DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after December 9, 2016, is being examined under the first inventor to file provisions of the AIA .
In an Amendment filed on July 6, 2026, claims 1, 2, 5, 6, 8, 12, 13, 15, 19, and 20 were amended.
Claims 1-21 are currently pending and under examination, of which claims 1, 8, and 15 are independent claims.
Response to Amendment
Applicants’ amendments to the claims have overcome the objections previously set forth.
Applicants’ amendments to the claims have overcome the rejections under 35 USC 112(b) previously set forth.
Applicants’ amendments to the claims have overcome the rejections under 35 USC 112(d) previously set forth.
Response to Arguments
Considering the claim amendments, Applicant’s arguments with respect to the 35 USC 102 and 35 USC 103 rejections of independent claims 1, 8, and 15 have been considered and are persuasive. However, the arguments do not apply to the newly cited portions of the prior art and the newly cited reference being used in the current rejections. Dependent claims 2-7, 9-14, and 16-21 depend directly, or indirectly, from independent claims 1, 8, and 15.
Claim Objections
The following claims are objected to for lack of antecedent support or for redundancies. The Examiner recommends the following changes:
Claim 1, line 11, insert “operation of” before “at least one renewable”.
Claim 8, line 15, insert “operation of” before “at least one renewable”.
Claim 15, line 12, insert “operation of” before “at least one renewable”.
Appropriate correction is respectfully requested.
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.
Claims 1-3, 5, 7-10, 12, 14-17, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US Patent Publication No. 2016/0273518 A1) (“Chen”) in view of Lin et al. (CN 110232640A) (“Lin”).
Regarding independent claim 1, Chen teaches:
A computer-implemented method of managing a power grid, comprising: Chen: Paragraph [0025] (“Along with an ever increasing penetration of large amount of renewable energy, especially wind power into the power grids around the world, existing EMSs and EMMSs require enhancements to manage, accommodate, and control wind power and other types of renewable energy (e.g., like solar power, run-of-river hydro power, etc.) to optimize economic and environmental benefits.”) Chen: Paragraph [0029] (“Turning now to FIG. 1, a portion of an example energy deliver system 100 according to embodiments of the present invention is provided. Independent System Operators (ISO) 102 operate control centers that can include an EMS 104. The EMS 104 can include a number of hardware and software components for monitoring, controlling, and optimizing the performance (e.g., in terms of minimizing cost, maximizing efficiency, and maximizing reliability) of the generation and transmission of the energy delivery system 100.”)
identifying, with a control system comprising one or more hardware processors, a plurality of energy generation resources electrically coupled within a power grid; Chen: Paragraphs [0025] and [0029] [As described above.] Chen: Paragraph [0019] (“Embodiments of the present invention include software applications and systems adapted to provide a wind power management (WPM) system within the EMS that provides security constrained dynamic dispatch (SCDD). The SCDD application is responsible for economically dispatching active power generation of all types of resources including wind farms subject to system, resource and network constraints over multiple time intervals in a manner such that as much as wind power can be injected the power grids without degrading system security and yet achieve the best possible system overall economics over the dispatch horizon.”) Chen: Paragraph [0030] (“The EMS 104 includes an automatic generation control (AGC) system 106 for adjusting the power output of multiple resources 108 (e.g., generators) at different power plants (e.g., utilities 110, independent power producers (IPP) and/or non-utility generators (NUG), etc.), in response to changes in the load created by consumers of the electricity. The generated power is delivered from the resources 108 to power consumers' loads 112 via transmission lines 114. Note that the utilities 110 can include an EMS 104 with an AGC system 106. Utilities 110′ that include one or more wind farms 118 include a wind power management (WPM) application 116 as part of their EMS 104. To support such utilities 110′, the ISO's EMS 104 also includes the WPM application 116. As will be described in detail below with respect to FIG. 2, the WPM application 116 includes a SCDD application. To facilitate communications and control between the EMSs 104, the EMSs 104 also implement an inter-control center protocol (ICCP) 120.”) Chen: Paragraph [0033] (“Where the grid has tie interconnections to adjacent control areas, the AGC system 106 helps maintain the power interchanges over the tie lines at the scheduled levels.”) [As shown in FIG. 1, the multiple resources including generators at power plants connected via transmission lines in a power grid reads on “identifying… a plurality of energy generation resources electrically coupled within a power grid”. The ISO control center reads on “a control system”.]
identifying, with the control system, at least one renewable energy source within the plurality of energy generation resources electrically coupled within the power grid; Chen: Paragraphs [0019], [0025], [0029], [0030], and [0033] [As described above.] [As shown in FIG. 1, the multiple resources including wind farms and other types of renewable energy (e.g., like solar power, run-of-river hydro power, etc.) connected via transmission lines in a power grid reads on “identifying… at least one renewable energy source within the plurality of energy generation resources electrically coupled within the power grid”.]
inputting, with the control system, grid and energy source data from the identified plurality of energy generation resources into an energy dispatch model; Chen: Paragraph [0039] (“The WPM application 116 also includes a Wind Power Forecast (WPF) function operative to make use of weather forecast data, historical actual weather data, especially wind speed and wind direction, current actual weather data, historical wind farm actual MW output, current actual wind farm MW output; takes advanced adaptive forecasting techniques; and produces reasonable wind power forecast results on the system, zone, and wind farm levels.”) Chen: Paragraph [0042] (“The data collection function module 302 obtains static and dynamic data from appropriate data sources including an interface to the SCADA/AGC for input data which includes a real-time snapshot of telemetry and dynamic unit parameters; an interface to a Current Operating Plan (COP) which includes schedule input data; and an interface to the TNA server 204 which includes critical constraint input data.”) Chen: Paragraph [0045] (“The optimization model formulation function module 308 is operative to map the internal data structures that hold the input data to the optimization model. In addition, internal indexes are built for fast access between internal data structures and internal model structures for the optimization model. The optimization model formulation function module 308 also builds the objective function, constraints and bounds in the standard formats.”) [The WPM application and/or the optimization model read on “an energy dispatch model”.]
executing, with the control system, an optimization algorithm with the energy dispatch model to optimize a plurality of objective functions that comprise a first objective function to minimize an operational cost and a second objective function to maximize at least one renewable energy source; Chen: Paragraphs [0025], [0039], [0042], and [0045] [As described above.] Chen: Paragraph [0024] (“Further, options for the objective function include minimizing total system cost and maximizing the use of renewable energy, especially wind power.”) Chen: Paragraph [0028] (“The SCDD application meets the challenging needs of modern control centers and provides a number of functions. Specifically, these functions include supporting the dispatch needs of a market environment where market resources compete for supplying power generation; supporting the dispatch needs of traditional EMS control centers where non-market resources are dispatched based on production cost consideration; supporting the dispatch needs of EMS control centers where a mix of market resources and non-market resources must be dispatched properly; supporting the use of renewable energy, especially wind power; supporting co-optimization of energy and ancillary services; dispatch generating resources over multiple consecutive time intervals in an optimal manner; respecting critical network security constraints; and incorporating inter-temporal ramping constraints on dispatchable units.”) Chen: Paragraph [0046] (“The solution function module 310 executes the optimization process. The optimization results are mapped back from internal model structures that hold optimization result data to the internal data structures for the output data.”) Chen: Paragraph [0044] (“The data pre-processing function module 306 performs extensive pre-processing after the input data has been validated. The pre-processing, performed prior to proceeding with the optimization model formulation can include determining the total generation to be dispatched along with any AS requirements for each time interval of the dispatch horizon; determining the list of generating units that will participate in the dispatch, along with their operating modes for each time interval of the dispatch horizon; determining the effective dispatch limits of each participating generating unit for each time interval of the dispatch horizon; determining the effective ramp rates of each participating generating unit for each time interval of the dispatch horizon; determining the resulting price/cost curve of each participating generating unit for each time interval of the dispatch horizon; determining the initial MW of each participating generating unit for each time interval of the dispatch horizon; and determining the list of network critical constraints to be considered during dispatch, along with their limits and shift factors for each time interval of the dispatch horizon.”) Chen: Paragraph [0055] (“The optimization problem can be thought of as minimizing the system production cost over all participating units and over all time intervals in the dispatch horizon subject to a number of categories of constraints. These categories include power system balance constraints, system regulation requirements, system reserve requirements, unit dispatch limits, unit regulation limits, unit reserve limits, unit ramp rate limits, unit prohibited regions, power plant/group limits, critical flow limits, wind farm's security limits which are considered in processing of unit dispatch limits.”) [The options for the objective functions to minimize total system cost and maximize the use of renewable energy, especially wind power reads on “a first objective function to minimize an operational cost and a second objective function to maximize at least one renewable energy source”.]
determining, with the control system, at least one energy system control command based on the executed optimization model; and Chen: Paragraph [0044] [As described above.] Chen: Paragraph [0047] (“The result post-processing function module 312 processes the solution of the optimization to determine the unit's final economic basepoints after the optimization has completed. The results of the post-processing will be written to the SCDD application 201 output tables of the WPM database 206. Exception handling is also conducted in cases where infeasibilities occur in the optimization process. The unit economic basepoints for the binding interval are also updated to the real-time AGC database in the case of a real-time user.”) Chen: Paragraph [0036] (“In operation, the ISO clears the real time market through its market optimization engine and then ISOs the dispatch instructions along with ancillary service awards (e.g., regulation, reserves, etc.) to individual power utilities through a transport mechanism (e.g., ICCP 120). The power utilities receive the dispatch instructions (e.g., via ICCP 120) and then make use of their AGC 106 to compute a power setpoint command for each AGC cycle for the resources under AGC control (i.e., AGC units).”)
controlling, with the control system, the plurality of energy generation resources to maximize a power output of the at least one renewable energy source based on the executed energy dispatch model… Chen: Paragraph [0044] [As described above.] Chen: Paragraph [0024] (“The SCDD application also provides an optimization process that ensures that the power grid's security is not compromised and properly dispatches a mix of market resources that are bid-based and non-market resources that are cost based. Further, options for the objective function include minimizing total system cost and maximizing the use of renewable energy, especially wind power. The SCDD application can also co-optimize the energy, regulation and spinning reserve to meet power balance, regulation requirements and reserve requirements. The SCDD application can dispatch the dispatchable generating units over multiple consecutive time intervals in an optimal manner to minimize the system production cost based on the unit's piece-wise linear or stair-case incremental cost curves and the total area generation to be dispatched (GTBD). Further, plant/group limits are included when enabled, unit limits and response rates are respected, unit prohibited regions are considered, critical network security constraints are respected when enabled, and inter-temporal ramping constraints on dispatchable units are incorporated.”) Chen: Paragraph [0038] (“A Wind Power Control (WPC) function provides a three-phase short-circuit contingency list and request for the TNA server's FC application 209 to conduct post-fault voltage values for the WPC function interested transmission nodes; ranks the contingencies; conducts the maximization of system wind power generation; conducts the minimization of system wind power generation loss; and determines the system maximum allowed wind power generation that can be integrated into the power grid.”) [The dispatching of the generating units including renewable energy units or wind farms reads on “controlling…the plurality of energy generation resources”.]
Chen does not expressly teach “operating at least one thermal energy generation resource at a minimal generation capacity during a peak generation period of the at least one renewable enemy source to minimize curtailment of the at least one renewable enemy source”. However, Lin describes an electric heating combined dispatching model considering heat load elasticity and heat supply network characteristics for wind power consumption. Lin teaches:
…controlling, with the control system, the plurality of energy generation resources to maximize a power output of the at least one renewable energy source based on the executed energy dispatch model by operating at least one thermal energy generation resource at a minimal generation capacity during a peak generation period of the at least one renewable enemy source to minimize curtailment of the at least one renewable enemy source. Lin: Abstract (“The invention provides an electric heating combined dispatching model considering heat load elasticity and heat supply network characteristics for wind power consumption, which comprises the following steps of: (1) analyzing the heat use comfort level of a heating user, and setting a PMV segmented limited range according to different day and night comfort level requirements of the user; (2) considering the required elasticity of the heat load, and converting the traditional heat balance equation constraint into an interval constraint; and (3) taking minimum total coal consumption and maximum wind power acceptance as an optimization target, and establishing an electric heating combined dispatching model considering heat supply network characteristics and heat load elasticity. The model is beneficial to broadening of a new idea of promoting wind power consumption by mining the adjustment potential of the demand side, and provides reference for electric heating economic dispatching underwind power integration.”) Lin: Page 3, third paragraph (“S1: The minimum total coal consumption of the system is minimized, and the wind power grid is maximized to meet the optimization goal. The electric heat joint scheduling model with heat balance interval constraint is established. The synergy between the thermal network characteristics and the thermal load elasticity gives the system flexible adjustment space. Minimize coal consumption costs while achieving wind power consumption:”) Lin: Page 11, fourth and fifth paragraphs (“Step 3: With the minimum total coal consumption and the maximum acceptance of wind power as the optimization goal, establish a combined electric and thermal scheduling model considering thermal load elasticity and thermal network characteristics: According to the proposed thermal grid characteristics and thermal load elastic electrothermal joint scheduling model, the results of the scheduling are: when the night wind is large, the output of the pure condensing unit and the thermoelectric unit in the system are reduced by 3.76% and 2.90%, respectively. Dropped to 55.29% and 87.61%), it increases the space for wind power grid in its adjustable range; the heat load of the thermal power unit is reduced, the heat output of the unit is reduced by about 16.87%, and the heating energy consumption is reduced; compared with conventional dispatching, The wind power grid rate has increased by 20.52%, and the wind power has been fully connected to the Internet. The daily coal consumption of the system has decreased by 4.38%, which effectively improves the operational economy of the system. The specific scheduling results are shown in Figure 5-8.”) [The electric heating combined dispatching model taking the minimum total coal consumption and maximum wind power during certain scheduling reads on “operating at least one thermal energy generation resource at a minimal generation capacity during a peak generation period of the at least one renewable enemy source”.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Chen and Lin before them, to provide a plurality of objective functions that comprise a first objective function to minimize an operational cost and a second objective function to maximize at least one renewable energy source and to operate at least one thermal energy generation resource at a minimal generation capacity during a peak generation period of the at least one renewable enemy source to minimize curtailment of the at least one renewable enemy source because the references are in the same field of endeavor as the claimed invention and they are focused on energy management.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would broaden a new idea of promoting wind power consumption by mining the adjustment potential of the demand side, and provides reference for electric heating economic dispatching underwind power integration. Lin Abstract
Regarding claim 2, Chen and Lin teach all the claimed features of claim 1, from which claim 2 depends. Chen further teaches:
The computer-implemented method of claim 1, wherein the controlling of the multiple energy generation resources to maximize the power output of the at least one renewable energy source based on the executed energy dispatch model comprises controlling, with the control system, the multiple energy generation resources to minimize curtailment of the at least one renewable energy source based on the executed energy dispatch model. Chen: Paragraph [0044] [As described in claim 1.] [The WPM application and/or the optimization model determining the effective dispatch limits of each participating generating unit including wind farms for each time interval reads on “the controlling of…the multiple energy generation resources to minimize curtailment of the at least one renewable energy source based on the executed energy dispatch model”.]
Regarding claim 3, Chen and Lin teach all the claimed features of claim 1, from which claim 3 depends. Chen further teaches:
The computer-implemented method of claim 2, wherein the at least one renewable energy source comprises a wind energy source. Chen: Paragraphs [0019] and [0025] [As described in claim 1.] [The wind farm producing wind power reads on “a wind energy source”.]
Regarding claim 5, Chen and Lin teach all the claimed features of claim 1, from which claim 5 depends. Chen further teaches:
The computer-implemented method of claim 1, wherein executing of the optimization algorithm with the energy dispatch model to optimize the plurality of objective functions comprises: executing, with the control system, the optimization algorithm with the energy dispatch model to minimize the operational with the first objective function; and Chen: Paragraphs [0025] and [0044] [As described in claim 1.] Chen: Paragraph [0024] (“Further, options for the objective function include minimizing total system cost and maximizing the use of renewable energy, especially wind power.”) [The optimization of cost of renewable energy reads on “minimize the operational cost with the first objective function”.]
executing, with the control system, the optimization algorithm with the energy dispatch model to maximize the usage of the at least one renewable energy source function with the second objective function. Chen: Paragraph [0024] [As described above.] Chen: Paragraph [0028] [As described in claim 1.] Chen: Paragraph [0023] (“In various embodiments, the SCDD application for the WPM system provides a number of features including taking into consideration the maximum allowed system wind generation for integration by enforcing the maximum wind generation allowable for integration obtained from a process of maximizing wind generation and minimizing wind generation loss based on a probabilistic approach. In addition, the maximum allowed wind farm's wind power generation is considered by enforcing the wind dispatch results obtained from the process of maximizing wind generation and minimizing wind generation loss based on a probabilistic approach.”)
Regarding claim 7, Chen and Lin teach all the claimed features of claim 1, from which claim 7 depends. Chen further teaches:
The computer-implemented method of claim 1, wherein the grid and energy source data comprise wind forecast systems data, auto load forecast data, distributed wind energy data, thermal energy generation data, auxiliary energy generation data, and aggregated energy generation data. Chen: Paragraph [0039] (“The WPM application 116 also includes a Wind Power Forecast (WPF) function operative to make use of weather forecast data, historical actual weather data, especially wind speed and wind direction, current actual weather data, historical wind farm actual MW output, current actual wind farm MW output; takes advanced adaptive forecasting techniques; and produces reasonable wind power forecast results on the system, zone, and wind farm levels.”) Chen: Paragraph [0084] (“The schedule data used from the Current Operating Plan (COP) includes unit outage schedules, unit derate schedules, unit energy bid price curves, unit ancillary service bid price curves, area net interchange schedule, and area load forecast.”) Chen: Paragraph [0024] (“Further, options for the objective function include minimizing total system cost and maximizing the use of renewable energy, especially wind power.”) Chen: Paragraph [0028] (“The SCDD application for WPM can be implemented as a core application within the WPM system…supporting the use of renewable energy, especially wind power; supporting co-optimization of energy and ancillary services; dispatch generating resources over multiple consecutive time intervals in an optimal manner; ...”) Chen: Paragraph [0033] (“With computer-based control systems and multiple inputs, an AGC system 106 can take into account such matters as the most economical units to adjust, the coordination of thermal, hydroelectric, wind, and other generation types, and constraints related to the stability of the system and capacity of interconnections to other power grids.”) Chen: Paragraph [0079] (“For thermal units with allowed regions (ARs) and prohibited regions (PRs), the dispatch first attempt to assign economic basepoints that are not in any of the PRs; if not possible due to the fact that the PRs are two wide to cross over in one time interval, ensure that the PR is crossed as fast as possible and penalize the basepoint that falls within any of the PRs.”) Chen: Paragraph [0025] (“Along with an ever increasing penetration of large amount of renewable energy, especially wind power into the power grids around the world, existing EMSs and EMMSs require enhancements to manage, accommodate, and control wind power and other types of renewable energy (e.g., like solar power, run-of-river hydro power, etc.) to optimize economic and environmental benefits.” Which reads on “auxiliary energy generation data”.) Chen: Paragraph [0044] (“The pre-processing, performed prior to proceeding with the optimization model formulation can include determining the total generation to be dispatched along with any AS requirements for each time interval of the dispatch horizon; determining the list of generating units that will participate in the dispatch,…” which reads on “aggregated energy generation data”.)
Regarding independent claim 8, Chen teaches:
A computing system, comprising: one or more memory modules configured to store an energy dispatch model of a power grid; and one or more hardware processors communicably coupled to the one or more memory modules and configured to execute instructions stored on the one or more memory modules to perform operations comprising: Chen: Paragraph [0103] (“The present disclosure may refer to a “control system”, application, or program. A control system, application, or program, as that term is used herein, may be a computer processor coupled with an operating system, device drivers, and appropriate programs (collectively “software”) with instructions to provide the functionality described for the control system. The software is stored in an associated memory device (sometimes referred to as a computer readable medium). While it is contemplated that an appropriately programmed general purpose computer or computing device may be used, it is also contemplated that hard-wired circuitry or custom hardware (e.g., an application specific integrated circuit (ASIC)) may be used in place of, or in combination with, software instructions for implementation of the processes of various embodiments. Thus, embodiments are not limited to any specific combination of hardware and software.”) Chen: Paragraph [0104] (“A “processor” means any one or more microprocessors, Central Processing Unit (CPU) devices, computing devices, microcontrollers, digital signal processors, or like devices. Exemplary processors are the INTEL PENTIUM or AMD ATHLON processors.”)
The remaining functions recited in the claim are similar limitations as in independent claim 1 and is rejected using the same teachings and rationale.
Regarding claim 9, the features recited in the claim are similar limitations as in claim 2 and is rejected using the same teachings and rationale.
Regarding claim 10, the features recited in the claim are similar limitations as in claim 3 and is rejected using the same teachings and rationale.
Regarding claim 12, the features recited in the claim are similar limitations as in claim 5 and is rejected using the same teachings and rationale.
Regarding claim 14, the features recited in the claim are similar limitations as in claim 7 and is rejected using the same teachings and rationale.
Regarding independent claim 15, Chen teaches:
An apparatus comprising a tangible, non-transitory computer readable memory comprising instructions for causing one or more processors to perform operations comprising: Chen: Paragraph [0105] (“The term “computer-readable medium” refers to any statutory medium that participates in providing data (e.g., instructions) that may be read by a computer, a processor or a like device... The terms “computer-readable memory” and/or “tangible media” specifically exclude signals, waves, and wave forms or other intangible or non-transitory media that may nevertheless be readable by a computer.
The remaining functions recited in the claim are similar limitations as in independent claim 1 and is rejected using the same teachings and rationale.
Regarding claim 16, the features recited in the claim are similar limitations as in claim 2 and is rejected using the same teachings and rationale.
Regarding claim 17, the features recited in the claim are similar limitations as in claim 3 and is rejected using the same teachings and rationale.
Regarding claim 19, the features recited in the claim are similar limitations as in claim 5 and is rejected using the same teachings and rationale.
Regarding claim 21, the features recited in the claim are similar limitations as in claim 7 and is rejected using the same teachings and rationale.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen, and Lin, and in view of Gu, Y., Jiang, H., Zhang, Y. and Gao, D.W., 2014, November. Statistical scheduling of economic dispatch and energy reserves of hybrid power systems with high renewable energy penetration. In 2014 48th Asilomar Conference on Signals, Systems and Computers (pp. 530-534). IEEE. (“Gu”).
Regarding claim 4, Chen and Lin teach all the claimed features of claim 1, from which claim 4 depends. Chen and Lin do not expressly teach the features of claim 4. However, Gu describes an optimal scheduling of economic dispatch and energy reserves in a hybrid power system. Gu teaches:
The computer-implemented method of claim 1, wherein the energy dispatch model comprises an IEEE RTS 24-Bus model. Gu: Abstract (“A statistical scheduling approach to economic dispatch and energy reserves is proposed in this paper. The proposed approach focuses on minimizing the overall power operating cost with considerations of renewable energy uncertainty and power system security. In such a system, it is challenging and yet an open question on the scheduling of economic dispatch together with energy reserves, due to renewable energy generation uncertainty, and spatially wide distribution of energy resources. The hybrid power system scheduling is formulated as a convex programming problem to minimize power operating cost, taking considerations of renewable energy generation, power generation-consumption balance and power system security. A genetic algorithm based approach is used for solving the minimization of the power operating cost. The IEEE 24-bus reliability test system (IEEE-RTS), which is commonly used for evaluating the price stability of power system and reliability, is used as the test bench for verifying and evaluating system performance of the proposed scheduling approach.”) Gu: Section II.A, page 530, second column (“In our test bench system, the IEEE 24-bus Reliability Testing System (IEEE-RTS), 4 wind farms are used to substitute the 4 conventional power plant in the IEEE-RTS. The 4 wind farms with nominal generation capacity of 150 MW, 250 MW, 400 MW and 600 MW, respectively.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Chen, Lin, and Gu before them, for the energy dispatch model of Chen to comprise an IEEE RTS 24-Bus model because the references are in the same field of endeavor as the claimed invention and they are focused on energy management.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it is a commonly used for evaluating the price stability of power system and reliability, is used as the test bench for verifying and evaluating system performance of the proposed scheduling approach. Gu Abstract
Regarding claim 11, the features recited in the claim are similar limitations as in claim 4 and is rejected using the same teachings and rationale.
Regarding claim 18, the features recited in the claim are similar limitations as in claim 4 and is rejected using the same teachings and rationale.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen, Lin, and in view Cruickshank, III (US Patent Publication No. 2021/0296897 A1) (“Cruickshank”).
Regarding claim 6, Chen and Lin teach all the claimed features of claim 5, from which claim 6 depends. Chen further teaches:
The computer-implemented method of claim 1, wherein the operational cost function comprises a thermal energy operational cost function, a renewable energy operational cost function, and ... Chen: Paragraph [0033] (“With computer-based control systems and multiple inputs, an AGC system 106 can take into account such matters as the most economical units to adjust, the coordination of thermal, hydroelectric, wind, and other generation types, and constraints related to the stability of the system and capacity of interconnections to other power grids.”) Chen: Paragraph [0079] (“For thermal units with allowed regions (ARs) and prohibited regions (PRs), the dispatch first attempt to assign economic basepoints that are not in any of the PRs; if not possible due to the fact that the PRs are two wide to cross over in one time interval, ensure that the PR is crossed as fast as possible and penalize the basepoint that falls within any of the PRs.”) Chen: Paragraph [0024] (“Further, options for the objective function include minimizing total system cost and maximizing the use of renewable energy, especially wind power.”) Chen: Paragraph [0028] (“The SCDD application for WPM can be implemented as a core application within the WPM system. The SCDD application meets the challenging needs of modern control centers and provides a number of functions. Specifically, these functions include supporting the dispatch needs of a market environment where market resources compete for supplying power generation; … supporting the use of renewable energy, especially wind power; supporting co-optimization of energy and ancillary services; dispatch generating resources over multiple consecutive time intervals in an optimal manner; respecting critical network security constraints; and incorporating inter-temporal ramping constraints on dispatchable units.”)
Chen and Lin do not expressly teach that the operational cost function comprises “an emissions operational cost function”. However, Cruickshank describes a system and method for providing a load shape signal for power networks. Cruickshank teaches:
…an emissions operational cost function. Cruickshank: Paragraph [0057] (“Automatic electric load shaping and subsequent modulation of power based on shaping the load can increase the efficiency of existing thermal power plants and facilitate a transition to carbon-free generation from renewables. The ongoing digitization of buildings, industry, and transportation presents an ever-expanding set of opportunities to use the Internet of Things to introduce load elasticity and alter the traditional electricity supply-follows-demand paradigm. The solution addressed here is to create a globally applicable framework to model the value of flexible residential load across a range of geographies in terms of electricity production cost and carbon dioxide emissions and develop a method system and apparatus to provide a load shaping signal, modify the load, and in so doing modulate the power generated by thermal power plants and further to renewable energy sources.”) Cruickshank: Paragraph [0112] (“Foresee was built on a multi-objective model predictive control framework, wherein the objectives consisted of minimizing energy cost and carbon emissions while maximizing thermal comfort and user convenience. Foresee learned user preferences on different objectives and acted on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Chen, Lin, and Cruickshank before them, for the energy dispatch model of Chen to comprise an IEEE RTS 24-Bus model because the references are in the same field of endeavor as the claimed invention and they are focused on energy management.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification to enable highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Cruickshank Paragraph [0112]
Regarding claim 13, the features recited in the claim are similar limitations as in claim 6 and is rejected using the same teachings and rationale.
Regarding claim 20, the features recited in the claim are similar limitations as in claim 6 and is rejected using the same teachings and rationale.
It is noted that any citations to specific paragraphs or figures in the prior art references and any interpretation of the reference should not be limited in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang et al. (CN111080177A) describes a direct current reactive power linearization processing method and system based on Taylor expansion, firstly establishing a converter station steady-state operation model; secondly, carrying out variable elimination and linearization processing on the converter station steady-state operation model to obtain a linearized converter station steady-state operation model; and finally, applying the direct-current reactive power linearization method to the optimization scheduling problem of the alternating-current and direct-current interconnected power grid, establishing an alternating-current and direct-current interconnected power grid safety constraint optimization scheduling model aiming at the minimum thermal power operation cost and the maximum wind power consumption, ensuring the fine modeling of the actions of reactive equipment such as an alternating-current filter.
Applicants’ amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicants are reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for replying 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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no case, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M. CHOI whose telephone number is (571)272-1473. The examiner can normally be reached on Monday - Friday 7:30 am to 5:00 pm.
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, Robert Fennema can be reached on 571-272-2748. 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). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117
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