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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action.
In response to Examiner's communication of 2/24/2026, Applicant responded on 3/24/2026. Amended claim 1-8.
Claims 1-10 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 1-8 are sufficient to overcome the 35 USC 112(b) rejections set forth in the previous action. However, Applicant’s amendments necessitated new grounds of 35 USC 112(b) rejections.
Applicant's amendments to claims 1-8 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1-8 are not sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…As clarified by the October Guidance, "A claim with limitation(s) that cannot be practically performed in the human mind does not recite a mental process."…adjusting electric energy supply of the electricity generation stations, the urban area, and the electricity storage devices according to the estimated electric energy data, and monitoring the electricity consumption data of the different levels of the urban area after being adjusted to obtain secondary electricity consumption data; and calling the electricity storage devices in surrounding regions according to the secondary electricity consumption data to supplement electric energy" cannot practically be performed in the human mind, and even cannot be performed by applying the abstract idea with the computer components. Similarly, claim 8 recites, inter alia, the above technical features.…they are about specific ways to collaboratively schedule hierarchical and graded source-network-load-storage multi subjects by adjusting, by the processor, electric energy supply of the electricity generation stations, the urban area, and the electricity storage devices according to the estimated electric energy data, and monitoring the electricity consumption data of the different levels of the urban area after being adjusted to obtain secondary electricity consumption data; and calling, by the processor, the electricity storage devices in surrounding regions according to the secondary electricity consumption data to supplement electric energy ", which cannot be performed or achieved using any mental process… these claims recite a practical application of collaboratively schedule hierarchical and graded source-network-load-storage multi subjects. For at least this reason, claims 1 and 8, and the remaining claims, based at least on their dependence from the independent claim 1, are believed patentable over the § 101 rejection….” The Examiner respectfully disagrees.
The claims and the argued elements, are directed to, …schedule hierarchical and graded source-network-load-storage multi subjects…adjusting electric energy supply of the electricity generation stations, the urban area, and the electricity storage devices according to the estimated electric energy data, and monitoring the electricity consumption data of the different levels of the urban area after being adjusted to obtain secondary electricity consumption data…, is a problem directed to mental process (i.e. humans monitoring and managing human urban power consumption and scheduling supplemental power with other power storage companies to meet human urban power consumption demand), organizing human activities (i.e. humans monitoring and managing human urban power consumption and scheduling supplemental power with other power storage companies to meet human urban power consumption demand), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components, i.e. computer. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer and electric power generation, performing extra solution activities (i.e. gathering power usage data and outputting notification calling and requesting for power). Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 or amount to significantly more in Step 2B.
Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
[C]laims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
[P]roduct claims reciting mental processes include:
A wide-area real-time performance monitoring system for monitoring and assessing dynamic stability of an electric power grid – Electric Power Group, 830 F.3d at 1351 and n.1, 119 USPQ2d at 1740 and n.1; and
The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
By way of example, in Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the claims described “the creation of a dynamic document based upon ‘management record types’ and ‘primary record types.’” 850 F.3d at 1339-40; 121 USPQ2d at 1945-46. The claims were found to be directed to the abstract idea of “collecting, displaying, and manipulating data.” 850 F.3d at 1340; 121 USPQ2d at 1946. In addition to the abstract idea, the claims also recited the additional element of modifying the underlying XML document in response to modifications made in the dynamic document. 850 F.3d at 1342; 121 USPQ2d at 1947-48. Although the claims purported to modify the underlying XML document in response to modifications made in the dynamic document, nothing in the claims indicated what specific steps were undertaken other than merely using the abstract idea in the context of XML documents. The court thus held the claims ineligible, because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words “apply it”. 850 F.3d at 1341-42; 121 USPQ2d at 1947-48 (citing Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 1743-44 (cautioning against claims “so result focused, so functional, as to effectively cover any solution to an identified problem”)).
[T]he courts have found to be insignificant extra-solution activity:
iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016); and
[T]he courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include:
vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…In the paragraph [0055], the applicant finds that the data which are preprogrammed based on historical data are thresholds, the thresholds can be such as percentage (40%, 60%, 75%, 100%
in the following text). This is different from features disclosed by claim 1 (obtaining historical data sets of the electricity generation stations, the urban area, and the electricity storage devices, and analyzing the power supply data, the electricity consumption data, and the energy storage data according to the historical data sets to obtain estimated electric energy data). In claim 1, the data which are obtained according to the historical data sets are the estimated electric energy data, which is different from the thresholds, therefore, claim 1 is cannot achieved by combining Li and Padmarao. Claim 1 is patentable by CN116683542A named Li in view of US 20210296898A named Padmarao.….” The Examiner respectfully disagrees.
Respectfully, Applicant’s argument requires that the each of the features of supporting references are bodily incorporated into primary reference that teach and every element is individually taught by a single reference. However, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one single or in all of the references. See id. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See id.; In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Furthermore, under the broadest reasonable interpretation, Li teaches:
obtaining, by the processor, historical data sets of the …, the urban area, and the electricity storage devices, and analyzing the power supply data, the electricity consumption data, and the energy storage data according to the historical data sets to obtain estimated electric energy data; (in at least [pg5] Step S3: Obtain the total power output, total energy storage and electricity load prediction value at the current moment based on the source, grid, load and storage digital twin. The total power output is the current moment of thermal power data, hydropower data, nuclear power data and new energy generation data. The total energy storage is the sum of all energy storage in the chemical battery energy storage data, hydropower station energy storage data, hydrogen energy storage data and supercapacitor energy storage data. The electricity load prediction value is the source grid load storage The digital twin is obtained by predicting based on historical civil load data and historical industrial load data; Specifically, in this embodiment, the source grid, load and storage digital twin obtains the total power output, total energy storage and electricity load prediction value at the current moment through data sensing and data processing to provide data support for the source grid, load and storage regulation and control decisions. The source network load storage digital twin in the embodiment interacts with the entity data and obtains the power resource data, energy storage resource data and load resource data generated by the entity in real time. [pg9] A processing unit configured to obtain the total power output, the total energy storage, and the electricity load prediction value at the current moment based on the source grid load and storage digital twin, where the total power output is the thermal power data, the hydropower data , the sum of the output power at the current moment of the nuclear power data and the new energy power generation data, the total energy storage is the chemical battery energy storage data, the water storage power station energy storage data, the hydrogen energy storage data and all The sum of all the energy stored in the supercapacitor energy storage data, the electricity load prediction value is obtained by predicting the source grid load storage digital twin based on historical civil load data and historical industrial load data;)
Although implied, Li does not expressly disclose the following limitations, which however, are taught by Padmarao,
…historical data sets of the electricity generation stations… (in at least [0028] During operation of plant 100, environmental conditions will change and affect the operations of wind assets 110 and solar assets 112. For example, a day may be cloudy and windy with low solar irradiance and high wind. Alternatively, a day may be sunny with high solar irradiance and little or no wind. Also at night, solar assets 112 and their associated inverters 114 may be unused due to the lack of solar irradiance. In other situations, the power generated by wind assets 110 and solar assets 112 may be greater than that required or allowed to be supplied to grid 102. In this situation, plant 100 may store at least a portion of the excess generated power in batteries 116. [0032] A plant controller 206 coordinates the operation of the various assets 210 of plant 100. Each asset 210 includes an asset controller 208 that controls the operation of individual asset 210. For example, if plant controller 206 instructs an asset 210 to produce 5 megawatts (MW) of power, asset controller 208 controls asset 210 to safely produce that amount of power. In some embodiments, asset controller 208 may also be in communication with one or more sensors that measure conditions at asset 210, including both environmental and operating conditions of asset 210. In some embodiments, a single asset controller 208 controls a plurality of assets 210. In other embodiments, each asset controller 208 controls a single asset 210. In some embodiments, plant controller 206 distributes the reactive power to asset controllers 208. [0055] power system management computer device 310 stores or accesses, such as through database 320 (shown in FIG. 3), other system information about plant 100 and assets 210 (shown in FIG. 2). This other system information may include, but is not limited to, rated wind speed for wind assets 110 (shown in FIG. 1), rated solar irradiation for solar assets 112 (shown in FIG. 1), a point of interconnect limit for transformer 108 (shown in FIG. 1), and an MVA rating of one or more assets 210. In the exemplary embodiment, power system management computer device 310 also access thresholds for Khigh for wind assets 110 and Klow for solar assets 112, as described below. These thresholds may be set by a user through a client system 325 (shown in FIG. 3) or be preprogrammed based on historical data. In the exemplary embodiment, process 700 is performed when the amount of solar power that would be generated is below a certain level (Klow) and the amount of wind power that would be generated is greater than a certain level (Khigh). For example, Klow may be set between 40% and 60% of the total power potentially generated based on the asset's rating, while Khigh is set between 75% and 100% of the total power potentially generated based on the asset's rating. In some embodiments, process 700 may be performed during nighttime, evenings, and cloudy days to increase the amount of power generated.)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Li as taught by Padmarao, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Li with the motivation of, …to managing reactive power in a hybrid power environment to improve active power generation…provide the reactive power support for the plant to meet the required reactive power generation. However, reactive power generation reduces the amount of active or real power that an asset is producing. When the active power production is high, the capability for reactive power production may be limited by the apparent power capability of the generator and the inverters.…operating the plant to optimize power generation based on current conditions. Accordingly, it would be useful to combine forecasted conditions with asset generation capabilities to optimize plant energy production.…(i) improved design of plants to maximize output; (ii) increased utilization of installed electrical components such as wind generators and inverters; (iii) increased annual energy production of the plant due to dynamic uprate of wind assets; (iv) reduction in collector system losses due to optimal distribution of reactive power among generation assets; (v) reduction in spill-over of energy during curtailment scenarios in a hybrid renewable plant; (vi) maximization of revenue generated during curtailment scenarios in a hybrid renewable plant; and (vii) minimization of negative impact on life of components impacted due to curtailment… to allow these three assets 110 to operate at higher levels and improve the revenue for plant 100…result in an improvement in the annual energy production of hybrid plant 100.…process 700 may be used for a net improvement in operation of corresponding plants 100. Furthermore, process 700 may be used to design high efficiency hybrid plants 100 based on a mix of solar, wind, and potentially battery or other sources.…, as recited in Padmarao.
Claim Rejections - 35 USC § 112(b)
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 9, 10 are rejected under is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant(s) regard as their invention.
Claim 9 recites “A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method according to claim 1 are realized”, it is unclear if these elements refer to the same elements introduced in Claim 1. Further, if these elements refer to the elements introduced in Claim 1, it is unclear if Claim 9 further narrows Claim 1. Appropriate correction is required.
Claim 10 recites “A computer-readable storage medium in which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method according to claim 1 are realized.”, it is unclear if these elements refer to the same elements introduced in Claim 1. Further, if these elements refer to the elements introduced in Claim 1, it is unclear if Claim 10 further narrows Claim 1. Appropriate correction is required.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 8) recites, “A hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method, applied to …, the method comprising:
…, to collaboratively schedule hierarchical and graded source-network-load-storage multi subjects;
obtaining, by the …, environment data, power data, and electricity generation data of electricity generation stations to obtain power supply data, obtaining electricity consumption data corresponding to different levels of an urban area, and obtaining energy storage data of …;
obtaining, by the …, historical data sets of the electricity generation stations, the urban area, and the electricity storage devices, and analyzing the power supply data, the electricity consumption data, and the energy storage data according to the historical data sets to obtain estimated electric energy data;
adjusting, by the …, electric energy supply of the electricity generation stations, the urban area, and the electricity storage devices according to the estimated electric energy data, and monitoring the electricity consumption data of the different levels of the urban area after being adjusted to obtain secondary electricity consumption data; and
calling, by the …, the … in surrounding regions according to the secondary electricity consumption data to supplement electric energy ….
Analyzing under Step 2A, Prong 1:
The limitations regarding, …to collaboratively schedule hierarchical and graded source-network-load-storage multi subjects; obtaining, by the …, environment data, power data, and electricity generation data of electricity generation stations to obtain power supply data, obtaining electricity consumption data corresponding to different levels of an urban area, and obtaining energy storage data of …; obtaining, by the …, historical data sets of the electricity generation stations, the urban area, and the electricity storage devices, and analyzing the power supply data, the electricity consumption data, and the energy storage data according to the historical data sets to obtain estimated electric energy data; adjusting, by the …, electric energy supply of the electricity generation stations, the urban area, and the electricity storage devices according to the estimated electric energy data, and monitoring the electricity consumption data of the different levels of the urban area after being adjusted to obtain secondary electricity consumption data; and calling, by the …, the … in surrounding regions according to the secondary electricity consumption data to supplement electric energy …, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the above identified limitations, therefore, the claims are directed to a mental process.
Further, …to collaboratively schedule hierarchical and graded source-network-load-storage multi subjects; obtaining, by the …, environment data, power data, and electricity generation data of electricity generation stations to obtain power supply data, obtaining electricity consumption data corresponding to different levels of an urban area, and obtaining energy storage data of …; obtaining, by the …, historical data sets of the electricity generation stations, the urban area, and the electricity storage devices, and analyzing the power supply data, the electricity consumption data, and the energy storage data according to the historical data sets to obtain estimated electric energy data; adjusting, by the …, electric energy supply of the electricity generation stations, the urban area, and the electricity storage devices according to the estimated electric energy data, and monitoring the electricity consumption data of the different levels of the urban area after being adjusted to obtain secondary electricity consumption data; and calling, by the …, the … in surrounding regions according to the secondary electricity consumption data to supplement electric energy …, are humans monitoring and managing human urban power consumption and scheduling supplemental power with other power storage companies to meet human urban power consumption demand, which are commercial interactions, managing interactions and relationship between people, therefore the claims, are directed to certain methods of organizing human activities.
Accordingly, the claims are directed to a mental process, certain methods of organizing human activities, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 8: a computer device comprising a memory and a processor, executing, by the processor, a plurality of instructions stored in the memory ,electricity storage devices, system
Claim 2: illumination sensors, wind speed sensors, photovoltaic electricity generation devices of a photovoltaic electricity generation station, wind electricity generation devices of a wind electricity generation station
Claim 9: A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program
Claim 10: A computer-readable storage medium in which a computer program is stored, wherein when the computer program is executed by a processor
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “…obtaining…”, “…calling…”, “…sends…” these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…obtaining…” data output – “…calling…”, “…sends…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0030] In order to solve the above technical problems, the present invention provides the following technical solution: a hierarchical and graded source-network-load-storage multi-subject collaborative scheduling system includes a data obtaining module, a data processing module, a primary electricity supply adjustment module, a secondary electricity supply adjustment module, and a database module.
[0031] The data obtaining module is used for obtaining environment data, power data, and electricity generation data of electricity generation stations to obtain power supply data, obtaining electricity consumption data corresponding to different levels of an urban area, and obtaining energy storage data of electricity storage devices.
[0032] The data processing module is used for obtaining historical data sets of the electricity generation stations, the urban area, and the electricity storage devices, and analyzing the power supply data, the electricity consumption data, and the energy storage data according to the historical data sets to obtain estimated electric energy data.
[0033] The primary electricity supply adjustment module is used for adjusting electric energy supply of the electricity generation stations, the urban area, and the electricity storage devices according to the estimated electric energy data, and monitoring the electricity consumption data of the different levels of the urban area after being adjusted to obtain secondary electricity consumption data.
[0034] The secondary electricity supply adjustment module is used for calling the electricity storage devices in surrounding regions according to the secondary electricity consumption data to supplement electric energy.
[0035] the database module is used for storing historical average data of the electricity generation stations, the urban area, and the electricity storage device, historical average data of an environment, illumination time periods of different regions, the maximum stored electricity quantity of the different electricity storage devices, the maximum photoelectric inversion coefficient, and the maximum wind electricity inversion coefficient.
[0036] A computer device includes a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method are realized.
[0037] A computer-readable storage medium in which a computer program is stored, where when the computer program is executed by a processor, the steps of the above-mentioned hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method are realized.
[0207] If a function is realized in the form of a software function unit and sold or used as an independent product, the function may be stored in a computer-readable storage medium. On the basis of this understanding, the technical solution of the present invention essentially or a part that contributes to the prior art, or a part of the technical solution may be embodied in the form of a software product, and the computer software product is stored in a storage medium and includes a plurality of instructions which are used for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or a part of the steps of the methods described in the various examples of the present invention. The above storage medium includes: various media that may store program codes, such as a IJSB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc.
[0208] Logics and/or steps expressed in the flow diagram or otherwise described herein, for example, may be considered as an ordered list of executable instructions used for realizing logical functions, and may be specifically realized in any computer-readable medium for being used by instruction execution systems, apparatuses, or devices (such as computer-based systems, systems including processors, or other systems that may acquire instructions from the instruction execution systems, the apparatuses, or the devices and execute the instructions), or used in conjunction with these instruction execution systems, apparatuses, or devices. With regard to the present specification, the “computer-readable medium” may be any apparatus that may contain, store, communicate, propagate or transmit a program for being used by the instruction execution systems, the apparatuses, or the devices, or used in conjunction with these instruction execution systems, apparatuses, or devices.
[0209] More specific instances of the machine-readable storage medium (non-exhaustive list) include: an electric connection part (an electronic apparatus) with one or more wires, a portable computer disk case (a magnetic apparatus), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or a flash memory), an optical fiber apparatus, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other appropriate media in which the program may be printed, due to that the program may be obtained in an electronic manner, for example, through optically scanning the paper or the other media, and then editing, interpreting or processing in other appropriate manners if necessary, and then the program is stored in a computer memory.
[0210] It should be understood that, each part of the present invention may be realized by hardware, software, firmware or a combination thereof. In the above implementation manners, a plurality of steps or methods may be realized by software or firmware stored in the memory and executed by the appropriate instruction execution systems. For example, if the plurality of steps or methods are realized by hardware, as in another implementation manner, the plurality of steps or methods may be realized by any one of the following technologies which are well known in the art or a combination thereof: a discrete logic circuit with a logic gate circuit for realizing a logic function for a data signal, an application-specific integrated circuit with an appropriate combinational logic gate circuit, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.
[0218] It should be noted that, the above examples are merely used for illustrating the technical solution of the present invention and are not for limitation, although the present invention is described in detail with reference to the preferred examples, those of ordinary skill in the art should understand that, the technical solutions of the present invention may be modified or equivalently substituted without departing from the spirit and scope of the technical solution of the present invention, and all those modifications or replacements should be included in the scope of the claims of the present invention.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-10 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 8, 9, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable by CN Patent Publication to CN116683542A to Li et al., (hereinafter referred to as “Li”) in view of US Patent Publication to US20210296898A1 to Padmarao et al., (hereinafter referred to as “Padmarao”)
As per Claim 1, Li teaches: A hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method, applied to a computer device comprising a memory and a processor, the method comprising: ([pg9])
executing, by the processor, a plurality of instructions stored in the memory, to collaboratively schedule hierarchical and graded source-network-load-storage multi subjects; ([pg9])
obtaining, by the processor, environment data, power data, and electricity generation data of electricity generation stations to obtain power supply data, obtaining electricity consumption data corresponding to different levels of an urban area, and obtaining energy storage data of electricity storage devices; (in at least [pg4-pg5] Step S1: Obtain power resource data, energy storage resource data and load resource data. The power resource data includes thermal power data, hydropower data, nuclear power data and new energy power generation data. The energy storage resource data includes chemical battery energy storage data and water storage power station data. Energy storage data, hydrogen energy storage data and supercapacitor energy storage data, load resource data includes civil load data and industrial load data; Specifically, the source grid load storage is the power supply, power grid, load and energy storage. By accurately controlling the power load and energy storage resources, the safe operation level of the grid is improved and problems such as grid volatility in the process of new energy consumption are solved. This embodiment , power resource data includes but is not limited to thermal power data, hydropower data, nuclear power data and new energy power generation data. New energy power generation data can be clean energy power generation data such as photovoltaic power generation data and wind power generation data. Energy storage resource data includes but is not limited to Chemical battery energy storage data, hydropower station energy storage data, hydrogen energy storage data, supercapacitor energy storage data and other data that can store electrical energy. Load resource data includes civil load data and industrial load data, among which civil load data and industrial load data It also includes controllable load data that can be interrupted by the power grid and uncontrollable load data that cannot be interrupted. The controllable load data is used for load storage deployment in the source network. It can be understood that in the actual application process, the relevant resource data is based on the implementation The data settings actually included in the area. For example, the energy storage resource data of a certain area only includes chemical battery energy storage data and water storage power station energy storage data. Then in the actual application process, only the chemical battery energy storage data and water storage power station data are required. Energy storage data. Specifically, the network in the source network load storage can be a power grid or a power supply network, including but not limited to substations, distribution stations, power lines (including cables) and other power supply facilities. It can be understood that the power grid here is an overall deployment The carrier of the solution, power is transmitted to the load end (power consumption side) through the power grid, and the excess power is transmitted to energy storage equipment or energy storage facilities through the power grid for energy storage. At the same time, with the development of artificial intelligence, source grid load storage The network in it can also refer to the Internet of Things, etc., which achieves more accurate and comprehensive acquisition and interaction of data to realize the deployment of source network load storage. At the same time, it can quickly identify and provide feedback for power grid operation faults to achieve milliseconds. level of response speed to reduce grid losses and maintain safe operation of the grid.)
obtaining, by the processor, historical data sets of the …, the urban area, and the electricity storage devices, and analyzing the power supply data, the electricity consumption data, and the energy storage data according to the historical data sets to obtain estimated electric energy data; (in at least [pg5] Step S3: Obtain the total power output, total energy storage and electricity load prediction value at the current moment based on the source, grid, load and storage digital twin. The total power output is the current moment of thermal power data, hydropower data, nuclear power data and new energy generation data. The total energy storage is the sum of all energy storage in the chemical battery energy storage data, hydropower station energy storage data, hydrogen energy storage data and supercapacitor energy storage data. The electricity load prediction value is the source grid load storage The digital twin is obtained by predicting based on historical civil load data and historical industrial load data; Specifically, in this embodiment, the source grid, load and storage digital twin obtains the total power output, total energy storage and electricity load prediction value at the current moment through data sensing and data processing to provide data support for the source grid, load and storage regulation and control decisions. The source network load storage digital twin in the embodiment interacts with the entity data and obtains the power resource data, energy storage resource data and load resource data generated by the entity in real time. [pg9] A processing unit configured to obtain the total power output, the total energy storage, and the electricity load prediction value at the current moment based on the source grid load and storage digital twin, where the total power output is the thermal power data, the hydropower data , the sum of the output power at the current moment of the nuclear power data and the new energy power generation data, the total energy storage is the chemical battery energy storage data, the water storage power station energy storage data, the hydrogen energy storage data and all The sum of all the energy stored in the supercapacitor energy storage data, the electricity load prediction value is obtained by predicting the source grid load storage digital twin based on historical civil load data and historical industrial load data;)
adjusting, by the processor, electric energy supply of the electricity generation stations, the urban area, and the electricity storage devices according to the estimated electric energy data, and monitoring the electricity consumption data of the different levels of the urban area after being adjusted to obtain secondary electricity consumption data; and (in at least [pg6-pg7] Step S43: When the power demand value is positive or zero, a primary source grid load storage regulation decision is generated; Step S44: When the power demand value is a negative number and the absolute value is less than or equal to the preset load threshold, a secondary source grid load storage regulation decision is generated; Step S45: When the power demand value is a negative number and the absolute value is greater than the preset load threshold, a three-level source-grid load-storage regulation decision is generated. Specifically, in this embodiment, the source-grid load-storage regulation decision-making is mainly to ensure the smooth operation of the power grid, and the stable operation of the power grid needs to be dynamically stable. Moreover, the integration of new energy and energy storage causes fluctuations in the operation of the power grid. Therefore, , in order to adapt to the instability of new energy power generation and formulate real-time adjustment of the source network load and storage control decisions, this embodiment needs to do a difference between the total power output and the predicted value of the electricity load to obtain the load difference, and then the load The sum of the difference and the total amount of energy storage is used to obtain the electricity demand value; the positive or negative value of the electricity demand value represents the relationship between the power generation and electricity consumption in the implementation area. When the electricity demand value is a positive number or zero, it indicates the implementation area. The current power generation can meet the electricity consumption. At this time, a first-level source grid load and storage regulation decision is generated to allocate the source grid load and storage. When the power demand value is a negative number, it means that the current power generation in the implementation area cannot meet the demand. Electricity consumption, therefore, it is necessary to compare the absolute value of the electricity demand value with the preset load threshold. When the absolute value of the electricity demand value is less than or equal to the preset load threshold, it means that the electricity gap is small. Therefore, Generate a second-level source-grid load-storage regulation decision; when the absolute value of the electricity demand value is greater than the preset load threshold, it indicates that the power gap is large. Therefore, a third-level source-grid load-storage regulation decision is generated, in which the preset load threshold is It is preset to the maximum load carrying capacity of the power grid operation, that is, the sum of the total power output, the total energy storage, and the controllable load data in the civil load data and industrial load data that can be interrupted by the power grid. It can be understood that the preset load threshold Represents the total amount of controllable power supply when the power grid is running. In actual application, if there are other power supplies, the total amount of controllable power supply must also be added to the preset load threshold, that is, if the power demand If the absolute value of the value is less than or equal to the maximum load carrying capacity, then the power gap can be replenished through these controllable resources. On the contrary, when the absolute value of the power demand value is greater than the maximum load carrying capacity, it means that the power gap has been greater than The total power supply is beyond the capacity of the power grid.)
calling, by the processor, the electricity storage devices in surrounding regions according to the secondary electricity consumption data to supplement electric energy. (in at least [pg7-pg8] Optionally, the secondary source grid load storage control decision-making includes: Step S441, obtain the absolute value of the load difference, perform difference processing between the absolute value of the load difference and the total amount of energy storage, and obtain the load demand value; Step S442, consume all the energy storage corresponding to the total energy storage amount, and increase the thermal power data, hydropower data and nuclear power data in the power source resource data according to the load demand value until the increased thermal power data, hydropower data and nuclear power data The sum of is greater than or equal to the load demand value, and the new energy power generation data remains unchanged. Specifically, the secondary source grid load and storage regulation decision in this embodiment can be understood as the deployment of source grid load and storage within the adjustable range. For a small power gap, only the energy storage needs to be released to increase thermal power generation, hydropower generation and Nuclear power generation can make up for the electricity gap. Due to the uncontrollability of new energy, in this embodiment, no adjustment is made to the new energy, and only its power generation output is monitored. Optionally, the three-level source network load storage control decision-making includes: Step S451, obtain the absolute value of the load difference, perform difference processing between the absolute value of the load difference and the total amount of energy storage, and obtain the load demand value; Step S452, consume all the energy storage corresponding to the total energy storage amount, increase the thermal power data, hydropower data and nuclear power data in the power resource data according to the load demand value, cut off the controllable loads in the civil load data and industrial load data, Until the absolute value of the power demand value is less than the preset load threshold; Among them, when the absolute value of the power demand value is less than or equal to the preset load threshold, the adjustable load is turned on. Specifically, in this embodiment, the three-level source-grid-load-storage control decision is the first-level strategy with the greatest deployment intensity. Since the power gap is too large, simply increasing power generation and energy storage and energy release cannot fill the power gap. Therefore, , it is necessary to cut off the controllable loads in the civil load data and industrial load data to temporarily alleviate the power gap until the absolute value of the power demand value is less than the preset load threshold. It is understandable that the load shedding operation time should not be too long. A long-term power outage can easily cause chaos in the electricity market and cause serious economic losses. Therefore, the three-level source, grid, load and storage regulation decisions can be understood as short-term adjustments, which only allocate the source, grid, load and storage in a short period of time. Optionally, when the power demand value is negative and the absolute value is greater than the preset load threshold, a three-level source-grid load-storage regulation decision is generated, which also includes: When the power demand value is a negative number, the absolute value of the power demand value is greater than the preset load threshold, and the duration is greater than the preset time threshold, adjust the power resource data of other power grid areas to the current power consumption area to meet the current power consumption regional electricity demand. Specifically, when the load shedding control lasts too long, in order to prevent chaos in the electricity market, it is necessary to timely call from other power grid areas, that is, to introduce power sources from other areas to the implementation area to meet the power demand in the implementation area. In one embodiment, a certain area adopts the source grid load and storage control method, and acquires thermal power data, hydropower data and photovoltaic data by constructing a source grid load and storage digital twin to monitor and perceive power resource data, energy storage resource data and load resource data in real time. Power generation data, because there is also a water storage power station in the area, the energy storage data of the water storage power station was obtained. At the same time, the area includes two factories, multiple shopping malls, office buildings, and multiple residential buildings. Relevant civil load data and industrial load data were obtained. According to the source The grid load and storage digital twin obtains the total power output, total energy storage and electricity load prediction value at 12 noon. After the source grid load and storage digital twin senses and processes it, due to the lunch break and meal peak at noon, the electricity consumption at this time The load prediction value increases and is greater than the total power output. After summing with the total energy storage, the power gap is still unable to be met. However, because the power gap is small, that is, the absolute value of the power demand value is less than the preset load threshold, Then start the secondary source grid load and storage regulation decision-making, and generate power generation instructions to increase thermal power generation and hydropower generation to make up for the power gap. When the power gap is met, the primary source grid load and storage regulation decision-making is restored. Through this implementation Based on the example of source grid load storage control method, digital simulation processing is performed before source grid load storage deployment, and then physical execution is performed after optimization. This avoids possible erroneous execution caused by direct operation, improves the accuracy of deployment, and makes the power grid operation more efficient. Safety.)
Although implied, Li does not expressly disclose the following limitations, which however, are taught by Padmarao,
…historical data sets of the electricity generation stations… (in at least [0028] During operation of plant 100, environmental conditions will change and affect the operations of wind assets 110 and solar assets 112. For example, a day may be cloudy and windy with low solar irradiance and high wind. Alternatively, a day may be sunny with high solar irradiance and little or no wind. Also at night, solar assets 112 and their associated inverters 114 may be unused due to the lack of solar irradiance. In other situations, the power generated by wind assets 110 and solar assets 112 may be greater than that required or allowed to be supplied to grid 102. In this situation, plant 100 may store at least a portion of the excess generated power in batteries 116. [0032] A plant controller 206 coordinates the operation of the various assets 210 of plant 100. Each asset 210 includes an asset controller 208 that controls the operation of individual asset 210. For example, if plant controller 206 instructs an asset 210 to produce 5 megawatts (MW) of power, asset controller 208 controls asset 210 to safely produce that amount of power. In some embodiments, asset controller 208 may also be in communication with one or more sensors that measure conditions at asset 210, including both environmental and operating conditions of asset 210. In some embodiments, a single asset controller 208 controls a plurality of assets 210. In other embodiments, each asset controller 208 controls a single asset 210. In some embodiments, plant controller 206 distributes the reactive power to asset controllers 208. [0055] power system management computer device 310 stores or accesses, such as through database 320 (shown in FIG. 3), other system information about plant 100 and assets 210 (shown in FIG. 2). This other system information may include, but is not limited to, rated wind speed for wind assets 110 (shown in FIG. 1), rated solar irradiation for solar assets 112 (shown in FIG. 1), a point of interconnect limit for transformer 108 (shown in FIG. 1), and an MVA rating of one or more assets 210. In the exemplary embodiment, power system management computer device 310 also access thresholds for Khigh for wind assets 110 and Klow for solar assets 112, as described below. These thresholds may be set by a user through a client system 325 (shown in FIG. 3) or be preprogrammed based on historical data. In the exemplary embodiment, process 700 is performed when the amount of solar power that would be generated is below a certain level (Klow) and the amount of wind power that would be generated is greater than a certain level (Khigh). For example, Klow may be set between 40% and 60% of the total power potentially generated based on the asset's rating, while Khigh is set between 75% and 100% of the total power potentially generated based on the asset's rating. In some embodiments, process 700 may be performed during nighttime, evenings, and cloudy days to increase the amount of power generated.)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Li as taught by Padmarao, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Li with the motivation of, …to managing reactive power in a hybrid power environment to improve active power generation…provide the reactive power support for the plant to meet the required reactive power generation. However, reactive power generation reduces the amount of active or real power that an asset is producing. When the active power production is high, the capability for reactive power production may be limited by the apparent power capability of the generator and the inverters.…operating the plant to optimize power generation based on current conditions. Accordingly, it would be useful to combine forecasted conditions with asset generation capabilities to optimize plant energy production.…(i) improved design of plants to maximize output; (ii) increased utilization of installed electrical components such as wind generators and inverters; (iii) increased annual energy production of the plant due to dynamic uprate of wind assets; (iv) reduction in collector system losses due to optimal distribution of reactive power among generation assets; (v) reduction in spill-over of energy during curtailment scenarios in a hybrid renewable plant; (vi) maximization of revenue generated during curtailment scenarios in a hybrid renewable plant; and (vii) minimization of negative impact on life of components impacted due to curtailment… to allow these three assets 110 to operate at higher levels and improve the revenue for plant 100…result in an improvement in the annual energy production of hybrid plant 100.…process 700 may be used for a net improvement in operation of corresponding plants 100. Furthermore, process 700 may be used to design high efficiency hybrid plants 100 based on a mix of solar, wind, and potentially battery or other sources.…, as recited in Padmarao.
As per Claim 2, Li teaches: The hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method according to claim 1,
wherein the obtaining power supply data comprises that the processor is associated with … and photovoltaic electricity generation devices of a photovoltaic electricity generation station to obtain …, a photovoltaic electricity generation quantity, a photovoltaic … power, and a photovoltaic … power of the photovoltaic electricity generation station; (in at least [pg4-pg5] Step S1: Obtain power resource data, energy storage resource data and load resource data. The power resource data includes thermal power data, hydropower data, nuclear power data and new energy power generation data. The energy storage resource data includes chemical battery energy storage data and water storage power station data. Energy storage data, hydrogen energy storage data and supercapacitor energy storage data, load resource data includes civil load data and industrial load data; Specifically, the source grid load storage is the power supply, power grid, load and energy storage. By accurately controlling the power load and energy storage resources, the safe operation level of the grid is improved and problems such as grid volatility in the process of new energy consumption are solved. This embodiment , power resource data includes but is not limited to thermal power data, hydropower data, nuclear power data and new energy power generation data. New energy power generation data can be clean energy power generation data such as photovoltaic power generation data and wind power generation data. Energy storage resource data includes but is not limited to Chemical battery energy storage data, hydropower station energy storage data, hydrogen energy storage data, supercapacitor energy storage data and other data that can store electrical energy. Load resource data includes civil load data and industrial load data, among which civil load data and industrial load data It also includes controllable load data that can be interrupted by the power grid and uncontrollable load data that cannot be interrupted. The controllable load data is used for load storage deployment in the source network. It can be understood that in the actual application process, the relevant resource data is based on the implementation The data settings actually included in the area. For example, the energy storage resource data of a certain area only includes chemical battery energy storage data and water storage power station energy storage data. Then in the actual application process, only the chemical battery energy storage data and water storage power station data are required. Energy storage data. Specifically, the network in the source network load storage can be a power grid or a power supply network, including but not limited to substations, distribution stations, power lines (including cables) and other power supply facilities. It can be understood that the power grid here is an overall deployment The carrier of the solution, power is transmitted to the load end (power consumption side) through the power grid, and the excess power is transmitted to energy storage equipment or energy storage facilities through the power grid for energy storage. At the same time, with the development of artificial intelligence, source grid load storage The network in it can also refer to the Internet of Things, etc., which achieves more accurate and comprehensive acquisition and interaction of data to realize the deployment of source network load storage. At the same time, it can quickly identify and provide feedback for power grid operation faults to achieve milliseconds. level of response speed to reduce grid losses and maintain safe operation of the grid.)
the processor is associated with … and wind electricity generation devices of a wind electricity generation station to obtain a …, a wind electricity generation quantity, a wind … power, and a wind … power of the wind electricity generation station; (in at least [pg4-pg5] Step S1: Obtain power resource data, energy storage resource data and load resource data. The power resource data includes thermal power data, hydropower data, nuclear power data and new energy power generation data. The energy storage resource data includes chemical battery energy storage data and water storage power station data. Energy storage data, hydrogen energy storage data and supercapacitor energy storage data, load resource data includes civil load data and industrial load data; Specifically, the source grid load storage is the power supply, power grid, load and energy storage. By accurately controlling the power load and energy storage resources, the safe operation level of the grid is improved and problems such as grid volatility in the process of new energy consumption are solved. This embodiment , power resource data includes but is not limited to thermal power data, hydropower data, nuclear power data and new energy power generation data. New energy power generation data can be clean energy power generation data such as photovoltaic power generation data and wind power generation data. Energy storage resource data includes but is not limited to Chemical battery energy storage data, hydropower station energy storage data, hydrogen energy storage data, supercapacitor energy storage data and other data that can store electrical energy. Load resource data includes civil load data and industrial load data, among which civil load data and industrial load data It also includes controllable load data that can be interrupted by the power grid and uncontrollable load data that cannot be interrupted. The controllable load data is used for load storage deployment in the source network. It can be understood that in the actual application process, the relevant resource data is based on the implementation The data settings actually included in the area. For example, the energy storage resource data of a certain area only includes chemical battery energy storage data and water storage power station energy storage data. Then in the actual application process, only the chemical battery energy storage data and water storage power station data are required. Energy storage data. Specifically, the network in the source network load storage can be a power grid or a power supply network, including but not limited to substations, distribution stations, power lines (including cables) and other power supply facilities. It can be understood that the power grid here is an overall deployment The carrier of the solution, power is transmitted to the load end (power consumption side) through the power grid, and the excess power is transmitted to energy storage equipment or energy storage facilities through the power grid for energy storage. At the same time, with the development of artificial intelligence, source grid load storage The network in it can also refer to the Internet of Things, etc., which achieves more accurate and comprehensive acquisition and interaction of data to realize the deployment of source network load storage. At the same time, it can quickly identify and provide feedback for power grid operation faults to achieve milliseconds. level of response speed to reduce grid losses and maintain safe operation of the grid.)
the processor is associated with a power grid of the urban area to obtain an electricity consumption quantity of a special level, an electricity consumption quantity of a residential level, and an electricity consumption quantity of industrial and commercial levels, so as to obtain electricity consumption data; (in at least [pg6-pg7] Obtain historical civil load data and historical industrial load data; The initial load prediction model is trained based on historical civil load data and historical industrial load data to obtain the initial power load prediction value; Calculate the loss based on the initial electricity load prediction value, historical civil load data and historical industrial load data, and obtain the loss function output; The model parameters of the initial load forecast model are corrected according to the loss function output until the loss function input meets the preset conditions, and the parameter-adjusted initial load forecast model is used as the load forecast model. Specifically, through the load prediction model of this embodiment, the power load prediction value is made more accurate, and the accuracy of subsequent source-grid load-storage regulation decisions is enhanced. Optionally, as shown in Figure 2, the source grid load and storage regulation decision is generated based on the total power output, the total energy storage amount and the electricity load prediction value, and the source grid load and storage deployment is controlled based on the source grid load and storage regulation decision, including: Step S41: Difference the total power output and the predicted electrical load to obtain the load difference; Step S42, obtain the electricity demand value based on the sum of the load difference and the total amount of energy storage; Step S43: When the power demand value is positive or zero, a primary source grid load storage regulation decision is generated; Step S44: When the power demand value is a negative number and the absolute value is less than or equal to the preset load threshold, a secondary source grid load storage regulation decision is generated; Step S45: When the power demand value is a negative number and the absolute value is greater than the preset load threshold, a three-level source-grid load-storage regulation decision is generated. Specifically, in this embodiment, the source-grid load-storage regulation decision-making is mainly to ensure the smooth operation of the power grid, and the stable operation of the power grid needs to be dynamically stable. Moreover, the integration of new energy and energy storage causes fluctuations in the operation of the power grid. Therefore, , in order to adapt to the instability of new energy power generation and formulate real-time adjustment of the source network load and storage control decisions, this embodiment needs to do a difference between the total power output and the predicted value of the electricity load to obtain the load difference, and then the load The sum of the difference and the total amount of energy storage is used to obtain the electricity demand value; the positive or negative value of the electricity demand value represents the relationship between the power generation and electricity consumption in the implementation area. When the electricity demand value is a positive number or zero, it indicates the implementation area. The current power generation can meet the electricity consumption. At this time, a first-level source grid load and storage regulation decision is generated to allocate the source grid load and storage. When the power demand value is a negative number, it means that the current power generation in the implementation area cannot meet the demand. Electricity consumption, therefore, it is necessary to compare the absolute value of the electricity demand value with the preset load threshold. When the absolute value of the electricity demand value is less than or equal to the preset load threshold, it means that the electricity gap is small. Therefore, Generate a second-level source-grid load-storage regulation decision; when the absolute value of the electricity demand value is greater than the preset load threshold, it indicates that the power gap is large. Therefore, a third-level source-grid load-storage regulation decision is generated, in which the preset load threshold is It is preset to the maximum load carrying capacity of the power grid operation, that is, the sum of the total power output, the total energy storage, and the controllable load data in the civil load data and industrial load data that can be interrupted by the power grid. It can be understood that the preset load threshold Represents the total amount of controllable power supply when the power grid is running. In actual application, if there are other power supplies, the total amount of controllable power supply must also be added to the preset load threshold, that is, if the power demand If the absolute value of the value is less than or equal to the maximum load carrying capacity, then the power gap can be replenished through these controllable resources. On the contrary, when the absolute value of the power demand value is greater than the maximum load carrying capacity, it means that the power gap has been greater than The total power supply is beyond the capacity of the power grid.)
the processor is associated with electricity storage devices to obtain device types and stored electric quantities of the electricity storage devices, so as to obtain energy storage data; (in at least [pg7] Specifically, in this embodiment, the source-grid load-storage regulation decision-making is mainly to ensure the smooth operation of the power grid, and the stable operation of the power grid needs to be dynamically stable. Moreover, the integration of new energy and energy storage causes fluctuations in the operation of the power grid. Therefore, , in order to adapt to the instability of new energy power generation and formulate real-time adjustment of the source network load and storage control decisions, this embodiment needs to do a difference between the total power output and the predicted value of the electricity load to obtain the load difference, and then the load The sum of the difference and the total amount of energy storage is used to obtain the electricity demand value; the positive or negative value of the electricity demand value represents the relationship between the power generation and electricity consumption in the implementation area. When the electricity demand value is a positive number or zero, it indicates the implementation area. The current power generation can meet the electricity consumption. At this time, a first-level source grid load and storage regulation decision is generated to allocate the source grid load and storage. When the power demand value is a negative number, it means that the current power generation in the implementation area cannot meet the demand. Electricity consumption, therefore, it is necessary to compare the absolute value of the electricity demand value with the preset load threshold. When the absolute value of the electricity demand value is less than or equal to the preset load threshold, it means that the electricity gap is small. Therefore, Generate a second-level source-grid load-storage regulation decision; when the absolute value of the electricity demand value is greater than the preset load threshold, it indicates that the power gap is large. Therefore, a third-level source-grid load-storage regulation decision is generated, in which the preset load threshold is It is preset to the maximum load carrying capacity of the power grid operation, that is, the sum of the total power output, the total energy storage, and the controllable load data in the civil load data and industrial load data that can be interrupted by the power grid. It can be understood that the preset load threshold Represents the total amount of controllable power supply when the power grid is running. In actual application, if there are other power supplies, the total amount of controllable power supply must also be added to the preset load threshold, that is, if the power demand If the absolute value of the value is less than or equal to the maximum load carrying capacity, then the power gap can be replenished through these controllable resources. On the contrary, when the absolute value of the power demand value is greater than the maximum load carrying capacity, it means that the power gap has been greater than The total power supply is beyond the capacity of the power grid. Optionally, as shown in Figure 3, the first-level source network load and storage control decisions include: Step S431, obtain the load difference and store the remaining power generation corresponding to the load difference; Step S432, compare the load difference with the sum of rated energy storage of chemical battery energy storage data, water storage power station energy storage data, hydrogen energy storage data and supercapacitor energy storage data;)
the processor takes the … and the … as environment data, takes the wind … power, the wind … power, the photovoltaic … power, and the photovoltaic … power as power data, takes the wind electricity generation quantity and the photovoltaic electricity generation quantity as electricity generation data, and takes the environment data, the power data and the electricity generation data as power supply data; and (in at least [pg4-pg5] Step S1: Obtain power resource data, energy storage resource data and load resource data. The power resource data includes thermal power data, hydropower data, nuclear power data and new energy power generation data. The energy storage resource data includes chemical battery energy storage data and water storage power station data. Energy storage data, hydrogen energy storage data and supercapacitor energy storage data, load resource data includes civil load data and industrial load data; Specifically, the source grid load storage is the power supply, power grid, load and energy storage. By accurately controlling the power load and energy storage resources, the safe operation level of the grid is improved and problems such as grid volatility in the process of new energy consumption are solved. This embodiment , power resource data includes but is not limited to thermal power data, hydropower data, nuclear power data and new energy power generation data. New energy power generation data can be clean energy power generation data such as photovoltaic power generation data and wind power generation data. Energy storage resource data includes but is not limited to Chemical battery energy storage data, hydropower station energy storage data, hydrogen energy storage data, supercapacitor energy storage data and other data that can store electrical energy. Load resource data includes civil load data and industrial load data, among which civil load data and industrial load data It also includes controllable load data that can be interrupted by the power grid and uncontrollable load data that cannot be interrupted. The controllable load data is used for load storage deployment in the source network. It can be understood that in the actual application process, the relevant resource data is based on the implementation The data settings actually included in the area. For example, the energy storage resource data of a certain area only includes chemical battery energy storage data and water storage power station energy storage data. Then in the actual application process, only the chemical battery energy storage data and water storage power station data are required. Energy storage data. Specifically, the network in the source network load storage can be a power grid or a power supply network, including but not limited to substations, distribution stations, power lines (including cables) and other power supply facilities. It can be understood that the power grid here is an overall deployment The carrier of the solution, power is transmitted to the load end (power consumption side) through the power grid, and the excess power is transmitted to energy storage equipment or energy storage facilities through the power grid for energy storage. At the same time, with the development of artificial intelligence, source grid load storage The network in it can also refer to the Internet of Things, etc., which achieves more accurate and comprehensive acquisition and interaction of data to realize the deployment of source network load storage. At the same time, it can quickly identify and provide feedback for power grid operation faults to achieve milliseconds. level of response speed to reduce grid losses and maintain safe operation of the grid.)
Although implied, Li does not expressly disclose the following limitations, which however, are taught by Padmarao,
… illumination sensors… to obtain illumination intensity, …, a photovoltaic active power, and a photovoltaic reactive power …; (in at least [0028] During operation of plant 100, environmental conditions will change and affect the operations of wind assets 110 and solar assets 112. For example, a day may be cloudy and windy with low solar irradiance and high wind. Alternatively, a day may be sunny with high solar irradiance and little or no wind. Also at night, solar assets 112 and their associated inverters 114 may be unused due to the lack of solar irradiance. In other situations, the power generated by wind assets 110 and solar assets 112 may be greater than that required or allowed to be supplied to grid 102. In this situation, plant 100 may store at least a portion of the excess generated power in batteries 116. [0029] utilizing inverters 114 associated with solar assets 112 and batteries 116 as a source of reactive power generation based on need and current conditions. The methods described herein use the fact that inverters 114 associated with solar assets 112 are generally underutilized at night. Additionally, when solar assets 112 of plant 100 are not generating power up to their rated generation capacity, the systems and methods described herein may be applied. For approximately 60% of the daytime (e.g., 8 out of 13 hours of daylight), inverter 114 capacity is underutilized. This underutilized capacity may also be used at night. Thus the systems described herein disclose shifting reactive power generation from wind assets 110 to inverters 114 when inverters 114 are lightly loaded. This allows wind assets 110 to operate at a higher kilowatt (kW) level and generate additional active power [0049] FIGS. 6A-6D illustrate apparent power capability curves including active and reactive power. FIGS. 6A-6D display active power (P) on the x-axis and reactive power (Q) on the y-axis. The Figures also show the apparent power curve, which illustrates the trade-off between active power and reactive power [0054] power system management computer device 310 receives 705 current conditions, such as current wind speed and current solar irradiation levels. In some embodiments, power system management computer device 310 receives 705 the current conditions from one or more sensors 305 (shown in FIG. 3). In some embodiments, the current conditions include a forecast of future conditions for a period of time, such as, an hour, day, week, or other period of time.)
… wind speed sensors … to obtain a wind speed …, a wind active power, and a wind reactive power of the wind electricity generation station; (in at least [0028] During operation of plant 100, environmental conditions will change and affect the operations of wind assets 110 and solar assets 112. For example, a day may be cloudy and windy with low solar irradiance and high wind. Alternatively, a day may be sunny with high solar irradiance and little or no wind. Also at night, solar assets 112 and their associated inverters 114 may be unused due to the lack of solar irradiance. In other situations, the power generated by wind assets 110 and solar assets 112 may be greater than that required or allowed to be supplied to grid 102. In this situation, plant 100 may store at least a portion of the excess generated power in batteries 116. [0029] utilizing inverters 114 associated with solar assets 112 and batteries 116 as a source of reactive power generation based on need and current conditions. The methods described herein use the fact that inverters 114 associated with solar assets 112 are generally underutilized at night. Additionally, when solar assets 112 of plant 100 are not generating power up to their rated generation capacity, the systems and methods described herein may be applied. For approximately 60% of the daytime (e.g., 8 out of 13 hours of daylight), inverter 114 capacity is underutilized. This underutilized capacity may also be used at night. Thus the systems described herein disclose shifting reactive power generation from wind assets 110 to inverters 114 when inverters 114 are lightly loaded. This allows wind assets 110 to operate at a higher kilowatt (kW) level and generate additional active power [0049] FIGS. 6A-6D illustrate apparent power capability curves including active and reactive power. FIGS. 6A-6D display active power (P) on the x-axis and reactive power (Q) on the y-axis. The Figures also show the apparent power curve, which illustrates the trade-off between active power and reactive power [0054] power system management computer device 310 receives 705 current conditions, such as current wind speed and current solar irradiation levels. In some embodiments, power system management computer device 310 receives 705 the current conditions from one or more sensors 305 (shown in FIG. 3). In some embodiments, the current conditions include a forecast of future conditions for a period of time, such as, an hour, day, week, or other period of time.)
… illumination intensity and the wind speed as environment data, takes the wind active power, the wind reactive power, the photovoltaic active power, and the photovoltaic reactive power as power data…; (in at least [0028] During operation of plant 100, environmental conditions will change and affect the operations of wind assets 110 and solar assets 112. For example, a day may be cloudy and windy with low solar irradiance and high wind. Alternatively, a day may be sunny with high solar irradiance and little or no wind. Also at night, solar assets 112 and their associated inverters 114 may be unused due to the lack of solar irradiance. In other situations, the power generated by wind assets 110 and solar assets 112 may be greater than that required or allowed to be supplied to grid 102. In this situation, plant 100 may store at least a portion of the excess generated power in batteries 116. [0029] utilizing inverters 114 associated with solar assets 112 and batteries 116 as a source of reactive power generation based on need and current conditions. The methods described herein use the fact that inverters 114 associated with solar assets 112 are generally underutilized at night. Additionally, when solar assets 112 of plant 100 are not generating power up to their rated generation capacity, the systems and methods described herein may be applied. For approximately 60% of the daytime (e.g., 8 out of 13 hours of daylight), inverter 114 capacity is underutilized. This underutilized capacity may also be used at night. Thus the systems described herein disclose shifting reactive power generation from wind assets 110 to inverters 114 when inverters 114 are lightly loaded. This allows wind assets 110 to operate at a higher kilowatt (kW) level and generate additional active power [0049] FIGS. 6A-6D illustrate apparent power capability curves including active and reactive power. FIGS. 6A-6D display active power (P) on the x-axis and reactive power (Q) on the y-axis. The Figures also show the apparent power curve, which illustrates the trade-off between active power and reactive power [0054] power system management computer device 310 receives 705 current conditions, such as current wind speed and current solar irradiation levels. In some embodiments, power system management computer device 310 receives 705 the current conditions from one or more sensors 305 (shown in FIG. 3). In some embodiments, the current conditions include a forecast of future conditions for a period of time, such as, an hour, day, week, or other period of time.)
The reason and rationale to combine Li and Padmarao is the same as recited above.
As per Claim 9, Li teaches: A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method according to claim 1 are realized. (in at least [pg9] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above source network load storage control method is implemented. It should be noted that the device can be a computer device such as a server or a mobile terminal. Figure 4 shows an internal structure diagram of a computer device in one embodiment. The computer device includes a processor, a memory, a network interface, an input device and a display screen connected through a system bus. Among them, memory includes non-volatile storage media and internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, it can enable the processor to implement a multi-function collaborative operation method. The internal memory may also store a computer program. When the computer program is executed by the processor, the computer program can cause the processor to execute a multi-function collaborative execution method. The display screen of the computer device may be a liquid crystal display or an electronic ink display, and the input device of the computer device may be a touch layer covered on the display screen, or it may be a button, trackball or touch pad provided on the computer device shell, or It can be an external keyboard, trackpad or mouse, etc. In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above source network load storage control method is implemented. Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing relevant hardware through computer programs. The programs can be stored in a non-volatile computer-readable storage medium. , when the program is executed, it may include the processes of the above-mentioned method embodiments. Any reference to memory, storage, database or other media used in the various embodiments provided by the present invention may include non-volatile and/or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.))
As per Claim 10, Li teaches: A computer-readable storage medium in which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method according to claim 1 are realized. (in at least [pg9] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above source network load storage control method is implemented. It should be noted that the device can be a computer device such as a server or a mobile terminal. Figure 4 shows an internal structure diagram of a computer device in one embodiment. The computer device includes a processor, a memory, a network interface, an input device and a display screen connected through a system bus. Among them, memory includes non-volatile storage media and internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, it can enable the processor to implement a multi-function collaborative operation method. The internal memory may also store a computer program. When the computer program is executed by the processor, the computer program can cause the processor to execute a multi-function collaborative execution method. The display screen of the computer device may be a liquid crystal display or an electronic ink display, and the input device of the computer device may be a touch layer covered on the display screen, or it may be a button, trackball or touch pad provided on the computer device shell, or It can be an external keyboard, trackpad or mouse, etc. In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above source network load storage control method is implemented. Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing relevant hardware through computer programs. The programs can be stored in a non-volatile computer-readable storage medium. , when the program is executed, it may include the processes of the above-mentioned method embodiments. Any reference to memory, storage, database or other media used in the various embodiments provided by the present invention may include non-volatile and/or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.))
As per Claim 8 for a system (see at least Li [pg9]), respectively, substantially recite the subject matter of Claim 1 and are rejected based on the same reasoning and rationale.
Claims 3, 4, 5, 6, 7, is/are rejected under 35 U.S.C. 103 as being unpatentable by CN Patent Publication to CN116683542A to Li et al., (hereinafter referred to as “Li”) in view of US Patent Publication to US20210296898A1 to Padmarao et al., (hereinafter referred to as “Padmarao”) in view of US Patent Publication to US20160018835A1 to Gaasch et al., (hereinafter referred to as “Gaasch”)
As per Claim 3, Li teaches: The hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method according to claim 2,
wherein the obtaining historical data sets of the …, the urban area, and the electricity storage devices comprises that the processor obtains historical … data of the …, the urban area, and the electricity storage devices …, and historical … data of an environment through a database, so as to obtain historical data sets; (in at least [pg5] Step S3: Obtain the total power output, total energy storage and electricity load prediction value at the current moment based on the source, grid, load and storage digital twin. The total power output is the current moment of thermal power data, hydropower data, nuclear power data and new energy generation data. The total energy storage is the sum of all energy storage in the chemical battery energy storage data, hydropower station energy storage data, hydrogen energy storage data and supercapacitor energy storage data. The electricity load prediction value is the source grid load storage The digital twin is obtained by predicting based on historical civil load data and historical industrial load data; Specifically, in this embodiment, the source grid, load and storage digital twin obtains the total power output, total energy storage and electricity load prediction value at the current moment through data sensing and data processing to provide data support for the source grid, load and storage regulation and control decisions. The source network load storage digital twin in the embodiment interacts with the entity data and obtains the power resource data, energy storage resource data and load resource data generated by the entity in real time. [pg9] A processing unit configured to obtain the total power output, the total energy storage, and the electricity load prediction value at the current moment based on the source grid load and storage digital twin, where the total power output is the thermal power data, the hydropower data , the sum of the output power at the current moment of the nuclear power data and the new energy power generation data, the total energy storage is the chemical battery energy storage data, the water storage power station energy storage data, the hydrogen energy storage data and all The sum of all the energy stored in the supercapacitor energy storage data, the electricity load prediction value is obtained by predicting the source grid load storage digital twin based on historical civil load data and historical industrial load data;)
….
Although implied, Li does not expressly disclose the following limitations, which however, are taught by Padmarao,
… historical data sets of the electricity generation stations… (in at least [0055] power system management computer device 310 stores or accesses, such as through database 320 (shown in FIG. 3), other system information about plant 100 and assets 210 (shown in FIG. 2). This other system information may include, but is not limited to, rated wind speed for wind assets 110 (shown in FIG. 1), rated solar irradiation for solar assets 112 (shown in FIG. 1), a point of interconnect limit for transformer 108 (shown in FIG. 1), and an MVA rating of one or more assets 210. In the exemplary embodiment, power system management computer device 310 also access thresholds for Khigh for wind assets 110 and Klow for solar assets 112, as described below. These thresholds may be set by a user through a client system 325 (shown in FIG. 3) or be preprogrammed based on historical data. In the exemplary embodiment, process 700 is performed when the amount of solar power that would be generated is below a certain level (Klow) and the amount of wind power that would be generated is greater than a certain level (Khigh). For example, Klow may be set between 40% and 60% of the total power potentially generated based on the asset's rating, while Khigh is set between 75% and 100% of the total power potentially generated based on the asset's rating. In some embodiments, process 700 may be performed during nighttime, evenings, and cloudy days to increase the amount of power generated.)
The reason and rationale to combine Li and Padmarao is the same as recited above.
Although implied, Li in view of Padmarao does not expressly disclose the following limitations, which however, are taught by Gaasch,
…obtains historical average data… in the past three years… (in at least [0046] energy transference media can include media such as electricity, natural gas, steam, hot water, chilled water and fuel oil, in various value types. For example, in some embodiments, the value types can include average, maximum, minimum, average during peak, average during off peak, power factor (of the electricity), and apparent power (of the electricity). In some embodiments, the value types can include values at various time steps such as monthly, daily, hourly and sub-hourly, for a certain duration of time (typically a year), and those that are associated with time stamps. In some other embodiments, weather data can be collected including time series outdoor weather values such as dry/wet bulb temperature, humidity, wind speed, cloud coverage, sunrise/sunset time and solar radiation that is measured from the same period energy data is collected. In some embodiments, energy tariff data can be taken including energy cost structure which could be a flat rate or time of use rates.)
average values of the historical data sets are calculated respectively, variances of the historical data sets are calculated respectively after the average values are evaluated, standard deviations of the historical data sets are calculated respectively after the variances are evaluated, and covariances of the historical data sets are calculated respectively after the standard deviations are evaluated through the processor (in at least [0061] FIG. 4, in some embodiments of the invention, the system 100 can implement methods comprising a series of steps 400 that include a weather correlation analysis (step 410) that evaluates the quality of regression using performance metrics of goodness-of-fit such as the coefficient of determination (“R2”), the root mean squared error (“RMSE”), and the coefficient of variance of the RMSE (“CVRMSE”). In some embodiments, a facility without an acceptable energy-weather correlation is benchmarked with simple metrics such as EUI, demand during different periods of days, etc., and visualized in step 404. Otherwise, in some embodiments, the facility's energy use data are analyzed through a series of pattern recognition and feature extraction (step 412) to detect characteristics such as occupancy schedule, heating and cooling types, exterior lighting, photovoltaic, power generation, etc. [0066] pattern-based use type detection system 408 (comprising the process 700 illustrated in FIG. 7) based on the hypothesis that time series energy use data (e.g., 15-minute electricity intervals) have longitudinal patterns that are unique to each facility 102 use type. Therefore, in some embodiments, a machine learning model can be trained using certain features of the energy use data to predict use types of facilities 102 with unknown use types. In some embodiments, to train the prediction model, the system 100 can use data comprising a set of facilities 102 with known use types 702 to build training data. In some embodiments, the system 100 converts the raw time series data into numeric variables (i.e., “features”) that are potentially correlated to use types. In some embodiments, the features can include variables comprising EUI, start/end time of operation and occupancy, distributions of daily usage in each month (e.g., percent occupied), and/or ratios of different usage metrics (maximum, minimum, mean, standard deviation, etc.) of different periods (parts of day, day types, months, seasons, etc.) In some embodiments, the computed features 706 are then evaluated using a variable subset selection algorithm such as a stepwise regression to filter out the most relevant features (in training step 708). In some embodiments, these selected features are then used to train a machine learning model 710 to predict facility 102 use types. In some embodiments, various supervised machine learning algorithms can be used in 710, such as logistic regression, artificial neural network (“ANN”), decision trees and support vector machines (“SVM”).)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Li in view of Padmarao as taught by Gaasch, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Li in view of Padmarao with the motivation of, …to improve the analysis if newer or better data are available…improved to an efficient model in step 1108 to reflect various energy efficiency measures or to comply with an energy efficiency standard. In some embodiments, the efficient model of step 1108 can then be compared to the facility's energy use data to determine energy savings potential… focusing on improving the operation of existing systems through controls…to improve the analysis of those facilities. In some embodiments, this can be an iterative process until no improvement can be made…compares the actual energy use data to the virtual efficient model at specific concurrent time periods on each end use category to derive energy savings potential and generate energy efficiency recommendations...provide methods and tools to leverage data analytics throughout the energy efficiency lifecycle. Such desired energy data analytics can be used to identify and prioritize customers with the greatest energy savings potential, engage customers with personalized insights, convert energy audits into efficiency projects, and dynamically track new efficiency opportunities and verify savings…, as recited in Gaasch.
As per Claim 4, Li teaches: The hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method according to claim 3,
wherein the obtaining estimated electric energy data comprises that … in the historical data sets are calculated respectively, …, and estimated electric energy data is calculated after the … are obtained through the processor. (in at least [pg8-pg9] In one embodiment, a certain area adopts the source grid load and storage control method, and acquires thermal power data, hydropower data and photovoltaic data by constructing a source grid load and storage digital twin to monitor and perceive power resource data, energy storage resource data and load resource data in real time. Power generation data, because there is also a water storage power station in the area, the energy storage data of the water storage power station was obtained. At the same time, the area includes two factories, multiple shopping malls, office buildings, and multiple residential buildings. Relevant civil load data and industrial load data were obtained. According to the source The grid load and storage digital twin obtains the total power output, total energy storage and electricity load prediction value at 12 noon. After the source grid load and storage digital twin senses and processes it, due to the lunch break and meal peak at noon, the electricity consumption at this time The load prediction value increases and is greater than the total power output. After summing with the total energy storage, the power gap is still unable to be met. However, because the power gap is small, that is, the absolute value of the power demand value is less than the preset load threshold, Then start the secondary source grid load and storage regulation decision-making, and generate power generation instructions to increase thermal power generation and hydropower generation to make up for the power gap. When the power gap is met, the primary source grid load and storage regulation decision-making is restored. Through this implementation Based on the example of source grid load storage control method, digital simulation processing is performed before source grid load storage deployment, and then physical execution is performed after optimization. This avoids possible erroneous execution caused by direct operation, improves the accuracy of deployment, and makes the power grid operation more efficient. Safety. Corresponding to the above-mentioned source network load and storage control method, embodiments of the present invention also provide a source network load and storage control device, including: Acquisition unit, used to acquire power resource data, energy storage resource data and load resource data, wherein the power resource data includes thermal power data, hydropower data, nuclear power data and new energy power generation data, and the energy storage resource data includes chemical batteries Energy storage data, hydropower station energy storage data, hydrogen energy storage data and supercapacitor energy storage data, the load resource data includes civil load data and industrial load data; A construction unit configured to construct a source grid load storage digital twin based on the power resource data, the energy storage resource data and the load resource data; A processing unit configured to obtain the total power output, the total energy storage, and the electricity load prediction value at the current moment based on the source grid load and storage digital twin, where the total power output is the thermal power data, the hydropower data , the sum of the output power at the current moment of the nuclear power data and the new energy power generation data, the total energy storage is the chemical battery energy storage data, the water storage power station energy storage data, the hydrogen energy storage data and all The sum of all the energy stored in the supercapacitor energy storage data, the electricity load prediction value is obtained by predicting the source grid load storage digital twin based on historical civil load data and historical industrial load data; The processing unit is also configured to generate a source grid load and storage regulation decision based on the total power output, the total energy storage amount, and the electricity load prediction value, and control the source grid load and storage deployment according to the source grid load and storage regulation decision.)
Although implied, Li does not expressly disclose the following limitations, which however, are taught by Padmarao,
….correlation coefficients in the historical data sets are calculated respectively…(in at least [0089] Supervised and unsupervised machine learning techniques may be used. In supervised machine learning, a processing element may be provided with example inputs and their associated outputs, and may seek to discover a general rule that maps inputs to outputs, so that when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output. In unsupervised machine learning, the processing element may be required to find its own structure in unlabeled example inputs. In one embodiment, machine learning techniques may be used to extract data about infrastructures and users associated with a building to detect events and correlations between detected events to identify trends.)
The reason and rationale to combine Li and Padmarao is the same as recited above.
Although implied, Li in view of Padmarao does not expressly disclose the following limitations, which however, are taught by Gaasch,
…regression coefficients in the historical data sets are calculated respectively after the correlation coefficients are evaluated, correction coefficients in the historical data sets are calculated respectively after the regression coefficients are evaluated to obtain regression equations… regression equations…(in at least [0061] the system 100 can implement methods comprising a series of steps 400 that include a weather correlation analysis (step 410) that evaluates the quality of regression using performance metrics of goodness-of-fit such as the coefficient of determination (“R2”), the root mean squared error (“RMSE”), and the coefficient of variance of the RMSE (“CVRMSE”). In some embodiments, a facility without an acceptable energy-weather correlation is benchmarked with simple metrics such as EUI, demand during different periods of days, etc., and visualized in step 404. Otherwise, in some embodiments, the facility's energy use data are analyzed through a series of pattern recognition and feature extraction (step 412) to detect characteristics such as occupancy schedule, heating and cooling types, exterior lighting, photovoltaic, power generation, etc. [0069] facility 102 energy use data for space heating and cooling are correlated to outdoor air temperature. Further, in some embodiments, correlation analyses such as the segmented linear regression can be performed between energy use and outdoor air temperature for each energy transference medium (e.g., electricity, natural gas, etc.) to determine if this energy transference medium is significantly used for facility heating or cooling. Taking electricity as an example, the plot 800 of FIG. 8 demonstrates an example in which the energy use data are in 15-minute intervals, and have two clusters of occupancy level. In some embodiments, each cluster of intervals and their corresponding dry bulb temperature values are correlated by a segmented linear regression line that has one inflection point (802 for the high cluster and 808 for the low cluster) and two line segments. In the high occupancy cluster, the slope of the line segment with lower temperature (804) can be defined as the heating indicator, and the slope of the line segment with higher temperature (806) can be defined as the cooling indicator. Similarly, in the low occupancy cluster, the slope of the line segment with lower temperature (810) is defined as the heating indicator, and the slope of the line segment with higher temperature (812) is the cooling indicator. In some embodiments, heating and cooling indicators are normalized by facility's floor area and time duration of each interval so that facilities 102 with different sizes and energy metering steps are comparable. In some further embodiments, if the heating indicator of a facility 102 is greater than a threshold, the facility 102 is most likely to have electric heating. On the contrary, in some other embodiments, if the heating indicator is smaller than the threshold, it is less likely to be electrically heated. In some further embodiments, the same approach can be applied to cooling as well.)
The reason and rationale to combine Li, Padmarao and Gaasch is the same as recited above.
As per Claim 5, Li teaches: The hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method according to claim 4,
wherein the adjusting electric energy supply of the electricity generation stations, the urban area, and the electricity storage devices comprises that the processor … (in at least [pg1] Obtain power resource data, energy storage resource data and load resource data, where the power resource data includes thermal power data, hydropower data, nuclear power data and new energy power generation data, and the energy storage resource data includes chemical battery energy storage data, storage Hydropower station energy storage data, hydrogen energy storage data and supercapacitor energy storage data, the load resource data includes civil load data and industrial load data; Construct a source grid load storage digital twin based on the power resource data, the energy storage resource data and the load resource data; The total power output, the total energy storage and the electricity load prediction value at the current moment are obtained according to the source, grid, load and storage digital twin, where the total power output is the thermal power data, the hydropower data, and the nuclear power data. and the sum of the output power of the new energy power generation data at the current moment, and the total energy storage is the chemical battery energy storage data, the water storage power station energy storage data, the hydrogen energy storage data and the supercapacitor energy storage The sum of all energy storage in the data, the electric load prediction value is obtained by predicting the source grid load storage digital twin based on historical civil load data and historical industrial load data;)
the electricity consumption quantity of the special level in the electricity consumption data is recorded as Ua, the electricity consumption quantity of the residential level is recorded as Ub, and the electricity consumption quantity of the industrial and commercial levels is recorded as Uc; (in at least [pg6] Obtain historical civil load data and historical industrial load data; The initial load prediction model is trained based on historical civil load data and historical industrial load data to obtain the initial power load prediction value; Calculate the loss based on the initial electricity load prediction value, historical civil load data and historical industrial load data, and obtain the loss function output; The model parameters of the initial load forecast model are corrected according to the loss function output until the loss function input meets the preset conditions, and the parameter-adjusted initial load forecast model is used as the load forecast model. Specifically, through the load prediction model of this embodiment, the power load prediction value is made more accurate, and the accuracy of subsequent source-grid load-storage regulation decisions is enhanced. Optionally, as shown in Figure 2, the source grid load and storage regulation decision is generated based on the total power output, the total energy storage amount and the electricity load prediction value, and the source grid load and storage deployment is controlled based on the source grid load and storage regulation decision, including: Step S41: Difference the total power output and the predicted electrical load to obtain the load difference; Step S42, obtain the electricity demand value based on the sum of the load difference and the total amount of energy storage; Step S43: When the power demand value is positive or zero, a primary source grid load storage regulation decision is generated; Step S44: When the power demand value is a negative number and the absolute value is less than or equal to the preset load threshold, a secondary source grid load storage regulation decision is generated; Step S45: When the power demand value is a negative number and the absolute value is greater than the preset load threshold, a three-level source-grid load-storage regulation decision is generated.)
device types in the energy storage data are read, and the stored electric quantities are recorded as s; and (in at least [pg2] Compare the load difference with the sum of rated energy storage of the chemical battery energy storage data, the water storage power station energy storage data, the hydrogen energy storage data and the supercapacitor energy storage data;)
comparing the stored electric quantities through the processor comprises that if s>(msi+msw), it indicates that an energy supply relationship does not need to be adjusted, if s< (msi+msw), it indicates that an electric power demand is increased, when s<(msi+msw), s-(Ua±Ub+Uc)>0, it indicates that the energy supply relationship does not need to be adjusted, if s-(Ua+Ub+Uc)<0, it indicates that electric power needs to be supplemented additionally, if s-(Ua+Ub+Uc)=0 , it indicates that the electric power demand is increased, and when s-(Ua+Ub+Uc) = 0, the processor compares an active power and adjusts the electricity generation stations, and the processor compares electricity usage data and adjusts electric power distribution; (in at least [pg9] A processing unit configured to obtain the total power output, the total energy storage, and the electricity load prediction value at the current moment based on the source grid load and storage digital twin, where the total power output is the thermal power data, the hydropower data , the sum of the output power at the current moment of the nuclear power data and the new energy power generation data, the total energy storage is the chemical battery energy storage data, the water storage power station energy storage data, the hydrogen energy storage data and all The sum of all the energy stored in the supercapacitor energy storage data, the electricity load prediction value is obtained by predicting the source grid load storage digital twin based on historical civil load data and historical industrial load data; [pg5-pg9] Step S4: Generate a source grid load and storage regulation decision based on the total power output, total energy storage and power load prediction value, and control the source grid load and storage deployment based on the source grid load and storage regulation decision. Specifically, this embodiment can also perform visualization processing to form a source network, load and storage visual monitoring platform, that is, gather the above data on the screen, and display each data, for example, the display of power supply in the source network, load and storage visual monitoring platform. , can include the cumulative power consumption of the day, the excess power of the day, etc. Through the data, you can intuitively see the use of power today. For the display of the power grid, you can display the AC power status and the DC power status, and you can intuitively see that both are in normal status. It is still in a fault state. At the same time, it can also provide audible and visual alarms to prompt staff to view and regulate. The source, network, load and storage visual monitoring platform can not only monitor the pre-processed and post-processed data in the source, network, load and storage digital twin. For display, the data changes and strategy content within each time period can also be stored to facilitate subsequent staff to view the power grid operation in the implementation area and provide data support, which is more convenient. Compared with the existing technology, the embodiment of the present invention constructs a source grid load and storage digital twin through power supply resource data, energy storage resource data and load resource data. Based on the source grid load and storage digital twin, the total power output and storage at the current moment are obtained. The total amount of energy and the predicted value of electricity load are digitally mapped through the source, network, load and storage digital twin, that is, the physical objects are constructed in the digital virtual world, and the power resource data, energy storage resource data and load resource data are digitized. Simulate to obtain the total power output, total energy storage and electricity load prediction value at the current moment, and then generate the source grid load and storage regulation decision, and control the source grid load and storage deployment according to the source grid load and storage regulation decision. This is achieved through embodiments of the present invention. Real-time monitoring and analysis of data related to source grid, load and storage, and combining the analyzed data to deploy source grid, load and storage, which improves the real-time and reliability of data. Compared with the "source follows load" scheduling in the existing technology The mode is more powerful for the deployment of energy storage and new energy, and the grid operation after proper integration of new energy is safer. Optionally, the source network load-storage digital twin includes a load forecast model, and the construction process of the load forecast model includes: Obtain historical civil load data and historical industrial load data; The initial load prediction model is trained based on historical civil load data and historical industrial load data to obtain the initial power load prediction value; Calculate the loss based on the initial electricity load prediction value, historical civil load data and historical industrial load data, and obtain the loss function output; The model parameters of the initial load forecast model are corrected according to the loss function output until the loss function input meets the preset conditions, and the parameter-adjusted initial load forecast model is used as the load forecast model. Specifically, through the load prediction model of this embodiment, the power load prediction value is made more accurate, and the accuracy of subsequent source-grid load-storage regulation decisions is enhanced. Optionally, as shown in Figure 2, the source grid load and storage regulation decision is generated based on the total power output, the total energy storage amount and the electricity load prediction value, and the source grid load and storage deployment is controlled based on the source grid load and storage regulation decision, including: Step S41: Difference the total power output and the predicted electrical load to obtain the load difference; Step S42, obtain the electricity demand value based on the sum of the load difference and the total amount of energy storage; Step S43: When the power demand value is positive or zero, a primary source grid load storage regulation decision is generated; Step S44: When the power demand value is a negative number and the absolute value is less than or equal to the preset load threshold, a secondary source grid load storage regulation decision is generated; Step S45: When the power demand value is a negative number and the absolute value is greater than the preset load threshold, a three-level source-grid load-storage regulation decision is generated. Specifically, in this embodiment, the source-grid load-storage regulation decision-making is mainly to ensure the smooth operation of the power grid, and the stable operation of the power grid needs to be dynamically stable. Moreover, the integration of new energy and energy storage causes fluctuations in the operation of the power grid. Therefore, , in order to adapt to the instability of new energy power generation and formulate real-time adjustment of the source network load and storage control decisions, this embodiment needs to do a difference between the total power output and the predicted value of the electricity load to obtain the load difference, and then the load The sum of the difference and the total amount of energy storage is used to obtain the electricity demand value; the positive or negative value of the electricity demand value represents the relationship between the power generation and electricity consumption in the implementation area. When the electricity demand value is a positive number or zero, it indicates the implementation area. The current power generation can meet the electricity consumption. At this time, a first-level source grid load and storage regulation decision is generated to allocate the source grid load and storage. When the power demand value is a negative number, it means that the current power generation in the implementation area cannot meet the demand. Electricity consumption, therefore, it is necessary to compare the absolute value of the electricity demand value with the preset load threshold. When the absolute value of the electricity demand value is less than or equal to the preset load threshold, it means that the electricity gap is small. Therefore, Generate a second-level source-grid load-storage regulation decision; when the absolute value of the electricity demand value is greater than the preset load threshold, it indicates that the power gap is large. Therefore, a third-level source-grid load-storage regulation decision is generated, in which the preset load threshold is It is preset to the maximum load carrying capacity of the power grid operation, that is, the sum of the total power output, the total energy storage, and the controllable load data in the civil load data and industrial load data that can be interrupted by the power grid. It can be understood that the preset load threshold Represents the total amount of controllable power supply when the power grid is running. In actual application, if there are other power supplies, the total amount of controllable power supply must also be added to the preset load threshold, that is, if the power demand If the absolute value of the value is less than or equal to the maximum load carrying capacity, then the power gap can be replenished through these controllable resources. On the contrary, when the absolute value of the power demand value is greater than the maximum load carrying capacity, it means that the power gap has been greater than The total power supply is beyond the capacity of the power grid. Optionally, as shown in Figure 3, the first-level source network load and storage control decisions include: Step S431, obtain the load difference and store the remaining power generation corresponding to the load difference; Step S432, compare the load difference with the sum of rated energy storage of chemical battery energy storage data, water storage power station energy storage data, hydrogen energy storage data and supercapacitor energy storage data; Step S433, when the load difference is less than or equal to the total rated energy storage, all remaining power generation is stored; Step S434: When the load difference is greater than the total rated energy storage, the remaining power generation is stored, and the civil load data and industrial load data are adjusted to consume the unused remaining power generation. Specifically, in this embodiment, the primary source grid load storage regulation decision-making is mainly based on energy storage, supplemented by energy consumption, and the remaining power generation is stored. If the rated storage energy of all energy storage equipment or facilities is full, then Adjust civil load data and industrial load data to consume unstored power generation to avoid energy waste. At the same time, in the actual application process, preferential power usage policies can be implemented for the remaining power generation, thereby increasing power demand and avoiding energy consumption. losses while reducing economic losses. Optionally, the secondary source grid load storage control decision-making includes: Step S441, obtain the absolute value of the load difference, perform difference processing between the absolute value of the load difference and the total amount of energy storage, and obtain the load demand value; Step S442, consume all the energy storage corresponding to the total energy storage amount, and increase the thermal power data, hydropower data and nuclear power data in the power source resource data according to the load demand value until the increased thermal power data, hydropower data and nuclear power data The sum of is greater than or equal to the load demand value, and the new energy power generation data remains unchanged. Specifically, the secondary source grid load and storage regulation decision in this embodiment can be understood as the deployment of source grid load and storage within the adjustable range. For a small power gap, only the energy storage needs to be released to increase thermal power generation, hydropower generation and Nuclear power generation can make up for the electricity gap. Due to the uncontrollability of new energy, in this embodiment, no adjustment is made to the new energy, and only its power generation output is monitored. Optionally, the three-level source network load storage control decision-making includes: Step S451, obtain the absolute value of the load difference, perform difference processing between the absolute value of the load difference and the total amount of energy storage, and obtain the load demand value; Step S452, consume all the energy storage corresponding to the total energy storage amount, increase the thermal power data, hydropower data and nuclear power data in the power resource data according to the load demand value, cut off the controllable loads in the civil load data and industrial load data, Until the absolute value of the power demand value is less than the preset load threshold; Among them, when the absolute value of the power demand value is less than or equal to the preset load threshold, the adjustable load is turned on. Specifically, in this embodiment, the three-level source-grid-load-storage control decision is the first-level strategy with the greatest deployment intensity. Since the power gap is too large, simply increasing power generation and energy storage and energy release cannot fill the power gap. Therefore, , it is necessary to cut off the controllable loads in the civil load data and industrial load data to temporarily alleviate the power gap until the absolute value of the power demand value is less than the preset load threshold. It is understandable that the load shedding operation time should not be too long. A long-term power outage can easily cause chaos in the electricity market and cause serious economic losses. Therefore, the three-level source, grid, load and storage regulation decisions can be understood as short-term adjustments, which only allocate the source, grid, load and storage in a short period of time. Optionally, when the power demand value is negative and the absolute value is greater than the preset load threshold, a three-level source-grid load-storage regulation decision is generated, which also includes: When the power demand value is a negative number, the absolute value of the power demand value is greater than the preset load threshold, and the duration is greater than the preset time threshold, adjust the power resource data of other power grid areas to the current power consumption area to meet the current power consumption regional electricity demand. Specifically, when the load shedding control lasts too long, in order to prevent chaos in the electricity market, it is necessary to timely call from other power grid areas, that is, to introduce power sources from other areas to the implementation area to meet the power demand in the implementation area. In one embodiment, a certain area adopts the source grid load and storage control method, and acquires thermal power data, hydropower data and photovoltaic data by constructing a source grid load and storage digital twin to monitor and perceive power resource data, energy storage resource data and load resource data in real time. Power generation data, because there is also a water storage power station in the area, the energy storage data of the water storage power station was obtained. At the same time, the area includes two factories, multiple shopping malls, office buildings, and multiple residential buildings. Relevant civil load data and industrial load data were obtained. According to the source The grid load and storage digital twin obtains the total power output, total energy storage and electricity load prediction value at 12 noon. After the source grid load and storage digital twin senses and processes it, due to the lunch break and meal peak at noon, the electricity consumption at this time The load prediction value increases and is greater than the total power output. After summing with the total energy storage, the power gap is still unable to be met. However, because the power gap is small, that is, the absolute value of the power demand value is less than the preset load threshold, Then start the secondary source grid load and storage regulation decision-making, and generate power generation instructions to increase thermal power generation and hydropower generation to make up for the power gap. When the power gap is met, the primary source grid load and storage regulation decision-making is restored. Through this implementation Based on the example of source grid load storage control method, digital simulation processing is performed before source grid load storage deployment, and then physical execution is performed after optimization. This avoids possible erroneous execution caused by direct operation, improves the accuracy of deployment, and makes the power grid operation more efficient. Safety.)
wherein …, …, Ua represents the electricity consumption quantity of the special level in the electricity consumption data, Ub represents the electricity consumption quantity of the residential level, and Uc represents the electricity consumption quantity of the industrial and commercial levels. (in at least [pg6] Obtain historical civil load data and historical industrial load data; The initial load prediction model is trained based on historical civil load data and historical industrial load data to obtain the initial power load prediction value; Calculate the loss based on the initial electricity load prediction value, historical civil load data and historical industrial load data, and obtain the loss function output; The model parameters of the initial load forecast model are corrected according to the loss function output until the loss function input meets the preset conditions, and the parameter-adjusted initial load forecast model is used as the load forecast model. Specifically, through the load prediction model of this embodiment, the power load prediction value is made more accurate, and the accuracy of subsequent source-grid load-storage regulation decisions is enhanced. Optionally, as shown in Figure 2, the source grid load and storage regulation decision is generated based on the total power output, the total energy storage amount and the electricity load prediction value, and the source grid load and storage deployment is controlled based on the source grid load and storage regulation decision, including: Step S41: Difference the total power output and the predicted electrical load to obtain the load difference; Step S42, obtain the electricity demand value based on the sum of the load difference and the total amount of energy storage; Step S43: When the power demand value is positive or zero, a primary source grid load storage regulation decision is generated; Step S44: When the power demand value is a negative number and the absolute value is less than or equal to the preset load threshold, a secondary source grid load storage regulation decision is generated; Step S45: When the power demand value is a negative number and the absolute value is greater than the preset load threshold, a three-level source-grid load-storage regulation decision is generated.)
Although implied, Li does not expressly disclose the following limitations, which however, are taught by Padmarao,
… records the photovoltaic reactive power in the power supply data as nip, and records the wind reactive power as nwp;… (in at least [0049] FIGS. 6A-6D illustrate apparent power capability curves including active and reactive power. FIGS. 6A-6D display active power (P) on the x-axis and reactive power (Q) on the y-axis. The Figures also show the apparent power curve, which illustrates the trade-off between active power and reactive power as shown in Equation 1 below. Q=√{square root over (S 2 −P 2)} EQ. 1where S is the apparent power. [0053] power system management computer device 310 performs process 700 to shift reactive power generation from wind assets 110 to solar assets 112 and solar inverters 114 (all shown in FIG. 1) during the occurrence of low solar irradiance and high wind. The electrical margin available to wind assets 110 is leveraged to generate more active power. The active power of solar inverter 114 is reduced as a function of the power factor, as shown in Equation 1.)
…msi represents the stored electric quantity of an estimated photovoltaic electricity generation quantity,…(in at least [0059] Power system management computer device 310 computes 740 the net additional active power that can be generated from select wind assets 110 as ΔP1. Further, power system management computer device 310 estimates 745 the reactive power requirement corresponding to the net plant active power generation after the uprate that may be shifted from wind assets 110 to solar assets 112. In addition, power system management computer device 310 determines 750 if inverters 114 (shown in FIG. 1) may support the power shift. If not, then process 700 ends and normal operation 770 of plant 100 continues. Otherwise, power system management computer device 310 estimates 755 the impact on the solar power generation due to the supply of reactive power as ΔP2, which includes the losses from moving generation from active to reactive power. Power system management computer device 310 determines 760 if the amount of active power gained is greater than the amount of active power that will be lost due to the shift (ΔP1>|ΔP2|). If not, then process 700 ends and normal operation 770 of plant 100 continues. Otherwise, power system management computer device 310 operates 765 wind assets 110 at the higher MW values and use solar inverters 114 to provide the reactive power.)
…msw represents the stored electric quantity of the wind electricity generation quantity,…(in at least [0058] power system management computer device 310 performs steps 720 through 735 for every wind asset 110 in plant 100. In other embodiments, power system management computer device 310 only performs steps 720 through 735 on a predetermined subset of wind assets 110. The power system management computer device 310 compares 720 the wind forecast to the Khigh threshold multiplied by the wind rated power of wind asset 110. If the forecast is greater, then power system management computer device 310 determines 725 if wind asset 110 has available margin for uprate (uprate margin) and is not currently under deration or curtailment. The available margin for uprate indicates if the available power generation of active power of that wind asset 110 may be increased. For example, assume a wind asset 110 is rated for (e.g., has a nameplate listing of) 3.98 MW. However, if the reactive power generation is adjusted, then the amount of active power that wind asset 110 generates could be increased to 4.2 MW. In this case, the uprate margin is 0.22 MW. This uprate is only the energy uprate and does not affect the mechanical operation of wind asset 110. Each wind asset 110 that is able to be uprated is selected 730 for uprate, and the next wind asset 110 is reviewed 735. In some embodiments, a wind asset 110 may not be available for uprate because it is not in the high wind region, and thus not able to produce additional active power. In some embodiments, each wind asset 110 is individually compared to the threshold to determine if that wind asset 110 is available for uprate.)
The reason and rationale to combine Li and Padmarao is the same as recited above.
As per Claim 6, Li teaches: The hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method according to claim 5,
wherein the obtaining secondary electricity consumption data comprises that after the electric energy supply adjustment for the electricity generation stations, the urban area, and the electricity storage devices is completed, the processor obtains the electricity consumption data of the different levels again, and records the electricity consumption data as Uaa, Ubb, and Ucc; (in at least [pg6] Obtain historical civil load data and historical industrial load data; The initial load prediction model is trained based on historical civil load data and historical industrial load data to obtain the initial power load prediction value; Calculate the loss based on the initial electricity load prediction value, historical civil load data and historical industrial load data, and obtain the loss function output; The model parameters of the initial load forecast model are corrected according to the loss function output until the loss function input meets the preset conditions, and the parameter-adjusted initial load forecast model is used as the load forecast model. Specifically, through the load prediction model of this embodiment, the power load prediction value is made more accurate, and the accuracy of subsequent source-grid load-storage regulation decisions is enhanced. Optionally, as shown in Figure 2, the source grid load and storage regulation decision is generated based on the total power output, the total energy storage amount and the electricity load prediction value, and the source grid load and storage deployment is controlled based on the source grid load and storage regulation decision, including: Step S41: Difference the total power output and the predicted electrical load to obtain the load difference; Step S42, obtain the electricity demand value based on the sum of the load difference and the total amount of energy storage; Step S43: When the power demand value is positive or zero, a primary source grid load storage regulation decision is generated; Step S44: When the power demand value is a negative number and the absolute value is less than or equal to the preset load threshold, a secondary source grid load storage regulation decision is generated; Step S45: When the power demand value is a negative number and the absolute value is greater than the preset load threshold, a three-level source-grid load-storage regulation decision is generated.)
the calculating secondary electricity consumption data through the processor is represented as ΔUU=(mUi+mUw)-(Uaa+Ubb+Ucc) (in at least [pg7-pg8] the secondary source grid load storage control decision-making includes: Step S441, obtain the absolute value of the load difference, perform difference processing between the absolute value of the load difference and the total amount of energy storage, and obtain the load demand value; Step S442, consume all the energy storage corresponding to the total energy storage amount, and increase the thermal power data, hydropower data and nuclear power data in the power source resource data according to the load demand value until the increased thermal power data, hydropower data and nuclear power data The sum of is greater than or equal to the load demand value, and the new energy power generation data remains unchanged. Specifically, the secondary source grid load and storage regulation decision in this embodiment can be understood as the deployment of source grid load and storage within the adjustable range. For a small power gap, only the energy storage needs to be released to increase thermal power generation, hydropower generation and Nuclear power generation can make up for the electricity gap. Due to the uncontrollability of new energy, in this embodiment, no adjustment is made to the new energy, and only its power generation output is monitored. Optionally, the three-level source network load storage control decision-making includes:Step S451, obtain the absolute value of the load difference, perform difference processing between the absolute value of the load difference and the total amount of energy storage, and obtain the load demand value; Step S452, consume all the energy storage corresponding to the total energy storage amount, increase the thermal power data, hydropower data and nuclear power data in the power resource data according to the load demand value, cut off the controllable loads in the civil load data and industrial load data, Until the absolute value of the power demand value is less than the preset load threshold; Among them, when the absolute value of the power demand value is less than or equal to the preset load threshold, the adjustable load is turned on. Specifically, in this embodiment, the three-level source-grid-load-storage control decision is the first-level strategy with the greatest deployment intensity. Since the power gap is too large, simply increasing power generation and energy storage and energy release cannot fill the power gap. Therefore, , it is necessary to cut off the controllable loads in the civil load data and industrial load data to temporarily alleviate the power gap until the absolute value of the power demand value is less than the preset load threshold. It is understandable that the load shedding operation time should not be too long. A long-term power outage can easily cause chaos in the electricity market and cause serious economic losses. Therefore, the three-level source, grid, load and storage regulation decisions can be understood as short-term adjustments, which only allocate the source, grid, load and storage in a short period of time. .Optionally, when the power demand value is negative and the absolute value is greater than the preset load threshold, a three-level source-grid load-storage regulation decision is generated, which also includes: When the power demand value is a negative number, the absolute value of the power demand value is greater than the preset load threshold, and the duration is greater than the preset time threshold, adjust the power resource data of other power grid areas to the current power consumption area to meet the current power consumption regional electricity demand. Specifically, when the load shedding control lasts too long, in order to prevent chaos in the electricity market, it is necessary to timely call from other power grid areas, that is, to introduce power sources from other areas to the implementation area to meet the power demand in the implementation area.)
wherein …
Although implied, Li does not expressly disclose the following limitations, which however, are taught by Padmarao,
…mUi represents the estimated photovoltaic electricity generation quantity…(in at least [0059] Power system management computer device 310 computes 740 the net additional active power that can be generated from select wind assets 110 as ΔP1. Further, power system management computer device 310 estimates 745 the reactive power requirement corresponding to the net plant active power generation after the uprate that may be shifted from wind assets 110 to solar assets 112. In addition, power system management computer device 310 determines 750 if inverters 114 (shown in FIG. 1) may support the power shift. If not, then process 700 ends and normal operation 770 of plant 100 continues. Otherwise, power system management computer device 310 estimates 755 the impact on the solar power generation due to the supply of reactive power as ΔP2, which includes the losses from moving generation from active to reactive power. Power system management computer device 310 determines 760 if the amount of active power gained is greater than the amount of active power that will be lost due to the shift (ΔP1>|ΔP2|). If not, then process 700 ends and normal operation 770 of plant 100 continues. Otherwise, power system management computer device 310 operates 765 wind assets 110 at the higher MW values and use solar inverters 114 to provide the reactive power.)
…mUw represents an estimated wind electricity generation quantity… (in at least [0058] power system management computer device 310 performs steps 720 through 735 for every wind asset 110 in plant 100. In other embodiments, power system management computer device 310 only performs steps 720 through 735 on a predetermined subset of wind assets 110. The power system management computer device 310 compares 720 the wind forecast to the Khigh threshold multiplied by the wind rated power of wind asset 110. If the forecast is greater, then power system management computer device 310 determines 725 if wind asset 110 has available margin for uprate (uprate margin) and is not currently under deration or curtailment. The available margin for uprate indicates if the available power generation of active power of that wind asset 110 may be increased. For example, assume a wind asset 110 is rated for (e.g., has a nameplate listing of) 3.98 MW. However, if the reactive power generation is adjusted, then the amount of active power that wind asset 110 generates could be increased to 4.2 MW. In this case, the uprate margin is 0.22 MW. This uprate is only the energy uprate and does not affect the mechanical operation of wind asset 110. Each wind asset 110 that is able to be uprated is selected 730 for uprate, and the next wind asset 110 is reviewed 735. In some embodiments, a wind asset 110 may not be available for uprate because it is not in the high wind region, and thus not able to produce additional active power. In some embodiments, each wind asset 110 is individually compared to the threshold to determine if that wind asset 110 is available for uprate.)
The reason and rationale to combine Li and Padmarao is the same as recited above.
As per Claim 7, Li teaches: The hierarchical and graded source-network-load-storage multi-subject collaborative scheduling method according to claim 6,
wherein the calling the electricity storage devices in surrounding regions to supplement electric energy comprises that whether the secondary electricity consumption data ΔUU is greater than 0 or not is judged through the processor, if ΔUU> 0, it indicates that electric energy supply is balanced, and if ΔUU<0, it indicates that electric power needs to be supplemented additionally from the electricity storage devices in surrounding regions, so that an electric quantity of |ΔUU| is supplemented. (in at least [pg8] the secondary source grid load storage control decision-making includes: Step S441, obtain the absolute value of the load difference, perform difference processing between the absolute value of the load difference and the total amount of energy storage, and obtain the load demand value; Step S442, consume all the energy storage corresponding to the total energy storage amount, and increase the thermal power data, hydropower data and nuclear power data in the power source resource data according to the load demand value until the increased thermal power data, hydropower data and nuclear power data The sum of is greater than or equal to the load demand value, and the new energy power generation data remains unchanged. Specifically, the secondary source grid load and storage regulation decision in this embodiment can be understood as the deployment of source grid load and storage within the adjustable range. For a small power gap, only the energy storage needs to be released to increase thermal power generation, hydropower generation and Nuclear power generation can make up for the electricity gap. Due to the uncontrollability of new energy, in this embodiment, no adjustment is made to the new energy, and only its power generation output is monitored. Optionally, the three-level source network load storage control decision-making includes: Step S451, obtain the absolute value of the load difference, perform difference processing between the absolute value of the load difference and the total amount of energy storage, and obtain the load demand value; Step S452, consume all the energy storage corresponding to the total energy storage amount, increase the thermal power data, hydropower data and nuclear power data in the power resource data according to the load demand value, cut off the controllable loads in the civil load data and industrial load data, Until the absolute value of the power demand value is less than the preset load threshold; Among them, when the absolute value of the power demand value is less than or equal to the preset load threshold, the adjustable load is turned on. Specifically, in this embodiment, the three-level source-grid-load-storage control decision is the first-level strategy with the greatest deployment intensity. Since the power gap is too large, simply increasing power generation and energy storage and energy release cannot fill the power gap. Therefore, , it is necessary to cut off the controllable loads in the civil load data and industrial load data to temporarily alleviate the power gap until the absolute value of the power demand value is less than the preset load threshold. It is understandable that the load shedding operation time should not be too long. A long-term power outage can easily cause chaos in the electricity market and cause serious economic losses. Therefore, the three-level source, grid, load and storage regulation decisions can be understood as short-term adjustments, which only allocate the source, grid, load and storage in a short period of time. .Optionally, when the power demand value is negative and the absolute value is greater than the preset load threshold, a three-level source-grid load-storage regulation decision is generated, which also includes: When the power demand value is a negative number, the absolute value of the power demand value is greater than the preset load threshold, and the duration is greater than the preset time threshold, adjust the power resource data of other power grid areas to the current power consumption area to meet the current power consumption regional electricity demand.Specifically, when the load shedding control lasts too long, in order to prevent chaos in the electricity market, it is necessary to timely call from other power grid areas, that is, to introduce power sources from other areas to the implementation area to meet the power demand in the implementation area.)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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