CN115983890A - SOC (system on chip) offset optimization control method and system of 5G base station energy storage system - Google Patents
SOC (system on chip) offset optimization control method and system of 5G base station energy storage system Download PDFInfo
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Abstract
The invention provides a method and a system for SOC (system on chip) offset optimization control of a 5G base station energy storage system, which relate to the technical field of energy storage system control optimization, and the specific scheme comprises the following steps: establishing an SOC offset control optimization model by taking the lowest total cost of the operation of the base station as a target function; analyzing the operation data of the base station energy storage system, and carrying out base station energy storage constraint on the optimization model; carrying out price type demand response constraint on the optimization model based on peak-valley time-of-use electricity price; solving the optimization model to obtain a solution result, and controlling the 5G base station energy storage system to perform operation scheduling according to the solution result; the invention establishes an optimization model, predicts the charge and discharge state of the energy storage system, keeps the charge state of the energy storage system in a stable interval, and real-time prediction optimization is added on the basis of a day-ahead scheduling plan, and the coordination control is implemented to correct the working condition, so that the charge state of the energy storage system is optimized more quickly.
Description
Technical Field
The invention belongs to the technical field of energy storage system control optimization, and particularly relates to an SOC (system on chip) offset optimization control method and system for an energy storage system of a 5G base station.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The fifth Generation Mobile Communication Technology (5 th Generation Mobile Communication Technology, 5G for short) is a new Generation broadband Mobile Communication Technology with the characteristics of high speed, low time delay and large connection, and the 5G Communication facility is a network infrastructure for realizing man-machine-object interconnection; with the development of society, 5G will permeate all fields of society and become a key novel infrastructure for supporting the digitization, networking and intelligent transformation of the economy and society; 5G is developed rapidly, and has the performances of high transmission rate, high bandwidth, high reliability, low time delay and the like, the power consumption of base station equipment is greatly improved, the requirement on an energy storage system of a base station is also greatly improved, and particularly, how to meet the basic standby power requirement needs, the capacity expansion accounting needs to be carried out on a storage battery pack. At present, most 5G base stations uniformly use lithium batteries as base station storage batteries to participate in power grid peak shaving, and the mode of participating in power grid peak shaving by energy storage of the 5G base stations, the input cost and the output economy need to be further researched.
In the prior art, a day-ahead scheduling optimization model of an active power distribution network is generally established, an optimization result of day-ahead scheduling is obtained by solving, the day-ahead scheduling optimization of the active power distribution network based on flexible loads is completed by using actual power, and a final-edition day-ahead optimization scheduling scheme is obtained, and the method specifically comprises the following steps:
clustering the flexible loads on each bus of the active power distribution network; according to the clustering result, establishing a day-ahead scheduling optimization model of the active power distribution network based on the flexible load cluster, solving to obtain a pre-optimization result of day-ahead scheduling, and obtaining reference power of each cluster; decomposing and optimizing the reference power of each cluster to obtain the actual power demand of each flexible load in the cluster; and finishing day-ahead scheduling final optimization of the active power distribution network based on the flexible load cluster by using the actual power demand to obtain a final day-ahead scheduling optimization scheme.
The day-ahead scheduling optimization scheme does not relate to scheduling optimization and optimization schemes except for the day-ahead stage, the operation interval of the SoC is a fixed value in one day, the optimization effect is poor, the operation reliability is low, and the efficient operation of the energy storage system is not facilitated.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method and a system for SOC offset optimization control of a 5G base station energy storage system, an optimization model is established, the charge-discharge power and the charge-discharge state of the energy storage system are predicted, the charge state of the energy storage system is kept in a stable interval, real-time prediction optimization is added on the basis of a day-ahead scheduling plan, the coordination control is carried out to correct the working condition, and the charge state of the energy storage system is optimized more quickly.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
the invention provides a SOC offset optimization control method of a 5G base station energy storage system in a first aspect;
the SOC offset optimization control method of the 5G base station energy storage system comprises the following steps:
establishing an SOC offset control optimization model by taking the lowest total cost of the operation of the base station as a target function;
analyzing the operation data of the base station energy storage system, and carrying out base station energy storage constraint on the optimization model;
carrying out price type demand response constraint on the optimization model based on peak-valley time-of-use electricity price;
solving the optimization model to obtain a solution result, and controlling the 5G base station energy storage system to perform operation scheduling according to the solution result;
and the solving result is the predicted time-sharing charge-discharge power and time-sharing charge-discharge state of the energy storage system.
Further, the objective function is:
F cost ＝min(C BESS +C PV +C rev +C grid )
wherein, F cost Represents the total cost of operation of the base station, C BESS Represents the operating cost of the energy storage system, C PV Represents the operating cost of the photovoltaic cell, C rev Indicating the electricity charge that should be paid after the user responds to the real-time demand, C grid And the interaction cost of the energy storage system of the base station and the main network power is represented.
Further, the operating cost C of the energy storage system BESS Comprises the following steps:
wherein,
representing the deterioration cost of the ith energy storage battery at time T, I being the set of energy storage batteries and T being 24 hours per day;
operating cost C of the photovoltaic PV Comprises the following steps:
wherein,
represents the unit operation and maintenance cost of the photovoltaic>
The input power of the photovoltaic panel at the ith energy storage battery at the moment t is represented, and tau represents the time interval between two adjacent moments;
the electric charge C which should be paid after the real-time demand response of the user rev Comprises the following steps:
wherein, pr j Represents the demand response electricity prices on electricity prices j,
ideal load response active power, alpha, representing unfulfilled demand response at time t j, Binary variable, alpha, representing whether or not to respond at electricity price j j, ∈{0,1}/>
L j Expressing the demand response rate under the electricity price J, wherein the J expresses the total number of the electricity prices in the electricity price standard;
the interactive cost C of the base station energy storage system and the main network power is represented grid Comprises the following steps:
wherein,
represents the price for purchasing electricity from the main network at time t, <' > is selected>
Representing the power deficit of the microgrid at time t,
represents the price at which electricity was sold to the main grid at time t, <' > based on the status of the electricity>
Representing the excess power of the microgrid at time t.
Further, the base station energy storage constraint means that the charging and discharging power of the energy storage battery of each base station is within the upper and lower limits of the input and output power.
Further, the base station energy storage constraint specifically includes:
wherein,
represents the internal charging and discharging power of the ith base station energy storage battery at the moment t, and->
Represents the charging power of the ith base station energy storage battery at the moment t>
Representing the discharge power, alpha, of the energy storage battery of the ith base station at time t ch Represents the charging efficiency, alpha, of the energy storage cell dis Representing the discharge efficiency, P, of the energy storage cell i,t Represents the charging and discharging power beta of the energy storage battery of the ith base station at the time t i,t Representing a charge and discharge binary decision variable of the ith energy storage at the moment t; />
Represents the charging binary decision variable for the ith energy storage at time t>
Representing the discharge binary decision variable of the ith energy storage at the moment t; />
The storage capacity of the ith energy storage at the time t is represented; />
The SoC lower limit value of the ith energy storage optimized at the moment t is represented;
and the upper limit value of the SoC of the ith energy storage optimized at the moment t is shown.
Further, the price type demand response constraint is that the electricity price at the peak time of electricity utilization is higher than the electricity price at the valley time of electricity utilization.
Further, the price type demand response constraint specifically includes:
p p -p v >0
p v -p min >0
wherein p is p Indicating the electricity price at peak electricity consumption, p v Indicating the electricity price at the time of electricity utilization valley; p is a radical of min The power generation cost of the power supply company.
The invention provides a system for optimizing and controlling the SOC offset of the energy storage system of the 5G base station in a second aspect.
The SOC offset optimization control system of the 5G base station energy storage system comprises a model building module, a first constraint module, a second constraint module and a model solving module:
a model building module configured to: establishing an SOC offset control optimization model by taking the lowest total cost of the operation of the base station as a target function;
a first constraint module configured to: analyzing the operation data of the base station energy storage system, and carrying out base station energy storage constraint on the optimization model;
a second constraint module configured to: carrying out price type demand response constraint on the optimization model based on peak-valley time-of-use electricity price;
a model solving module configured to: solving the optimization model to obtain a solution result, and controlling the 5G base station energy storage system to perform operation scheduling according to the solution result;
and the solving result is the predicted time-sharing charge-discharge power and time-sharing charge-discharge state of the energy storage system.
A third aspect of the present invention provides a computer readable storage medium, on which a program is stored, which when executed by a processor implements the steps in the SOC offset optimization control method for a 5G base station energy storage system according to the first aspect of the present invention.
A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, where the processor implements the steps in the SOC offset optimization control method for a 5G base station energy storage system according to the first aspect of the present invention when executing the program.
The above one or more technical solutions have the following beneficial effects:
according to the method, an optimization model is established, an electricity price constraint condition and an energy storage system constraint condition are established, a target function is solved by combining a large amount of historical data such as charge-discharge frequency and charge-discharge power, the charge-discharge power and the charge-discharge state of the energy storage system are predicted, the charge state of the energy storage system is kept in a stable interval, real-time prediction optimization is added on the basis of a day-ahead scheduling plan, the coordination control is implemented to correct the working condition, and the charge state of the energy storage system is optimized more quickly.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of the method of the first embodiment.
Fig. 2 is a time-of-use electricity rate diagram and peak-to-valley time division of the first embodiment.
Fig. 3 is a system configuration diagram of the second embodiment.
Detailed Description
The invention is further described with reference to the following figures and examples.
The SOC is short for state-of-charge, is the state of charge of the battery, is also called residual capacity, and represents the capacity of the battery for continuous operation; SOC is generally the ratio of charge capacity to rated capacity, expressed as a percentage; a battery generally has a rated capacity, and when the battery is charged for a certain time at a certain multiplying power, the charging capacity can be obtained, the ratio of the capacity to the rated capacity is the SOC, and the value range of the SOC is 0-1, when the SOC =0, the battery is completely discharged, and when the SOC =1, the battery is completely charged.
In reality, due to factors such as the charging and discharging direction, the charging and discharging times and the charging and discharging power change of the battery energy storage system, the charging and discharging amount of the energy storage system is unbalanced, and the SoC of the battery energy storage system is likely to deviate gradually, so that the research on the SoC deviation control method of the 5G base station energy storage system is very important.
When the charging and discharging strategy of the energy storage system is formulated, additional control on the SOC (State of Charge) of the energy storage system is added, so that the power fluctuation of the wind power plant can be stabilized, the overcharge and overdischarge of the energy storage system can be avoided, the output power of the wind power plant can be smoothed for a long time, and the output reliability of the energy storage system is ensured when the energy storage system participates in power grid interaction.
The invention provides a method for optimizing SOC state deviation control of a 5G base station energy storage system, which comprises the steps of establishing an optimization model, predicting the charging and discharging power and the charging and discharging state of the energy storage system, keeping the charging state SoC of the energy storage system in a stable interval, adding real-time prediction optimization on the basis of a day-ahead scheduling plan, implementing coordinated control to correct working conditions, and optimizing the charging state of the energy storage system more quickly so as to ensure the reliability of output when the energy storage system participates in power grid interaction.
Example one
The embodiment discloses a SOC offset optimization control method of a 5G base station energy storage system;
as shown in fig. 1, the SOC offset optimization control method for a 5G base station energy storage system includes:
step S1: establishing an SOC offset control optimization model by taking the lowest total cost of the operation of the base station as a target function;
in order to adjust and optimize the SoC of the energy storage system and reduce the total running cost of the base station as much as possible, the 5G base station energy storage system is analyzed, an SOC offset control optimization model is established by combining energy storage data, and the objective function is as follows:
F cost ＝min(C BESS +C PV +C rev +C grid )
wherein, F cost Represents the total cost of operation of the base station, C BESS Represents the operating cost of the energy storage system, C PV Represents the operating cost of the photovoltaic cell, C rev Indicating the electricity charge that should be paid after the user responds to the real-time demand, C grid And the interaction cost of the energy storage system of the base station and the main network power is represented.
Operating cost C of energy storage system BESS Comprises the following steps:
wherein,
representing the deterioration cost of the ith energy storage battery at the moment T, l is the set of energy storage batteries, and T is 24 hours per day;
operating cost C of photovoltaic PV Comprises the following steps:
wherein,
represents the unit operation and maintenance cost of the photovoltaic>
The input power of the photovoltaic panel at the ith energy storage battery at the moment t is represented, and tau represents the time interval between two adjacent moments;
electric charge C that should be paid after user real-time demand response rev Comprises the following steps:
wherein, pr j Indicating demand response electricity prices on electricity prices j,
ideal load response active power, alpha, representing unfulfilled demand response at time t j，t Binary variable, alpha, representing whether or not to respond at electricity price j j，t ∈{0，1}/>
L j The demand response rate at the electricity rate J is shown, J represents the total number of electricity rates in the time-of-use electricity rate table, which is shown in fig. 2.
Representing interaction cost C of base station energy storage system and main network power grid Comprises the following steps:
wherein,
represents the price for purchasing electricity from the main network at time t, <' > is selected>
Representing the power deficit of the microgrid at time t,
represents the price at which electricity was sold to the main grid at time t, <' > based on the status of the electricity>
Representing the excess power of the microgrid at time t.
The objective function of the optimization model relates to base station energy storage and time-of-use electricity price, so that a large number of historical operating data of a base station energy storage system, such as the charging and discharging frequency of an energy storage battery, the charging and discharging real-time power of the energy storage battery and the like, are acquired, and the objective function is constrained.
Step S2: analyzing the operation data of the base station energy storage system, and carrying out base station energy storage constraint on the optimization model:
(1) The charging and discharging power of each base station energy storage battery is within the range of the upper limit and the lower limit of the input and output power, and the method specifically comprises the following steps:
wherein,
represents the internal charging and discharging power of the ith base station energy storage battery at the moment t, and->
Represents the charging power of the ith base station energy storage battery at the moment t>
Represents the discharge power of the energy storage battery of the ith base station at the time t, alpha ch Representing the charging efficiency, alpha, of the energy storage cell dis Representing the discharge efficiency, P, of the energy storage cell i，t Represents the charging and discharging power beta of the energy storage battery of the ith base station at the time t i，t Representing a charge and discharge binary decision variable of the ith energy storage at the moment t; />
Represents the charging binary decision variable of the i-th energy store at the instant t, in conjunction with the evaluation of the charging state>
And (4) representing the discharge binary decision variable of the ith energy storage at the time t.
(2) The SoC value of each base station energy storage battery is within the SoC upper and lower limits, specifically:
wherein,
the storage capacity of the ith energy storage at the moment t is represented; />
Representing the SoC lower limit value of the ith energy storage optimized at the moment t; />
And the upper limit value of the SoC of the ith energy storage optimized at the moment t is shown.
And step S3: and carrying out price type demand response constraint on the optimization model based on the peak-valley time-of-use electricity price:
the price of electricity when the power consumption peak will be higher than the price of electricity when the power consumption low ebb, specifically is:
p p -p v ＞0
p v -P min ＞0
wherein p is p Indicating the electricity price at peak electricity consumption, p v Represents the electricity price at the time of electricity consumption valley; p is a radical of min The cost of power generation for the power supply company.
And step S4: solving the optimization model to obtain a solution result, and controlling the 5G base station energy storage system to perform operation scheduling according to the solution result;
the invention converts a multi-optimization problem into a single-target optimization problem, and utilizes the particle swarm algorithm to solve according to the target function and the constraint condition, and the particle swarm algorithm has the advantages of less parameters, simple structure, capability of rapidly solving the optimal solution of the problem and the like, thereby being widely applied to solving the optimal solution problem of the power system.
Setting the attribute of the particles as the charging power of the ith base station energy storage battery at the time t
Discharging power ^ of the ith base station energy storage battery at time t>
Charging binary decision variable ^ at time t for ith energy storage>
And a firsti discharge binary decision variables which store energy at the instant t>
According to the peak-valley time division and the time-of-use electricity price table, as shown in fig. 2, the time-of-use electricity price is taken as a known condition, and the particle swarm algorithm is adopted to solve the SOC deviation control optimization model to obtain the optimal SOC deviation control optimization model
And &>
Namely the time-sharing charge-discharge power and the time-sharing charge-discharge state of the energy storage system.
The particle swarm algorithm is a commonly used evolutionary algorithm, and the solving process is not described herein again.
The running state of the energy storage of two adjacent time intervals can be changed, and a binary variable b is used i，t Whether the operating state of the energy storage system BESS changes or not is represented as follows:
wherein, b i,t Is the running state flag bit of the ith base station energy storage battery, when the running state of the ith base station energy storage battery is the same in two adjacent time intervals, namely, the charging and discharging state of the last moment is continuously kept, b i，t =1; when the energy storage battery of the ith base station has different running states at two adjacent time intervals, namely the charging and discharging states are changed, b i,t ＝0。
According to the operating state flag bit b i，t And calculating the energy accumulation of the ith energy storage battery at the time t
The method specifically comprises the following steps:
wherein,
representing the energy accumulation of the ith energy storage cell at time t, b i，t A flag bit indicating the operation status is set,
indicates the energy accumulation of the i-th energy storage cell at the moment t-1, is/are>
Represents the power of charging and discharging the energy storage battery of the ith base station in the time t, and tau represents the time interval between two adjacent times, and is combined with the power of charging and discharging the energy storage battery of the ith base station in the time t>
Represents the system capacity value of the i-th energy storage cell, based on the measured value>
Represents the deterioration cost of the ith energy storage battery at the time t, C loss Representing the actual cost of degradation.
The energy storage battery plays an auxiliary role and is mainly maintained by a power grid, if the power grid is not supported enough, the energy storage battery is used for compensation, namely the binary decision variable action is 1, and the non-action is 0.
Example two
The embodiment discloses an SOC offset optimization control system of a 5G base station energy storage system;
as shown in fig. 3, the SOC offset optimization control system of the 5G base station energy storage system includes a model building module, a first constraint module, a second constraint module, and a model solving module:
a model building module configured to: establishing an SOC (system on chip) offset control optimization model by taking the lowest total cost of the operation of the base station as a target function;
a first constraint module configured to: analyzing the operation data of the base station energy storage system, and carrying out base station energy storage constraint on the optimization model;
a second constraint module configured to: carrying out price type demand response constraint on the optimization model based on peak-valley time-of-use electricity price;
a model solving module configured to: solving the optimization model to obtain a solution result, and controlling the 5G base station energy storage system to perform operation scheduling according to the solution result;
and the solving result is the predicted time-sharing charge-discharge power and time-sharing charge-discharge state of the energy storage system.
EXAMPLE III
An object of the present embodiment is to provide a computer-readable storage medium.
The system comprises a computer readable storage medium, a computer program stored thereon, and a processor, wherein the computer program is used for implementing the steps in the SOC offset optimization control method of the 5G base station energy storage system according to the first embodiment of the disclosure.
Example four
An object of the present embodiment is to provide an electronic apparatus.
The electronic device comprises a memory, a processor and a program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the SOC offset optimization control method of the 5G base station energy storage system according to the first embodiment of the disclosure.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
- The SOC offset optimization control method of the energy storage system of the 1.5G base station is characterized by comprising the following steps:establishing an SOC offset control optimization model by taking the lowest total cost of the operation of the base station as a target function;analyzing the operation data of the base station energy storage system, and carrying out base station energy storage constraint on the optimization model;carrying out price type demand response constraint on the optimization model based on peak-valley time-of-use electricity price;solving the optimization model to obtain a solution result, and controlling the 5G base station energy storage system to perform operation scheduling according to the solution result;and the solving result is the predicted time-sharing charge-discharge power and time-sharing charge-discharge state of the energy storage system.
- 2. The SOC offset optimization control method of the 5G base station energy storage system according to claim 1, wherein the objective function is:F cost ＝min(C BESS +C PV +C rev +C grid )wherein, F cost Represents the total cost of operation of the base station, C BESS Represents the operating cost of the energy storage system, C PV Represents the operating cost of the photovoltaic, C rev Indicating the electricity charge that should be paid after the user responds to the real-time demand, C grid And the interaction cost of the energy storage system of the base station and the main network power is represented.
- 3. The SOC offset optimization control method of the 5G base station energy storage system of claim 2, wherein the operating cost C of the energy storage system BESS Comprises the following steps:
wherein,representing the degradation cost of the ith energy storage battery at the moment T, wherein I is the energy storage battery set, and T is 24 hours per day;
operating cost C of the photovoltaic PV Comprises the following steps:
wherein,represents the unit operation and maintenance cost of the photovoltaic>
The input power of the photovoltaic panel at the ith energy storage battery at the moment t is represented, and tau represents the time interval between two adjacent moments;
the electric charge C which should be paid after the real-time demand response of the user rev Comprises the following steps:
wherein, pr j Represents the demand response electricity prices on electricity prices j,ideal load response active power, alpha, representing unfulfilled demand response at time t j, A binary variable representing whether a response is made at power price j, based on the value of the variable, and>
L j expressing the demand response rate under the electricity price J, wherein the J expresses the total number of the electricity prices in the electricity price standard;
the interactive cost C of the base station energy storage system and the main network power is represented grid Comprises the following steps:
wherein,represents the price for purchasing electricity from the main network at time t, <' > is selected>
Represents the power shortage at time t microgrid>
Represents the price at which electricity was sold to the main grid at time t, <' > based on the status of the electricity>
Representing the excess power of the microgrid at time t.
- 4. The SOC offset optimization control method of the 5G base station energy storage system according to claim 1, wherein the base station energy storage constraints are that the charging and discharging power of each base station energy storage battery is within the upper and lower limits of input and output power.
- 5. The SOC offset optimization control method of the 5G base station energy storage system according to claim 1, wherein the base station energy storage constraints specifically include:
wherein,represents the internal charging and discharging power of the ith base station energy storage battery at the moment t, and->
Represents the charging power of the ith base station energy storage battery at the moment t>
Representing the discharge power, alpha, of the energy storage battery of the ith base station at time t ch Representing the charging efficiency, alpha, of the energy storage cell dis Indicating the discharge efficiency, P, of the energy storage cell i,t Represents the charging and discharging power beta of the energy storage battery of the ith base station at the time t i,t Representing a charge and discharge binary decision variable of the ith energy storage at the moment t; />
Represents the charging binary decision variable for the ith energy storage at time t>
A discharge binary decision variable representing the ith energy storage at the time t; />
The storage capacity of the ith energy storage at the moment t is represented; />
Representing the SoC lower limit value of the ith energy storage optimized at the moment t; />
And the upper limit value of the SoC of the ith energy storage optimized at the moment t is shown.
- 6. The method of claim 1, wherein the price type demand response constraint is that the electricity price is higher during peak hours than during valley hours.
- 7. The SOC offset optimization control method of the 5G base station energy storage system according to claim 6, wherein the price type demand response constraint specifically is:p p -p v >0p v -p min >0wherein p is p Indicating the electricity price at peak electricity consumption, p v Represents the electricity price at the time of electricity consumption valley; p is a radical of formula min The power generation cost of the power supply company.
- The SOC offset optimization control system of the 8.5G base station energy storage system is characterized by comprising a model building module, a first constraint module, a second constraint module and a model solving module:a model building module configured to: establishing an SOC offset control optimization model by taking the lowest total cost of the operation of the base station as a target function;a first constraint module configured to: analyzing the operation data of the base station energy storage system, and carrying out base station energy storage constraint on the optimization model;a second constraint module configured to: carrying out price type demand response constraint on the optimization model based on peak-valley time-of-use electricity price;a model solving module configured to: solving the optimization model to obtain a solution result, and controlling the 5G base station energy storage system to perform operation scheduling according to the solution result;and the solving result is the predicted time-sharing charge-discharge power and time-sharing charge-discharge state of the energy storage system.
- 9. An electronic device, comprising:a memory for non-transitory storage of computer readable instructions; anda processor for executing the computer readable instructions,wherein the computer readable instructions, when executed by the processor, perform the method of any of claims 1-7.
- 10. A storage medium storing non-transitory computer-readable instructions, wherein the non-transitory computer-readable instructions, when executed by a computer, perform the instructions of the method of any one of claims 1-7.
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