DETAILED ACTION
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 .
Claim Objections
Claims 4 and 6-8 are objected to because of the following informalities:
Claim 4 recites the phrase, “… the process further comprises…” which is incorrect because claim 4 is about the system of claim 2 not process. To maintain consistency with rest of the claim language, the word “process” needs to be corrected to “system”.
Claims 6-8 recite the phrase, “… the process further comprises…” which is incorrect because claims 6-7 are about the system of claim 2 and claim 8 is about system of claim 1, not process. To maintain consistency with rest of the claim language, the word “process” needs to be corrected to “system”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation "the future time period" in line 9. There is insufficient antecedent basis for this limitation in the claim. Claim 2 depends from claim 1 which recites a period of time, not future period of time.
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) 1,8,9 and 16 are rejected under 35 U.S.C.103 as being unpatentable over Drees et al. (US 20170102162 A1) in view of Engelstein et al. (US 20200333767 A1).
Regarding claim 1, Drees et al. teaches, a system for controlling components (a central plant and building management system, [0046]) in a facility based on energy hedges (the central plant system determining control parameter to meet demand based on energy contract between the electrical supplier and participant in the system, [0215] and [0341]), the system comprising:
a controller configured to control the components in the facility (BMS controller controlling several downstream building systems and subsystems, [0073] and [0074]), wherein the controller comprises processors and memory storing instructions that, when executed by the processors (BMS controller includes processor and memory where processor execute instructions stored in the memory, [0080]), causes the controller to perform a process comprising:
monitoring real-time energy market conditions based on real-time energy
information from an energy grid data source (demand response layer of the BMS controller receives time of use prices for energy and other data from the utility provider (energy grid) that is BMS controller monitoring real time energy market condition based on data from a grid/utility, [0086] and [0127]);
generating component control instructions based on current operating conditions (current energy used by the components of the building, [0086], [0114] and [0115]), energy capacity of the facility (the high level optimizer of BMS controller makes control decisions such as charge/discharge rate for each storage sub-plants. In order to make decisions, the high level optimizer must know the current condition of the storage sub-plants that is energy capacity of the facility, [0131] and [0086]), and the monitored real-time energy market conditions (actual utility rates and other data from the utility are received and monitored by the BMS controller to monitor energy market condition, [0086], [0087] and [0127]); and
executing the component control instructions to cause the components in the facility to perform the operational tasks (demand response layer of the BMS controller automatically changing setpoints of the monitored systems and sub-systems based on the inputs received such as energy prices, current load of the building and others to optimize energy cost while meeting demand, [0086]-[0088]).
Drees et al. does not teach the details of receiving pre-purchased energy hedge information for a period of time. However Drees et al. explicitly teaches in [0105] and [0215] that based on certain inputs such as whether there is an energy contract (similar to energy hedge) between the building system participant and the electrical supplier and other energy related information, the controller makes control decisions such as the optimal amount of energy to be purchased, control setpoints for various systems and sub-systems as taught in [0088].
On the other hand, Engelstein et al. teaches, receiving pre-purchased energy hedge information for a period of time (buying future electricity contracts for the predicted peak demand in a future time predicted by the system and based on this information and other information, generate control signals to increase or decrease energy consumption of a load, [0342] and [0334]);
the pre-purchased energy hedge information (future electricity contracts, [0342] and [0334]).
Drees et al. and Engelstein et al. are analogous art because they are from same field of endeavor that is predicting energy scheduling for a future time period based on certain conditions.
Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the system for controlling components in a facility based energy market condition, energy capacity of the facility and current operating conditions as taught by Drees et al. by applying the known technique of using pre-purchased energy hedge information (future electricity contracts) for a future period of time as taught by Engelstein et al. as an improvement to control energy usage of the system to yield predictable results for minimizing energy cost while proactively shopping for lower cost electricity contracts to cover future peak demand as taught by Engelstein et al. in [0342].
Drees et al. teach:
[0215] Still referring to FIG. 8, high level optimizer 632 is shown to include an incentive program module 822. Incentive program module 822 may modify the optimization problem to account for revenue from participating in an incentive-based demand response (IBDR) program. IBDR programs may include any type of incentive-based program that provides revenue in exchange for resources (e.g., electric power) or a reduction in a demand for such resources. For example, central plant system 500 may provide electric power to an energy grid or an independent service operator as part of a frequency response program (e.g., PJM frequency response) or a synchronized reserve market. In a frequency response program, a participant contracts with an electrical
supplier to maintain reserve power capacity that can be supplied or removed from an energy grid by tracking a supplied signal.1 The participant is paid by the amount of power capacity required to maintain in reserve. In other types of IBDR programs, central plant system 500 may reduce its demand for resources from a utility as part of a load shedding program. It is contemplated that central plant system 500 may participate in any number and/or type of IBDR programs.
[0074] In some embodiments, AHU controller 330 receives information from BMS controller 366 (e.g., commands, setpoints, operating boundaries, etc.) and provides information to BMS controller 366 (e.g., temperature measurements, valve or actuator positions, operating statuses, diagnostics, etc.). For example, AHU controller 330 may provide BMS controller 366 with temperature measurements from temperature sensors 362-364, equipment on/off states, equipment operating capacities, and/or any other information that can be used by BMS controller 366 to monitor or control a variable state or condition within building zone 306.
[0086] Demand response layer 414 may be configured to optimize resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage in response to satisfy the demand of building 10. The optimization may be based on time-of-use prices, curtailment signals, energy availability, or other data received from utility providers2, distributed energy generation systems 424, energy storage 427 (e.g., hot TES 242, cold TES 244, electrical energy storage, etc.), or from other sources. Demand response layer 414 may receive inputs from other layers of BMS controller 366 (e.g., building subsystem integration layer 420, integrated control layer 418, etc.). The inputs received from other layers may include environmental or
sensor inputs such as temperature, carbon dioxide levels, relative humidity levels, air quality sensor outputs, occupancy sensor outputs, room schedules, and the like. The inputs may also include inputs such as electrical use (e.g., expressed in kWh), thermal load measurements, pricing information, projected pricing, smoothed pricing, curtailment signals from utilities, and the like.
[0127] Load/rate predictor 622 is shown receiving utility rates from utilities 510. Utility rates may indicate a cost or price per unit of a resource (e.g., electricity, natural gas, water, etc.) provided by utilities 510 at each time step k in the prediction window3. In some embodiments, the utility rates are time variable rates. For example, the price of electricity may be higher at certain times of day or days of the week (e.g., during high demand periods) and lower at other times of day or days of the week (e.g., during low demand periods). The utility rates may define various time periods and a cost per unit of a resource during each time period. Utility rates may be actual rates received from utilities 510 or predicted utility rates estimated by load/rate predictor 622.
[0115] BMS 606 may receive control signals from central plant controller 506 specifying on/off states, charge/discharge rates, and/or setpoints for the subplant equipment. BMS 606 may control the equipment (e.g., via actuators, power relays, etc.) in accordance with the control signals provided by central plant controller 506. For example, BMS 606 may operate the equipment using closed loop control to achieve the setpoints specified by central plant controller 5064. In various embodiments, BMS 606 may be combined with central plant controller 506 or may be part of a separate building management
system. According to an exemplary embodiment, BMS 606 is a METASYS® brand building management system, as sold by Johnson Controls, Inc.
[0131] Still referring to FIG. 6, memory 610 is shown to include an demand response optimizer 630. Demand response optimizer 630 may perform a cascaded optimization process to optimize the performance of central plant system 500. For example, demand response optimizer 630 is shown to include a high level optimizer 632 and a low level optimizer 634. High level optimizer 632 may control an outer (e.g., subplant level) loop of the cascaded optimization. High level optimizer 632 may determine an optimal set of control decisions for each time step in the prediction window in order to optimize
(e.g., maximize) the value of operating central plant system 500. Control decisions made by high level optimizer may include, for example, load setpoints for each of generator subplants 520, charge/discharge rates for each of storage subplants 530, resource purchase amounts for each type of resource purchased from utilities 510, and/or an amount of each resource sold to energy purchasers 504. In other words, the control decisions may define resource allocation at each time step. The control decisions made by high level optimizer 632 are based on the statistical estimates of incentive event probabilities and revenue generation potential for various IBDR events as well as the load and rate predictions.
[0087] According to an exemplary embodiment, demand response layer 414 includes control logic for responding to the data and signals it receives. These responses can include communicating with the control algorithms in integrated control layer 418, changing control strategies, changing setpoints, or activating/deactivating building equipment or subsystems in a controlled manner. Demand response layer 414 may also include control logic configured to determine when to utilize stored energy. For example, demand response layer 414 may determine to begin using energy from energy storage 427 just prior to the beginning of a peak use hour.
Engelstein et al. teach:
[0342] The load prediction may be used to allow a user to commit a load shed event in advance of the actual demand interval. For instance, traditionally, when a particular load exceeds an alarm, then a load shedding event will occur. In accordance with the techniques of the present disclosure, the user will be warned well into the future, e.g., by e-mail, text message, etc., sent by system 1400 and well before the alarm condition ever occurred. This will allow the user to shed at least one load proactively to a peak demand as opposed to reactively after an alarm was set. Another possibility is that a user may be able to buy future contracts for electricity at that time based on the predictions of system 1400so that the energy purchase could hedge the cost of the electricity during the peak demand situation predicted by the systems 5and methods of the present disclosure. Even if they cannot shed at least one load because the processes are mandatory, the user will have time to proactively shop for lower cost electricity contracts to minimize their high demand condition.
[0334] Referring to FIG. 15, a method for using machine learning system 1100 is shown in accordance with the present disclosure. In step 1202, a plurality of data samples are stored in data library 1102. As described above, the data samples may be received from various sources (e.g., IEDs, clients, etc.). Instep 1204, machine learning module 1104 receives data samples from the data library 1102. In step1206, machine learning module processes the data samples in accordance with at least one machine learning algorithm (e.g., stored in a memory of server 424/524). In step 1208, based on the processing of the data samples received, machine learning module outputs at least one recommendation and/or prediction (e.g., a predicted energy usage, a predicted fault, a recommendation to increase or decrease energy consumption by a load, etc.) In step 1210, action module 1106 receives the at least one recommendation and/or prediction from machine learning module 1104. In step 1212, action module 1106 performs at least one action based on the recommendation and/or prediction. For example, the action may include sending a communication signal to at least one client device (e.g., a user's computing device, a service personnel's computing device, etc.) including the recommendation and/or prediction. The action may include sending a control signal to at least one client device and/or one or more IEDs, where the control signal is generated based on the recommendation and/or prediction. For example, the control signal may include increasing or decreasing the energy consumption of a load, turning on or off a load, rebooting, shutting down, and/or updating one or more IEDs, etc6.
Regarding claim 8, combination of Drees et al. and Engelstein et al. teach the system of claim 1. In addition Drees et al. teaches, wherein the process further comprises:
determining, based on monitoring the real-time energy market conditions (actual utility rates and other data from the utility are received and monitored by the BMS controller to monitor energy market condition, [0086], [0087] and [0127]), an upcoming energy event (demand response layer of the BMS controller predicts the building is close to peak demand-upcoming energy event, [0087] and [0112]), the upcoming energy event being at least one of an energy shortage and an energy surplus in the energy market (the demand response layer knows when is the off-peak hours so the energy market will have surplus energy to charge batteries at lower cost and also when the market will have peak demand that is energy shortage, so the building will use stored energy during energy market shortage hours, [0087] and [0103]);
generating, based on the upcoming energy event and the current operating conditions, a recommendation for a facility adjustment to maintain facility operations at or below a predetermined facility operational setpoint during a time corresponding to the upcoming energy event (based on the received inputs, the demand response layer knows when there will be peak demand and where there will be surplus energy in non-peak hours during an optimization window. Based on these information, the demand response layer generate setpoints to control the building components by turning some of the components off, or operating the chiller at variable capacity during peak to curtail energy usage or with stored electrical energy during peaks hours to reduce electricity usage from the grid (load shifting) that is operating the facility at or below operational setpoint and purchase energy from the grid during non-peak hours to charge electrical energy storages, [0086], [0087], [0103]-[0105], [0107] and [0108]); and
returning the recommendation to a centralized controller (BMS controller in communication with the demand response layer, [0074] and [0086]) of the facility for automatic execution by components in the facility (“…In some embodiments, demand response layer 414 includes a control module configured to actively initiate control actions (e.g., automatically changing setpoints)7 which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.)...”, [0088] and [0087], see also [0115]).
Regarding claim 9, combination of Drees et al. and Engelstein et al. teach the system of claim 8. In addition Drees et al. teaches, wherein generating the recommendation comprises at least one of:
(i) determining a threshold amount of energy usage to cut during the time corresponding to the upcoming energy event (the controller determines the predetermined amount of power (threshold amount) to be shed during peak usage (upcoming energy event), [0111],[0112] and [0092]),
(ii) determining a threshold quantity of energy hedges of the facility to sell back to the energy market while maintaining execution of the facility operations at or below the predetermined facility operational setpoint (demand response optimizer of the controller determines for each time step of the optimization window exactly what amount of energy to be sold to the utility (threshold quantity of energy hedge), control setpoints for various components of the sub-plant of the building such the reducing energy usage while still meeting required load setpoint (operational setpoint) of the sub-plant of the building, [0112], [0131] and [0137]), or
(iii) determining a threshold quantity of energy hedges to store during the time corresponding to the upcoming energy event to maintain execution of the facility operations at or below the predetermined facility operational set point (demand response optimizer of the controller determines for each time step of the optimization window exactly what amount of energy to be purchased from the utility and charge/discharge rates of the storage sub-plant (threshold quantity of energy hedge to store), control setpoints for various components of the sub-plant of the building such the reducing energy usage while still meeting required load setpoint (operational setpoint) of the sub-plant of the building, [0112], [0131] and [0137]).
Regarding claim 16, combination of Drees et al. and Engelstein et al. teach the system of claim 1. In addition Drees et al. teaches, wherein the controller is further configured to:
receive facility information and energy market conditions information (the controller receives occupancy status, room schedules, energy use of the building, price of energy usage and other data, [0086] and [0114]), wherein the facility information indicates an amount of energy that was used to control the components in the facility to perform the operational tasks (controller receives electrical use, thermal load measurements of the building and other additional data from various sources, [0086], [0114] and [0134]);
determine a quantity of energy hedges to sell back to the energy market based on the amount of energy that was used to control the components in the facility to perform the operational tasks and the market conditions information (based on the received inputs including energy use of the building as taught in [0086],
demand response optimizer of the controller determines for each time step of the optimization window exactly what amount of energy to be sold to the utility (energy hedge to sell back), control setpoints for various components of the sub-plant of the building such the reducing energy usage while still meeting required load setpoint (operational setpoint) of the sub-plant of the building, [0112], [0131] and [0137]); and
return a recommendation to sell the quantity of the energy hedges back to an energy market over a future period of time (decisions made by the controller including the amount of energy to sell back, store and others are presented to the user in real time as system health dashboard, [0137], [0134], [0131] and [0240]).
Claim(s) 2,4,6 and 7are rejected under 35 U.S.C.103 as being unpatentable over Drees et al. (US 20170102162 A1) in view of Engelstein et al. (US 20200333767 A1) and Sun et al. (US 20220284458 A1).
Regarding claim 2 combination of Drees et al. and Engelstein et al. teach the system of claim 1. In addition Drees et al. teaches, wherein the controller is configured to generate the pre-purchased energy hedge information (future electricity contracts in view of Engelstein et al.) based on:
receiving facility information and energy market conditions information for a predetermined period of time (the demand response layer of the BMS controller receiving room schedules, thermal load measurement of the building, energy price data and energy availability data from the utility plurality of time steps of the optimization window – predetermined period of time, [0086], [0114], [0116] and [0127]);
generating, based on the output, recommendations for purchasing or selling a portion of the predicted block energy hedges over the future period of time (based on the received inputs, the demand response layer knows when there will be peak demand and where there will be surplus energy in non-peak hours during an optimization window-future period of time. Based on these information, the demand response layer generate setpoints to control the building components by turning some of the components off, or operating the chiller at variable capacity during peak to curtail energy usage or with stored electrical energy during peaks hours to reduce electricity usage from the grid (load shifting) that is operating the facility at or below operational setpoint and purchase energy from the grid during non-peak hours to charge electrical energy storages and also determine exact amount to energy to sell back to the utility during a particular time step of the optimization window and purchase exact amount of energy from the utility for a certain time step of the optimization window, [0086], [0087], [0103]-[0105], [0108] and [0112]); and
returning the recommendations for purchasing the portion of the predicted block energy hedges over the future period of time (demand response optimizer of the controller determines for each time step of the optimization window exactly what amount of energy to be purchased from the utility (purchasing energy from utility in a certain time in future that is a time step of the optimization window, [0105], [0112], [0131] and [0137]).
Neither in combination nor individually Drees et al. and Engelstein et al. teach the details of retrieving, from a data store, a model that was trained to predict energy market conditions over a future period of time; providing, to the model, at least a portion of the received information as input; receiving, from the model, output indicating the predicted energy market conditions over the future period of time, wherein the model was trained to predict block energy hedges over the future period of time and quantify energy prices based on the predicted block energy hedges. However Drees et al. teaches to receive utility price data in real time and during optimization window to in addition to other data to determine control decisions to run the building as taught in [0127] and [0086].
On the other hand Sun et al. teaches, retrieving, from a data store (memory, [0069]), a model that was trained to predict energy market conditions over a future period of time (VPP optimization models for determining optimal scheduling and trading strategy for the VPP (virtual power plant) retrieved from the memory, [0064] and [0069]);
providing, to the model, at least a portion of the received information as input (the VPP optimization models receive data related to power consumption and production forecast, future market pricing, future market uncertainty and other data, [0019], [0065] and [0069]);
receiving, from the model, output indicating the predicted energy market conditions over the future period of time (forecasting selling price for the pool market, forecasting buying and selling strip prices in the future market during the future time horizon-future period of time, [0032] and [0084]), wherein the model was trained to predict block energy hedges over the future period of time and quantify energy prices based on the predicted block energy hedges (“…Specifically, the algorithm8 outputs the amount of energy that should be charged or discharged, the amount of power consumption that should be curtailed, as well as how much powers should be bought or sold at the pool or future markets during the time horizon to maximize total revenue for energy reading while minimizing total cost for production9. This process or portions thereof may be repeated at the beginning of each future/pool time interval to maximize profits while satisfying the constraints such as keeping the storage level within its minimum and maximum capacity at all times…”, [0084], [0019], [0032], [0065] and [0069]).
Drees et al., Engelstein et al. and Sun et al. are analogous art because they are from same field of endeavor that is predicting energy scheduling for a future time period based on certain conditions.
Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the system controlling components of a facility based on facility information and energy market condition as taught by combination of Drees et al. and Engelstein et al. by applying the known technique of using a trained model for predicting energy market condition and quantify energy prices based on the predicted block energy hedges (buying and selling energy strip price over a future time) as taught by Sun et al. as an improvement to the system to yield predictable results of predicting future market energy price information for buying or selling energy to maximize total future market revenue by strategically mixing selling and buying decisions for future Option and strip contracts as taught by Sun et al. in [0032].
Sun et al. teach:
[0032] The Optimized solution is performed to maximize an expected total pool market revenue and an expected total future market revenue, while minimizing an expected total energy cost for the VPP. For example, the VPP CC system can respond to pricing signals and accommodate market requirements within the identified risk tolerance level in real-time. For example, the embodiments of the present disclosure can: (1) maximize the expected total pool revenue by adjusting storage discharging and charging with respect to the variation of renewable and load demands to offer sustaining powers for favorable pool price periods; (2) maximize the total future market revenue by strategically mixing selling and buying decisions for future Option and strip contracts and coordinating with pool offerings; and (3) while minimizing a total energy cost for the energy system by considering wear costs for power producing and storing for renewable plants and energy storages, and the purchasing costs for customer power consumptions. Thus, the systems and methods of the present disclosure consider in the Optimized solution some VPP aspects for each time period including: (1) forecasting of total power generation; (2) forecasting of total local power load, (3) identifying energy storage current level and maintenance schedule; (4) forecasting selling price(s)for pool market; (5) forecasting buying and selling strip price(s) in future market;10 and (6) forecasting buying and selling Option premium and execution price(s) in future market.
Regarding claim 3, combination of Drees et al., Engelstein et al. and Sun et al. teach the details of the system of claim 2. In addition Drees et al. teaches, wherein returning the recommendations comprises:
transmitting, to a user device, the recommendations and at least a portion of the predicted energy market conditions (system health dashboard provided to user with information related energy market condition, building energy usage, setpoints and others, [0134] and [0241]),
wherein the user device is configured to present, in the GUIs (dashboard, [0134]), the recommendations, the at least portion of the predicted energy market conditions, and a recommendation to purchase a predetermined quantity of the predicted block energy hedges over the future period of time (the dashboard also presents recommended optimized amount of energy to be sold or purchased by the system as taught in [0086], [0105] and [0134]).
Regarding claim 4, combination of Drees et al., Engelstein et al. and Sun et al. teach the details of the system of claim 2. In addition Sun et al. teaches, wherein based on the model output, the process further comprises:
determining, based on identifying that the facility is operating at or below a facility operational set point (in view of Drees et al., reducing energy usage while still meeting required load setpoint (operational setpoint) of the sub-plant of the building, [0112], [0131] and [0137]), (i) a quantity of the predicted block energy hedges to buy in the energy market (forecasting buying and selling strip prices in future market, [0032]) and (ii) a segment of time over the future period of time at which to buy the quantity of the predicted block energy hedges (optimized energy trading and scheduling showing how much energy should be sold or purchased during certain time horizon of the future market, [0084] and [0032]).
Regarding claim 6, combination of Drees et al., Engelstein et al. and Sun et al. teach the details of the system of claim 2. In addition Sun et al. teaches, wherein the process further comprises:
determining (i) timing, (ii) quantity, and (iii) cost for purchasing the predicted block energy hedges based at least in part on a joint probability of block energy hedge price and facility load distributions over the future period of time (based on tolerable profit probability distribution which is determined based on future pool market price, energy generation, local demand of the system, forecast the buying and selling strip price (energy hedge) in a certain time in a future market, [0065],[0032] and [0084]).
Regarding claim 7, combination of Drees et al., Engelstein et al. and Sun et al. teach the details of the system of claim 2. In addition Drees et al. teaches, wherein the process further comprises:
determining at least one of (i) a facility operational setpoint to reduce facility load over the future period of time and (ii) component operational setpoints to reduce respective component loads over the future period of time (based on the received inputs, the demand response layer knows when there will be peak demand and where there will be surplus energy in non-peak hours during an optimization window-future period of time. Based on these information, the demand response layer generate setpoints to control the building components by turning some of the components off, or operating the chiller at variable capacity during upcoming peak (future time) to curtail energy usage or with stored electrical energy during upcoming peaks hours to reduce electricity usage from the grid (load shifting) that is operating the facility at or below operational setpoint and purchase energy from the grid during non-peak hours in the future to charge electrical energy storages and also determine exact amount to energy to sell back to the utility during a particular time step of the optimization window and purchase exact amount of energy from the utility for a certain time step of the optimization window, [0086], [0087], [0103]-[0105], [0108] and [0112]).
Claim 20 is rejected under 35 U.S.C.103 as being unpatentable over Drees et al. (US 20170102162 A1) in view of Engelstein et al. (US 20200333767 A1) and Green et al. (US 20170288402 A1).
Regarding claim 20 combination of Drees et al. and Engelstein et al. teach the system of claim 1. In addition Drees et al. teaches, executing the component control
Instructions (controller automatically changing setpoints,[0088] and [0074]).
Neither in combination nor individually Drees et al. and Engelstein et al. teach the details of activating a blast cell to perform item freezing operations using energy
that is stored by an energy storage system in response to detecting an energy surplus in the energy market.
Green teaches, activating a blast cell to perform item freezing operations using energy that is stored by an energy storage system in response to detecting an energy surplus in the energy market (turning on a freezer (blast cell) when excess power is available especially when energy is stored as some physical parameter or variable, [0008]).
Drees et al., Engelstein et al. and Green are analogous art because they are from same field of endeavor that is predicting energy scheduling for a future/desired time period based on certain conditions.
Therefore it would have been obvious before effective filing date of the claimed invention to a person of ordinary skill in the art to modify the system controlling components in a facility based on energy hedges as taught by combination of Drees et al. and Engelstein et al. by applying the known technique of turning on/activating a freezer (blast cell) when surplus energy is available as taught by Green as an improvement to the system’s energy management to yield predictable results of controlling power load distributions to the loads with additional degree of freedom during times of power shortage and excess power as taught by Green in [0006] and [0008].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Takriti et al. (US 5,974,403) teaches a computer implemented tool which forecasts probabilistic distributions of the electric load and the cost of function of each utility to build probabilistic distributions for the spot market prices and electric trades in the system.
Naserimojarad (US 20250251974 A1) teaches a system and method for intelligent power orchestration where based energy hedge agreement, the energy usage of the components of the datacenter is modulated.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANZUMAN SHARMIN whose telephone number is (571)272-7365. The examiner can normally be reached M and Th 7:00am - 3:00pm and Tue 8:00am-12:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KAMINI SHAH can be reached at (571)272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANZUMAN SHARMIN/Examiner, Art Unit 2115
/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2115
1 Energy contract between participant and the utility.
2 Energy market condition.
3 Price of energy in future time window.
4 Automatically controlling the equipment of the system based on received/determined control signals.
5 Pre-purchased information of energy hedge.
6 Taking control decision based on energy hedge information.
7 Automatic execution of the setpoints by the components in facility/building by the demand response layer in communication with the BMS controller (central controller).
8 The model.
9 Predicting block energy hedge over future period of time and forcasting buying and selling strip price (hedge price) in the future market as taught in [0032].
10 Quantify energy prices based on predicted energy block energy hedges.