v(nt), the state, co-state, and stationary condition equations can be used to solve for optimal u* (relaxation not optimal input) using the following equations (dropping the subscript n from x, u and y to simplify notation for solving the differential equation system).
GENC does not disclose locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
Shaffer teaches locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods (see Shaffer figure 4-5 and paragraph 0026 i.e. FIG. 4 illustrates an example result of independently optimized energy policies. The goal of each individual policy may be to reduce the energy levels of each company, such as turning off lights if a facility is not occupied, and, more particularly, to stagger start-up of devices 110 such as heaters, air conditioners, generators, etc. to reduce spikes/peak consumption, etc. As shown (for illustrative explanation purposes only), "company A" has an un-managed power consumption 405 with a large peak at a particular time T1. Company A's "policy A" may be used to provide local optimization, albeit without knowing the operating environment of its neighboring companies which draw energy from the same distribution grid transformer or substation, to spread out the power consumption 410 over time (e.g., altering start-up times), and effectively reducing the peak consumption (now at time T2) and paragraph 0043 i.e. shown in FIG. 5, in which the energy utilization result of the two independently optimized soft policies of FIG. 4 have been adjusted such that their respective peaks (usage 510 peaking at T2 and 520 peaking at T4) do not overlap, and the energy consumption result 530 of the globally optimized policy is reduced (in comparison to the energy consumption 430 shown in FIG. 4)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GENC in view of Shaffer to have companies both local optimize power consumption to spread out the power consumption over time (e.g., altering start-up times), and effectively reducing the peak consumption while also globally optimizing energy utilization of each independently locally optimized soft policies such that their respective peaks do not overlap and the energy consumption result is reduced in comparison as a way to reduce peak load (see Shaffer 0043). Therefore one would have been motivated to have locally optimizing or improving activities and aggregating load in each group agent based on sums of energy consumption profiles for selected time periods.
Allowable Subject Matter
Claims 2, 4, 6, 8, 10, 12 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
With respect to claims 2, 4, 6, 8, 10, 12 and 14 the prior art does not teach wherein the managing comprises updating locally a Lagrange, (λK +1), multiplier using a projection of λk + αk * Ʃi Ʃjxijk+1, on A, where λk is a Lagrange multiplier: αk is a constraint: xijk+1, is the load: A is a set.
With respect to claim 22-28 the prior art does not teach wherein the system selectively manages a peak load of the system based on at least the aggregated information to preserve privacy and attain improved, near-optimal or optimal solutions by linear calculation of peak load given by (P); and employs a decomposition to solve (P) by using a Lagrange function, L, where L is given as:
PNG
media_image1.png
61
438
media_image1.png
Greyscale
where λk is a Lagrange multiplier; A is a set; xi is a set; where xi = {xij}jej, and λ = {λt}teT.
Prior art of Record
Ye (2021/0365568) titled “PRIVACY PRESERVING APPROACH TO PEAK LOAD MANAGEMENT”.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN E ALMEIDA whose telephone number is (571)270-1018. The examiner can normally be reached on Monday-Thursday from 7:30 A.M. to 5:00 P.M. The examiner can also be reached on alternate Fridays from 7:30 A.M. to 4:00 P.M.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Dharia Rupal, can be reached on 571-272-3880. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/DEVIN E ALMEIDA/Examiner, Art Unit 2492 /RUPAL DHARIA/Supervisory Patent Examiner, Art Unit 2492