DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Response to Arguments
Applicant has amended the claim set to incorporate the subject matter of dependent claim 2 into independent claim 1, and has consequently cancelled claim 2.
Claim Rejections under 35 U.S.C. 112(a)
Claim 1 is rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. Specifically claim 1 recites the limitation “disorderly charging loads of electric vehicles”, which does not provide enough detail to determine the scope of “disorderly charging” wherein emphasis has been placed on the term with insufficient written description. Applicant cites specification ¶0004 which discloses “an electric vehicle serving as a transportation means capable of consuming clean energy attracts much attention. A lot of electric vehicles are charged disorderly, possibly leading to the problem of ‘peak on peak’ of the power grid, the problem of intensified traffic jam and the like. In order to positively consume renewable energy sources and improve the utilization rate of wind power…”. The plain definition of disorderly is lacking organization, chaotic, or messy.
Applicant specification ¶0072 discloses “a method of acquiring the curve of the disorderly charging loads of the electric vehicles comprises: acquiring information, such as the quantity, the charging/discharging power, the charging/discharging electric quantity, the travel time proportion and the like of various types of electric vehicles in an area, to obtain a curve of independent charging/discharging loads of the electric vehicles”, which appears to teach the opposite by ordering the charging/discharging loads of the electric vehicles into a curve based on vehicle information. There is no visual representation of the curve of disorderly charging loads of the electric vehicles. The final statement regarding disorderly charging in the specification is in ¶0092 “Therefore, the method can be used for solving the problems of a lot of wind abandonment phenomena and disorderly charging of the electric vehicles.” The written description of what is meant by the term “disorderly” is not defined in the specification, and the teachings of the specification appear to be teaching the opposite of the term “disorderly”.
Claim Rejections under 35 U.S.C. 102(a)(1)
Claim 1 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhou et al (US 20170337646 A1), applicant has amended claim 1 to incorporate the subject matter of claim 2. Applicant argues that Zhou fails to teach “the model that the electric vehicles participate in wind power consumption to minimize the remaining blocked quantity of the wind power”. Zhou FIG 2 depicts a flowchart illustrating the particle swarm optimization algorithm, the first box states “Input the output powers of PV and WT and the total load
P
l
o
a
d
”. Thereby selecting the renewable energy sources of wind photovoltaic power and wind turbine power as the preferred power source, if no photovoltaic power is available then all of the renewable energy sources would be wind turbines. The following box states “PV and WT do not meet the load demand”, further emphasizing prioritizing using renewable energy sources. This would result in minimizing the remaining quantity of wind power.
Claim Rejections under 35 U.S.C. 103
Claim 6 is rejected under 35 U.S.C. 103 as being anticipated by Zhou modified by Uyeki et al (US 20140091747 A1), applicant argues that claim 6 fails to remedy the limitations of Zhou.
Objection to Drawings
Applicant has amended the limitation “curve of disorderly charging loads of electric vehicles” to “disorderly charging loads of electric vehicles” and argues that the objection is moot. Examiner respectfully disagrees as the curve of disorderly charging loads is pertinent information to defining the term “disorderly” as described above in the response to arguments regarding rejection under 35 U.S.C. 112(a).
Objection to Specification
Applicant has amended the typographical error, Examiner thanks the applicant and withdraws the objection.
Applicant's arguments filed 15 January 2026 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim 1 rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor at the time the application was filed, had possession of the claimed invention. Claim 1 contains the limitation "acquiring disorderly charging loads of electric vehicles", Claim 1 as originally filed recites “acquiring a curve of disorderly charging loads”. The limitation as described in ¶0009 and ¶0045 as "acquiring disorderly charging loads of the electric vehicles", which does not provide enough detail to determine the scope of "disorderly charging loads of the electric vehicles". The limitation is further described in ¶0072 as "a method of acquiring the curve of the disorderly charging loads of the electric vehicles comprises: acquiring information, such as the quantity, the charging/discharging power, the charging/discharging electric quantity, the travel time proportion and the like of various types of electric vehicles in an area, to obtain a curve of independent charging/discharging loads of the electric vehicles". These descriptions do not further the reader’s understanding of what “disorderly” means within this context. Applicant Specification ¶0004 discloses “lot of electric vehicles are charged disorderly, possibly leading to the problem of ‘peak on peak’ of the power grid, the problem of intensified traffic jam and the like”. The final statement regarding disorderly charging in the specification is in ¶0092 “Therefore, the method can be used for solving the problems of a lot of wind abandonment phenomena and disorderly charging of the electric vehicles.” The written description of what is meant by the term “disorderly” is not defined in the specification, and the teachings of the specification appear to be teaching the away from the term “disorderly”.
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.
Claim 1 rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claim 1 contains the limitation " wherein the disorderly charging loads of electric vehicles are loads when electric vehicles are charged disorderly", which is a recursive definition resulting in the use of the plain definition of disorderly. For the purpose of examination disorderly is being interpreted as without order or chaotic.
Claim 6 rejected under 35 U.S.C. 112(b) as being incomplete for omitting essential steps, such omission amounting to a gap between the steps. See MPEP § 2172.01. Claim 6 recites the limitation "according to a prediction curve of wind power output on a next day". The omitted step is calculating or determining a prediction curve of wind power output on a next day. Specification FIG 1 step S2 recites “Acquiring a curve of disorderly charging loads of the electric vehicles”, which provides a description for how a prediction curve would be determined.
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 and 3-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more.
Step 1: Is the Claim to a Process, Machine, Manufacture or Composition of Matter?
Each of claims 1 and 3-6 falls within the statutory category of Process. See MPEP § 2106.03.
Step 2A – Prong 1: Does the Claim Recite an Abstract Idea?
The claims on the whole are directed towards mathematical concepts and data gathering.
Representative Claim 1 recites:
A method for optimizing dispatching of charging loads of electric vehicles to promote wind power consumption, comprising the following steps: acquiring blocked electric quantity of wind power at a peak down-regulation period; (Abstract Idea: Mental Process of gathering data regarding the blocked electric quantity of wind power)
acquiring disorderly charging loads of electric vehicles; (Abstract Idea: Mental Process of gathering data for the disorderly charging loads)
establishing a model for optimizing the charging loads of the electric vehicles to promote wind power consumption, wherein an objective function of the model refers to that the electric vehicles participate in wind power consumption to minimize the remaining blocked quantity of the wind power, and the total charging cost of the electric vehicles is lowest; (Abstract Idea: Mathematical Concept because it uses adaptive mutation particle swarm optimization algorithm to solve a model.)
and acquiring constraint conditions of the model; (Abstract Idea: Mental Process of gathering data for a mathematical model)
solving the optimization model by adopting an adaptive mutation particle swarm optimization algorithm, to obtain target charging/discharging electric quantity and target charging/discharging power of the electric vehicles; (Abstract Idea: Mathematical Concept because it uses adaptive mutation particle swarm optimization algorithm to solve a model.)
wherein the disorderly charging loads of electric vehicles are loads when electric vehicles are charged disorderly; (Abstract Idea: Mathematical Concept uses a mathematical algorithm.)
wherein a method of establishing the model for optimizing the charging loads of the electric vehicles to promote wind power consumption comprises: (Abstract Idea: Mathematical Concept and Human Activity of data gathering for a mathematical model.)
establishing a model that the electric vehicles participate in wind power consumption to minimize the remaining blocked quantity of the wind power:
f
1
=
min
E
B
,
t
-
E
E
,
t
,
t
∈
T
E
E
V
,
t
=
∑
i
=
1
N
E
V
P
c
,
i
t
Δ
t
wherein in the formulas,
f
1
represents the remaining blocked quantity of the wind power;
E
B
,
t
, represents the blocked electric quantity at the peak down-regulation period;
E
V
,
t
represents the charging electric quantity of the electric vehicles; T represents the peak down-regulation period;
P
c
,
i
t
represents the charging power of an i-th electric vehicle a period t;
N
E
V
represents the number of the electric vehicles; and
Δ
t
represents the time scale; (Abstract Idea: Mathematical Concept because it is uses a mathematical algorithm.)
establishing the objective function that the total charging cost of the electric vehicles is lowest:
f
2
=
min
∑
t
=
1
n
∑
i
=
1
N
E
V
P
c
,
i
t
*
F
c
,
t
-
∑
t
=
1
n
∑
i
=
1
N
E
V
P
f
,
i
t
*
F
f
,
t
wherein in the formula
f
s
resents the total charging cost of the electric vehicles;
P
c
,
i
t
and
P
f
,
i
t
respectively represent the charging power and the discharging power of the i* electric vehicle at the period t: and
F
c
,
t
and
F
f
,
t
, respectively represent charging fees and discharging fees of the electric vehicles at the period t. (Abstract Idea: Mathematical Concept uses a mathematical algorithm.)
Step 2A – Prong 2: Does the Claim Recite Additional Elements
that Amount to Significantly More than the Abstract Idea?
Claims 1 and 3-6 do not include additional elements (when considered individually, as an order combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
Representative Claim 3 recites:
The method for optimizing dispatching of charging loads of electric vehicles to promote wind power consumption according to claim 1, (Abstract Idea: Mathematical Concept and Mental Process as detailed above)
wherein the constraint conditions of the model comprise a power balance constraint of a system, an output constraint of a wind power plant and relevant constraints of the electric vehicles. (Abstract Idea: Mathematical Concept because it uses a mathematical algorithm.)
Representative Claim 4 recites:
The method for optimizing dispatching of charging loads of electric vehicles to promote wind power consumption according to claim 3, (Abstract Idea: Mathematical Concept as detailed above)
wherein the relevant constraints of the electric vehicles comprise an electric quantity constraint of the electric vehicles, a charging/discharging constraint of the electric vehicles, an SOC (State or Charge) constraint and an online time constraint of the electric vehicles. (Abstract Idea: Mathematical Concept because it is further defining the constraint conditions for the mathematical model.)
Representative Claim 5 recites:
The method for optimizing dispatching of charging loads of electric vehicles to promote wind power consumption according to claim 4, (Abstract Idea: Mathematical Concept as detailed above)
wherein the power balance constraint of the system is:
P
F
,
t
+
∑
j
=
1
n
G
u
j
*
P
G
,
j
t
=
P
L
,
t
+
∑
i
=
1
N
E
V
(
P
c
,
i
t
*
V
i
,
t
+
P
f
,
i
t
*
V
i
,
t
)
wherein in the formula
P
F
,
t
represents the discharging power of the electric vehicles at the period t;
P
G
,
t
represents the active power output of a conventional power supply j at the period t;
P
L
,
t
represents the value of a system load at the period t;
P
c
,
i
t
and
P
f
,
i
t
respectively represent the charging power and the discharging power of the i-th electric vehicle at the period t;
u
j
=
1
represents that units operate normally, and
u
j
=
0
represents that the units stop operating;
V
i
,
t
represents a charging state and a discharging state of the ith electric vehicle at the period t;
V
i
,
t
=
1
represents that the vehicle is in the charging state, and
V
i
,
t
=
-
1
represents that the vehicle is in the discharging state;
n
G
represents the number of units; and
N
E
V
represents the number of the electric vehicles; (Abstract Idea: Mathematical Concept defining the mathematical equation for the optimization algorithm)
the output constraint of the wind power plant is:
min
P
F
,
t
≤
P
F
,
t
≤
max
P
F
,
t
wherein in the formula
min
P
F
,
t
and
max
P
F
,
t
, respectively represent the upper limit and the lower limit of power of wind power output at the t-th period; (Abstract Idea: Mathematical Concept defining the mathematical equation for the optimization algorithm)
the electric quantity constraint of the electric vehicles is:
Q
i
≥
Q
i
,
t
n
1
-
P
f
,
t
t
*
Δ
t
f
+
P
c
,
i
t
*
Δ
t
c
wherein in the formula
Q
i
, represents the electric quantity after the electric vehicles are charged/discharged;
Q
i
,
t
n
1
represents the electric quantity before the electric vehicles are charged/discharged:
Δ
t
c
and
Δ
t
f
respectively represent the charging duration and the discharging duration; (Abstract Idea: Mathematical Concept defining the mathematical equation for the optimization algorithm)
the charging/discharging constraint of the electric vehicles is:
0
≤
P
c
,
i
t
≤
P
c
,
m
a
x
0
≤
P
f
,
i
t
≤
P
f
,
m
a
x
P
c
,
i
t
*
P
f
,
i
t
=
wherein in the formulas,
P
c
,
m
a
x
represents the upper limit of the charging power of the electric vehicles, and
P
f
,
m
a
x
represents the upper limit of the discharging power of the electric vehicles; (Abstract Idea: Mathematical Concept defining the mathematical equation for the optimization algorithm)
the SOC constraint is:
S
O
C
d
,
i
≤
S
O
C
e
,
i
≤
S
O
C
m
a
x
wherein in the formula,
S
O
C
e
,
i
represents an SOC of the i-th electric vehicle when the charging is ended;
S
O
C
d
,
i
represents an expected SOC of the ith electric vehicle; and
S
O
C
m
a
x
represents the upper limit of charging, which is set by a power battery; (Abstract Idea: Mathematical Concept defining the mathematical equation for the optimization algorithm)
the online time constraint of the electric vehicles is: wherein in the formulas, TI represents the network access time of the electric vehicles; T represents the charging time of the electric vehicles; TU, represents the off-network time of the electric vehicles; and T, represents the discharging time of the electric vehicles. (Abstract Idea: Mathematical Concept defining the mathematical equation for the optimization algorithm)
Representative Claim 6 recites:
The method for optimizing dispatching of charging loads of electric vehicles to promote wind power consumption according to claim 1, (Abstract Idea: Mathematical Concept as detailed above for claim 1)
wherein a method of acquiring the blocked electric quantity of the wind power at the peak down-regulation period comprises: solving the predicted electric quantity
E
F
,
w
i
n
d
t
of wind power at each period
Δ
t
according to a prediction curve of wind power output on a next day:
E
F
,
w
i
n
d
t
=
P
F
,
w
i
n
d
t
*
Δ
t
wherein in the formula,
Δ
t
represents the time scale, and
P
F
,
w
i
n
d
t
represents the power of the wind power output; (Abstract Idea: Mathematical Concept defining the mathematical equation for the optimization algorithm)
setting a peak down-regulation period and a peak non-down-regulation period of the system and acquiring the blocked electric quantity of the wind power:
T
=
T
|
E
F
,
w
i
n
d
t
≥
E
p
,
w
i
n
d
t
,
t
∈
T
wherein in the formula,
E
p
,
w
i
n
d
t
represents planned wind power quantity, and T represents the peak down-regulation period; acquiring the blocked electric quantityof the wind power at the peak down-regulation period; (Abstract Idea: Mathematical Concept defining the mathematical equation for the optimization algorithm)
acquiring the blocked electric quantity
E
B
,
t
of the wind power at the peak down-regulation period:
E
B
,
t
=
E
F
,
w
i
n
d
t
-
E
p
,
w
i
n
d
t
,
t
∈
T
(Abstract Idea: Mathematical Concept defining the mathematical equation for the optimization algorithm)
In view of the above, the examiner finds no additional elements in the dependent claims and thereby no integration into a practical application of the abstract idea.
Step 2B
Claim 1 does not include additional elements and as a whole there is no finding of significantly more than the abstract idea.
Claim(s) 3-6 does/do not include additional elements, when considered individually and similar to the analysis of claim 1, there is no finding of significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhou et al (US 20170337646 A1).
Regarding claim 1, Zhou teaches a method for optimizing dispatching of charging loads of electric vehicles to promote wind power consumption, (¶0072 "the [phot voltaic] and the [wind turbine] both apply the control method of maximum power tracking output, which enables to make full use of solar energy and wind energy")
comprising the following steps: acquiring blocked electric quantity of wind power at a peak down-regulation period; (¶0070 "The output power P.sub.WT of the [wind turbine], is obtained through equation (2)", ¶0097 "Step 4.1, dividing 24 hours of one day into three time periods: peak time period, flat time period and valley time period according to the peak-valley time-of- use price applied by the main grid")
acquiring disorderly charging loads of electric vehicles; (¶0096 "Step 4, determining the amount, the starting and ending time, the starting and ending state of charge, and other basic calculating data of the EV accessing the microgrid under time-of-use price")
establishing a model for optimizing the charging loads of the electric vehicles to promote wind power consumption, (¶0071 "in equation (2), a and b are the coefficient of the output power P.sub. WT of the WT")
wherein an objective function of the model refers to that the electric vehicles participate in wind power consumption to minimize the remaining blocked quantity of the wind power, and the total charging cost of the electric vehicles is lowest; (¶0079 "Step 2, establishing an optimal scheduling objective function of the microgrid considering the depreciation cost of the EV battery under time-of-use price")
and acquiring constraint conditions of the model; (¶0088 equations 10-16 describe constraint conditions)
solving the optimization model by adopting an adaptive mutation particle swarm optimization algorithm, to obtain target charging/discharging electric quantity and target charging/discharging power of the electric vehicles; (¶0101 "Step 5, as shown in FIG. 2, determining the charge and discharge power of the EV when accessing the microgrid, by solving the optimal scheduling model of the microgrid with the PSO algorithm")
wherein the disorderly charging loads of electric vehicles are loads when electric vehicles are charged disorderly; (¶0096 "Step 4, determining the amount, the starting and ending time, the starting and ending state of charge, and other basic calculating data of the EV accessing the microgrid under time-of-use price")
wherein a method of establishing the model for optimizing the charging loads of the electric vehicles to promote wind power consumption comprises:
establishing a model that the electric vehicles participate in wind power consumption to minimize the remaining blocked quantity of the wind power:
f
1
=
min
E
B
,
t
-
E
E
,
t
,
t
∈
T
E
E
V
,
t
=
∑
i
=
1
N
E
V
P
c
,
i
t
Δ
t
wherein in the formulas,
f
1
represents the remaining blocked quantity of the wind power;
E
B
,
t
, represents the blocked electric quantity at the peak down-regulation period;
E
V
,
t
represents the charging electric quantity of the electric vehicles; T represents the peak down-regulation period;
P
c
,
i
t
represents the charging power of an i-th electric vehicle a period t;
N
E
V
represents the number of the electric vehicles; and
Δ
t
represents the time scale; (¶0072 and ¶0098 as described below)
establishing the objective function that the total charging cost of the electric vehicles is lowest:
f
2
=
min
∑
t
=
1
n
∑
i
=
1
N
E
V
P
c
,
i
t
*
F
c
,
t
-
∑
t
=
1
n
∑
i
=
1
N
E
V
P
f
,
i
t
*
F
f
,
t
wherein in the formula
f
s
resents the total charging cost of the electric vehicles;
P
c
,
i
t
and
P
f
,
i
t
respectively represent the charging power and the discharging power of the i* electric vehicle at the period t: and
F
c
,
t
and
F
f
,
t
, respectively represent charging fees and discharging fees of the electric vehicles at the period t. (¶0081 " In equation (6), C is the total operation cost of the microgrid; N is the total amount of the distributed generators within the microgrid; T is the total amount of the time intervals of a scheduling cycle of the microgrid; t is the number of the time intervals; P.sub.i(t) is the output power of distributed generator i within time interval t; F.sub.i(P.sub.i(t)) is the fuel cost of distributed generator i within time interval t; OM.sub.i(P.sub.i(t)) is the operation and maintenance cost of distributed generator i within time interval t, and is obtained through equation (7)")
The disclosed equation for f₁ is aimed at minimizing the remaining power from wind generation, which corresponds to maximizing the amount of power generated by the wind which is taught in Zhou ¶0072 "the [phot voltaic] and the [wind turbine] both apply the control method of maximum power tracking output, which enables to make full use of solar energy and wind energy". The total load produced by photo voltaic and wind turbine means is further discussed in Zhou ¶0098 "Step 4.2, determining the total load demand P.sub.LOAD and output power of the [phot voltaic] and the [wind turbine], respectively".
Equation 6, as taught by Zhou, minimizes the operational cost of charging an electric vehicle with multiple sources of power generation including wind power generation. This functionally gets to the same intent as the equation for ƒ₂ as disclosed herein
Regarding claim 3, Zhou teaches the method for optimizing dispatching of charging loads of electric vehicles to promote wind power consumption according to claim 1. Zhou further teaches wherein the constraint conditions of the model comprise a power balance constraint of a system, (¶0089 "Equation (10) represents power equilibrium constraint; P.sub.i is the actual output power of distributed generator i; P.sub.GRID is the actual interactive energy of the microgrid with the main grid; P.sub.EV is the net output power of all the EVs in the microgrid; P.sub.LOAD is the total load demand of the microgrid users")
an output constraint of a wind power plant (¶0090 " Equation (11) represents the constraint of the own power generation capacity of distributed generator i, wherein P.sub.i.sup.max and P.sub.i.sup.min are the upper and lower limit of the output power of the distributed generator i, respectively", particularly wherein index i is wind turbine)
and relevant constraints of the electric vehicles. (¶0092 "Equation (13) represents the state of charge constraint of EV j; SOC.sub.j represents the state of charge of the battery of the EV j; SOC.sub.j.sup.max and SOC.sub.j.sup.min represent the upper and lower limit of the state of charge of the battery of the EVj, respectively").
Regarding claim 4, Zhou teaches the method for optimizing dispatching of charging loads of electric vehicles to promote wind power consumption according to claim 3. Zhou further teaches wherein the relevant constraints of the electric vehicles comprise an electric quantity constraint of the electric vehicles, (¶0086 In equation (9), n is the amount of the EVs accessing the microgrid, C.sub.REP is the battery replacement cost of the EV, E.sub.PUT is the total energy throughput of the EV during the lifetime of the battery thereof")
a charging/discharging constraint of the electric vehicles, (¶0087 "Step 3, determining the constraint conditions of each distributed generator and EV battery; and forming an optimal scheduling model of the microgrid together with the optimal scheduling objective function of the microgrid"),
an SOC (State Of Charge) constraint (¶0092 "Equation (13) represents the state of charge constraint of EV j; SOC.sub.j represents the state of charge of the battery of the EV j; SOC.sub.j.sup.max and SOC.sub.j.sup.min represent the upper and lower limit of the state of charge of the battery of the EVj, respectively").
and an online time constraint of the electric vehicles. (¶0096 "Step 4, determining the amount, the starting and ending time, the starting and ending state of charge, and other basic calculating data of the EV accessing the microgrid under time-of-use price")
Regarding claim 5, Zhou teaches the method for optimizing dispatching of charging loads of electric vehicles to promote wind power consumption according to claim 4. Zhou further teaches wherein the power balance constraint of the system is:
P
F
,
t
+
∑
j
=
1
n
G
u
j
*
P
G
,
j
t
=
P
L
,
t
+
∑
i
=
1
N
E
V
(
P
c
,
i
t
*
V
i
,
t
+
P
f
,
i
t
*
V
i
,
t
)
wherein in the formula
P
F
,
t
represents the discharging power of the electric vehicles at the period t;
P
G
,
t
represents the active power output of a conventional power supply j at the period t;
P
L
,
t
represents the value of a system load at the period t;
P
c
,
i
t
and
P
f
,
i
t
respectively represent the charging power and the discharging power of the i-th electric vehicle at the period t;
u
j
=
1
represents that units operate normally, and
u
j
=
0
represents that the units stop operating;
V
i
,
t
represents a charging state and a discharging state of the ith electric vehicle at the period t;
V
i
,
t
=
1
represents that the vehicle is in the charging state, and
V
i
,
t
=
-
1
represents that the vehicle is in the discharging state;
n
G
represents the number of units; and
N
E
V
represents the number of the electric vehicles; (The power balance constraint equation as disclosed herein functions as the power equilibrium constraint equation 10 as taught by Zhou ¶0089 "Equation (10) represents power equilibrium constraint; P.sub.i is the actual output power of distributed generator i; P.sub.GRID is the actual interactive energy of the microgrid with the main grid; P.sub.EV is the net output power of all the EVs in the microgrid; P.sub.LOAD is the total load demand of the microgrid users")
the output constraint of the wind power plant is:
min
P
F
,
t
≤
P
F
,
t
≤
max
P
F
,
t
wherein in the formula
min
P
F
,
t
and
max
P
F
,
t
, respectively represent the upper limit and the lower limit of power of wind power output at the t-th period; (¶0093 " Equation (14) represents the charge and discharge power constraint of the EV; P.sub.j.sup.max represents the upper limit of the discharge power of EV j; P.sub.j.sup.min represents the lower limit of the charge power of EVj, which is normally determined by the type of the EV battery")
the electric quantity constraint of the electric vehicles is:
Q
i
≥
Q
i
,
t
n
1
-
P
f
,
t
t
*
Δ
t
f
+
P
c
,
i
t
*
Δ
t
c
wherein in the formula
Q
i
, represents the electric quantity after the electric vehicles are charged/discharged;
Q
i
,
t
n
1
represents the electric quantity before the electric vehicles are charged/discharged:
Δ
t
c
and
Δ
t
f
respectively represent the charging duration and the discharging duration; (¶0086 "P.sub.j.sup.EV(t) is the charge and discharge power of the battery of EV j within time interval t after accessing the microgrid")
the charging/discharging constraint of the electric vehicles is:
0
≤
P
c
,
i
t
≤
P
c
,
m
a
x
0
≤
P
f
,
i
t
≤
P
f
,
m
a
x
P
c
,
i
t
*
P
f
,
i
t
=
wherein in the formulas,
P
c
,
m
a
x
represents the upper limit of the charging power of the electric vehicles, and
P
f
,
m
a
x
represents the upper limit of the discharging power of the electric vehicles; (¶0093 "Equation (14) represents the charge and discharge power constraint of the EV; P.sub.j.sup.max represents the upper limit of the discharge power of EV j; P.sub.j.sup.min represents the lower limit of the charge power of EV j, which is normally determined by the type of the EV battery")
the SOC constraint is:
S
O
C
d
,
i
≤
S
O
C
e
,
i
≤
S
O
C
m
a
x
wherein in the formula,
S
O
C
e
,
i
represents an SOC of the i-th electric vehicle when the charging is ended;
S
O
C
d
,
i
represents an expected SOC of the ith electric vehicle; and
S
O
C
m
a
x
represents the upper limit of charging, which is set by a power battery; (¶0092 "Equation (13) represents the state of charge constraint of EV j; SOC.sub.j represents the state of charge of the battery of the EV j; SOC.sub.j.sup.max and SOC.sub.j.sup.min represent the upper and lower limit of the state of charge of the battery of the EV j, respectively")
the online time constraint of the electric vehicles is:wherein in the formulas,TI represents the network access time of the electric vehicles; T represents the charging time of the electric vehicles; TU, represents the off-network time of the electric vehicles; and T, represents the discharging time of the electric vehicles. (¶0096 "Step 4, determining the amount, the starting and ending time, the starting and ending state of charge, and other basic calculating data of the EV accessing the microgrid under time-of-use price")
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou modified by Uyeki et al (US 20140091747 A1).
Regarding claim 6, Zhou teaches the method for optimizing dispatching of charging loads of electric vehicles to promote wind power consumption according to claim 1. Zhou further teaches wherein a method of [acquiring the blocked electric quantity of the wind power at the peak down-regulation period comprises: solving the predicted electric quantity
E
F
,
w
i
n
d
t
of wind power at each period
Δ
t
according to a prediction curve of wind power output on a next day:
E
F
,
w
i
n
d
t
=
P
F
,
w
i
n
d
t
*
Δ
t
wherein in the formula,
Δ
t
represents the time scale, and
P
F
,
w
i
n
d
t
represents the power of the wind power output;]
setting a peak down-regulation period and a peak non-down-regulation period of the system and acquiring the blocked electric quantity of the wind power:
T
=
T
|
E
F
,
w
i
n
d
t
≥
E
p
,
w
i
n
d
t
,
t
∈
T
wherein in the formula,
E
p
,
w
i
n
d
t
represents planned wind power quantity, and T represents the peak down-regulation period; acquiring the blocked electric quantityof the wind power at the peak down-regulation period; (¶0096 "Step 4, determining the amount, the starting and ending time, the starting and ending state of charge, and other basic calculating data of the EV accessing the microgrid under time-of-use price")
acquiring the blocked electric quantity
E
B
,
t
of the wind power at the peak down-regulation period:
E
B
,
t
=
E
F
,
w
i
n
d
t
-
E
p
,
w
i
n
d
t
,
t
∈
T
(¶0070 "The output power P.sub.WT of the WT, is obtained through equation (2)").
Zhou does not teach acquiring the blocked electric quantity of the wind power at the peak down-regulation period comprises: solving the predicted electric quantity
E
F
,
w
i
n
d
t
of wind power at each period
Δ
t
according to a prediction curve of wind power output on a next day:
E
F
,
w
i
n
d
t
=
P
F
,
w
i
n
d
t
*
Δ
t
wherein in the formula,
Δ
t
represents the time scale, and
P
F
,
w
i
n
d
t
represents the power of the wind power output.
Uyeki teaches acquiring the blocked electric quantity of the wind power at the peak down-regulation period comprises: solving the predicted electric quantity
E
F
,
w
i
n
d
t
of wind power at each period
Δ
t
(¶0019 "from the historical information, the utility company 103 predicts that at 7:00 pm on May 25.sup.th of the current year, 2 percent of the total available energy on the grid 101 is provided by wind power")
according to a prediction curve of wind power output on a next day:
E
F
,
w
i
n
d
t
=
P
F
,
w
i
n
d
t
*
Δ
t
wherein in the formula,
Δ
t
represents the time scale, and
P
F
,
w
i
n
d
t
represents the power of the wind power output. (¶0018 "utility company 103 may identify historical information describing the total amount of renewable energy generated by a type of renewable energy source (e.g., solar thermal electric plant or wind turbine) during time periods of the specific day from previous years").
The model produced by the utility company 103, as taught by Uyeki, is able to predict the total power generation due to wind turbines for a given year. It would be obvious to one of ordinary skill in the art, before the time of the effective filing date, to modify the method as taught by Zhou wherein the method of acquiring the blocked electric quantity of the wind power comprises solving the predicted electric quantity of wind power at each period t as taught by Uyeki. The modification would be obvious because one of ordinary skill in the art would be motivated to optimize power produced by wind turbines and increasing customer satisfaction with minimizing operational costs and optimizing use of green energy during off-peak hours.
Drawings
The subject matter of this application admits of illustration by a drawing to facilitate understanding of the invention, as is stated in exemplary claim 1 "disorderly charging loads of electric vehicles" where the term “disorderly” is indefinite as detailed above to reject claim 1 under 35 U.S.C. 112(a). Applicant is required to furnish a drawing under 37 CFR 1.81. No new matter may be introduced in the required drawing. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.121(d).
The subject matter of this application admits of illustration by a drawing to facilitate understanding of the invention, as is stated in exemplary claim 6 "prediction curve of wind power output on a next day". Applicant is required to furnish a drawing under 37 CFR 1.81. No new matter may be introduced in the required drawing. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.121(d).
Claim Objections
Claim 6 is objected to because of the following informalities: typographical error. Claim 6 begins with a pair of empty parentheses, which appear to be a typographical error. Appropriate correction is required.
Prior Art Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited by Examiner attached to this correspondence.
Liu et al (US 20220147670 A1) discloses optimizing allocation of stored energy to electric vehicles using a Monte Carlo statistical model for ordering data.
Hirose et al (US 20220242263 A1) discloses a method of using stored energy from renewable sources to charge an electric vehicle during off-peak hours.
Nesler et al (US 20100017045 A1) discloses a power distribution system which minimizes costs and reliance on the power grid by prioritizing renewable power sources such as wind turbines and solar energy.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/LISA KOTOWSKI/Examiner, Art Unit 2859
/TAELOR KIM/Supervisory Patent Examiner, Art Unit 2836
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