DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/24/2024, and 02/12/2025, have been acknowledged. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the Examiner. The initialed and dated copies of Applicant’s IDS forms, 1449, are attached to the instant Office Action.
Status of Claims
This is a Non-Final Action on the merits in response to the claims filed on 07/24/2024.
Claims 1 – 20 are currently pending in this application.
Claim Rejections – 35 U.S.C. § 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 – 20, are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
Claims 1, 9, and 13:
charging and discharging scheduling method, comprising: setting a scheduling model for charging/discharging, based on a constraint function and an objective function considering a contract for difference, a plus demand response (DR), and a national DR;
optimizing the set scheduling model;
and scheduling using the optimized scheduling model;
wherein the constraint function includes a function for at least one of or any combination of restrictions on charging/discharging an upper limit on a charging/discharging amount considering a vehicle entry time, satisfaction of a customer-set target battery charging amount, compliance with winning bid power when participating in a Plus DR market, restrictions on arbitrage services, and recognition of an amount of reduction up to a customer baseline load power amount when participating in the national DR;
and wherein the objective function includes a regular scheduling function considering the contract for difference and the plus DR market, and an irregular scheduling function considering the contract for difference, the plus DR market, and a national DR market.
Applicant’s claim 1 recites the abstract idea mathematical concepts and particularly, mathematical relationships. For example claim 1 recites, charging and discharging scheduling method, setting a scheduling model for charging/discharging, based on a constraint function and an objective function considering a contract for difference, a plus demand response (DR), and a national DR; optimizing the set scheduling model; scheduling using the optimized scheduling model; wherein the constraint function includes a function for at least one of or any combination of restrictions on charging/discharging an upper limit on a charging/discharging amount considering a vehicle entry time, satisfaction of a customer-set target battery charging amount, compliance with winning bid power when participating in a Plus DR market, restrictions on arbitrage services, and recognition of an amount of reduction up to a customer baseline load power amount when participating in the national DR; and wherein the objective function includes a regular scheduling function considering the contract for difference and the plus DR market, and an irregular scheduling function considering the contract for difference, the plus DR market, and a national DR market; where these limitations all organize information and manipulate the data using mathematical functions. Claims 9 and 13, are substantially similar and recite the same subject matter as claim 1. Accordingly, claims 1, 9, and 13 recite mathematical relationships.
The dependent claims encompass the same abstract ideas as well. For instance, claims 2, 10, and 14 are directed towards observing a restriction function for the restrictions on charging/discharging is configured to set a charging decision variable and a discharging decision variable; claims 3, 11, and 15 are directed towards observing an upper limit function for the upper limit on the charging/discharging amount considering the vehicle entry time is set, such that a charging capacity variable is set to be less than or equal to a product of charging upper limit power and a charging decision variable, and a discharge capacity variable is set to be less than or equal to a product of discharge upper limit power and a discharging decision variable, and wherein the charging upper limit power or the discharge upper limit power is set in consideration of an entry time; claims 4, 12, and 16 are directed towards observing a satisfaction function for satisfaction of the customer-set target battery charging amount is set such that a battery charging amount at a specific time is set to be greater than or equal to the customer-set target battery charging amount in response to the customer-set target battery charging amount being present at the specific time; claims 5 and 17 are directed towards observing a prevention function; claims 6 and 18 are directed towards observing a complying function for complying with the winning bid; claims 7 and 19 are directed towards observing a recognition function for recognition of the amount of reduction up to the customer baseline load power amount for participating in the national DR is set in consideration of a Customer Baseline Load (CBL) as a settlement standard; and claims 8 and 20 are directed towards observing the optimizing the scheduling model includes optimizing the scheduling model to significantly reduce power charges or significantly increase revenue using the constraint function and the objective function. Accordingly, the dependent claims encompass the same abstract idea.
These judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of an electric vehicle, smart charging, performing electric vehicle charging and discharging, prevention of a reversed power flow when applying Vehicle to Home (V2H), and resources directly connected to a grid; claim 9 recites the same additional elements as claim 1; and in addition to reciting the additional elements of claim 1, claim 13 recites the additional elements of an electric vehicle charging/discharging scheduling device comprising: one or more processors; and a storage medium storing computer-readable instructions that, when executed by the one or more processors, enable the one or more processors. However, the additional elements of an electric vehicle, smart charging, performing electric vehicle charging and discharging, prevention of a reversed power flow when applying Vehicle to Home (V2H), resources directly connected to a grid, an electric vehicle charging/discharging scheduling device comprising: one or more processors; and a storage medium storing computer-readable instructions that, when executed by the one or more processors, enable the one or more processors are generic computer components as per Applicant’s Specifications shown below:
“[0117] The processor 401 may enable the computing device 400 to operate according to the example embodiments mentioned above. For example, the processor 401 may execute one or more programs stored in the computer-readable storage medium 402. The one or more programs may include one or more computer-executable instructions, and the computer-executable instructions, when executed by the processor 401, may be configured to cause the computing device 400 to perform operations according to example embodiments”
and thus are not practically integrated nor significantly more.
The claims do not include additional elements that are sufficient to amount significantly more than the judicial exception. As stated above the additional elements of an electric vehicle, smart charging, performing electric vehicle charging and discharging, prevention of a reversed power flow when applying Vehicle to Home (V2H), resources directly connected to a grid, an electric vehicle charging/discharging scheduling device comprising: one or more processors; and a storage medium storing computer-readable instructions that, when executed by the one or more processors, enable the one or more processors are considered generic computer components performing generic computer functions and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 2 – 8, 10 – 12, and 14 – 20, when analyzed both individually and in combination are also held to be ineligible for the same reason above, and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. 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 amount to significantly more than the abstract idea itself. Therefore, claims 1 – 20, are not patent eligible.
Claim Rejections – 35 U.S.C. § 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 (i.e., changing from AIA to pre-AIA ) 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(a) 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.
5. Claims 1 – 20, are rejected under 35 U.S.C. 103 as being unpatentable over Luo, Bo-te (CN 115860365) in view of Huang, Yu-Ping (CN 115882494) in view of Otsuki, Tomoshi et al. (JP 2022139181 A).
Claims 1, 9, and 13:
An electric vehicle charging and discharging scheduling method, comprising: setting a scheduling model for charging/discharging an electric vehicle, based on a constraint function and an objective function considering a contract for difference, smart charging, a plus demand response (DR), and a national DR; Luo teaches in Description, Contents of the invention, pg. 3, ¶ 4, for the electric vehicle charging and discharging scheduling task carried out in the smart park, construct a multi-objective optimization model and the constraint conditions of the decision variables in the multi-objective optimization model; wherein, the multi-objective optimization model includes A first optimization model for the power grid, a second optimization model for charging stations in the smart park, and a third optimization model for electric vehicles contracted with the charging stations;
optimizing the set scheduling model; Luo teaches in Contents of the invention, ¶ 5, pg. 3, taking the minimization of the net load variance of the power grid in the first optimization model, the operating cost of the charging station in the second optimization model and the total charging cost of the electric vehicle in the third optimization model as the optimization goal, combining the constraints Conditionally solving the multi-objective optimization model to obtain a game equilibrium solution of the decision variables in the multi-objective optimization model;
and performing electric vehicle charging and discharging scheduling using the optimized scheduling model; Luo teaches in Contents of the invention, ¶ 6, pg. 3, according to the game equilibrium solution of the decision variables in the multi-objective optimization model, the charging and discharging scheduling task of the electric vehicle is executed. Luo teaches in ¶ 7, pg. 3, this application is aimed at the charging and discharging scheduling tasks of electric vehicles in the smart park. By signing contracts with the charging stations in the smart park and electric vehicles, the disorderly charging behavior of electric vehicles can be transformed into an orderly charging and discharging behavior that can be coordinated. Furthermore, considering the balance of interests among the power grid, charging stations and electric vehicles contracted with the charging stations in the smart park, a multi-objective optimization model is constructed from the three levels of "network-station-vehicle" and the multi-objective optimization model constraints on the decision variables. Wherein, the multi-objective optimization model includes a first optimization model for the power grid in the smart park, a second optimization model for charging stations in the smart park, and a second optimization model for electric vehicles contracted with the charging station. Three optimization models. Luo teaches in ¶ 8, pg. 3, then take minimizing the net load variance of the power grid in the first optimization model, the operating cost of the charging station in the second optimization model, and the total charging cost of electric vehicles in the third optimization model as the optimization target, combined with the Solving the multi-objective optimization model with constraint conditions to obtain a game equilibrium solution of the decision variables in the multi-objective optimization model; according to the game equilibrium solution of the decision variables in the multi-objective optimization model, execute the electric vehicle charging and discharging schedule The mission is to effectively suppress the net load fluctuation of the power grid, reduce the operating cost of the charging station, and reduce the charging fee of the electric vehicle contracted with the charging station, so as to achieve the win-win effect of the "network-station-vehicle" tripartite balance.
wherein the constraint function includes a function for at least one of or any combination of restrictions on charging/discharging for each electric vehicle, an upper limit on a charging/discharging amount considering a vehicle entry time, satisfaction of a customer-set target battery charging amount, compliance with winning bid power when participating in a Plus DR market, restrictions on arbitrage services for resources directly connected to a grid, and recognition of an amount of reduction up to a customer baseline load power amount when participating in the national DR; Luo teaches in ¶ 5, pg. 8, among them, f ev,c represents the third optimization model with the optimization goal of minimizing the total charging cost of electric vehicles; C a is the power consumption cost of the electric vehicle driving to the charging station; C s is the charging service fee of the charging station Cost, ε is the discount coefficient (ε<1) of the electric vehicle contracted with the charging station, μ is the step reward coefficient of the charging times of the electric vehicle contracted with the charging station (μ<1); C e is the electric vehicle The cost corresponding to the electricity consumption from the charging station to the destination; C se is the electricity sales revenue of the electric energy stored in the idle state of the electric vehicle contracted with the charging station, and λ is the sales revenue of the electric vehicle contracted with the charging station Electricity ladder reward coefficient (λ>1). Luo teaches in 2. ¶ 11, pg. 8, the lower limit value and upper limit value of electric vehicle charging/discharging/electric power. Luo teaches in ¶ 3, pg. 12, Aiming at the electricity consumption tasks of residents in the smart park, the target optimization model and the constraint conditions of the decision variables in the target optimization model can be constructed; wherein, the target optimization model includes the first model for the power grid in the smart park. The second model is aimed at the contracted residents in the smart park, and the third model is aimed at the electric vehicles contracted with the charging station; to minimize the net load variance of the power grid in the first model, and the contracted electric vehicle in the second model. The electricity consumption cost of the household and the total charging cost of the electric vehicle in the third model are optimization targets, and the target optimization model is solved in combination with the constraint conditions, and the game equilibrium solution of the decision variables in the target optimization model is obtained; according to The game equilibrium solution of the decision variables in the objective optimization model is to execute the contracted car charging task in the smart park, and to achieve the minimum total charging cost of the contracted electric vehicle in the smart park and the operating cost of the charging station under the condition that the comfort of the contracted car owner allows. Minimum, the net load variance of the grid is minimum.
wherein the optimizing the scheduling model includes optimizing the scheduling model to significantly reduce power charges or significantly increase revenue using the constraint function and the objective function; Luo teaches in ¶ 1, pg. 12, After the server obtains the game equilibrium solution of the decision variables in the multi-objective optimization model, it can execute the electric vehicle charging and discharging scheduling task according to the game equilibrium solution of the decision variables in the multi-objective optimization model, so as to effectively suppress the power grid Net load fluctuations, reduce the operating costs of charging stations, and reduce the charging fees of electric vehicles contracted with charging stations, to achieve a balanced and win-win effect for the three parties of "network-station-car".
While Luo teaches signing contracts with charging stations in the park, electric vehicles can be charged at preferential and low prices participate in demand response, selling power profit, and reduce charging costs, Luo does not explicitly teach profit maximization. However, Huang teaches the following:
and wherein the objective function includes a regular scheduling function considering the contract for difference and the plus DR market, and an irregular scheduling function considering the contract for difference, the plus DR market, and a national DR market; Huang teaches in Abstract, the method comprises a V2G demand response and scheduling process and a vehicle charging and discharging scheduling model with optimal benefits for an electric vehicle service main body. Huang teaches in claim 1, a hierarchical V2G scheduling optimization method considering vehicle grid-connected service duration difference is characterized by comprising the following steps: the method comprises the following steps: the dispatching center imports initial information of electric vehicle charge-discharge control and economic dispatching control, and performs matching analysis on power supply, load demand and electric vehicle capacity among the main bodies; step two: the dispatching center optimizes the demand of the power grid load balance by taking the minimum response cost of the regional demand side as a target, and sends an optimization result to a V2G load aggregation operator; step three: AGs send a V2G service invitation to the EV cluster; step four: AGs acquire offer information returned by EV users, and distribute EV vehicle groups to corresponding V2G service points for vehicle grid connection; step five: AGs construct a vehicle optimization scheduling model based on mixed integers according to the information of respective areas, and set a user profit maximization target equation of an area V2G scheduling system; step six: AGs issue a charge and discharge operation instruction of a specified vehicle to a charging pile; step seven: the EV starts to execute charge and discharge invitations, the AGs adjusts the charge and discharge power of the EVs in real time according to the load, and the V2G invitations are continuously sent to the off-network EV cluster; step eight: updating the EVs execution condition, and feeding back the load total response execution condition to the dispatching center; and returning to the first step for continuous execution, and performing loop iteration updating. Huang teaches in the Summary, the dispatch center optimizes the grid load balancing requirements with the goal of minimizing the regional demand-side response cost, and sends the optimization results to the V2G load aggregator; Huang teaches in claim 5, price profit maximization scheduling model wherein in step five, the building of the hybrid integer-based vehicle optimal scheduling model sets a user profit maximization profit objective equation of a regional V2G scheduling system to be:
PNG
media_image1.png
70
928
media_image1.png
Greyscale
Huang teaches in ¶¶ 5 – 6, pg. 33, Under the benchmark electricity price, the peak value of the system base load when electric vehicles are not involved in charging and discharging scheduling is 6600kWh,the valley value is 1470 kWh, and the peak-to-valley difference is 5130kWh. When 100 electric vehicles participate in charging and discharging optimization scheduling, the load peak is 6456kWh, which is 144kWh less than before. The valley value is 1900kWh, which is 430kWh more than before the electric vehicles participate in charging and discharging optimization scheduling. The peak-to-valley difference is 4556kWh, which is 574kWh less than before scheduling. Under the peak-valley-flat electricity price mechanism, the peak value of the system base load when electric vehicles are not involved in charging and discharging scheduling is 6600kWh, the valley value is 1470kWh, and the peak-valley difference is 5130kWh. When 100 electric vehicles participate in charging and discharging optimization scheduling, the load peak is 5900kWh, which is 700kWh less than before. The valley value is 1966kWh, which is 496kWh more than before the electric vehicles participate in charging and discharging optimization scheduling. The peak-valley difference is 3934kWh, which is 1196kWh less than before scheduling.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method comprises the following steps: aiming at the electric automobile charging and discharging scheduling task, a multi-objective optimization model and a constraint condition of a decision variable in the multi-objective optimization model of Luo with a V2G demand response and scheduling process and a vehicle charging and discharging scheduling model with optimal benefits for an electric vehicle of Huang to assist businesses with building optimization models aimed at profit maximization when charging and discharging optimization scheduling of electric vehicles (Huang ¶ 7, pg. 33).
While Luo teaches charging stations in the park, electric vehicles can be charged at preferential and low prices participate in demand response, selling power profit, and reduce charging costs, and Huang teaches profit maximization, neither Luo nor Huang explicitly teach prevention of a reverse power flow. However, Otsuki teaches the following:
prevention of a reversed power flow when applying Vehicle to Home (V2H); Otsuki teaches in ¶ 8, pg. 8, the grid supply upper limit power 223 is set on the residential side based on the contract power. The system supply lower limit power 224 is set on the residential side as power to be kept so that reverse power flow does not occur due to discharge. Assuming that the demand, which is the actual power consumption, is x (W) and the charge/discharge output is 3000 (W), the output when charging is x+3000 when the customer requests charging, and the request for discharging is x+3000.The output when discharged should be x-3000. However, in reality, it cannot take a value less than the lower limit power supply for the system or larger than the upper limit power for public holidays. For example, if the grid supply lower limit power 224 is 100W, the meter value will not be less than 100W.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method comprises the following steps: aiming at the electric automobile charging and discharging scheduling task, a multi-objective optimization model and a constraint condition of a decision variable in the multi-objective optimization model of Luo and a V2G demand response and scheduling process and a vehicle charging and discharging scheduling model with optimal benefits for an electric vehicle of Huang with a charging and discharging planning unit for preparing a charging and discharging plan of a storage battery owned by a customer of Otsuki to assist businesses with building optimization models aimed at including constraints of reverse power flow (Otsuki, ¶ 1, pg. 2).
Claims 2, 10, and 14:
Luo, Huang, and Otsuki teach claims 1, 9, and 13. Luo further teaches the following:
wherein a restriction function for the restrictions on charging/discharging for each electric vehicle is configured to set a charging decision variable and a discharging decision variable according to a V1G type or a V2G type in response to the electric vehicle is parked, wherein the V1G type is discharging being limited and charging being allowed, and wherein the V2G type is discharging and charging being allowed, in response to the electric vehicle being the V1G type; Luo teaches in claim 1, a charge and discharge scheduling method of an electric vehicle is characterized by comprising the following steps: aiming at an electric automobile charging and discharging scheduling task performed in an intelligent park, a multi- objective optimization model and a constraint condition of a decision variable in the multi-objective optimization model are constructed; the multi-objective optimization model comprises a first optimization model aiming at a power grid in a smart park, a second optimization model aiming at charging stations in the smart park and a third optimization model aiming at electric vehicles contracted with the charging stations; Luo teaches in claim 2, predicting the value of a decision variable in the multi-target optimization model according to the load historical data of the power grid in the intelligent park, the charge discharge historical data of the charging station and the charge-discharge historical data of the electric vehicle signed with the charging station to obtain a prediction value set of the decision variable; Luo teaches in Background technique, ¶ 1, new energy electric vehicles have been vigorously promoted and applied due to their advantages of high efficiency, environmental protection, and energy saving. The large-scale construction of matching charging piles and charging stations is imperative. As a micro-unit of the city, the smart park is an important entry point for new infrastructure. It can provide convenient services of "parking and charging integration" in the parking lot of the smart park. Luo teaches in ¶ 14, pg. 6, in one example, formula (1) shows the multi-objective model established from three levels of power grid, charging station, and electric vehicles contracted with the charging station for the scheduling task of electric vehicle charging and discharging in the smart park. Optimize the model. Luo teaches in ¶¶ 1 – 3, among them, F represents the multi-objective optimization model. fgrid represents a collection of first optimization models at the power grid level, which may contain multiple different first optimization models for the power grid in the smart park, fgrid, i , i represents the i-th first optimization model, and the value is [1, n]. fev,j represents a set of third optimization models at the electric vehicle level, which may include multiple different third optimization models fev,j for electric vehicles contracted with the charging station, and j represents the jth third optimization model, the value is [1, m]. f station represents a set of second optimization models at the charging station level, which may contain multiple different second optimization models f station for charging stations in the smart park, h , h represents the hth second optimization model, take the value is [1,l]. At the grid level, considering the demand for stable power supply, a first optimization model is constructed with the optimization goal of minimizing the net load variance of the grid, and the decision variable of the first optimization model is the net load power of the grid. In an example, the expression form of the first optimization model constructed with the optimization objective of minimizing the net load variance of the power grid is shown in formula (2).
wherein the charging decision variable is set to be greater than or equal to 0 and less than or equal to 1 and wherein the discharging decision variable is set to be 0, and wherein a sum of the charging decision variable and the discharging decision variable is set to be greater than or equal to 0 and less than or equal to 1 in response to the electric vehicle being the V2G type; Huang teaches in ¶ 4, pg. 23, Indicates that the i-th electric vehicle changes from discharging to idling state in period t, 1 indicates that the i-th electric vehicle changes from discharging to idling in period t, and 0 indicates that the i-th electric vehicle continues to discharge in period t; Huang teaches in ¶ 2, pg. 7, the change of the ith electric automobile from idle to charging in the t period is represented, 1 represents that the ith electric automobile is converted from idle to charging in the t period, and 0 represents that the ith electric automobile is not charged in the t period. Huang teaches in claim 8, The method for optimizing hierarchical V2G scheduling considering vehicle grid-connected service duration differences according to claim 6, wherein the charging and discharging operation process constraints of the electric vehicle. Huang teaches in pg. 8, Equation 16.
Claims 3, 11, and 15:
Luo, Huang, and Otsuki teach claims 1, 9, and 13. Luo further teaches the following:
wherein an upper limit function for the upper limit on the charging/discharging amount considering the vehicle entry time is set, such that a charging capacity variable is set to be less than or equal to a product of charging upper limit power and a charging decision variable, and a discharge capacity variable is set to be less than or equal to a product of discharge upper limit power and a discharging decision variable, and wherein the charging upper limit power or the discharge upper limit power is set in consideration of an entry time of the electric vehicle; Luo teaches in 2. The charging and discharging power of the electric vehicle is within the preset power range, ¶¶ 10 – 11, pg. 8, As shown in the formula (6), the charging and discharging power limitation constraint of the electric vehicle contracted with the charging station: Pev,min ≤ |Pev (t)|≤ Pev,max (6). Among them, Pev,min and Pev,max are the lower limit value and upper limit value of electric vehicle charging/discharging/electric power, respectively. Luo teaches in claim 4, The power stored in the energy storage module connected to the charging station is within the preset power range. Equation (8) shows the equality constraints and inequality constraints of the energy storage module:
PNG
media_image2.png
338
910
media_image2.png
Greyscale
Wherein, Eess (t) represents the electric quantity of the energy storage module, wherein ηch represents its charging efficiency, and ηdis represents its discharging efficiency. Eess, min and Eess, max are the lower limit and upper limit of the electric quantity of the energy storage module. The energy storage module can only be discharged or charged per unit time, and sch (t) and sdis (t) are binary variables representing its charging and discharging status.
Claims 8 and 20:
Luo, Huang, and Otsuki teach claims 1, 9, and 13. Luo further teaches the following:
wherein the optimizing the scheduling model includes optimizing the scheduling model to significantly reduce power charges or significantly increase revenue using the constraint function and the objective function; Luo teaches in ¶ 1, pg. 12, After the server obtains the game equilibrium solution of the decision variables in the multi-objective optimization model, it can execute the electric vehicle charging and discharging scheduling task according to the game equilibrium solution of the decision variables in the multi-objective optimization model, so as to effectively suppress the power grid Net load fluctuations, reduce the operating costs of charging stations, and reduce the charging fees of electric vehicles contracted with charging stations, to achieve a balanced and win-win effect for the three parties of "network-station-car". At the grid level, through the orderly charging and discharging of electric vehicles contracted with charging stations, the randomness and intermittent nature of distributed power generation and the disorderly charging behavior of electric vehicles will have a negative impact on the grid, reducing net load fluctuations and the difference between peak and valley improves the load characteristics of the power grid. At the charging station level in the park, electricity purchase and operation and maintenance costs can be reduced by charging electric vehicles contracted with the charging station at low valley prices. At the level of contracting electric vehicles, by signing contracts with charging stations in the park, they can charge at a preferential and low price, participate in demand response, and reduce charging costs. Luo teaches in claim 9, the method of claim 1, wherein the decision variables in the multi-objective optimization model comprise at least one of: grid payload power in the first optimization model; the electricity purchase cost of the charging stations in the second optimization model, the electricity saving cost from unordered charging to ordered charging and discharging of the electric vehicles signed with the charging stations, and the electricity saving cost for power generation of the distributed power supplies connected with the charging stations; the electric quantity consumption cost of the electric vehicle signed with the charging station and traveling to the charging station, the charging service cost of the charging station, the electric quantity consumption cost of the electric vehicle signed with the charging station and traveling from the charging station to the destination, and the electricity selling income of the stored electric energy signed with the charging station in the third optimization model.
6. Claims 4, 12, and 16, are rejected under 35 U.S.C. 103 as being unpatentable over Luo, Bo-te (CN 115860365) in view of Huang, Yu-Ping (CN 115882494) in view of Otsuki, Tomoshi et al. (JP 2022139181 A) in view of Collado, Edwin, et al. "Profit maximization with customer satisfaction control for electric vehicle charging in smart grids." AIMS Energy 5.3 (2017).
Claims 4, 12, and 16:
While Luo, Huang, and Otsuki teach claims 1, 9, and 13, neither Luo, Huang, nor Otsuki explicitly teach satisfaction. However Collado teaches the following:
wherein a satisfaction function for satisfaction of the customer-set target battery charging amount is set such that a battery charging amount at a specific time is set to be greater than or equal to the customer-set target battery charging amount in response to the customer-set target battery charging amount being present at the specific time; Collado, Edwin, et al. "Profit maximization with customer satisfaction control for electric vehicle charging in smart grids." AIMS Energy 5.3 (2017) teaches in Introduction, ¶ 2, pg. 531, a user satisfaction factor control. Collado teaches in Methodology 2.2 Offline EV charging-scheduling problem, ¶ 1, pg. 532, In the offline charging scenario, we assume the station is equipped with an web-based reservation system such that every EV owner can book both the parking lot and charging needs (i.e., [ri , di ] and wi). The station utilizes the above information to design the charging strategy to obtain the maximum profit. In the offline EV charging-scheduling problem the goal is to maximize the unified overall profit for the charging station by jointly optimizing over the EV scheduling, the charging power, and the user satisfaction factor that is defined as the percentage of charging given the desired target energy wi . The scheduling of EVs at time t is represented by Xt = {xti,j}, where 1 ≤ i ≤ N, 1 ≤ j ≤ C, and xti,j is given by
PNG
media_image3.png
52
514
media_image3.png
Greyscale
Then, the overall scheduling is denoted by X = {X1 , X2 , . . . , XT}. Similarly, the charging power at time slot t is represented by Pt = {pti,j}, where pti,j is the charging power level of the jth machine for the ith EV at time t. Here, we assume that the pti,j is limited by a safe maximum charging power psafe, which is a system constant set by the operator in advance. The overall power allocation is denoted by P = {P1 , P2, . . . , PT}. Note that we normalize the slot length such that the power allocation vector is also representing the energy charging vector. Now, let γ = {γ1, γ2, . . . , γN} denote the set of user satisfaction factors, at which each vehicle is serviced by the end of the schedule, where γi ∈ [γmin, 1] for 1 ≤ i ≤ N. Here, we assume that the minimum user satisfaction factor γmin is also a system constant set by the operator in advance. Collado teaches in pg. 533, Equation 1.2; Collado further teaches in ¶ 1, pg. 534, (1.2) guarantees that every EV will be charged at or above the percentage defined by the satisfaction factor.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method comprises the following steps: aiming at the electric automobile charging and discharging scheduling task, a multi-objective optimization model and a constraint condition of a decision variable in the multi-objective optimization model of Luo and a V2G demand response and scheduling process and a vehicle charging and discharging scheduling model with optimal benefits for an electric vehicle of Huang and a charging and discharging planning unit for preparing a charging and discharging plan of a storage battery owned by a customer of Otsuki with a novel profit maximization framework for station operation in both offline and online charging scenarios, under certain customer satisfaction constraints of Collado to assist businesses with building EV charging optimization models aimed at profit maximization with customer satisfaction constraints (Collado, Abstract).
Claims 5 and 17:
Luo, Huang, and Otsuki teach claims 1, 9, and 13. Otsuki further teaches the following:
wherein a prevention function for prevention of the reversed power flow when applying the V2H is set such that a sum of a predicted power usage in a home and charging/discharging power of the electric vehicle at a specific time does not generate the reversed power flow and does not exceed an upper power limit of the home; Otsuki teaches in Details of consumer information ¶ 3, pg. 7, the system supply upper limit power is power is power corresponding to the contract amount of the consumer. In the case of consumer A, the system supply upper limit power 223 is 5000W. The system supply lower limit power 224 is power corresponding to the minimum value of the meter value that should be kept even during discharge. In the case of consumer A, the system supply lower limit power 224 is 100W. The system supply lower limit power 224 is sometimes referred to as discharge lower limit power, reverse power flow prevention power, or the like. From the viewpoint of preventing reverse power flow, the value of the system supply lower limit power needs to be positive, but it may take a negative value when allowing reverse power flow. In many cases, the system supply upper limit power 223 and the system supply lower limit power 224 are set values of the controller of the storage battery for home use, and are often set by the supplier when the storage battery is introduced.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method comprises the following steps: aiming at the electric automobile charging and discharging scheduling task, a multi-objective optimization model and a constraint condition of a decision variable in the multi-objective optimization model of Luo and a V2G demand response and scheduling process and a vehicle charging and discharging scheduling model with optimal benefits for an electric vehicle of Huang with a charging and discharging planning unit for preparing a charging and discharging plan of a storage battery owned by a customer of Otsuki to assist businesses with building optimization models aimed at including constraints of reverse power flow (Otsuki, ¶ 1, pg. 2).
7. Claims 6 and 18, are rejected under 35 U.S.C. 103 as being unpatentable over Luo, Bo-te (CN 115860365) in view of Huang, Yu-Ping (CN 115882494) in view of Otsuki, Tomoshi et al. (JP 2022139181 A) in view of Park, Gun Bae (KR 20230042903 A).
Claims 6 and 18:
While Luo, Huang, and Otsuki teach claims 1, 9, and 13, neither Luo, Huang, nor Otsuki explicitly teach plus DR winning bid. However, Park teaches the following:
wherein a complying function for complying with the winning bid power for participating in the Plus DR market is set in consideration of a Contracted Power Capacity (CPC) awarded on a previous day and a Customer Baseline Load (CBL) as a settlement standard; Park teaches in ¶ 3, pg. 3, in another embodiment of the present invention, (a) the mediation server receives metering data for each time period of a plurality of charging stations, selects one of the plurality of charging stations according to a preset selection criterion, and generates selection information. doing; (b) generating bidding information by a plurality of demand management service provider servers using the selection information, and bidding on a preset Demand Response (DR) algorithm using the bidding information; (c) generating successful bidding information by applying the bidding information to the demand response resource algorithm by the power exchange server; and (d) transmitting Demand Response resource implementation information for implementing Demand Response resources to a customer terminal of an electric vehicle owner by using the successful bidding information from a plurality of charging company servers. Park teaches in ¶ 1, pg. 6, In addition, in order to induce the participation of electric vehicle owners during Plus DR hours, incentives must be provided to customers who charge during that time period, and the most effective method is to discount electric vehicle charging rates. To this end, a charging service provider should selectively apply a discounted rate to customers charging at a specific charging station and at a specific time, but such a function is not currently provided. That is, a rate discount function is required. Park teaches in ¶ 2, pg. 6, In addition, in order to provide more incentives to charging operators and electric vehicle owners participating in Plus DR, CBL (Customer Baseline, standard rate), which is the standard for calculating settlement payments, is calculated by charging station and time slot, and bidding is conducted at the charging station with the lowest CBL. it is necessary to do If participating in Plus DR without considering CBL, the performance settlement amount is lower than the amount provided for the discount of charging fee, which may cause damage to the business operator. That is, it is necessary to calculate CBL. Park teaches in ¶ 18, pg. 8 and ¶ 1, pg. 9, the mediation server 210 is linked with the electric power company's MDMS (Meter Data Management System) and receives hourly metering data of charging stations (not shown) linked to the roaming platform 140 every day (①). In addition, the mediation server 210 calculates the CBL for each time zone of the charging station associated with the roaming flat of KEPCO using the metering data for each time zone, and based on the calculated CBL (Customer Base Line: Customer Base Load) for each time zone of the charging station Demand management (DR) Participation The optimal charging station, participation time, and participation capacity are calculated to generate selection information. CBL is the load that is the standard for calculating the amount of decrease or increase in demand. CBL is calculated as the average of the median value of the 4-day power consumption out of the 6 most recent weekdays from the date of increased demand. Park teaches in ¶¶ 2 – 5, pg. 9, In addition, the mediation server 210 enters into an agreement with the electric power company and is a demand management operator server 220 linked between systems. Plus DR participation by 09:00 the day before the implementation of Plus DR (D-1) Optimal charging station, participation time, participation transfer capacity. The demand management operator server 220 is based on the Plus DR participation optimal charging station, participation time, and participation capacity provided by the mediation server 210 for EV flexible resource mediation, until 14:00 the day before Plus DR implementation (D-1). A plus DR bidding is executed to the G Power Exchange server 230 . That is, bidding information is generated and transmitted to the power exchange server 230 (②). The power exchange server 230 transmits the Plus DR winning bid information to the demand management service provider server 220 at 15:30 on the day before the implementation of the Plus DR (D-1) (③). The winning bid information may include a charging station in which Plus DR is implemented, a time zone, and a winning bid capacity. In other words, the power exchange server 230 may include bidding information of a charging station that wants to use electricity and bidding information of a power generation company (or power company) capable of supplying power to the charging station (available power, time zone, region, etc.) ), and the bidding information is mutually matched to complete the transaction event. Accordingly, successful bidding information is generated. A plus DR (Demand Response) algorithm that generates transaction events and successful bidding information through such matching is implemented. Programs, data, etc. are constructed for this algorithm.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method comprises the following steps: aiming at the electric automobile charging and discharging scheduling task, a multi-objective optimization model and a constraint condition of a decision variable in the multi-objective optimization model of Luo and a V2G demand response and scheduling process and a vehicle charging and discharging scheduling model with optimal benefits for an electric vehicle of Huang and a charging and discharging planning unit for preparing a charging and discharging plan of a storage battery owned by a customer of Otsuki with an electric vehicle (EV) flexibility resource brokerage system that allows an operator (demand management operator, mobility service provider (MSP), and charge point operator (CPO)) to participate in demand response (DR) using electric vehicle charging of Park to assist businesses with building optimization models aimed at transmitting successful bid information (Park, ¶ 6, pg. 5).
8. Claims 7 and 19, are rejected under 35 U.S.C. 103 as being unpatentable over Luo, Bo-te (CN 115860365) in view of Huang, Yu-Ping (CN 115882494) in view of Otsuki, Tomoshi et al. (JP 2022139181 A) in view of Kim Ku Hwan (KR 20220077088A).
Claims 7 and 19:
While Luo, Huang, and Otsuki teach claims 1, 9, and 13, neither Luo, Huang, nor Otsuki explicitly teach reduction up to the customer baseline load power amount. However, Kim teaches the following:
wherein a recognition function for recognition of the amount of reduction up to the customer baseline load power amount for participating in the national DR is set in consideration of a Customer Baseline Load (CBL) as a settlement standard; Kim teaches in ¶ 12, pg. 3, and ¶ 1, pg. 4, the management server 100 informs the customer terminal 400 of the DR command ( S210 ), and the customer decides whether to participate in DR. Step (S220) in which the data 220 is transmitted to the customer terminal 400, the customer determines whether to participate in DR from the received EV data and responds from the customer terminal 400 to the management server 100 (S230); when the customer's DR participation is determined, the management server 100 sends a power reduction instruction to the charger 200 that the customer's electric vehicle 200 can charge and transmits a charging control command including charging delay, charging stop, or reducing the amount of charge (S240), the customer terminal 400 receives the EV data of the customer electric vehicle 200 participating in DR and monitors the EV in real time (S250), or the customer terminal 400 is reduced to DR participation to calculate the settlement amount of the power exchange 500 At least one of the step S270 of transmitting the power reduction data from the management server 100 to the power exchange 500 may be included. Kim teaches in ¶ 13, pg. 7, and ¶ 1, pg. 8, display the remaining battery level of the customer electric vehicle 200 and the drivable distance calculated accordingly on the screen unit 410, and may indicate the degree of national DR issuance probability according to time of day or time of season. 7 may show charging information including elapsed time and remaining time information when the customer electric vehicle 200 is being charged. display at least one of information including the vehicle number and year of the customer electric vehicle 200, drivable distance, remaining battery capacity, total mileage, fuel economy, and a smart charging option change function. 9 may be a mode in which the details of points due to participation in the national DR of the customer electric vehicle 200 can be checked, and the details of the points may include at least one of national DR accumulation points, carbon emission credit accumulation points, or coupon exchange. Kim teaches in ¶ 2, pg. 8, In the step S120 of determining whether to automatically participate in DR, the customer may select in advance whether to automatically participate in DR or autonomously participate in DR when DR is issued. Auto-participation in DR may be to participate in DR automatically without a specific action from the customer, and autonomous participation in DR may be to decide whether to participate in DR after the issuance of DR according to the customer's decision. Kim teaches in ¶ 3, pg. 9, In the settlement step (S300), the power exchange (S500) calculates the settlement amount from the power reduction data (S270) received from the management server 100 and pays the settlement amount to the management server 100 (S320), or the management server ( 100) may include a step (S340) of dividing the settlement money received from the power exchange 500 according to a predetermined ratio and paying the settlement money back to the customer terminal (S340). Kim teaches in ¶ 4, pg. 9, In the settlement step (S300), the power exchange 500 or the management server 100 calculates the customer baseline load (CBL, Customer Baseline Load) (S302), or the power exchange 500 or the management server 100 is the customer A step (S304) of comparing the reference load curve profit and the DR participation settlement amount and presenting it to the customer terminal 400 may be additionally included. Kim teaches in ¶ 5, pg. 9, the customer reference load (CBL) may be a value that predicted the usual power usage that the participating customer would have used if the power load had not been reduced. This may mean a load that is a standard for reducing power demand in the corresponding time period. The customer standard load may be a criterion for selecting and evaluating the reduction in power load of customers participating in the demand resource trading market by time period, and either mid (4/6) or mid (8/10), the power exchange 500 will designate on a monthly basis. (e.g., use mid(4/6) when summer or winter temperature fluctuations are expected to be large).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method comprises the following steps: aiming at the electric automobile charging and discharging scheduling task, a multi-objective optimization model and a constraint condition of a decision variable in the multi-objective optimization model of Luo and a V2G demand response and scheduling process and a vehicle charging and discharging scheduling model with optimal benefits for an electric vehicle of Huang and a charging and discharging planning unit for preparing a charging and discharging plan of a storage battery owned by a customer of Otsuki with a demand management method in connection with an electric vehicle charger that stops charging of an electric vehicle and receives a demand management settlement when a power peak or fine dust alarm is issued of Kim to assist businesses with building optimization models aimed at calculating a customer baseline in electric vehicle power management (Kim, ¶ 11, pg. 2).
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference Steven, Alain P. et al. (U.S. 2014/0316973 A1) discloses an apparatus, systems and methods herein facilitate generation of energy-related revenue for an energy customer of an electricity supplier.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Frank Alston whose telephone number is 703-756-4510. The Examiner can normally be reached 9:00 AM – 5:00 PM Monday - Friday. Examiner can be reached via Fax at 571-483-7338. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor Beth Boswell can be reached at (571) 272-6737.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
02/12/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625