CN116757445A - Method, device, equipment and medium for quickly distributing adjustment capability of virtual power plant - Google Patents
Method, device, equipment and medium for quickly distributing adjustment capability of virtual power plant Download PDFInfo
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Abstract
The application relates to a method, a device, equipment and a medium for rapidly distributing adjustment capability of a virtual power plant, wherein the method comprises the following steps: constructing a virtual power plant model containing various resources; predicting the balance power demands of users in the area to obtain a demand predicted value; dividing the resources into quick adjustment type resources and optimization adjustment type resources according to the characteristics of the resources, the demand predicted values, the geographic areas and the market information; establishing a virtual energy storage model, an evaluation system and a quick calling model which participate in the quick regulation type resource; establishing an optimization calling model participating in optimization regulation of class resources; according to the type of the demand, a regulation strategy is formed by adopting a mode of solving the optimization calling model or a mode of calling the rapid calling model; and distributing the adjustment capacity of the virtual power plant by adopting the adjustment strategy. The application can realize quick response to the adjustment requirement.
Description
Technical Field
The application relates to the technical field of virtual power plants, in particular to a method, a device, equipment and a medium for rapidly distributing the adjustment capacity of a virtual power plant.
Background
The virtual power plant performs aggregation management and optimal control on distributed resources such as distributed power sources, energy storage, adjustable loads and the like through advanced digital technology, control technology, internet of things technology and information communication technology, so that the adjustment potential of the distributed resources is utilized, adjustable capacity is provided for power grid operation, and the virtual power plant has a good application prospect.
In the process of participating in the regulation of the virtual power plant, quick response is often required to be realized, the existing resource optimization configuration method generally carries out optimization solution on an objective function and constraint conditions through an artificial intelligent algorithm, an optimization scheme under a certain expected target can be formed, the solution time is long, and the flexibility is still required to be further improved. For example, the prior published patent document CN115034513a discloses a photovoltaic and energy storage optimizing configuration method for a load-oriented virtual power plant, which effectively solves the problems that the nodes of a clustering integration method are not concentrated enough and the photovoltaic output curve difference in different weather scenes affects capacity configuration and operation, but the optimization response time of the method is long and the requirement of rapid adjustment cannot be met. Meanwhile, the current balance is more electric quantity balance in a large range, and the conditions of real-time power balance capability and shortage of local area balance requirements need to be further improved.
Disclosure of Invention
The application provides a method, a device, equipment and a medium for quickly distributing the adjustment capability of a virtual power plant, which solve the problem of quickly distributing the adjustment capability of the virtual power plant and provide quick balancing capability for a balancing capability demand party in an area, and realize power balance in the area.
The technical scheme adopted for solving the technical problems is as follows: the utility model provides a quick distribution method of the adjustment capability of a virtual power plant, which comprises the following steps:
constructing a virtual power plant model containing various resources, and determining the adjustable capacity of the virtual power plant;
predicting the balance power demands of users in the area to obtain a demand predicted value;
dividing the resources into quick adjustment type resources and optimization adjustment type resources according to the characteristics of the resources, the demand predicted values, the geographic areas and the market information;
establishing a virtual energy storage model, an evaluation system and a rapid scheduling model which participate in the rapid adjustment type resource by combining the adjustable capacity of the virtual power plant;
establishing an optimization calling model participating in optimization regulation of class resources by combining the adjustable capacity of the virtual power plant;
according to the type of the demand, a regulation strategy is formed by adopting a mode of solving the optimization calling model or a mode of calling the rapid calling model;
and distributing the adjustment capacity of the virtual power plant by adopting the adjustment strategy.
The virtual power plant model comprises characteristics of adjustable resources of a virtual power plant, an adjustable capacity model and a balance power prediction model required by self renewable resources; the characteristics and the adjustable capacity model of the adjustable resources of the virtual power plant are as follows:wherein, the method comprises the steps of, wherein,UAindicating the ability to up-regulate,DAindicating the ability to be turned down,Pin order to predict the operating power of the vehicle,P min at the level of the minimum operating power to be achieved,P max at the time of the maximum operating power to be reached,P 0 in order to adjust the power of the target,αto adjust the rate, deltaTTo adjust the time; the balance power prediction model required by the self renewable resource is as follows: />Where delta represents the balance capacity required by the self-renewable resource, delta n Is the firstnThe balancing capacity required for renewable resources of the region itself,P cn is the firstnThe region itself may regenerate resources to predict the output power,δ n is the firstnThe region is predicted to have a deviation rate,Nis the total number of regions.
The quick adjustment type resource comprises quick in-situ balance resource and quick external adjustment resource; the optimizing and adjusting the class resource comprises optimizing the in-situ balance resource and optimizing the external adjustment resource.
The virtual energy storage model is constructed by packing the resources into a plurality of resource packages according to the resource characteristics, the resource positions and the economy level of the resources and based on the adjustable capacity of the plurality of resource packages, wherein the upper limit of the integral adjustable capacity of the resource packages is virtualized to be the maximum energy storage capacity, and the adjustment rate of the resource packages is virtualized to be the energy storage charging and discharging power; the evaluation system comprises an economic index, a regulation performance index and an environmental protection index; the quick calling model is that when the same type of resources are called, the virtual energy storage model is represented by a square frame with a fixed size on an x-axis, each resource package is ordered according to the evaluation system, each resource package is arranged at fixed intervals, the y-axis represents adjustable capacity, a calling curve is formed, and when the resources are regulated, the total regulation quantity requirement is quickly distributed to each resource package according to a preset proportion.
The economic indicators include adjusting cost and revenue levels; the regulation performance indexes comprise adjustable capacity, regulation quality, regulation speed, response speed, standard reaching rate and performance rate; the environmental indicators include carbon emission levels.
The calling curve is as follows:wherein, the method comprises the steps of, wherein,y t represent the firsttThe adjustable capacity of the time period is such that,x t represent the firsttThe abscissa of the resource packages of the time period,k t calling coefficients for a resourceb i,t Represent the firsttTime period of firstiThe adjustment amount of each resource package is adjusted in the next time,x ,t1 the abscissa representing the resource package with the largest adjustable capacity,M i,t represent the firsttTime period of firstiThe secondary total adjustment amount required is calculated,Mindicating the number of resource packages.
The optimization calling model takes the maximum net profit as an objective function, and is expressed as:wherein, the method comprises the steps of, wherein,C 0 indicating a net profit of the user,C F indicating that the participation in the auxiliary service benefit,C D indicating participation in the electric energy market revenue,C N the cost of the energy source is represented,C W representing the cost of operation and maintenance,C Z representing the cost of storing energy,C H representing environmental costs; constraint conditions of the optimization calling model comprise equipment power constraint, power balance constraint and equipment adjustment rate constraint; the device power constraint is: />，P h Representation devicehIs used for the power of (a),P h min representation devicehThe minimum power at which the operation is performed,P h max representation devicehThe maximum power at which the device can operate,P j,t representing a virtual power plant at the firsttThe time period participates in the power of the auxiliary service,P d,t representing a virtual power plant at the firsttThe time period is the power involved in the trade of electrical energy,P t min representing a virtual power plant at the firsttThe minimum regulated power for the time period,P t max representing a virtual power plant at the firsttMaximum regulated power for a time period; the power balance constraint is: />，P h,t Representation oftTime equipmenthThe output force or the electric power is used,P g,t representation oftThe power is interacted with the power grid at any time,Hrepresenting the number of devices; the device adjustment rate constraint is:wherein, the method comprises the steps of, wherein,φ h representation devicehIs used for the rate of adjustment of (a),φ h,min representation devicehIs used to adjust the rate of adjustment of the (c) in the (c),φ h,max representation devicehIs used for the maximum adjustment rate of (a).
The participation assisting service benefitC F Expressed as:wherein, the method comprises the steps of, wherein,Jindicating the total number of types of participating in the auxiliary service,R j , t represent the firsttTime period to participate in the firstjThe average price of the auxiliary service is kept,L j t, represent the firsttTime period to participate in the firstjAn adjustment amount of the seed auxiliary service; the participation in electric energy market benefitC D Expressed as:wherein, the method comprises the steps of, wherein,R t G denoted as the firsttThe purchase price of electricity in the time period,P t G represent the firsttAverage power purchased from the power grid in a time period, deltaT t Represent the firsttThe time of the time period is set to be,R t S denoted as the firsttThe selling price of electricity in the time period,P t S represent the firsttAverage power of selling electricity to the power grid in a time period; the energy costC N Expressed as: />Wherein, the method comprises the steps of, wherein,Kindicating the total number of types of energy purchased,R k t, represent the firsttTime period purchase of the firstkThe average price of the seed energy source,L k t, represent the firsttTime period purchase of the firstkThe total amount of seed energy; the operation and maintenance costsC W Expressed as: />Wherein, the method comprises the steps of, wherein,Hrepresenting the total number of devices,R h t, represent the firsttTime period devicehIs added to the running maintenance cost of the (a),L h t, represent the firsttTime period devicehIs set to the operating power of (a); the energy storage costC Z Expressed as: />Wherein, the method comprises the steps of, wherein,R se t, represents the breaking cost of the energy storage unit time,N c represent the firsttThe number of charges in the time period,N F represent the firsttThe number of discharges in a time periodT t Represent the firsttThe time of the time period; the environmental costC H Expressed as: />Wherein, the method comprises the steps of, wherein,Dindicating the total number of types of contaminants,R d t, represent the firsttTime period of firstdThe treatment cost of the seed pollutant,L d t, represent the firsttTime period of firstdAnd the discharge amount of pollutants.
And forming a regulation strategy by adopting a mode of solving the optimization calling model or a mode of calling the rapid calling model according to the type of the demand, wherein the regulation strategy comprises the following concrete steps:
obtaining a demand through predictive analysis and market trading modes of a virtual power plant, and judging the type of the demand;
when the type of the requirement is a quick adjustment requirement, calling a quick adjustment class resource, and forming a quick adjustment strategy according to the calling sequence and the quick adjustment model; the calling sequence is determined according to comprehensive evaluation, and the quick on-site balance resource is preferentially called to meet the balance power required by the virtual power plant;
when the type of the requirement is a non-rapid adjustment requirement, invoking an optimization adjustment class resource, and solving the optimization invocation model according to the invocation sequence and an artificial intelligent algorithm to form an optimization adjustment strategy; and determining the calling sequence according to comprehensive evaluation, and preferentially calling the optimized on-site balance resource to meet the balance power required by the virtual power plant.
The technical scheme adopted for solving the technical problems is as follows: provided is a fast allocation device for adjusting capacity of a virtual power plant, comprising:
the building module is used for building a virtual power plant model containing various resources and determining the adjustable capacity of the virtual power plant;
the prediction module is used for predicting the balanced power demands of users in the area to obtain a demand predicted value;
the dividing module is used for dividing the resources into quick adjustment type resources and optimized adjustment type resources according to the characteristics of the resources, the demand predicted value, the geographic area and the market information;
the first building module is used for building a virtual energy storage model, an evaluation system and a rapid calling model which participate in the rapid regulation type resource by combining the adjustable capacity of the virtual power plant;
the second building module is used for building an optimization calling model participating in optimization regulation of class resources by combining the adjustable capacity of the virtual power plant;
the strategy forming module is used for forming a regulation strategy by adopting a mode of solving the optimization calling model or a mode of calling the rapid calling model according to the type of the demand;
and the distribution module is used for distributing the adjustment capacity of the virtual power plant by adopting the adjustment strategy.
The technical scheme adopted for solving the technical problems is as follows: an electronic device is provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method for quickly distributing the adjustment capability of the virtual power plant are realized when the processor executes the computer program.
The technical scheme adopted for solving the technical problems is as follows: there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the above-described method for fast allocation of virtual power plant capacity.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the application has the following advantages and positive effects: the application can realize quick response to the adjustment requirement through the resource prediction, evaluation, classification and quick distribution technology, establishes the resource characteristic index of time and space dimension, and preferentially calls the on-site adjustment resource. The application establishes a comprehensive evaluation system of resources, and for the same type of resources, more resources with strong adjustment capability, high credit and economy and green are called, so that the performance, the adjustability and the environmental protection level of the resources are promoted.
Drawings
FIG. 1 is a flow chart of a method for rapid allocation of capacity for a virtual power plant according to a first embodiment of the present application;
FIG. 2 is a schematic diagram illustrating the operation of a method for rapid allocation of capacity for a virtual power plant according to a first embodiment of the present application;
FIG. 3 is a diagram of a fast tuning class resource call in accordance with a first embodiment of the present application.
Detailed Description
The application will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present application and are not intended to limit the scope of the present application. Furthermore, it should be understood that various changes and modifications can be made by one skilled in the art after reading the teachings of the present application, and such equivalents are intended to fall within the scope of the application as defined in the appended claims.
The first embodiment of the application relates to a method for quickly distributing the adjustment capability of a virtual power plant, which is shown in fig. 1 and 2, and comprises the following steps:
step S1, constructing a virtual power plant model containing resources such as distributed photovoltaic, distributed fans, distributed gas turbines, distributed energy storage, electric vehicles, adjustable loads and the like. In this step, the constructed virtual power plant model is an overall adjustable capacity model, and includes two parts, which are respectively: the characteristics of the adjustable resources of the virtual power plant, the adjustable capacity model and the balance power prediction model required by the renewable resources (wind power and photovoltaic) of the virtual power plant. In this embodiment, the regulation capability of the virtual power plant refers to the regulation capability after deducting the self-balancing demand.
The characteristics and adjustable capacity model of the adjustable resources of the virtual power plant are expressed as follows:。
wherein, the liquid crystal display device comprises a liquid crystal display device,UAindicating the up-regulation capability (kW),DAindicating the turndown capability (kW),Pto predict operating power (kW),P min for a minimum operating power (kW),P max for maximum operating power (kW),P 0 to adjust the target power (kW),αto adjust the rate (kW/h), the delta is adjustedTFor the adjustment of time (h).
The model for predicting the balance power required by self renewable resources is as follows:。
wherein Δ represents the balance capacity (kW) required for self-renewable resources, delta n Is the firstnThe balancing capacity (kW) required for renewable resources of the area itself,P cn is the firstnThe renewable resources of the region itself predict the output power (kW),δ n is the firstnThe region is predicted to have a deviation rate,Nis the total number (number) of regions.
And S2, predicting the balance power demand of the user in the area before the day or longer time scale by using an artificial intelligence algorithm to obtain a demand predicted value. In the step, users in the area mainly refer to key users forming alliance with the virtual power plant or expected to need balance capacity in the area, and the expected balance capacity is predicted by using an artificial intelligence algorithm according to historical energy utilization information of the users, expected demands provided by the users, climate information and the like to obtain balance power required by the users and a first balance powernThe area user needs balanced power.
And S3, dividing the resources into quick adjustment type resources and optimization adjustment type resources according to the factors such as the resource characteristics, the demand predicted value, the geographic area, the market information and the like. The quick adjustment type resource and the optimization adjustment type resource can be respectively divided into an external adjustment type resource and an in-situ balance type resource, namely, the quick in-situ balance resource K1, the quick external adjustment resource K2, the optimization in-situ balance resource Y1 and the optimization external adjustment resource Y2. In the aspect of the calling sequence, the quick adjustment class resource and the optimized adjustment class resource are called mainly according to the adjustment requirement, and when the quick adjustment class resource or the optimized adjustment class resource is called, the on-site balance resource is preferentially called so as to meet the balance power required by the virtual power plant, thereby preferentially promoting the realization of regional power matching.
And S4, establishing a virtual energy storage model, an evaluation system and a rapid scheduling model which participate in rapid resource scheduling by combining the adjustable capacity of the virtual power plant.
The virtual energy storage model in the step is constructed by packing the resources into a plurality of resource packages according to the resource characteristics, the resource positions and the economy level of the resources and based on the adjustable capacity of the plurality of resource packages, wherein the upper limit of the integral adjustable capacity of the resource packages is virtual into the maximum energy storage capacity, and the adjustment rate of the resource packages is virtual into the energy storage charging and discharging power.
The evaluation system comprises an economic index, a regulation performance index and an environmental protection index; wherein the economic indicators include adjusting cost and revenue levels; the regulation performance indexes comprise adjustable capacity, regulation quality, regulation speed, response speed, standard reaching rate and performance rate; the environmental indicators include carbon emission levels.
The quick calling model is that when the same type of resource is called, the virtual energy storage model is represented by a box with a fixed size on an x-axis, and each resource package is sequenced according to the evaluation system, and when the resource package is sequenced, the resource packages can be sequenced according to a descending order, namely, the higher the score is, the more the position is, the smaller the serial number is, and the resource packages can be sequenced according to an ascending order, namely, the lower the score is, the more the position is, the smaller the serial number is. As shown in fig. 3, the resource packages are arranged at fixed intervals, the y-axis represents the adjustable capacity, a call curve is formed, and the total adjustment amount requirement is rapidly allocated to the resource packages according to a preset proportion during adjustment.
The first embodimenttThe quick adjustment resource call curve for the time period is expressed as:wherein, the method comprises the steps of, wherein,y t represent the firsttThe adjustable capacity of the time period is such that,x t represent the firsttThe abscissa of the resource packages of the time period,k t calling coefficients for a resourceb i,t Represent the firsttTime period of firstiSecondary each resource package adjustment amount (kW),x ,t1 the abscissa representing the resource package with the largest adjustable capacity,M i,t represent the firsttTime period of firstiSecondary total regulated quantity demand (kW),Mindicating the number (number) of resource packages.
It should be noted that the calling curve is not limited to be linear, but may be other curves or be called proportionally according to the evaluation score.
And S5, establishing an optimization calling model participating in optimizing and adjusting resources by combining the adjustable capacity of the virtual power plant. The optimization calling model established in the step takes the maximum net profit as an objective function, and is expressed as:wherein, the method comprises the steps of, wherein,C 0 indicating a net profit (element),C F representing participation in an auxiliary serviceRevenue (meta) for the user,C D indicating participation in the electric energy market revenue (meta),C N representing the energy costs (meta),C W representing the operational maintenance costs (meta),C Z representing the cost of storing energy (meta),C H representing the environmental cost (meta).
Participation in auxiliary service revenuesC F Can be expressed as:wherein, the method comprises the steps of, wherein,Jindicating the total number of types of participating in the auxiliary service,R j , t represent the firsttTime period to participate in the firstjThe average price of the auxiliary service is kept,L j t, represent the firsttTime period to participate in the firstjThe adjustment amount or adjustment mileage of the auxiliary service.
Participation in electric energy market revenueC D Can be expressed as:wherein, the method comprises the steps of, wherein,R t G denoted as the firsttThe electricity purchase price (Yuan/(kW.h)) of the time period,P t G represent the firsttAverage power (kW) of the time period purchase to the grid is deltaT t Represent the firsttThe time (h) of the time period,R t S denoted as the firsttThe electricity selling price (yuan/(kWh)) of the time period,P t S represent the firsttAverage power (kW) of selling electricity to the grid for a period of time.
Cost of energyC N Can be expressed as:wherein, the method comprises the steps of, wherein,Kindicating the total number of types of energy purchased,R k t, represent the firsttTime period purchase of the firstkAverage price of seed energy (yuan/kg),L k t, represent the firsttTime period purchase of the firstkTotal amount of seed energy (kg).
Cost of operation and maintenanceC W Can be expressed as:wherein, the method comprises the steps of, wherein,Hrepresenting the total number of devices,R h t, represent the firsttTime period devicehOperating maintenance costs (yuan/kW),L h t, represent the firsttTime period devicehOperating power (kW) of (a).
The energy storage costC Z Can be expressed as:wherein, the method comprises the steps of, wherein,R se t, represents the energy storage unit time breakage cost (element/(secondary h)),N c represent the firsttThe number of charges (times) in the period,N F represent the firsttThe number of discharges (times) in the time period, and the delta is calculatedT t Represent the firsttTime (h) of the time period.
The environmental costC H Can be expressed as:wherein, the method comprises the steps of, wherein,Dindicating the total number of types of contaminants,R d t, represent the firsttTime period of firstdThe treatment cost (yuan/kg) of the seed pollutant,L d t, represent the firsttTime period of firstdEmissions (kg) of seed contaminants.
Constraints of the optimization calling model in this step include device power constraints, power balance constraints, and device adjustment rate constraints.
The device power constraints are:，P h representation devicehIs set to a power (kW),P h min representation devicehMinimum power (kW) of operation,P h max representation devicehMaximum power of operation (kW),P j,t representing a virtual power plant at the firsttThe time period participates in the power (kW) of the auxiliary service,P d,t representing a virtual power plant at the firsttThe time period is the power (kW) involved in the electric energy transaction,P t min representing a virtual power plant at the firsttMinimum regulated power (kW) for a time period,P t max representing a virtual power plant at the firsttMaximum regulated power (kW) for a time period.
The power balance constraint is:，P h,t representation oftTime equipmenthThe output or the power consumption (kW),P g,t representation oftThe time of day power (kW) interacting with the grid,Hindicating the number of devices.
The device adjustment rate constraint is:wherein, the method comprises the steps of, wherein,φ h representation devicehIs set up in terms of the rate of regulation (kW/h),φ h,min representation devicehIs a minimum regulation rate (kW/h),φ h,max representation devicehMaximum regulation rate (kW/h).
And S6, forming a regulation strategy by adopting a mode of optimizing and calling a model solution or a mode of calling a quick calling model according to the requirement type and the principle of calling local resources preferentially. The method specifically comprises the following steps:
obtaining a demand through predictive analysis and market trading modes of a virtual power plant, and judging the type of the demand;
when the type of the requirement is a quick adjustment requirement, calling a quick adjustment class resource, and forming a quick adjustment strategy according to the calling sequence and the quick adjustment model; the calling sequence is determined according to comprehensive evaluation, and the quick on-site balance resource is preferentially called to meet the balance power required by the virtual power plant;
when the type of the requirement is a non-rapid adjustment requirement, invoking an optimization adjustment class resource, and solving the optimization invocation model according to the invocation sequence and an artificial intelligent algorithm to form an optimization adjustment strategy; and determining the calling sequence according to comprehensive evaluation, and preferentially calling the optimized on-site balance resource to meet the balance power required by the virtual power plant.
And S7, distributing the adjustment capacity of the virtual power plant by adopting the adjustment strategy.
It is easy to find that the application can realize the fast response to the adjustment demand through the resource prediction, evaluation, classification and fast allocation technology, the application establishes the resource characteristic index of time and space dimension, and preferentially invokes the on-site adjustment resource, and meanwhile, the application is not limited to the traditional energy balance, more attention is paid to the real-time power matching, and the on-site power matching of the resource is preferentially realized. The application establishes a comprehensive evaluation system of resources, and for the same type of resources, more resources with strong adjustment capability, high credit and economy and green are called, so that the performance, the adjustability and the environmental protection level of the resources are promoted.
A second embodiment of the present application relates to a device for rapidly distributing capacity of a virtual power plant, including:
the building module is used for building a virtual power plant model containing various resources and determining the adjustable capacity of the virtual power plant;
the prediction module is used for predicting the balanced power demands of users in the area to obtain a demand predicted value;
the dividing module is used for dividing the resources into quick adjustment type resources and optimized adjustment type resources according to the characteristics of the resources, the demand predicted value, the geographic area and the market information;
the first building module is used for building a virtual energy storage model, an evaluation system and a rapid calling model which participate in the rapid regulation type resource by combining the adjustable capacity of the virtual power plant;
the second building module is used for building an optimization calling model participating in optimization regulation of class resources by combining the adjustable capacity of the virtual power plant;
the strategy forming module is used for forming a regulation strategy by adopting a mode of solving the optimization calling model or a mode of calling the rapid calling model according to the type of the demand;
and the distribution module is used for distributing the adjustment capacity of the virtual power plant by adopting the adjustment strategy.
It can be understood that the present embodiment corresponds to the first embodiment, and specific details thereof may be referred to each other, which will not be described herein.
A third embodiment of the application relates to an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method for fast allocation of capacity of a virtual power plant of the first embodiment when executing the computer program.
A fourth embodiment of the application relates to a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the virtual power plant adjustment capability quick allocation method of the first embodiment.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (11)
1. A method for rapidly distributing the adjustment capability of a virtual power plant is characterized by comprising the following steps:
constructing a virtual power plant model containing various resources, and determining the adjustable capacity of the virtual power plant;
predicting the balance power demands of users in the area to obtain a demand predicted value;
dividing the resources into quick adjustment type resources and optimization adjustment type resources according to the characteristics of the resources, the demand predicted values, the geographic areas and the market information;
establishing a virtual energy storage model, an evaluation system and a rapid scheduling model which participate in the rapid adjustment type resource by combining the adjustable capacity of the virtual power plant; the virtual energy storage model is constructed by packing the resources into a plurality of resource packages according to the resource characteristics, the resource positions and the economy level of the resources and based on the adjustable capacity of the plurality of resource packages, wherein the upper limit of the integral adjustable capacity of the resource packages is virtualized to be the maximum energy storage capacity, and the adjustment rate of the resource packages is virtualized to be the energy storage charging and discharging power; the evaluation system comprises an economic index, a regulation performance index and an environmental protection index; the quick calling model is characterized in that when the same type of resources are called, a square frame with a fixed size is used for representing the virtual energy storage model on an x-axis, each resource packet is sequenced according to the evaluation system, each resource packet is arranged at fixed intervals, the y-axis is used for representing adjustable capacity, a calling curve is formed, and when the resources are regulated, the total regulation quantity requirement is quickly distributed to each resource packet according to a preset proportion;
establishing an optimization calling model participating in optimization regulation of class resources by combining the adjustable capacity of the virtual power plant;
according to the type of the demand, a regulation strategy is formed by adopting a mode of solving the optimization calling model or a mode of calling the rapid calling model;
and distributing the adjustment capacity of the virtual power plant by adopting the adjustment strategy.
2. The method for rapid allocation of capacity for virtual power plant as claimed in claim 1, wherein the virtual power plant model includes characteristics of the virtual power plant adjustable resources and the demand for the adjustable capacity model and self-renewable resourcesA balanced power prediction model of (2); the characteristics and the adjustable capacity model of the adjustable resources of the virtual power plant are as follows:wherein, the method comprises the steps of, wherein,UAindicating the ability to up-regulate,DAindicating the ability to be turned down,Pin order to predict the operating power of the vehicle,P min at the level of the minimum operating power to be achieved,P max at the time of the maximum operating power to be reached,P 0 in order to adjust the power of the target,αto adjust the rate, deltaTTo adjust the time; the balance power prediction model required by the self renewable resource is as follows: />Where delta represents the balance capacity required by the self-renewable resource, delta n Is the firstnThe balancing capacity required for renewable resources of the region itself,P cn is the firstnThe region itself may regenerate resources to predict the output power,δ n is the firstnThe region is predicted to have a deviation rate,Nis the total number of regions.
3. The method for quickly allocating capacity for regulating a virtual power plant according to claim 1, wherein the quickly regulating class resources include quickly in-place balance resources and quickly out-regulating resources; the optimizing and adjusting the class resource comprises optimizing the in-situ balance resource and optimizing the external adjustment resource.
4. The method for rapid allocation of capacity for a virtual power plant according to claim 1, wherein the economic indicators include regulation cost and revenue levels; the regulation performance indexes comprise adjustable capacity, regulation quality, regulation speed, response speed, standard reaching rate and performance rate; the environmental indicators include carbon emission levels.
5. The method for quickly allocating capacity of a virtual power plant according to claim 1, wherein the calling curve is:wherein, the method comprises the steps of, wherein,y t represent the firsttThe adjustable capacity of the time period is such that,x t represent the firsttThe abscissa of the resource packages of the time period,k t calling coefficients for a resourceb i,t Represent the firsttTime period of firstiThe adjustment amount of each resource package is adjusted in the next time,x ,t1 the abscissa representing the resource package with the largest adjustable capacity,M i,t represent the firsttTime period of firstiThe secondary total adjustment amount required is calculated,Mindicating the number of resource packages.
6. The method for quickly distributing capacity of a virtual power plant according to claim 1, wherein the optimization calling model is expressed as a net profit maximization as an objective function:wherein, the method comprises the steps of, wherein,C 0 indicating a net profit of the user,C F indicating that the participation in the auxiliary service benefit,C D indicating participation in the electric energy market revenue,C N the cost of the energy source is represented,C W representing the cost of operation and maintenance,C Z representing the cost of storing energy,C H representing environmental costs; constraint conditions of the optimization calling model comprise equipment power constraint, power balance constraint and equipment adjustment rate constraint; the device power constraint is: />，P h Representation devicehIs used for the power of (a),P h min representation devicehThe minimum power at which the operation is performed,P h max representation devicehThe maximum power at which the device can operate,P j,t representing a virtual power plant at the firsttThe time period participates in the power of the auxiliary service,P d,t representation ofVirtual Power plant at the firsttThe time period is the power involved in the trade of electrical energy,P t min representing a virtual power plant at the firsttThe minimum regulated power for the time period,P t max representing a virtual power plant at the firsttMaximum regulated power for a time period; the power balance constraint is: />，P h,t Representation oftTime equipmenthThe output force or the electric power is used,P g,t representation oftThe power is interacted with the power grid at any time,Hrepresenting the number of devices; the device adjustment rate constraint is:wherein, the method comprises the steps of, wherein,φ h representation devicehIs used for the rate of adjustment of (a),φ h,min representation devicehIs used to adjust the rate of adjustment of the (c) in the (c),φ h,max representation devicehIs used for the maximum adjustment rate of (a).
7. The method for quickly assigning capacity for regulating power plant according to claim 6, wherein said participating auxiliary service gainsC F Expressed as:wherein, the method comprises the steps of, wherein,Jindicating the total number of types of participating in the auxiliary service,R j , t represent the firsttTime period to participate in the firstjThe average price of the auxiliary service is kept,L j t, represent the firsttTime period to participate in the firstjAn adjustment amount of the seed auxiliary service; the participation in electric energy market benefitC D Expressed as: />Wherein, the method comprises the steps of, wherein,R t G denoted as the firsttThe purchase price of electricity in the time period,P t G represent the firsttAverage power purchased from the power grid in a time period, deltaT t Represent the firsttThe time of the time period is set to be,R t S denoted as the firsttThe selling price of electricity in the time period,P t S represent the firsttAverage power of selling electricity to the power grid in a time period; the energy costC N Expressed as: />Wherein, the method comprises the steps of, wherein,Kindicating the total number of types of energy purchased,R k t, represent the firsttTime period purchase of the firstkThe average price of the seed energy source,L k t, represent the firsttTime period purchase of the firstkThe total amount of seed energy; the operation and maintenance costsC W Expressed as: />Wherein, the method comprises the steps of, wherein,Hrepresenting the total number of devices,R h t, represent the firsttTime period devicehIs added to the running maintenance cost of the (a),L h t, represent the firsttTime period devicehIs set to the operating power of (a); the energy storage costC Z Expressed as:wherein, the method comprises the steps of, wherein,R se t, represents the breaking cost of the energy storage unit time,N c represent the firsttThe number of charges in the time period,N F represent the firsttThe number of discharges in a time periodT t Represent the firsttThe time of the time period; the environmental costC H Expressed as: />Wherein, the method comprises the steps of, wherein,Dindicating the total number of types of contaminants,R d t, represent the firsttTime periodFirst, thedThe treatment cost of the seed pollutant,L d t, represent the firsttTime period of firstdAnd the discharge amount of pollutants.
8. The method for quickly distributing the adjustment capability of the virtual power plant according to claim 3, wherein the adjustment strategy is formed by adopting a mode of solving the optimization calling model or a mode of calling the quick calling model according to the type of the demand, specifically:
obtaining a demand through predictive analysis and market trading modes of a virtual power plant, and judging the type of the demand;
when the type of the requirement is a quick adjustment requirement, calling a quick adjustment class resource, and forming a quick adjustment strategy according to the calling sequence and the quick adjustment model; the calling sequence is determined according to comprehensive evaluation, and the quick on-site balance resource is preferentially called to meet the balance power required by the virtual power plant;
when the type of the requirement is a non-rapid adjustment requirement, invoking an optimization adjustment class resource, and solving the optimization invocation model according to the invocation sequence and an artificial intelligent algorithm to form an optimization adjustment strategy; and determining the calling sequence according to comprehensive evaluation, and preferentially calling the optimized on-site balance resource to meet the balance power required by the virtual power plant.
9. A virtual power plant regulation capacity quick distribution device, comprising:
the building module is used for building a virtual power plant model containing various resources and determining the adjustable capacity of the virtual power plant;
the prediction module is used for predicting the balanced power demands of users in the area to obtain a demand predicted value;
the dividing module is used for dividing the resources into quick adjustment type resources and optimized adjustment type resources according to the characteristics of the resources, the demand predicted value, the geographic area and the market information;
the first building module is used for building a virtual energy storage model, an evaluation system and a rapid calling model which participate in the rapid regulation type resource by combining the adjustable capacity of the virtual power plant; the virtual energy storage model is constructed by packing the resources into a plurality of resource packages according to the resource characteristics, the resource positions and the economy level of the resources and based on the adjustable capacity of the plurality of resource packages, wherein the upper limit of the integral adjustable capacity of the resource packages is virtualized to be the maximum energy storage capacity, and the adjustment rate of the resource packages is virtualized to be the energy storage charging and discharging power; the evaluation system comprises an economic index, a regulation performance index and an environmental protection index; the quick calling model is characterized in that when the same type of resources are called, a square frame with a fixed size is used for representing the virtual energy storage model on an x-axis, each resource packet is sequenced according to the evaluation system, each resource packet is arranged at fixed intervals, the y-axis is used for representing adjustable capacity, a calling curve is formed, and when the resources are regulated, the total regulation quantity requirement is quickly distributed to each resource packet according to a preset proportion;
the second building module is used for building an optimization calling model participating in optimization regulation of class resources by combining the adjustable capacity of the virtual power plant;
the strategy forming module is used for forming a regulation strategy by adopting a mode of solving the optimization calling model or a mode of calling the rapid calling model according to the type of the demand;
and the distribution module is used for distributing the adjustment capacity of the virtual power plant by adopting the adjustment strategy.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method for fast allocation of capacity of a virtual power plant according to any one of claims 1-8 when the computer program is executed.
11. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the method for fast allocation of capacity of a virtual power plant according to any one of claims 1-8.
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