CN112785337A - Price excitation method and device applied to virtual power plant - Google Patents
Price excitation method and device applied to virtual power plant Download PDFInfo
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Abstract
The embodiment of the invention discloses a price incentive method applied to a virtual power plant, which comprises the following steps: the method comprises the steps of constructing a fourth model reflecting the income of a virtual power plant through a first model reflecting the total excitation of the virtual power plant from a power grid, a second model reflecting the total excitation of the virtual power plant to aggregated resources and a third model reflecting the capital consumption of the virtual power plant, and calculating the internal pricing parameter of each aggregated resource through the fourth model and the constraint condition of the fourth model by taking the maximization of the income of the virtual power plant as a target. Therefore, the incentive strategy of the virtual power plant realized by the scheme deeply excavates the potential of the aggregate resource participation demand response capability, and effectively improves the overall profit of the virtual power plant participating demand response while ensuring that the virtual power plant meets the overall response index.
Description
Technical Field
The invention relates to the field of data processing, in particular to a price excitation method and a Zhuang-Han-Zi applied to a virtual power plant.
Background
The virtual power plant polymerization schedulable resource comprises: the system comprises directly controllable scheduling resources and indirectly excited scheduling resources, wherein the directly controllable scheduling resources refer to equipment which can directly control the power of devices such as an electric load and an energy storage device by sending a regulation instruction by an energy management system of a virtual power plant. The indirectly controllable scheduling resource refers to an aggregated resource which cannot be directly controlled by an energy management system, such as residential electricity, and is autonomously controlled by residents.
Currently, in order to ensure safe and stable operation of a power grid, external reliable power supply and orderly operation of various power production works, the polymerization schedulable resources of the virtual power plant are generally optimized and scheduled, but only directly controllable scheduling resources are generally considered, and indirectly excited scheduling resources are not considered. Therefore, the optimization degree of the virtual power plant on the polymerization schedulable resource is lacked.
Disclosure of Invention
In view of this, the embodiment of the invention discloses a price incentive method applied to a virtual power plant, which makes up for the deficiency that indirect incentive resources are not applied to virtual power plant scheduling optimization in the prior art, and further obtains a better optimization effect.
The embodiment of the invention discloses a price incentive method applied to a virtual power plant, which comprises the following steps:
constructing a first model reflecting total excitation obtained by the virtual power plant from the power grid;
constructing a second model reflecting the total excitation of the virtual power plant to the aggregated resources; the first model and the second model both comprise internal pricing parameters of the virtual power plant to the aggregated resources;
constructing a third model reflecting the capital consumption of the virtual power plant;
constructing a fourth model reflecting the benefit of the virtual power plant by a first model reflecting the total excitation of the virtual power plant from the power grid, a second model reflecting the total excitation of the virtual power plant to the aggregated resource and a third model reflecting the fund consumption of the virtual power plant;
determining constraints of a fourth model reflecting the benefits of the virtual power plant;
and calculating the internal pricing parameter of each aggregated resource through a fourth model and the constraint condition of the fourth model by taking the maximization of the income of the virtual power plant as a target.
Optionally, the method further includes:
constructing a meter and uncertainty model reflecting the participation degree of the aggregated resources in the demand response; the uncertainty model represents the relationship between the excitation level of the aggregation resource and the participation degree of the aggregation resource in the demand response;
acquiring the maximum response capacity of each aggregation resource;
and constructing a fifth model reflecting the actual response capability of each aggregation resource according to the uncertainty model and the maximum response capability of each aggregation resource, which are used for reflecting the participation degree of the aggregation resources in the demand response.
Optionally, the constructing a first model reflecting the total excitation obtained by the virtual power plant from the power grid includes:
determining a power grid excitation price declared to a power grid by the virtual power plant;
and constructing a first model reflecting the total excitation obtained by the virtual power plant from the power grid through the power grid excitation price declared by the virtual power plant to the power grid and a fifth model reflecting the actual response capability of each aggregated resource.
Optionally, the constructing a second model reflecting the total excitation of the virtual power plant to the aggregated resource includes:
and constructing a second model reflecting the total excitation of the virtual power plant to the aggregated resources through the internal pricing parameters of each aggregated resource and a fifth model reflecting the actual response capability of each aggregated resource.
Optionally, the constructing a third model reflecting the virtual power plant capital consumption includes:
determining punishment price of the power grid to the virtual power plant and total amount of user demand response declared to the power grid by the virtual power plant;
and constructing a sixth model reflecting the negative excitation of the power grid to the virtual power plant through the punishment price of the power grid to the virtual power plant, the total amount of user demand response declared to the power grid by the virtual power plant and the fifth model reflecting the actual response capacity of each aggregated resource.
Optionally, the constructing a third model reflecting the virtual power plant capital consumption includes:
constructing a fuel cost model, an operation and maintenance cost model and an environment cost model;
and constructing a cost model of the virtual power plant through the fuel cost model, the operation and maintenance cost model and the environment cost model.
Optionally, the calculating, with the goal of maximizing the profit of the virtual power plant, the internal pricing parameter of each aggregated resource through a fourth model and a constraint condition of the fourth model includes:
determining initial values of all parameters in the fourth model and initial values of genetic algorithm parameters;
performing iterative computation on the fourth model reflecting the income of the virtual power plant according to the initial values of the genetic algorithm and the parameters in the fourth model and the initial values of the parameters of the genetic algorithm;
when a condition for terminating an iteration is reached, a value of an internal pricing parameter of the aggregated resource at the termination of the iteration is determined.
The embodiment of the invention also discloses a price exciting device applied to the virtual power plant, which comprises:
the first model building unit is used for building a first model reflecting total excitation obtained by the virtual power plant from the power grid;
the second model building unit is used for building a second model reflecting the total excitation of the virtual power plant to the polymerization resources; the first model and the second model both comprise internal pricing parameters of the virtual power plant to the aggregated resources;
the third model building unit is used for building a third model reflecting the fund consumption of the virtual power plant;
the fourth model building unit is used for building a fourth model reflecting the income of the virtual power plant through a first model reflecting the total excitation of the virtual power plant from the power grid, a second model reflecting the total excitation of the virtual power plant to the aggregated resources and a third model reflecting the fund consumption of the virtual power plant;
the constraint condition construction unit is used for determining constraint conditions of a fourth model reflecting the income of the virtual power plant;
and the calculation unit is used for calculating the internal pricing parameter of each aggregated resource through a fourth model and the constraint condition of the fourth model by taking the maximization of the income of the virtual power plant as a target.
Optionally, the method further includes:
the uncertainty model construction unit is used for constructing a consideration uncertainty model reflecting the participation degree of the aggregated resources in the demand response; the uncertainty model represents the relationship between the excitation level of the aggregation resource and the participation degree of the aggregation resource in the demand response;
an obtaining unit, configured to obtain a maximum response capability of each aggregation resource;
and the fifth model building unit is used for building a fifth model reflecting the actual response capability of each aggregation resource according to the uncertainty model reflecting the participation degree of the aggregation resources in the demand response and the maximum response capability of each aggregation resource.
The embodiment of the invention also discloses an electronic device, which comprises:
a memory and a processor;
the memory is used for storing programs;
the processor, when executing the program in the memory, performs the method of any of claims 1-7.
The embodiment of the invention discloses a price incentive method applied to a virtual power plant, which comprises the following steps: the method comprises the steps of constructing a fourth model reflecting the income of a virtual power plant through a first model reflecting the total excitation of the virtual power plant from a power grid, a second model reflecting the total excitation of the virtual power plant to aggregated resources and a third model reflecting the capital consumption of the virtual power plant, and calculating the internal pricing parameter of each aggregated resource through the fourth model and the constraint condition of the fourth model by taking the maximization of the income of the virtual power plant as a target. Therefore, the incentive strategy of the virtual power plant realized by the scheme deeply excavates the potential of the aggregate resource participation demand response capability, and effectively improves the overall profit of the virtual power plant participating demand response while ensuring that the virtual power plant meets the overall response index.
Furthermore, in this embodiment, the cost of the virtual power plant is also considered, and the consideration of the directly controllable distributed resources by the virtual power plant represented by the cost, that is, the calculation of the pricing of the incentive of the virtual power plant, and the directly controllable distributed resources of the virtual power plant are also considered, so that the accuracy of the pricing result of the incentive of the aggregated resources by the virtual power plant is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a price incentive method applied to a virtual power plant according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a fifth model for constructing an actual response capability reflecting each aggregated resource according to the embodiment of the present invention;
FIG. 3 illustrates an example schematic of engagement of an aggregated resource in a demand response versus an aggregated resource incentive level;
FIG. 4 is a flow chart illustrating a method for calculating an internal pricing parameter for each aggregated resource according to an embodiment of the invention;
FIG. 5 is a schematic structural diagram of a price incentive device applied to a virtual power plant according to an embodiment of the present invention;
fig. 6 shows a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a schematic flow chart of a price incentive method applied to a virtual power plant according to an embodiment of the present invention is shown, in this embodiment, the method includes:
s101: constructing a first model reflecting total excitation obtained by the virtual power plant from the power grid;
in this embodiment, the total incentive obtained by the virtual power plant from the power grid is a total incentive amount applied by the virtual power plant to the power grid. Specifically, a first model reflecting the total excitation obtained by the virtual power plant from the power grid can be constructed by the following method:
determining a power grid excitation price declared to a power grid by the virtual power plant;
and constructing a first model reflecting the total excitation obtained by the virtual power plant from the power grid through the power grid excitation price declared by the virtual power plant to the power grid and a fifth model reflecting the actual response capability of each aggregated resource.
The fifth model reflecting the actual response capability of each aggregation resource may be constructed by a model reflecting the participation degree of the user in the demand response and the maximum response capability of each aggregation resource, and a specific construction method will be described in detail below, which is not described in detail in this embodiment. And, the actual response capability of the aggregated resource can be understood as the total actual power usage of the aggregated resource.
And the power grid excitation price declared to the power grid by the virtual power plant is the excitation unit price.
In this embodiment, the power grid excitation price declared by the virtual power plant to the power grid is related to the actual response capability of all aggregated resources (i.e., the total actual power consumption of the aggregated resources), and the power grid excitation price declared by the virtual power plant to the power grid is multiplied by the actual response capability of the aggregated resources, so as to obtain the total excitation obtained by the virtual power plant from the power grid.
For example, the following steps are carried out: the first model reflecting the total excitation of the virtual power plant from the grid can be represented by equation 1) as follows:
wherein R isgridRepresenting the total excitation, r, obtained by the virtual power plant from the gridgridIncentive price, more precisely r, declared to the grid for a virtual power plantgridReporting the power grid excitation price in the demand response participation amount to the power grid for the virtual power plant;
and the fifth model is a fifth model reflecting the total amount of the ith aggregate resource actually participating in demand response in the virtual power plant.
It is to be understood that the incentive of the grid to the virtual power plant is an incentive price when the virtual power plant declares the demand response participation amount to the grid, but the incentive is not enjoyed any more than the declared demand response participation amount.
Wherein the first model of the total excitation obtained by the virtual power plant from the power grid is related to the actual response capability of the aggregated resource, which is related to the excitation level of the aggregated resource (pricing parameter of the virtual power plant). Thus, the first model includes the internal pricing parameters of the virtual plant to the aggregated resources.
S102: constructing a second model reflecting the total excitation of the virtual power plant to the aggregated resources; the second model comprises internal pricing parameters of each aggregated resource;
in this embodiment, the virtual power plant may specify a subsidy unit price (internal pricing of each aggregated resource) of each incentive resource according to a price incentive policy, thereby maximally incentivizing users to participate in demand response of each resource.
In this embodiment, the virtual power plant determines the incentive level of the virtual power plant to the aggregated resource under the condition of guaranteeing the profit, or represents the internal price of the virtual power plant to the aggregated resource, and the price is lower than the price declared by the virtual power plant from the power grid. Wherein, the total incentive of the virtual power plant to the aggregation resource is related to the internal price of the aggregation resource and the actual response capability reflecting each aggregation resource, specifically, S102 includes:
and constructing a second model reflecting the total excitation of the virtual power plant to the aggregated resources through the internal pricing parameters of each aggregated resource and a fifth model reflecting the actual response capability of each aggregated resource.
For example, the following steps are carried out: the second model reflecting the total excitation of the virtual power plant to the aggregated resource may be represented by equation 2) as follows:
wherein x isiAn internal pricing parameter representing the ith aggregated resource,
and the fifth model is a fifth model reflecting the total amount of the ith aggregate resource actually participating in demand response in the virtual power plant.
S103: constructing a third model reflecting the capital consumption of the virtual power plant;
in this embodiment, the fund consumption is expressed as a fund paid by the virtual power plant, which may include: negative excitation of the virtual power plant by the power grid and the cost of the virtual power plant.
The excitation of the power grid to the virtual power plant is an excitation price when the virtual power plant declares the participation quantity of the demand response to the power grid, but the excitation is not enjoyed after the participation quantity of the declared demand response is exceeded, namely the power grid punishs the price of the virtual power plant after the participation quantity of the declared demand response is exceeded, and the power grid punishs the price of the virtual power plant, so that the power grid is considered as negative excitation of the virtual power plant.
The virtual power plant cost is the distributed resource directly controllable by the power plant, such as the combined cooling heating and power gas turbine unit CCHP, and the virtual power plant operator needs to be responsible for the operation cost, and these costs may consume the expenditure of the virtual power plant.
The virtual power plant negative excitation, the punishment price of the power grid to the virtual power plant and the value of the total amount of the actual demand response of the aggregated resources exceed the total amount of the demand response declared to the power grid by the virtual power plant.
In this embodiment, the negative excitation of the power grid to the virtual power plant may be determined through the negative excitation of the virtual power plant and the total amount of the penalty price of the power grid to the virtual power plant and the actual demand response of the aggregated resources, specifically, the method includes:
determining punishment price of the power grid to the virtual power plant and total amount of aggregated resource demand response declared to the power grid by the virtual power plant;
and constructing a sixth model reflecting negative excitation of the power grid to the virtual power plant through the punishment price of the power grid to the virtual power plant, the total quantity of aggregated resource demand response declared to the power grid by the virtual power plant and the fifth model reflecting the actual response capacity of each aggregated resource.
For example, the following steps are carried out: a sixth model reflecting the negative excitation of the virtual power plant by the grid may be formulated as follows
Formula 3) represents:
wherein the content of the first and second substances,
for the penalty price of the grid to the virtual power plant,
for the total amount of demand responses that the virtual power plant claims to the grid,
and the fifth model is a fifth model reflecting the total amount of the ith aggregate resource actually participating in demand response in the virtual power plant.
In this embodiment, the method for constructing the cost model reflecting the cost of the distributed resources of the virtual power plant includes multiple methods, which are not limited in this embodiment, and specifically, the method for constructing the cost model of the virtual power plant includes:
constructing a fuel cost model, an operation and maintenance cost model and an environment cost model;
and constructing a cost model of the virtual power plant through the fuel cost model, the operation and maintenance cost model and the environment cost model.
Wherein, the cost model of the virtual power plant can be represented by the following formula 4):
wherein the content of the first and second substances,
for output of jth unit, λfuelThe heat-generating price of the natural gas is low,
the efficiency of the jth unit, L is the natural gas low heating value,
for operation and maintenance cost coefficient, vkThe amount of the pollutant k discharged per unit power generation amount,
environmental remediation costs for class k pollutants.
Wherein the content of the first and second substances,
in order to be a model of the cost of the fuel,
in order to provide an operation and maintenance cost model,
is an environmental cost.
Therefore, the accuracy of the pricing result of the virtual power plant for the aggregated resource incentive is improved by considering the directly controllable distributed resources of the virtual power plant.
S104, constructing a fourth model reflecting the income of the virtual power plant through a first model reflecting the total excitation of the virtual power plant from the power grid, a second model reflecting the total excitation of the virtual power plant to the aggregated resources and a third model reflecting the fund consumption of the virtual power plant;
in this embodiment, the profit of the virtual power plant may be expressed as: the virtual power plant subtracts the difference between the total incentive amount of the virtual power plant to the aggregated resource and the capital consumption of the virtual power plant from the total incentive amount obtained by the power grid.
Wherein the fund consumption comprises: negative excitation of the virtual power plant by the power grid and/or cost of the virtual power plant.
For example, the following steps are carried out: in the case where the capital consumption includes negative excitation of the virtual power plant by the power grid and the cost of the virtual power plant, the fourth model reflecting the profit of the virtual power plant may be represented by the following equation 5):
5)JVPP＝Rgid-RVPP-Rpunish-Rchp；
specifically, as shown in equation 6):
wherein the content of the first and second substances,
a first model reflecting the total excitation obtained by the virtual power plant from the grid is represented,
representing a second model reflecting a total incentive of the virtual power plant to the aggregated resource;
a sixth model representing a negative excitation of the virtual power plant by the power grid;
a cost model reflecting the cost consumption of the virtual power plant is represented.
S105: determining constraints of a fourth model reflecting the benefits of the virtual power plant;
in this embodiment, the constraint condition is a value range of each parameter in the fourth model, and the value range of each parameter may be set according to an actual situation.
Wherein, when the fourth model is expressed by the above equation 5), the constraint condition may be expressed by the following equation 7):
wherein the actual response capability of each aggregated resource
The participation degree of the users in the demand response is determined, P {. is the probability that the actual response quantity of the aggregated resources in the virtual power plant is greater than the overall declaration quantity,
the maximum value of the output of the jth unit.
S106: and calculating the internal pricing parameter of each aggregated resource through the fourth model and the constraint condition of the fourth model by taking maximization of the income of the virtual power plant as a target.
In this embodiment, the internal pricing of each aggregated resource by the virtual power plant can be calculated on the basis of ensuring the maximum profit of the virtual power plant. That is, the internal pricing of each aggregated resource is calculated on the basis of guaranteeing the maximization of the revenue of the power plant through the fourth model reflecting the virtual power plant revenue.
The calculation of the corresponding internal pricing parameter may be performed in a variety of ways, and in this embodiment, without limitation, for example, the opportunity constraint may be converted into a deterministic plan, where for the opportunity constraint plan whose objective function is linear, the constraint parameter of the model may be converted into an equivalent deterministic plan under the condition that the constraint parameter obeys normal distribution and exponential distribution.
Further, in order to obtain the optimal solution, the internal pricing parameter of each aggregated resource may be calculated through a genetic algorithm, and a specific method will be described below, which is not described in detail in this embodiment.
In this embodiment, the value of the internal pricing parameter of each aggregated resource is obtained by constructing a fourth model reflecting the profit of the virtual power plant and solving the fourth model while ensuring the maximum profit. And, by the value of the internal pricing parameter for each aggregated resource, the user is compensated as incentive for the aggregated resource. Therefore, the virtual power plant is ensured to meet the overall response index, and the overall income maximization of the virtual power plant participating in demand response is realized.
In addition to this, the impact of distributed resources on the aggregated resource incentives is also taken into account,
referring to fig. 2, a schematic flowchart of a fifth model for constructing an actual response capability reflecting each aggregated resource according to an embodiment of the present invention is shown, in this embodiment, the method includes:
s201: constructing a meter and uncertainty model reflecting the participation degree of the user in the demand response; the uncertainty model represents the relationship between the excitation level of the aggregation resource and the participation degree of the aggregation resource in the demand response;
in this embodiment, the aggregated resource may be understood as a unit of electricity or a unit of power generation, and the unit of electric energy needs to be purchased from the virtual power plant. The electricity consumption unit can be a household, an enterprise, and the like.
In this embodiment, the aggregate resources participating in the demand response in the virtual power plant have individual differences in the incentive policy of the virtual power plant operator, and both present a difference threshold value and a saturation value. In the embodiment, the relationship between the excitation level of the virtual power plant to the aggregation resources and the participation degree of the aggregation resource participation demand response is embodied by constructing and calculating the uncertainty model.
For example, the following steps are carried out: assuming that the participation degree of the aggregated resources in the virtual power plant participating in the demand response is represented as xii(x) In which ξi(x) Expressed as the following equations 8), 9), and 10):
wherein ξupperAs a function of the upper bound, ξ, of demand response engagementlowerIs a lower bound function of the demand response participation degree, i refers to the ith aggregation resource of the virtual power plant, and x is the relative level of the demand response excitation (or is expressed as the internal definition of the aggregation resource)Price parameter) whose value is the ratio of the incentive price to the maximum incentive price, x0Is the lowest critical incentive price, x1At the upper boundary maximum critical excitation level, x2The lower boundary highest critical level.
For the upper bound function, when the incentive price x is greater than x1And if so, entering the dead interval by the participation curve of the upper boundary, namely the participation is 100%, which indicates that the participation of the demand response of the aggregation resource i cannot be changed by continuously increasing the incentive price.
For the lower bound function, when the incentive price x is greater than x2And if so, entering the dead interval by the participation curve of the lower boundary, namely the participation is 100%, which indicates that the participation of the demand response of the aggregation resource i cannot be changed by continuously increasing the incentive price.
Further, to more clearly show the relationship between the incentive level of the aggregated resource and the engagement demand response of the aggregated resource, as shown in fig. 3, an example schematic diagram of the relationship between the engagement of the aggregated resource and the incentive level of the aggregated resource is shown, wherein an abscissa is the incentive level of the aggregated resource, a total coordinate is the engagement of the demand response, an upper curve represents an upper bound function of the engagement of the aggregated resource and the demand response, a lower curve represents a lower bound function of the engagement of the aggregated resource and the demand response, and a shaded portion represents the range of uncertainty.
S202: acquiring the maximum response capacity of each aggregation resource;
each aggregated resource can reach an agreement with the virtual power plant in advance, and the maximum influence capacity of the aggregated resource is agreed, wherein the maximum response capacity can be expressed as the maximum power consumption of the aggregated resource.
S203: and constructing a fifth model reflecting the actual response capability of each aggregation resource according to the uncertainty model and the maximum response capability of each aggregation resource, which reflect the participation degree of the user in the demand response.
In this embodiment, the actual response capability of each aggregation resource is related to the participation degree of the user in the demand response and the maximum response capability of the aggregation resource, and the maximum response capability of each aggregation resource may be obtained from the power grid or may also be determined according to an actual situation.
For example, the following steps are carried out: the fifth model reflecting the actual response capability of each aggregated resource can be represented by the following equation 11):
wherein the content of the first and second substances,
representing the actual response capability, ξ, of each aggregated resourcei(x) Indicating the engagement of each aggregated resource in the demand response,
representing the maximum response capability of each aggregated resource.
In this embodiment, the uncertainty model is taken into account to reflect the participation degree of the aggregated resources in the demand response, and the uncertainty model and the maximum response capability of each aggregated resource are taken into account to construct a fifth model reflecting the actual response capability of each aggregated resource. Therefore, the participation degree of users of different aggregation resources participating in demand response can be reasonably reflected by considering the uncertainty model, and the actual response capability of the aggregation resources can be more reasonably reflected by considering the fifth model which is constructed by the uncertainty model and reflects the actual response capability.
Referring to fig. 4, a flowchart of a method for calculating an internal pricing parameter of each aggregated resource according to an embodiment of the present invention is shown, and in this embodiment, the method includes:
s401: determining initial values of all parameters in the fourth model and initial values of genetic algorithm parameters;
in this embodiment, the fourth model is a model reflecting the profit of the virtual power plant, and the parameters in the fourth model include, for example: distributed resources, economic parameters, electricity price information, and the like.
The distributed resources are some parameter information contained in the cost model, and the economic resources include some pricing information, for example, including: the incentive price of the power grid to the virtual power plant, and the price of the internal pricing of the virtual power plant to the aggregated resources.
Wherein the determination of the initial value needs to be determined based on a constraint condition.
S402: performing iterative computation on the fourth model reflecting the income of the virtual power plant according to the initial values of the genetic algorithm and the parameters in the fourth model and the initial values of the parameters of the genetic algorithm;
s403: when a condition for terminating an iteration is reached, a value of an internal pricing parameter of the aggregated resource at the termination of the iteration is determined.
The iteration termination condition may include multiple conditions, which are not limited in this embodiment, and may be, for example: and when the difference value between the power plant income of the Nth iteration result and the power plant income of the (N-1) th iteration result is smaller than a preset threshold value, indicating that an iteration termination condition is reached, stopping iteration, and taking the value of the internal pricing parameter of the polymerization resource during the Nth iteration as a final output value.
In the embodiment, the model is solved through a genetic algorithm, a target approximate optimal solution of the opportunity constraint planning problem is obtained through solving, and the difference of price incentive levels of different aggregation resources can be self-adapted.
Referring to fig. 5, a schematic structural diagram of a price incentive device applied to a virtual power plant according to an embodiment of the present invention is shown, in this embodiment, the device includes:
a first model building unit 501, configured to build a first model reflecting a total excitation obtained by the virtual power plant from the power grid;
a second model building unit 502, configured to build a second model reflecting the total excitation of the virtual power plant to the aggregated resource; the first model and the second model both comprise internal pricing parameters of the virtual power plant to the aggregated resources;
a third model construction unit 503, configured to construct a third model reflecting the virtual power plant capital consumption;
a fourth model construction unit 504, configured to construct a fourth model reflecting the profit of the virtual power plant by using the first model reflecting the total incentive obtained by the virtual power plant from the power grid, the second model reflecting the total incentive of the virtual power plant to the aggregated resource, and the third model reflecting the fund consumption of the virtual power plant;
a constraint condition constructing unit 505, configured to determine a constraint condition of a fourth model reflecting the profit of the virtual power plant;
a calculating unit 506, configured to calculate an internal pricing parameter of each aggregated resource through a fourth model and a constraint condition of the fourth model with a goal of maximizing the profit of the virtual power plant.
Optionally, the method further includes:
the uncertainty model construction unit is used for constructing a consideration uncertainty model reflecting the participation degree of the aggregated resources in the demand response; the uncertainty model represents the relationship between the excitation level of the aggregation resource and the participation degree of the aggregation resource in the demand response;
an obtaining unit, configured to obtain a maximum response capability of each aggregation resource;
and the fifth model building unit is used for building a fifth model reflecting the actual response capability of each aggregation resource according to the uncertainty model reflecting the participation degree of the aggregation resources in the demand response and the maximum response capability of each aggregation resource.
Optionally, the first model building unit includes:
the power grid excitation price determining unit is used for determining the power grid excitation price declared to the power grid by the virtual power plant;
and the first model building subunit is used for building a first model reflecting the total excitation obtained by the virtual power plant from the power grid through the power grid excitation price declared by the virtual power plant to the power grid and a fifth model reflecting the actual response capacity of each aggregated resource.
Optionally, the second model building unit includes:
and constructing a second model reflecting the total excitation of the virtual power plant to the aggregated resources through the internal pricing parameters of each aggregated resource and a fifth model reflecting the actual response capability of each aggregated resource.
Optionally, the constructing a third model reflecting the virtual power plant capital consumption includes:
determining punishment price of the power grid to the virtual power plant and total amount of user demand response declared to the power grid by the virtual power plant;
and constructing a sixth model reflecting the negative excitation of the power grid to the virtual power plant through the punishment price of the power grid to the virtual power plant, the total amount of user demand response declared to the power grid by the virtual power plant and the fifth model reflecting the actual response capacity of each aggregated resource.
Optionally, the third model building unit includes:
the first cost model construction subunit is used for constructing a fuel cost model, an operation and maintenance cost model and an environment cost model;
and the second cost model construction subunit is used for constructing a cost model of the virtual power plant through the fuel cost model, the operation and maintenance cost model and the environment cost model.
Optionally, the computing unit includes:
an initial value determining subunit, configured to determine an initial value of each parameter in the fourth model and an initial value of a genetic algorithm parameter;
the iteration subunit is used for carrying out iterative computation on the fourth model reflecting the benefits of the virtual power plant according to the initial values of the genetic algorithm and the parameters in the fourth model and the initial values of the parameters of the genetic algorithm;
when a condition for terminating an iteration is reached, a value of an internal pricing parameter of the aggregated resource at the termination of the iteration is determined.
According to the device, the fourth model reflecting the income of the virtual power plant is constructed, and the fourth model is solved under the condition of guaranteeing the maximum income, so that the value of the internal pricing parameter of each aggregated resource is obtained. And, by the value of the internal pricing parameter for each aggregated resource, the user is compensated as incentive for the aggregated resource. Therefore, the virtual power plant is ensured to meet the overall response index, and the overall income maximization of the virtual power plant participating in demand response is realized.
Referring to fig. 6, a schematic structural diagram of an electronic device according to an embodiment of the present invention is shown, where in this embodiment, the electronic device includes:
a memory 601 and a processor 602;
the memory 601 is used for storing programs;
the processor 601, when executing the program stored in the memory, executes the following price incentive method applied to a virtual power plant, including:
constructing a first model reflecting total excitation obtained by the virtual power plant from the power grid;
constructing a second model reflecting the total excitation of the virtual power plant to the aggregated resources; the first model and the second model both comprise internal pricing parameters of the virtual power plant to the aggregated resources;
constructing a third model reflecting the capital consumption of the virtual power plant;
constructing a fourth model reflecting the benefit of the virtual power plant by a first model reflecting the total excitation of the virtual power plant from the power grid, a second model reflecting the total excitation of the virtual power plant to the aggregated resource and a third model reflecting the fund consumption of the virtual power plant;
determining constraints of a fourth model reflecting the benefits of the virtual power plant;
and calculating the internal pricing parameter of each aggregated resource through a fourth model and the constraint condition of the fourth model by taking the maximization of the income of the virtual power plant as a target.
Optionally, the method further includes:
constructing a meter and uncertainty model reflecting the participation degree of the aggregated resources in the demand response; the uncertainty model represents the relationship between the excitation level of the aggregation resource and the participation degree of the aggregation resource in the demand response;
acquiring the maximum response capacity of each aggregation resource;
and constructing a fifth model reflecting the actual response capability of each aggregation resource according to the uncertainty model and the maximum response capability of each aggregation resource, which are used for reflecting the participation degree of the aggregation resources in the demand response.
Optionally, the constructing a first model reflecting the total excitation obtained by the virtual power plant from the power grid includes:
determining a power grid excitation price declared to a power grid by the virtual power plant;
and constructing a first model reflecting the total excitation obtained by the virtual power plant from the power grid through the power grid excitation price declared by the virtual power plant to the power grid and a fifth model reflecting the actual response capability of each aggregated resource.
Optionally, the constructing a second model reflecting the total excitation of the virtual power plant to the aggregated resource includes:
and constructing a second model reflecting the total excitation of the virtual power plant to the aggregated resources through the internal pricing parameters of each aggregated resource and a fifth model reflecting the actual response capability of each aggregated resource.
Optionally, the constructing a third model reflecting the virtual power plant capital consumption includes:
determining punishment price of the power grid to the virtual power plant and total amount of user demand response declared to the power grid by the virtual power plant;
and constructing a sixth model reflecting the negative excitation of the power grid to the virtual power plant through the punishment price of the power grid to the virtual power plant, the total amount of user demand response declared to the power grid by the virtual power plant and the fifth model reflecting the actual response capacity of each aggregated resource.
Optionally, the constructing a third model reflecting the virtual power plant capital consumption includes:
constructing a fuel cost model, an operation and maintenance cost model and an environment cost model;
and constructing a cost model of the virtual power plant through the fuel cost model, the operation and maintenance cost model and the environment cost model.
Optionally, the calculating, with the goal of maximizing the profit of the virtual power plant, the internal pricing parameter of each aggregated resource through a fourth model and a constraint condition of the fourth model includes:
determining initial values of all parameters in the fourth model and initial values of genetic algorithm parameters;
performing iterative computation on the fourth model reflecting the income of the virtual power plant according to the initial values of the genetic algorithm and the parameters in the fourth model and the initial values of the parameters of the genetic algorithm;
when a condition for terminating an iteration is reached, a value of an internal pricing parameter of the aggregated resource at the termination of the iteration is determined.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A price incentive method applied to a virtual power plant is characterized by comprising the following steps:
constructing a first model reflecting total excitation obtained by the virtual power plant from the power grid;
constructing a second model reflecting the total excitation of the virtual power plant to the aggregated resources; the first model and the second model both comprise internal pricing parameters of the virtual power plant to the aggregated resources;
constructing a third model reflecting the capital consumption of the virtual power plant;
constructing a fourth model reflecting the benefit of the virtual power plant by a first model reflecting the total excitation of the virtual power plant from the power grid, a second model reflecting the total excitation of the virtual power plant to the aggregated resource and a third model reflecting the fund consumption of the virtual power plant;
determining constraints of a fourth model reflecting the benefits of the virtual power plant;
and calculating the internal pricing parameter of each aggregated resource through a fourth model and the constraint condition of the fourth model by taking the maximization of the income of the virtual power plant as a target.
2. The method of claim 1, further comprising:
constructing a meter and uncertainty model reflecting the participation degree of the aggregated resources in the demand response; the uncertainty model represents the relationship between the excitation level of the aggregation resource and the participation degree of the aggregation resource in the demand response;
acquiring the maximum response capacity of each aggregation resource;
and constructing a fifth model reflecting the actual response capability of each aggregation resource according to the uncertainty model and the maximum response capability of each aggregation resource, which are used for reflecting the participation degree of the aggregation resources in the demand response.
3. The method of claim 2, wherein constructing the first model reflecting the total excitation obtained by the virtual power plant from the power grid comprises:
determining a power grid excitation price declared to a power grid by the virtual power plant;
and constructing a first model reflecting the total excitation obtained by the virtual power plant from the power grid through the power grid excitation price declared by the virtual power plant to the power grid and a fifth model reflecting the actual response capability of each aggregated resource.
4. The method of claim 2, wherein constructing a second model reflecting total incentives for the virtual plant to aggregate resources comprises:
and constructing a second model reflecting the total excitation of the virtual power plant to the aggregated resources through the internal pricing parameters of each aggregated resource and a fifth model reflecting the actual response capability of each aggregated resource.
5. The method of claim 1, wherein constructing a third model reflecting virtual plant capital consumption comprises:
determining punishment price of the power grid to the virtual power plant and total amount of user demand response declared to the power grid by the virtual power plant;
and constructing a sixth model reflecting the negative excitation of the power grid to the virtual power plant through the punishment price of the power grid to the virtual power plant, the total amount of user demand response declared to the power grid by the virtual power plant and the fifth model reflecting the actual response capacity of each aggregated resource.
6. The method of claim 1, wherein constructing a third model reflecting virtual plant capital consumption comprises:
constructing a fuel cost model, an operation and maintenance cost model and an environment cost model;
and constructing a cost model of the virtual power plant through the fuel cost model, the operation and maintenance cost model and the environment cost model.
7. The method of claim 1, wherein calculating internal pricing parameters for each aggregated resource through a fourth model and constraints of the fourth model with the goal of maximizing revenue for the virtual power plant comprises:
determining initial values of all parameters in the fourth model and initial values of genetic algorithm parameters;
performing iterative computation on the fourth model reflecting the income of the virtual power plant according to the initial values of the genetic algorithm and the parameters in the fourth model and the initial values of the parameters of the genetic algorithm;
when a condition for terminating an iteration is reached, a value of an internal pricing parameter of the aggregated resource at the termination of the iteration is determined.
8. A price incentive device applied to a virtual power plant, comprising:
the first model building unit is used for building a first model reflecting total excitation obtained by the virtual power plant from the power grid;
the second model building unit is used for building a second model reflecting the total excitation of the virtual power plant to the polymerization resources; the first model and the second model both comprise internal pricing parameters of the virtual power plant to the aggregated resources;
the third model building unit is used for building a third model reflecting the fund consumption of the virtual power plant;
the fourth model building unit is used for building a fourth model reflecting the income of the virtual power plant through a first model reflecting the total excitation of the virtual power plant from the power grid, a second model reflecting the total excitation of the virtual power plant to the aggregated resources and a third model reflecting the fund consumption of the virtual power plant;
the constraint condition construction unit is used for determining constraint conditions of a fourth model reflecting the income of the virtual power plant;
and the calculation unit is used for calculating the internal pricing parameter of each aggregated resource through a fourth model and the constraint condition of the fourth model by taking the maximization of the income of the virtual power plant as a target.
9. The apparatus of claim 8, further comprising:
the uncertainty model construction unit is used for constructing a consideration uncertainty model reflecting the participation degree of the aggregated resources in the demand response; the uncertainty model represents the relationship between the excitation level of the aggregation resource and the participation degree of the aggregation resource in the demand response;
an obtaining unit, configured to obtain a maximum response capability of each aggregation resource;
and the fifth model building unit is used for building a fifth model reflecting the actual response capability of each aggregation resource according to the uncertainty model reflecting the participation degree of the aggregation resources in the demand response and the maximum response capability of each aggregation resource.
10. An electronic device, comprising:
a memory and a processor;
the memory is used for storing programs;
the processor, when executing the program in the memory, performs the method of any of claims 1-7.
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