CN115545806A - Profit determination method and device and related product - Google Patents
Profit determination method and device and related product Download PDFInfo
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Abstract
The application discloses a profit determination method, a profit determination device and a related product, and belongs to the technical field of electric power. Wherein, the method comprises the following steps: determining a first parameter and a second parameter, wherein the first parameter is the optimal electricity selling price of the virtual power plant to the electric equipment, and the second parameter is the optimal buyback price of the virtual power plant to the electric equipment; determining a first power according to the first parameter, and determining a second power according to the second parameter, wherein the first power is the charging power of the electric equipment in the coverage area of the virtual power plant, and the second power is the discharging power of the electric equipment in the coverage area of the virtual power plant; determining a profit value for the virtual power plant based on the first parameter, the second parameter, the first power, and the second power.
Description
Technical Field
The application belongs to the technical field of electric power, and particularly relates to a revenue determination method, a revenue determination device and a relevant product.
Background
The virtual power plant is a coordination management system which realizes the aggregation and collaborative optimization of various distributed resources such as a distributed power supply, energy storage, controllable load, an electric automobile and the like through an information technology and a software system, so as to be used as a special power plant which can be used as a positive power plant to supply power to the system and can also be used as a negative power plant to consume the power of the system, and participate in the operation of a power market and a power grid.
The existing virtual power plant optimization scheduling strategy lacks consideration of benefits of all main bodies, and does not take benefits of a virtual power plant and benefits of an electric vehicle owner as key points, so that the participation degree of the electric vehicle owner is poor, and the energy storage advantage of the electric vehicle cannot be fully exerted, therefore, the profitability of the virtual power plant is low, and the peak clipping and valley filling efficiency of the virtual power plant is limited.
Disclosure of Invention
The embodiment of the application aims to provide a profit determination method, a profit determination device and a related product, which can solve the problems that the profitability of a virtual power plant is low and the peak clipping and valley filling efficiency of the virtual power plant is limited.
In a first aspect, an embodiment of the present application provides a method for determining a profit, where the method includes: determining a first parameter and a second parameter, wherein the first parameter is the optimal electricity selling price of the virtual power plant to the electric equipment, and the second parameter is the optimal buyback price of the virtual power plant to the electric equipment; determining a first power according to the first parameter, and determining a second power according to the second parameter, wherein the first power is the charging power of the electric equipment in the coverage area of the virtual power plant, and the second power is the discharging power of the electric equipment in the coverage area of the virtual power plant; determining a profit value for the virtual power plant based on the first parameter, the second parameter, the first power, and the second power.
In a second aspect, an embodiment of the present application provides a profit determination apparatus, including: and determining a module. The system comprises a determining module, a processing module and a processing module, wherein the determining module is used for determining a first parameter and a second parameter, the first parameter is the optimal electricity selling price of the income determining device for the electric equipment, and the second parameter is the optimal buyback electricity price of the income determining device for the electric equipment; determining a first power according to the first parameter, and determining a second power according to the second parameter, wherein the first power is a charging power of the electric equipment in the coverage area of the profit determining apparatus, and the second power is a discharging power of the electric equipment in the coverage area of the profit determining apparatus; and determining a benefit value for the benefit determination apparatus based on the first parameter, the second parameter, the first power, and the second power.
In a third aspect, embodiments of the present application provide an electronic device, which includes a processor and a memory, where the memory stores a program or instructions executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method according to the first aspect.
In a fourth aspect, embodiments of the present application provide a readable storage medium, on which a program or instructions are stored, which when executed by a processor, implement the steps of the method according to the first aspect.
In a fifth aspect, an embodiment of the present application provides a chip, where the chip includes a processor and a communication interface, where the communication interface is coupled to the processor, and the processor is configured to execute a program or instructions to implement the method according to the first aspect.
In a sixth aspect, embodiments of the present application provide a computer program product, which is stored in a storage medium and executed by at least one processor to implement the method according to the first aspect.
In the embodiment of the application, a virtual power plant can determine a first parameter and a second parameter, wherein the first parameter is the optimal electricity selling price of the virtual power plant to the electric equipment, and the second parameter is the optimal purchase back price of the virtual power plant to the electric equipment; determining a first power according to the first parameter, and determining a second power according to the second parameter, wherein the first power is the charging power of the electric equipment in the coverage area of the virtual power plant, and the second power is the discharging power of the electric equipment in the coverage area of the virtual power plant; determining a profit value for the virtual power plant based on the first parameter, the second parameter, the first power, and the second power. Because virtual power plant can directly acquire virtual power plant to the best of electric equipment sell the price of electricity with virtual power plant is right the best buys back the price of electricity of electric equipment, and confirm in the coverage area of virtual power plant the charging power of electric equipment with in the coverage area of virtual power plant the discharge power of electric equipment, virtual power plant can directly confirm the income value of virtual power plant like this, and need not lack among the prior art and to each main part interest's consideration, do not use virtual power plant income, electric vehicle owner's income as the key point, consequently, full play electric automobile energy storage advantage. Therefore, the profitability of the virtual power plant can be improved, and the efficiency of peak clipping and valley filling of the virtual power plant is prevented from being limited.
Drawings
FIG. 1 is a schematic flow chart diagram of a profit determination method provided in an embodiment of the present application;
FIG. 2 is a second schematic flow chart of a profit determination method according to an embodiment of the present application;
FIG. 3 is a third schematic flow chart of a profit determination method according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a profit determination apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present disclosure.
The terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that embodiments of the application are capable of operation in sequences other than those illustrated or described herein, and that the terms "first," "second," etc. are generally used in a generic sense and do not limit the number of terms, e.g., a first term can be one or more than one. In addition, "and/or" in the specification and claims means at least one of connected objects, a character "/", and generally means that the former and latter related objects are in an "or" relationship.
The benefit determining method, apparatus and related products provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings through specific embodiments and application scenarios thereof.
At present, in the related art, a virtual power plant is a coordination management system which realizes aggregation and collaborative optimization of various distributed resources such as a distributed power supply, an energy storage system, a controllable load system, an electric vehicle and the like through an information technology and a software system, so that the virtual power plant can be used as a power plant with special power which can be used as a positive power plant to supply power to a system and can also be used as a negative power plant to consume the system, and participates in the operation of a power market and a power grid. The existing virtual power plant optimization scheduling strategy lacks consideration of benefits of all main bodies, and does not take benefits of a virtual power plant and benefits of an electric vehicle owner as key points, so that the participation degree of the electric vehicle owner is poor, and the energy storage advantage of the electric vehicle cannot be fully exerted, therefore, the profitability of the virtual power plant is low, and the peak clipping and valley filling efficiency of the virtual power plant is limited.
However, in the embodiment of the present application, the virtual power plant may determine a first parameter and a second parameter, where the first parameter is an optimal sale price of the virtual power plant to the electric device, and the second parameter is an optimal buy-back price of the virtual power plant to the electric device; determining a first power according to the first parameter, and determining a second power according to the second parameter, wherein the first power is the charging power of the electric equipment in the coverage area of the virtual power plant, and the second power is the discharging power of the electric equipment in the coverage area of the virtual power plant; determining a profit value for the virtual power plant based on the first parameter, the second parameter, the first power, and the second power. Because virtual power plant can directly acquire virtual power plant to the best of electric equipment sell the price of electricity with virtual power plant is right the best buys back the price of electricity of electric equipment, and confirm in the coverage area of virtual power plant the charging power of electric equipment with in the coverage area of virtual power plant the discharge power of electric equipment, virtual power plant can directly confirm the income value of virtual power plant like this, and need not lack among the prior art and to each main part interest's consideration, do not use virtual power plant income, electric vehicle owner's income as the key point, consequently, full play electric automobile energy storage advantage. Therefore, the profitability of the virtual power plant can be improved, and the efficiency of peak clipping and valley filling of the virtual power plant is prevented from being limited.
Fig. 1 shows a schematic flow chart of a profit determination method provided in an embodiment of the present application. As shown in fig. 1, the benefit determining method provided by the embodiment of the present application includes steps 101 to 103 described below.
In this embodiment, the first parameter is an optimal electricity selling price of the virtual power plant for the electric device, and the second parameter is an optimal electricity purchasing price of the virtual power plant for the electric device.
Optionally, in this embodiment of the application, the virtual power plant may determine the first parameter and the second parameter by using an optimization solution algorithm.
And 102, the virtual power plant determines first power according to the first parameter, and determines second power according to the second parameter.
In an embodiment of the application, the first power is charging power of an electric device in a coverage area of the virtual power plant, and the second power is discharging power of an electric device in the coverage area of the virtual power plant.
Optionally, in this embodiment of the present application, with reference to fig. 1 and as shown in fig. 2, the step 102 may be specifically implemented by the following step 102a and step 102 b.
And 102a, the virtual power plant determines a fourth parameter according to the first parameter and the third parameter.
In this application embodiment, the third parameter is that public power grid is right the power supply price of virtual power plant, and the fourth parameter indicates in the coverage area of virtual power plant the electric equipment to the enthusiasm that virtual power plant charges.
Optionally, in this embodiment of the application, the virtual power plant may calculate a fourth parameter according to the first parameter and the third parameter by using a first algorithm.
Wherein, the first algorithm may be:
wherein,
as the fourth parameter, the first parameter is,
is a first parameter of the plurality of parameters,
is the third parameter.
And step 102b, the virtual power plant determines the first power according to the fourth parameter and the fifth parameter.
In an embodiment of the application, the fifth parameter is an average charging power of the electric device in a coverage area of the virtual power plant.
Optionally, in this embodiment of the application, the virtual power plant may calculate the first power according to the fourth parameter and the fifth parameter by using a second algorithm.
Wherein the second algorithm is:
wherein,
is a fifth parameter that is a function of,
an upper power rating for charging the electrically powered device.
Optionally, in this embodiment of the application, as shown in fig. 3 in combination with fig. 2, the step 102 may be specifically implemented by a step 102c and a step 102d described below.
And 102c, the virtual power plant determines a seventh parameter according to the second parameter, the sixth parameter and the third parameter.
In an embodiment of the application, the sixth parameter is that the public power grid is right to the electricity purchase price of the virtual power plant, and the seventh parameter indicates the aggressiveness of the electric equipment to sell electricity in the coverage area of the virtual power plant.
Optionally, in this embodiment of the application, the virtual power plant may determine the seventh parameter according to the second parameter, the sixth parameter, and the third parameter by using a third algorithm.
Optionally, in this embodiment of the application, the virtual power plant may determine the seventh parameter according to the second parameter, the sixth parameter, and the third parameter by using a fourth algorithm.
Wherein the fourth algorithm is:
wherein,
as the seventh parameter, the parameter is,
as the second parameter, the parameter is,
as a sixth parameter of the number one,
is the third parameter.
And step 102d, the virtual power plant determines the second power according to the seventh parameter and the eighth parameter.
In an embodiment of the application, the eighth parameter is an average discharge power of the electric device in a coverage area of the virtual power plant.
Optionally, in this embodiment of the application, the virtual power plant may determine the second power according to a seventh parameter and an eighth parameter by using a fifth algorithm.
Wherein, the fifth algorithm is:
wherein,
is the second power, and is,
as the seventh parameter, the parameter is,
as the eighth parameter, the parameter is,
is the upper discharge rated power limit of the electrically powered device.
The embodiments of the present application show a specific example for the purpose of detailed description.
The virtual power plant can set the electric vehicle purchasing and selling activity parameters and the constraint conditions in the virtual power plant, and then determine the first parameter and the second parameter so as to calculate the income value of the virtual power plant.
Wherein, it specifically can be to set up electric automobile and purchase, sell the electric enthusiasm parameter at virtual power plant:
Since the electric automobile mainly consumes electric energy to run and the electric automobile has more electricity purchasing requirements in a virtual power plant, the electricity purchasing positivity of the electric automobile is preferably calculated.
According to the fitting of the survey results of the sampling questionnaire, the hyperbolic tangent curve is selected to represent the positivity of the electric automobile in the electricity purchasing of the virtual power plant:
only the electric vehicle in a non-charging state can perform a discharging operation, so the enthusiasm of the electric vehicle to sell electricity to the virtual power plant is expressed as follows:
the constraint condition may specifically be:
(1) Based on the actual pricing strategy of the non-loss operation of the participants, the price should meet the following conditions at any time t:
in the formula
The highest electricity selling price of the virtual power plant agreed by the national price guide department,
the cost price of the charge and discharge energy loss of the electric vehicle is high.
(2) Electric quantity restraint of electric automobile
In the formula:
for the charge capacity of the v-th electric vehicle for the period t,
、
the upper limit and the lower limit of the electric storage amount of the v-th EV are provided.
V is the total quantity of electric automobiles of all users which can participate in a certain area of the virtual power plant accessories in the area.
(3) Electric vehicle charging and discharging power constraint
In the formula:
、
and the average charging and discharging power of the v-th electric automobile in the t period.
、
The charging and discharging rated power upper limit of the v-th electric automobile;
charging power of all electric vehicles in virtual power plant area
Charging power of all electric vehicles in virtual power plant area
Therefore, the virtual power plant can construct a revenue function, and a revenue value of the virtual power plant is determined according to the first parameter, the second parameter, the first power and the second power by adopting the revenue function.
103, the virtual power plant determines a profit value of the virtual power plant based on the first parameter, the second parameter, the first power and the second power.
According to the income determining method provided by the embodiment of the application, a virtual power plant can determine a first parameter and a second parameter, the first parameter is the optimal selling price of the virtual power plant to the electric equipment, and the second parameter is the optimal buyback price of the virtual power plant to the electric equipment; determining a first power according to the first parameter, and determining a second power according to the second parameter, wherein the first power is the charging power of the electric equipment in the coverage area of the virtual power plant, and the second power is the discharging power of the electric equipment in the coverage area of the virtual power plant; determining a profit value for the virtual power plant based on the first parameter, the second parameter, the first power, and the second power. Because virtual power plant can directly acquire virtual power plant to the best of electric equipment sell the price of electricity with virtual power plant is right the best buys back the price of electricity of electric equipment, and confirm in the coverage area of virtual power plant the charging power of electric equipment with in the coverage area of virtual power plant the discharge power of electric equipment, virtual power plant can directly confirm the income value of virtual power plant like this, and need not lack among the prior art and to each main part interest's consideration, do not use virtual power plant income, electric vehicle owner's income as the key point, consequently, full play electric automobile energy storage advantage. Therefore, the profitability of the virtual power plant can be improved, and the efficiency of peak clipping and valley filling of the virtual power plant is prevented from being limited.
Alternatively, in this embodiment of the present application, the step 103 may be specifically implemented by the following steps 103a to 103 c.
Step 103a, calculating by the virtual power plant according to the first parameter, the third parameter and the first power to obtain a first profit value.
In this embodiment, the third parameter is a power supply price of the virtual power plant, and the first profit value is a charging profit value of the electric device in a coverage area of the virtual power plant.
And 103b, calculating by the virtual power plant according to the second parameter, the sixth parameter and the second power to obtain a second profit value.
In this embodiment, the sixth parameter is that the public power grid is right to the electricity purchase price of the virtual power plant, and the second profit value is in the coverage area of the virtual power plant the discharge profit of the electric device.
And 103c, calculating by the virtual power plant based on the first profit value and the second profit value to obtain a profit value of the virtual power plant.
Alternatively, in this embodiment of the present application, the step 103c may be specifically implemented by the following steps 103c1 and 103c 2.
And 103c1, the virtual power plant determines a target profit value according to the sum of the first profit value and the second profit value.
In an embodiment of the present application, the target profit value indicates a gross profit value of the virtual power plant.
And 103c2, the virtual power plant determines the income value of the virtual power plant according to the difference between the target income value and the cost expense value.
In this application embodiment, the cost value is an operation and maintenance cost value of the virtual power plant.
Optionally, in this embodiment of the application, the virtual power plant may use a preset algorithm to calculate a profit value of the virtual power plant.
Wherein, the preset algorithm is as follows:
wherein,
and simulating the operation and maintenance cost value of the power plant in the time period.
The duration of the charging period. T is the total number of periods (per grid convention, per 24 hour point price peak valley, when T = 24).
As will be specifically exemplified below, how the virtual power plant determines the first parameter and the second parameter by using an optimization solution algorithm.
Alternatively, in this embodiment of the present application, the step 101 may be specifically implemented by the following steps 101a to 101 c.
Step 101a, the virtual power plant obtains M target parameter sets.
In the embodiment of the present application, each target parameter group includes at least one parameter, and M is a positive integer greater than 1.
And step 101b, the virtual power plant determines an optimal parameter with an optimal parameter position from each target parameter group respectively so as to determine M optimal parameters.
It is understood that each target parameter group can be regarded as an airborne troops, and an optimal parameter with optimal parameter position in each target parameter group can be regarded as a commander of the squad of the airborne troops.
And 101c, the virtual power plant determines a target parameter based on the target optimal parameter in the M optimal parameters.
In this embodiment of the present application, the target parameter is any one of the first parameter and the second parameter.
Optionally, in this embodiment of the application, the target optimal parameter may specifically be an optimal parameter with an optimal parameter position in the M optimal parameters.
It can be understood that the target optimal parameter can be regarded as one squad commander with the best position among the M squad commanders.
Alternatively, in this embodiment of the application, the step 101c may be specifically realized by the following steps 101c1 to 101c 4.
And 101c1, determining the optimal target parameter with the optimal parameter position from the M optimal parameters by the virtual power plant.
And step 101c2, controlling M-1 target parameter groups by the virtual power plant, and searching and calculating the parameter positions of the target optimal parameters to update the parameter positions of the M-1 target parameter groups.
In the embodiment of the present application, the M-1 target parameter sets are parameter sets corresponding to M-1 optimal parameters one to one, and the M-1 optimal parameters are optimal parameters of the M optimal parameters except for the target optimal parameters.
It can be understood that M-1 squad commanders can search and advance to the parameter position of the target optimal parameter, and soldiers passing through each squad can respectively adjust to the position of each squad commander and move to the positions.
Alternatively, in this embodiment of the application, the step 101c2 may be specifically realized by the following steps 101c2a and 101c2 b.
And step 101c2a, the virtual power plant controls the optimal parameters in the first parameter group and adjusts the parameter positions of the target optimal parameters.
And step 101c2b, the virtual power plant controls other parameters in the first parameter group, and modulates the parameter position of the optimal parameter in the first parameter group after adjustment.
Wherein, the first parameter group is any one of the M-1 target parameter groups; the other parameters are parameters in the first parameter group except for the optimal parameters in the first parameter group.
And step 101c3, the virtual power plant determines a first optimal parameter with an optimal parameter position from each target parameter group in the M-1 target parameter groups after the parameter positions are updated, so as to determine M-1 first optimal parameters.
And step 101c4, the virtual power plant determines the target parameters with optimal parameter positions from the M-1 first optimal parameters and the target optimal parameters.
Optionally, in this embodiment of the present application, when the number of iterations determined by the virtual power plant is greater than or equal to a preset number, the target parameter with an optimal parameter position is determined from the M-1 first optimal parameters and the target optimal parameter.
In the case that the virtual power plant determines that the iteration number is smaller than the preset number, the virtual power plant may perform the above steps 101c1 to 101c3 again until the iteration number is greater than or equal to the preset number.
The following will exemplify a specific example.
According to the above description of the embodiments, it can be known that the optimal solution process of the model is to find the optimal one
、
And the income f of the virtual power plant is maximized.
1. Dividing all airborne soldiers into M sub-teams, carrying out air drop in a target area, and randomly distributing drop points of each soldier.
The position of the jth airborne soldier of the ith squad is shown. The current iteration number n is given an initial value of 1, and N represents the total iteration number.
2. The positions of the airborne troops in each squad are compared, and the squad command with the optimal position is selectedThe officer.
Indicating the commander location of the ith squad.
3. Comparing the positions of all commanders to select the best position
。
4. And (5) judging that the iteration number N is less than N, and otherwise, jumping to the step 9.
5. The commanders are directed to progress toward the target search.
The commanders are constantly approaching the target. Sometimes, the commander must leave the current best position to find a better position, so that the capability of the algorithm to jump out of the local optimal value is effectively improved:
in the formula
Is the optimal position;
is [0,1 ]]The random vector of the inner one of the vectors,
、
is [ -1,1 [ ]]Random number within.
6. Soldiers approach to commanders of respective teams
The soldiers adjust according to the position of the respective commander and move towards the soldiers.
7. Soldier random search
When the soldier does not act as a commander of the squad where the soldier is located, the soldier further performs a deep random search. The new position updating mode of the individual soldiers is as follows:
in the formula,
is [0,1 ]]Random vector of (2).
Soldier random search motions enable the algorithm to fully explore the search space, so if the algorithm falls into local optimality, such behavior will help the algorithm jump out in time.
8. And (5) if the iteration number n = n +1, entering the step 2.
9. Outputting the optimum position
I.e. the target parameter.
According to the income determining method provided by the embodiment of the application, a virtual power plant can determine a first parameter and a second parameter, the first parameter is the optimal selling price of the virtual power plant to the electric equipment, and the second parameter is the optimal buyback price of the virtual power plant to the electric equipment; determining a first power according to the first parameter, and determining a second power according to the second parameter, wherein the first power is the charging power of the electric equipment in the coverage area of the virtual power plant, and the second power is the discharging power of the electric equipment in the coverage area of the virtual power plant; determining a profit value for the virtual power plant based on the first parameter, the second parameter, the first power, and the second power. Because virtual power plant can directly acquire virtual power plant to the best of electric equipment sell the price of electricity with virtual power plant is right the best buys back the price of electricity of electric equipment, and confirm in the coverage area of virtual power plant the charging power of electric equipment with in the coverage area of virtual power plant the discharge power of electric equipment, virtual power plant can directly confirm the income value of virtual power plant like this, and need not lack among the prior art and to each main part interest's consideration, do not use virtual power plant income, electric vehicle owner's income as the key point, consequently, full play electric automobile energy storage advantage. Therefore, the profitability of the virtual power plant can be improved, and the efficiency of peak clipping and valley filling of the virtual power plant is prevented from being limited.
According to the profit determining method provided by the embodiment of the application, the execution subject can be a profit determining device. In the embodiment of the present application, the revenue determining apparatus executes the revenue determining method as an example, and the revenue determining apparatus provided in the embodiment of the present application is described.
Fig. 4 is a schematic structural diagram illustrating a profit determination apparatus according to an embodiment of the present application. As shown in fig. 4, the profit determination apparatus 50 provided by the embodiment of the present application may include: a determination module 51.
The determining module 51 is configured to determine a first parameter and a second parameter, where the first parameter is an optimal electricity selling price of the revenue determining apparatus for the electric device, and the second parameter is an optimal electricity purchase price of the revenue determining apparatus for the electric device; determining a first power according to the first parameter, and determining a second power according to the second parameter, wherein the first power is a charging power of the electric equipment in the coverage area of the profit determination device, and the second power is a discharging power of the electric equipment in the coverage area of the profit determination device; and determining a benefit value for the benefit determination apparatus based on the first parameter, the second parameter, the first power, and the second power.
In a possible implementation manner, the determining module 51 is specifically configured to determine a fourth parameter according to the first parameter and the third parameter, where the third parameter is a power supply price of a public power grid to the benefit determining apparatus, and the fourth parameter indicates an aggressiveness of charging the benefit determining apparatus by the electric device in a coverage area of the benefit determining apparatus; and determining the first power according to the fourth parameter and a fifth parameter, wherein the fifth parameter is the average charging power of the electric equipment in the coverage area of the profit determination device.
In a possible implementation manner, the determining module 51 is specifically configured to determine a seventh parameter according to the second parameter, a sixth parameter and the third parameter, where the sixth parameter is an electricity purchase price of the public power grid for the profit determining apparatus, and the seventh parameter indicates an aggressiveness of the electric equipment in a coverage area of the profit determining apparatus to sell electricity; determining the second power according to the seventh parameter and an eighth parameter, wherein the eighth parameter is the average discharge power of the electric equipment in the coverage area of the benefit determination device.
In a possible implementation manner, the determining module 51 is specifically configured to calculate a first profit value according to the first parameter, a third parameter and the first power, where the third parameter is a power supply price of a public power grid to the profit determining apparatus, and the first profit value is a charging profit value of the electric device in a coverage area of the profit determining apparatus; calculating to obtain a second profit value according to the second parameter, a sixth parameter and the second power, wherein the sixth parameter is the electricity purchase price of the public power grid to the profit determination device, and the second profit value is the discharge profit value of the electric equipment in the coverage area of the profit determination device; and calculating the profit value of the profit determining device based on the first profit value and the second profit value.
In a possible implementation manner, the determining module 51 is specifically configured to determine a target profit value according to a sum of the first profit value and the second profit value, where the target profit value indicates a gross profit value of the profit determining apparatus; and determining the profit value of the profit determination device according to the difference between the target profit value and the cost expense value, wherein the cost expense value is the operation and maintenance expense value of the profit determination device.
In a possible implementation manner, the determining module 51 is specifically configured to obtain M target parameter sets, where each target parameter set includes at least one parameter, and M is a positive integer greater than 1; respectively determining an optimal parameter with an optimal parameter position from each target parameter group to determine M optimal parameters; and determining a target parameter based on a target optimal parameter in the M optimal parameters, wherein the target parameter is any one of the first parameter and the second parameter.
In a possible implementation manner, the determining module 51 is specifically configured to determine the target optimal parameter with the optimal parameter position from the M optimal parameters; controlling M-1 target parameter groups, and performing search calculation on the parameter positions of the optimal target parameters to update the parameter positions of the M-1 target parameter groups, wherein the M-1 target parameter groups are parameter groups corresponding to the M-1 optimal parameters one by one, and the M-1 optimal parameters are optimal parameters except the optimal target parameters in the M optimal parameters; respectively determining a first optimal parameter with an optimal parameter position from each target parameter group in the M-1 target parameter groups after the parameter positions are updated so as to determine M-1 first optimal parameters; and determining the target parameters with optimal parameter positions from the M-1 first optimal parameters and the target optimal parameters.
In a possible implementation manner, the determining module 51 is specifically configured to control an optimal parameter in a first parameter group, and adjust a parameter position of the target optimal parameter; controlling other parameters in the first parameter group, and modulating the parameter position of the optimal parameter in the adjusted first parameter group; wherein, the first parameter group is any one of the M-1 target parameter groups; the other parameters are parameters in the first parameter group except for the optimal parameters in the first parameter group.
The income confirmation device that this application embodiment provided, because the income confirmation device can directly acquire the income confirmation device to the electric equipment the best electricity price of selling with the income confirmation device is right the electric equipment's the best buys back electricity price, and confirm in the coverage area of income confirmation device the charging power of electric equipment with in the coverage area of income confirmation device the discharge power of electric equipment, the income confirmation device can directly confirm the income value of income confirmation device like this, and need not lack among the prior art the consideration to each main part interests, does not take income confirmation device income, electric vehicle owner's income as the key point, consequently, full play electric vehicle energy storage advantage. Thus, the profitability of the profit determination apparatus can be improved, and the peak clipping and valley filling efficiency of the profit determination apparatus is prevented from being limited.
The benefit determination apparatus in the embodiment of the present application may be an electronic device, or may be a component in the electronic device, such as an integrated circuit or a chip. The electronic device may be a terminal, or may be a device other than a terminal. The electronic device may be, for example, a mobile phone, a tablet computer, a notebook computer, a palm top computer, a vehicle-mounted electronic device, a Mobile Internet Device (MID), an Augmented Reality (AR)/Virtual Reality (VR) device, a robot, a wearable device, a super-mobile personal computer (UMPC), a netbook or a Personal Digital Assistant (PDA), and the like, and may also be a server, a Network Attached Storage (NAS), a Personal Computer (PC), a Television (TV), a teller machine or a self-service machine, and the like, and the embodiments of the present application are not limited in particular.
The benefit determination device in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system (Android), an iOS operating system, or other possible operating systems, which is not specifically limited in the embodiments of the present application.
The benefit determining apparatus provided in the embodiment of the present application can implement each process implemented in the method embodiments of fig. 1 to fig. 3, and is not described here again to avoid repetition.
Optionally, in this embodiment, as shown in fig. 5, an electronic device 60 is further provided in this embodiment, and includes a processor 61 and a memory 62, where the memory 62 stores a program or an instruction that can be executed on the processor 61, and when the program or the instruction is executed by the processor 61, the process steps of the embodiment of the benefit determining method are implemented, and the same technical effect can be achieved, and in order to avoid repetition, details are not repeated here.
It should be noted that the electronic device in the embodiment of the present application includes the mobile electronic device and the non-mobile electronic device described above.
The embodiment of the present application further provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or the instruction is executed by a processor, the program or the instruction implements each process of the above benefit determining method embodiment, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read only memory ROM, a random access memory RAM, a magnetic or optical disk, and the like.
The embodiment of the present application further provides a chip, where the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to run a program or an instruction to implement each process of the above benefit determining method embodiment, and can achieve the same technical effect, and is not described herein again to avoid repetition.
It should be understood that the chips mentioned in the embodiments of the present application may also be referred to as a system-on-chip, or a system-on-chip.
Embodiments of the present application provide a computer program product, which is stored in a storage medium and executed by at least one processor to implement the processes of the foregoing embodiment of the benefit determining method, and achieve the same technical effects, and in order to avoid repetition, the details are not repeated here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a component of' 8230; \8230;" does not exclude the presence of another like element in a process, method, article, or apparatus that comprises the element. Further, it should be noted that the scope of the methods and apparatus of the embodiments of the present application is not limited to performing the functions in the order illustrated or discussed, but may include performing the functions in a substantially simultaneous manner or in a reverse order based on the functions involved, e.g., the methods described may be performed in an order different than that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application or portions thereof that contribute to the prior art may be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (which may be a mobile phone, a computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present application.
While the present embodiments have been described with reference to the accompanying drawings, it is to be understood that the present embodiments are not limited to those precise embodiments, which are intended to be illustrative rather than restrictive, and that various changes and modifications may be effected therein by one skilled in the art without departing from the scope of the appended claims.
Claims (10)
1. A profit determination method is applied to a virtual power plant and is characterized by comprising the following steps:
determining a first parameter and a second parameter, wherein the first parameter is the optimal electricity selling price of the virtual power plant to the electric equipment, and the second parameter is the optimal buyback price of the virtual power plant to the electric equipment;
determining a first power according to the first parameter, and determining a second power according to the second parameter, wherein the first power is the charging power of the electric equipment in the coverage area of the virtual power plant, and the second power is the discharging power of the electric equipment in the coverage area of the virtual power plant;
determining a profit value for the virtual power plant based on the first parameter, the second parameter, the first power, and the second power.
2. The method of claim 1, wherein determining the first power based on the first parameter comprises:
determining a fourth parameter according to the first parameter and the third parameter, wherein the third parameter is the power supply price of a public power grid to the virtual power plant, and the fourth parameter indicates the charging activity of the electric equipment in the coverage area of the virtual power plant to the virtual power plant;
determining the first power according to the fourth parameter and a fifth parameter, wherein the fifth parameter is the average charging power of the electric equipment in the coverage area of the virtual power plant.
3. The method of claim 2, wherein determining the second power according to the second parameter comprises:
determining a seventh parameter according to the second parameter, a sixth parameter and the third parameter, wherein the sixth parameter is the electricity purchase price of the public power grid to the virtual power plant, and the seventh parameter indicates the enthusiasm of the electric equipment in the coverage area of the virtual power plant to sell electricity;
determining the second power according to the seventh parameter and an eighth parameter, wherein the eighth parameter is the average discharge power of the electric equipment in the coverage area of the virtual power plant.
4. A method according to claim 1, wherein the determining a value of return for the virtual power plant based on the first parameter, the second parameter, the first power, and the second power comprises:
calculating to obtain a first profit value according to the first parameter, a third parameter and the first power, wherein the third parameter is a power supply price of a public power grid to the virtual power plant, and the first profit value is a charging profit value of the electric equipment in a coverage area of the virtual power plant;
calculating to obtain a second profit value according to the second parameter, a sixth parameter and the second power, wherein the sixth parameter is the electricity purchase price of the public power grid to the virtual power plant, and the second profit value is the discharge profit value of the electric equipment in the coverage area of the virtual power plant;
and calculating to obtain the profit value of the virtual power plant based on the first profit value and the second profit value.
5. The method of claim 4, wherein calculating the value of revenue for the virtual power plant based on the first value of revenue and the second value of revenue comprises:
determining a target profit value according to the sum of the first profit value and the second profit value, wherein the target profit value indicates the gross profit value of the virtual power plant;
and determining the income value of the virtual power plant according to the difference between the target income value and the cost value, wherein the cost value is the operation and maintenance cost value of the virtual power plant.
6. The method of claim 1, wherein determining the first parameter and the second parameter comprises:
acquiring M target parameter groups, wherein each target parameter group comprises at least one parameter, and M is a positive integer greater than 1;
respectively determining an optimal parameter with an optimal parameter position from each target parameter group to determine M optimal parameters;
and determining a target parameter based on a target optimal parameter in the M optimal parameters, wherein the target parameter is any one of the first parameter and the second parameter.
7. The method of claim 6, wherein determining the target parameter based on the target optimal parameter of the M optimal parameters comprises:
determining the optimal target parameter with the optimal parameter position from the M optimal parameters;
controlling M-1 target parameter groups, and performing search calculation on the parameter positions of the target optimal parameters to update the parameter positions of the M-1 target parameter groups, wherein the M-1 target parameter groups are parameter groups corresponding to the M-1 optimal parameters one by one, and the M-1 optimal parameters are optimal parameters except the target optimal parameters in the M optimal parameters;
respectively determining a first optimal parameter with an optimal parameter position from each target parameter group in the M-1 target parameter groups after the parameter positions are updated so as to determine M-1 first optimal parameters;
and determining the target parameters with optimal parameter positions from the M-1 first optimal parameters and the target optimal parameters.
8. The method of claim 7, wherein the controlling M-1 sets of target parameters to perform search calculation to the parameter position of the target optimal parameter comprises:
controlling the optimal parameters in the first parameter group, and adjusting the parameter position of the target optimal parameters;
controlling other parameters in the first parameter group, and modulating the parameter position of the optimal parameter in the adjusted first parameter group;
wherein the first parameter group is any one of the M-1 target parameter groups; the other parameters are parameters in the first parameter group except for the optimal parameters in the first parameter group.
9. A revenue determination apparatus, the revenue determination apparatus comprising: a determination module;
the determining module is used for determining a first parameter and a second parameter, wherein the first parameter is the optimal electricity selling price of the income determining device for the electric equipment, and the second parameter is the optimal buyback electricity price of the income determining device for the electric equipment; determining a first power according to the first parameter, and determining a second power according to the second parameter, wherein the first power is a charging power of the electric equipment in the coverage area of the profit determination device, and the second power is a discharging power of the electric equipment in the coverage area of the profit determination device; and determining a benefit value for the benefit determination device based on the first parameter, the second parameter, the first power, and the second power.
10. An electronic device comprising a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions when executed by the processor implementing the steps of the benefit determination method of any of claims 1 to 8.
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| CN202310505185.0A CN116228299A (en) | 2022-11-29 | 2023-05-08 | Virtual power plant price determining method, device and system |
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| CN114862460B (en) * | 2022-04-28 | 2024-07-12 | 国网江苏省电力有限公司电力科学研究院 | Pricing method for virtual power plant operators of electric vehicles based on master-slave gaming |
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