CN115222298B - Virtual power plant adjustable capacity construction method and device, storage medium and electronic equipment - Google Patents
Virtual power plant adjustable capacity construction method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The invention discloses a method and a device for constructing an adjustable capacity of a virtual power plant, a storage medium and electronic equipment, wherein the method comprises the following steps: dividing internal resources of a virtual power plant into uncertain resources and adjustable resources according to the operating characteristics of the internal resources of the virtual power plant; determining a running baseline of the uncertain resources and a set of output values of the uncertain resources; determining a running baseline of the adjustable resource, an output value of the adjustable resource, and an output constraint set of the adjustable resource; and determining the adjustable capacity of the virtual power plant according to the operation baseline of the uncertain resources, the output value set of the uncertain resources, the operation baseline of the adjustable resources, the output value of the adjustable resources and the output constraint set of the adjustable resources.
Description
Technical Field
The invention relates to the technical field of power distribution network regulation and control, in particular to a method and a device for constructing an adjustable capacity of a virtual power plant, a storage medium and electronic equipment.
Background
The virtual power plant can flexibly participate in the electric power market as a fusion point of energy supply and energy consumption, provides auxiliary service for the electric power system, can effectively improve energy efficiency, reduces energy cost, promotes new energy consumption, and promotes the construction and development of novel electric power systems in China. When a virtual power plant operator participates in the auxiliary service market, information such as a quotation curve and a load curve needs to be reported to a power grid in the day ahead. The virtual power plant operator needs to consider the regulation and control capabilities of all resources which are possibly aggregated in the next day in the future reporting link, but how to evaluate the regulation and control capabilities of the virtual power plant according to different aggregation conditions of the virtual power plant and make a scheduling strategy for the current bidding of the virtual power plant still needs to be solved at present.
The existing virtual power plant regulation capacity assessment method based on multi-resource aggregation is used for assessing the regulation capacity and response capacity of various resources by establishing a physical analysis model and selecting assessment indexes. Meanwhile, an uncertainty model brought by considering energy storage stabilizing new energy is established, and the operation risk condition of the virtual power plant under each scene is evaluated, so that the adjusting capacity of various resources can be evaluated. The method adopts a traditional physical modeling method, only models photovoltaic, energy storage and temperature control loads, the types of internal resources of the virtual power plant are few, and the model establishment has particularity and poor generalization capability, so that the universal value cannot be generated for the diversified dynamic aggregation of the virtual power plant. The method only performs analysis aiming at the uncertainty of the stored energy, and the uncertainty model has a small application range. The method is mainly developed around the energy storage regulation capacity, does not have the function of estimating the dynamic regulation of the virtual power plant under the diversity aggregation, and has poor universality.
Therefore, the existing virtual power plant cannot fully schedule various resources when participating in the power market.
Disclosure of Invention
The invention provides a method and a device for constructing an adjustable capacity of a virtual power plant, a storage medium and electronic equipment, aiming at the technical problem that various resources cannot be fully scheduled when the virtual power plant participates in a power market in the prior art.
According to one aspect of the invention, a method for constructing the adjustable capacity of the virtual power plant is provided, which comprises the following steps:
dividing internal resources of a virtual power plant into uncertain resources and adjustable resources according to the operating characteristics of the internal resources of the virtual power plant;
determining a running baseline of the uncertain resources and a set of output values of the uncertain resources;
determining a running baseline of the adjustable resource, an output value of the adjustable resource, and an output constraint set of the adjustable resource;
and determining the adjustable capacity of the virtual power plant according to the operation baseline of the uncertain resources, the output value set of the uncertain resources, the operation baseline of the adjustable resources, the output value of the adjustable resources and the output constraint set of the adjustable resources.
Optionally, determining the uncertainty resourceComprises: determining a running baseline for the uncertain resource by the following formula
：/>
Is of the formula in the step (1), the first step,
for the output predicted value of the uncertainty resource j at the moment t, the judgment is carried out>
And the number of uncertain resources in the virtual power plant.
Optionally, determining the set of contribution values of the uncertainty resource comprises:
modeling the predicted value and fluctuation interval of the uncertain resources by using a preset uncertainty analysis method, and preliminarily determining the output value set of the uncertain resources;
and optimizing the preliminarily determined output value set by considering the electricity price influence factor to obtain the output value set of the uncertain resources.
Optionally, modeling the predicted value and the fluctuation interval of the uncertain resource by using a preset uncertainty analysis method, and preliminarily determining the output value set of the uncertain resource, including:
modeling the predicted value and the fluctuation interval of the uncertain resources by using a dynamic robust constraint method, and preliminarily determining the output value set of the uncertain resources as follows:
in the formula,
、/>
、/>
actual output values of conventional load, wind power and photovoltaic in the virtual power plant at the moment t are respectively obtained; />
The actual output value of the uncertain resource j in the virtual power plant at the moment t is obtained; />
The output predicted value of the uncertain resource j at the moment t is obtained; />
Predicting the maximum fluctuation deviation of the output of the uncertain resource j at the moment t; />
、/>
When the variable is 0-1 and the value is 1, respectively indicating that the output of the uncertain resources reaches the upper bound/lower bound of the prediction interval, or else, indicating the output is an expected value; />
、/>
For the precalculated value of uncertainty, i.e. the maximum space, the maximum number of times of fluctuation of the output value to the predicted value within a scheduling period, for the number of times of maximum time, for which the output value fluctuates>
Acting on a fluctuating parameter, which is collected indefinitely on a spatial scale, for a demand response>
Outputting a fluctuation parameter of an uncertain set on a time scale for demand response, namely a maximum value of deviation between a predicted value and an actual value in a scheduling period;
and considering the electricity price influence factor, optimizing the preliminarily determined output value set to obtain the output value set of the uncertain resources as follows:
in the formula,
、/>
、/>
actual output values of conventional load, wind power and photovoltaic in the virtual power plant at the moment t are respectively obtained; />
The output predicted value of the uncertain resource j at the moment t is obtained; />
Predicting the maximum fluctuation deviation of the output of the uncertain resource j at the moment t; />
、/>
And when the variable is a variable between 0 and 1 and the value is 1, the output of the uncertain resources reaches the upper bound/lower bound of the prediction interval respectively, otherwise, the output is an expected value.
Optionally, determining the adjustable resourcesA running baseline of sources comprising: determining a running baseline for the adjustable resource by the following formula
：
In the formula,
for adjusting the output prediction value of the resource i at the moment t>
And adjusting the number of resources in the virtual power plant.
Optionally, determining an output value of the adjustable resource comprises:
determining the increased/reduced output value of the scheduled operation of the adjustable resource as the output value of the adjustable resource;
and the increased/reduced output value of the scheduled operation of the adjustable resource is the difference value between the actual output value of the scheduled operation of the adjustable resource and the output predicted value of the scheduled operation of the adjustable resource.
Optionally, determining a set of output constraints for the adjustable resource comprises:
determining an output boundary constraint for the adjustable resource;
determining a transmit power constraint for the adjustable resource;
determining the electric quantity recovery constraint of the adjustable resource after the scheduling period is finished;
and determining a contribution constraint set of the adjustable resource according to the contribution boundary constraint, the power generation and power consumption constraint and the power recovery constraint.
Optionally, the set of output constraints of the adjustable resource is:
in the formula,
、/>
respectively a minimum adjustable power and a maximum adjustable power; />
The actual force output value of the adjustable resource after scheduling; />
The initial electric quantity at the scheduling moment; />
Is the maximum available remaining power or maximum power demand; />
Is the minimum available remaining power or the minimum power demand; />
Expected electricity generation and consumption expected values obtained through the uncertainty resource prediction curve; />
And (4) the output predicted value of the adjustable resource i at the moment t.
Optionally, determining the adjustable capacity of the virtual power plant according to the operation baseline of the uncertain resource, the output value set of the uncertain resource, the operation baseline of the adjustable resource, the output value of the adjustable resource, and the output constraint set of the adjustable resource, includes:
determining an operation baseline of the resources inside the virtual power plant according to the operation baseline of the uncertain resources and the operation baseline of the adjustable resources;
determining the actual operation characteristics of the virtual power plant after scheduling according to the output value set of the uncertain resources, the output value of the adjustable resources and the output constraint set of the adjustable resources;
and solving the difference value between the actual operation characteristic after the virtual power plant is scheduled and the operation baseline of the internal resources of the virtual power plant to obtain the adjustable capacity of the virtual power plant.
Optionally, the adjustable capacity of the virtual power plant
The calculation formula of (2) is as follows:
in the formula,
actual operating characteristics of the virtual power plant after scheduling; />
The operation base line of the internal resources of the virtual power plant is obtained; />
Adjusting the number of resources for the virtual power plant; />
The number of uncertain resources in the virtual power plant is obtained; />
The actual force output value of the adjustable resource after scheduling; />
A set of contribution values for said uncertainty resources; />
A running baseline for the adjustable resource; />
A running baseline for the uncertainty resource; />
The output predicted value of the adjustable resource i at the moment t is obtained; />
The actual output value of the uncertain resource j in the virtual power plant at the moment t is obtained; />
And (4) the output predicted value of the uncertain resource j at the time t.
According to another aspect of the present invention, there is provided a virtual power plant adjustable capacity construction apparatus, including:
the resource dividing module is used for dividing internal resources of the virtual power plant into uncertain resources and adjustable resources according to the operating characteristics of the internal resources of the virtual power plant;
the first determining module is used for determining a running baseline of the uncertain resources and a contribution value set of the uncertain resources;
a second determining module, configured to determine a running baseline of the adjustable resource, an output value of the adjustable resource, and an output constraint set of the adjustable resource;
and the third determining module is used for determining the adjustable capacity of the virtual power plant according to the running base line of the uncertain resources, the output value set of the uncertain resources, the running base line of the adjustable resources, the output value of the adjustable resources and the output constraint set of the adjustable resources.
Optionally, the first determining module is specifically configured to: determining a running baseline for the uncertain resource by the following equation
：
In the formula,
for the output predicted value of the uncertainty resource j at the moment t, the judgment is carried out>
And the number of uncertain resources in the virtual power plant.
Optionally, the first determining module is specifically configured to:
modeling the predicted value and fluctuation interval of the uncertain resources by using a preset uncertainty analysis method, and preliminarily determining the output value set of the uncertain resources;
and optimizing the preliminarily determined output value set by considering the electricity price influence factor to obtain the output value set of the uncertain resources.
Optionally, the first determining module is further specifically configured to:
modeling the predicted value and the fluctuation interval of the uncertain resources by using a dynamic robust constraint method, and preliminarily determining the output value set of the uncertain resources as follows:
in the formula,
、/>
、/>
actual output values of internal conventional load, wind power and photovoltaic of the virtual power plant at the moment t are respectively obtained; />
The actual output value of the uncertain resource j in the virtual power plant at the moment t is obtained; />
The output predicted value of the uncertain resource j at the moment t is obtained; />
Predicting the maximum fluctuation deviation of output at the moment t for the uncertain resources j; />
、/>
The variable is 0-1, when the value is 1, the output of the uncertain resources is respectively expressed to reach the upper limit/lower limit of the prediction interval, otherwise, the output is an expected value; />
、/>
For the precalculated value of uncertainty, i.e. the maximum space, the maximum number of times of fluctuation of the output value to the predicted value within a scheduling period, for the number of times of maximum time, for which the output value fluctuates>
Exerting an uncertainty over a spatial scale on a demand response>
Outputting a fluctuation parameter of an uncertain set on a time scale for demand response, namely a maximum value of deviation between a predicted value and an actual value in a scheduling period;
and considering the electricity price influence factor, optimizing the preliminarily determined output value set to obtain the output value set of the uncertain resources as follows:
in the formula,
、/>
、/>
actual output values of conventional load, wind power and photovoltaic in the virtual power plant at the moment t are respectively obtained; />
The output predicted value of the uncertain resource j at the moment t is obtained; />
Prediction of uncertainty resource j at time tMaximum force fluctuation deviation; />
、/>
And the variable is a variable between 0 and 1, when the value is 1, the output of the uncertain resource is respectively expressed to reach the upper limit/lower limit of the prediction interval, and otherwise, the output is an expected value.
Optionally, the second determining module is specifically configured to: determining a running baseline for the adjustable resource by the following formula
：
In the formula,
for adjusting the output predicted value of the resource i at the moment t>
And adjusting the number of resources in the virtual power plant.
Optionally, the second determining module is specifically configured to:
determining the increased/reduced output value of the scheduled operation of the adjustable resource as the output value of the adjustable resource;
and the increased/reduced output value of the scheduled operation of the adjustable resource is the difference value between the actual output value of the scheduled operation of the adjustable resource and the output predicted value of the scheduled operation of the adjustable resource.
Optionally, the second determining module is specifically configured to:
determining an output boundary constraint for the adjustable resource;
determining a transmit power constraint for the adjustable resource;
determining the electric quantity recovery constraint of the adjustable resource after the scheduling period is finished;
and determining a contribution constraint set of the adjustable resource according to the contribution boundary constraint, the power generation and power consumption constraint and the power recovery constraint.
Optionally, the set of output constraints of the adjustable resource is:
in the formula,
、/>
respectively minimum adjustable power and maximum adjustable power; />
The actual force output value of the adjustable resource after scheduling; />
The initial electric quantity is the scheduling moment; />
The maximum available remaining power or the maximum power demand; />
Is the minimum available remaining power or the minimum power demand; />
Expected values of the expected power generation and consumption obtained through the uncertainty resource prediction curve; />
And (4) the output predicted value of the adjustable resource i at the moment t.
Optionally, the third determining module is specifically configured to:
determining an operation baseline of the resources in the virtual power plant according to the operation baseline of the uncertain resources and the operation baseline of the adjustable resources;
determining the actual operation characteristics of the virtual power plant after scheduling according to the output value set of the uncertain resources, the output value of the adjustable resources and the output constraint set of the adjustable resources;
and solving a difference value between the actual operation characteristic after the virtual power plant is scheduled and the operation baseline of the internal resource of the virtual power plant to obtain the adjustable capacity of the virtual power plant.
Optionally, the adjustable capacity of the virtual power plant
The calculation formula of (2) is as follows:
in the formula,
actual operating characteristics of the virtual power plant after scheduling; />
The operation base line of the internal resources of the virtual power plant is obtained; />
Adjusting the number of resources for the interior of the virtual power plant; />
The number of uncertain resources in the virtual power plant is obtained; />
The actual force output value of the adjustable resource after scheduling; />
A set of output values for the uncertainty resource; />
A running baseline for the adjustable resource; />
A running baseline for the uncertainty resource; />
The output predicted value of the adjustable resource i at the moment t is obtained; />
The actual output value of the uncertain resource j in the virtual power plant at the moment t is obtained; />
And (4) the output predicted value of the uncertain resource j at the time t.
According to a further aspect of the invention, there is provided a computer readable storage medium having stored thereon a computer program for executing the method of any of the above aspects of the invention.
According to still another aspect of the present invention, there is provided an electronic apparatus including: a processor; a memory for storing the processor-executable instructions; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of the above aspects of the present invention.
Therefore, the method firstly divides the internal resources of the virtual power plant into two types of uncertain resources and adjustable resources according to different operating characteristics of the internal resources of the virtual power plant. And then, predicting the output of uncertain resources in the virtual power plant, analyzing the uncertainty of the internal resources in the virtual power plant, and further acquiring an operation baseline and an output value set of the uncertain resources. Secondly, the running base line of the adjustable resource, the output value of the adjustable resource and the output constraint set of the adjustable resource are determined by analyzing the running characteristics of the resource in the virtual power plant. And finally, combining the operation baseline of the uncertain resources, the output value set of the uncertain resources, the operation baseline of the adjustable resources, the output value of the adjustable resources and the output constraint set of the adjustable resources to obtain the adjustable capacity of the whole virtual power plant. The invention considers the uncertainty of the virtual power plant polymerization resource, is not limited to model establishment under a certain specific polymerization mode, and is convenient for the virtual power plant to participate in the research of the optimized scheduling bidding strategy integrally. Meanwhile, if the virtual power plant operator aggregates new resources (such as comprehensive energy, gas or heat energy and the like), classification is only carried out according to the operation characteristics. Further, if one virtual power plant operator manages a plurality of virtual power plants in different aggregation forms in subsequent research, parameters of the virtual power plants can be directly superposed to obtain the adjustable capacity of the virtual power plant operator, and the whole virtual power plant operator does not need to be modeled again. When the virtual power plant participates in the electric power market, the adjustability of the virtual power plant can be fully evaluated, the operating characteristics and the output condition of various resources can be fully considered, various resources can be fully scheduled, and the operating reliability and the economical efficiency of the virtual power plant are balanced.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
FIG. 1 is a schematic flow chart diagram of a method for constructing an adjustable capacity of a virtual power plant according to an exemplary embodiment of the present invention;
FIG. 2 is a diagram of the basic components of a virtual power plant provided in an exemplary embodiment of the present invention;
FIG. 3 is a schematic illustration of the adjustable capacity of a virtual power plant provided by an exemplary embodiment of the present invention;
FIG. 4 is a diagram of an operational framework for a virtual power plant operator to participate in an auxiliary service market provided by an exemplary embodiment of the present invention;
FIG. 5 is a schematic diagram of a virtual power plant adjustable capacity construction apparatus according to an exemplary embodiment of the present invention;
fig. 6 is a structure of an electronic device according to an exemplary embodiment of the present invention.
Detailed Description
Hereinafter, example embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of embodiments of the invention and not all embodiments of the invention, with the understanding that the invention is not limited to the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
It will be understood by those of skill in the art that the terms "first," "second," and the like in the embodiments of the present invention are used merely to distinguish one element, step, device, module, or the like from another element, and do not denote any particular technical or logical order therebetween.
It should also be understood that in embodiments of the present invention, "a plurality" may refer to two or more and "at least one" may refer to one, two or more.
It is also to be understood that any reference to any component, data, or structure in the embodiments of the invention may be generally understood as one or more, unless explicitly defined otherwise or stated to the contrary hereinafter.
In addition, the term "and/or" in the present invention is only one kind of association relationship describing the associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in the present invention generally indicates that the preceding and succeeding related objects are in an "or" relationship.
It should also be understood that the description of the embodiments of the present invention emphasizes the differences between the embodiments, and the same or similar parts may be referred to each other, so that the descriptions thereof are omitted for brevity.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Embodiments of the invention are operational with numerous other general purpose or special purpose computing system environments or configurations, and with numerous other electronic devices, such as terminal devices, computer systems, servers, etc. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with electronic devices, such as terminal devices, computer systems, servers, and the like, include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, networked personal computers, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
Exemplary method
Fig. 1 is a schematic flow chart of a method for constructing an adjustable capacity of a virtual power plant according to an exemplary embodiment of the present invention. The embodiment may be applied to an electronic device, and as shown in fig. 1, the method 100 for constructing an adjustable capacity of a virtual power plant includes the following steps:
In the embodiment of the present invention, fig. 2 shows a typical virtual power plant structure, and a virtual power plant operator performs optimized scheduling by aggregating internal resources to purchase and sell electricity with a power grid. As shown in fig. 2, the virtual plant internal resources include, for example and without limitation: wind power, photovoltaics, conventional loads, flexible loads, energy storage, gas turbines, electric vehicles, and the like. Internal resources of a virtual power plant may be divided into uncertain resources and adjustable resources based on their operating characteristics. The uncertain resources comprise renewable distributed power sources such as wind power and photovoltaic, conventional loads and the like. The adjustable resources comprise flexible loads, energy storage, and controllable distributed power supplies such as gas turbines and electric automobiles.
And 102, determining a running baseline of the uncertain resources and a contribution value set of the uncertain resources.
In the embodiment of the invention, after the internal resources of the virtual power plant are divided into the uncertain resources and the adjustable resources according to different operating characteristics, the uncertain resources and the adjustable resources are required to be respectively modeled, and then a unified adjustable capacity model of the virtual power plant is established.
The uncertain resources mainly comprise renewable distributed power sources such as wind power and photovoltaic, conventional loads and the like. In the decision making process of the virtual power plant operator, the output prediction value of uncertain resources is required
Making statistics and reporting the operation base line of a certain uncertain resource of the virtual power plant on the next day>
。
Optionally, determining a running baseline of the uncertainty resource comprises: determining a running baseline for the uncertain resource by the following formula
：
In the formula,
for the output predicted value of the uncertainty resource j at the moment t, the judgment is carried out>
And the number of uncertain resources in the virtual power plant.
In the embodiment of the invention, a preset uncertainty analysis method can be applied to model the predicted value and the fluctuation interval of the uncertainty resource, and the output value set of the uncertainty resource is preliminarily determined. The predetermined uncertainty analysis method includes, but is not limited to: an uncertainty analysis method under dynamic robust constraint and an analysis method for acquiring specific data and applying data drive. Namely, in the uncertain analysis process aiming at wind power, photovoltaic and load output, the uncertain analysis is not limited to the uncertain analysis under the dynamic robust constraint. In the optimization scheduling process of some specific implementation virtual power plants, the uncertainty of output can be analyzed by taking specific data acquisition into consideration and using data driving and other modes.
And modeling the uncertain resource output by adopting a robust optimization method in consideration of randomness of the output of the uncertain resource. The robust optimization method can further construct a worst scene of an uncertain condition by modeling a predicted value and a fluctuation interval of the uncertain resource, guarantee the feasibility of a scheduling result under an extreme condition, and keep a decision result conservative. And the conservative degree of solving can be adjusted by dynamic robust optimization, and the safety and the economy of the participation of the virtual power plant in scheduling decision are balanced.
Therefore, a unified characterization model of the virtual power plant adjustability uncertainty set under the dynamic robustness constraint is provided. Firstly, in the optimization scheduling process in the day ahead, the output value set of the uncertain resources of the virtual power plant is expressed as follows:
in the formula,
、/>
、/>
actual output values of internal conventional load, wind power and photovoltaic of the virtual power plant at the moment t are respectively obtained; />
The actual output value of the uncertain resource j in the virtual power plant at the moment t is obtained; />
The output predicted value of the uncertain resource j at the moment t is obtained; />
Predicting the maximum fluctuation deviation of output at the moment t for the uncertain resources j; />
、/>
When the variable is 0-1 and the value is 1, respectively indicating that the output of the uncertain resources reaches the upper bound/lower bound of the prediction interval, or else, indicating the output is an expected value; />
、/>
For the precalculated value of uncertainty, i.e. the maximum space, the maximum number of times of fluctuation of the output value to the predicted value within a scheduling period, for the number of times of maximum time, for which the output value fluctuates>
Acting on a fluctuating parameter, which is collected indefinitely on a spatial scale, for a demand response>
And outputting a fluctuation parameter of the uncertain set of output on a time scale for demand response, namely a maximum value of deviation between a predicted value and an actual value in a scheduling period.
Further, since the virtual power plant participates in the electric power market transaction, the load output is influenced by the market price of electricity, and the conventional load output in the virtual power plant should be further refined and modeled. The invention divides the load into a normal load and a translatable load based on the self-property and the response capability of the load. Conventional loads impose price concerns during day-ahead scheduling. The translatable loads have corresponding desired power usage plans and the translation and adjustment of electrical energy is performed over time according to operator schedules. In the time-of-use electricity price environment, the conventional load response prediction problem can be regarded as a load prediction problem in different electricity price environments. Therefore, the load output in the uncertain set is specifically the uncertain modeling of the conventional load, the conventional load in the uncertain resource set of the virtual power plant is added with the electricity price influence factor,
therefore, the power rate influence factor needs to be considered, the preliminarily determined output value set is optimized, and the output value set of the uncertain resource is obtained as follows:
in the formula,
、/>
、/>
actual output values of conventional load, wind power and photovoltaic in the virtual power plant at the moment t are respectively obtained; />
The output predicted value of the uncertain resource j at the moment t is obtained; />
Predicting the maximum fluctuation deviation of output at the moment t for the uncertain resources j; />
、/>
And when the variable is a variable between 0 and 1 and the value is 1, the output of the uncertain resources reaches the upper bound/lower bound of the prediction interval respectively, otherwise, the output is an expected value.
Wherein, when the uncertainty resource j is the normal load
At that time, the normal load->
The predicted value of the output at time t->
Expressed as:
in the formula,
the self-elastic coefficient of the load at the moment t; />
The rate of change of electricity price at the time t; />
Predicting a conventional load prediction value in the virtual power plant; />
And predicting the conventional load of the virtual power plant.
In embodiments of the invention, the adjustable resources include, but are not limited to, controllable distributed power sources including flexible loads, energy storage, and gas turbines and electric vehicles. In the decision making process of the virtual power plant operator, firstly, the decision making process needs to be carried outCounting output predicted values of the adjustable resources, and reporting the operation base line of the adjustable resources of the virtual power plant in the next day
。
Optionally, determining the running baseline of the adjustable resource includes: determining a running baseline for the adjustable resource by the following formula
：
In the formula,
for adjusting the output predicted value of the resource i at the moment t>
And adjusting the number of resources in the virtual power plant.
Optionally, determining an output value of the adjustable resource comprises: determining the increased/reduced output value of the scheduled operation of the adjustable resource as the output value of the adjustable resource; and the increased/reduced output value of the scheduled operation of the adjustable resource is the difference value between the actual output value of the scheduled operation of the adjustable resource and the output predicted value of the scheduled operation of the adjustable resource.
Optionally, determining a set of output constraints for the adjustable resource comprises: determining an output boundary constraint for the adjustable resource; determining a transmit power constraint for the adjustable resource; determining the electric quantity recovery constraint of the adjustable resource after the scheduling period is finished; and determining a contribution constraint set of the adjustable resource according to the contribution boundary constraint, the power generation and power consumption constraint and the power recovery constraint.
In the embodiment of the invention, the main information needed to make decisions by the virtual power plant operator is as follows:
1) The expected power utilization plan of the load can be translated, and the scheduling range and unit scheduling cost of each time interval can be reduced;
2) The conventional output and the schedulable power maximum/minimum value of the controllable distributed power supply are controlled;
3) Capacity of the energy storage unit, maximum/minimum charge/discharge capacity, and initial capacity required for a scheduling period.
It can be seen that such adjustable resources have clear parameters such as capacity, output range, etc., and the virtual power plant operator can determine the adjustable capability thereof according to the aggregated resource parameters, which are embodied as increasing/reducing the output value after the operation scheduling of the adjustable resources. Can be expressed as the difference between the actual output value of the scheduled adjustable resource and the expected power output of the translatable load or the conventional output of the controllable distributed power supply (which is equivalent to the output predicted value of the adjustable resource).
Based on the analysis, the actual output value of the adjustable resource of the virtual power plant is modeled. Although different adjustable resources operate in different modes, the output range, the adjustable capacity and the output expected value can be formed according to different data. The output constraints for aggregating any adjustable resource in the virtual power plant are as follows:
in the formula,
、/>
respectively minimum adjustable power and maximum adjustable power; />
The actual force output value of the adjustable resource after scheduling; />
The initial electric quantity is the scheduling moment; />
The maximum available remaining power or the maximum power demand; />
Is the minimum available remaining power or the minimum power demand; />
Expected values of the expected power generation and consumption obtained through the uncertainty resource prediction curve; />
And (4) the output predicted value of the adjustable resource i at the moment t.
Wherein, the output constraint concentration formula (6) represents the adjustable resource output boundary constraint; the formula (7) with concentrated output constraint indicates that the power generation and consumption of the adjustable resources in any time period are required to be within the adjustable capacity range; and (3) after the optimal scheduling period is finished, the output constraint concentration formula (8) can adjust the resources to restore the electric quantity to the initial level (energy storage resources) or reach the target electric quantity (load resources).
And 104, determining the adjustable capacity of the virtual power plant according to the operation baseline of the uncertain resources, the output value set of the uncertain resources, the operation baseline of the adjustable resources, the output value of the adjustable resources and the output constraint set of the adjustable resources.
Optionally, determining the adjustable capacity of the virtual power plant according to the running baseline of the uncertain resource, the output value set of the uncertain resource, the running baseline of the adjustable resource, the output value of the adjustable resource, and the output constraint set of the adjustable resource, includes: determining an operation baseline of the resources in the virtual power plant according to the operation baseline of the uncertain resources and the operation baseline of the adjustable resources; determining the actual operation characteristics of the virtual power plant after scheduling according to the output value set of the uncertain resources, the output value of the adjustable resources and the output constraint set of the adjustable resources; and solving the difference value between the actual operation characteristic after the virtual power plant is scheduled and the operation baseline of the internal resources of the virtual power plant to obtain the adjustable capacity of the virtual power plant.
In an embodiment of the present invention, fig. 3 shows a schematic diagram of the adjustable capacity of the virtual power plant. Fig. 4 is a specific application scenario of the adjustable capacity of the virtual power plant. As shown in fig. 4, in the process of participating in the day-ahead power market, the virtual power plant reports the operation baseline of the next day according to the relevant electricity price information issued by the main energy market of the grid side, and simultaneously performs optimal scheduling on the adjustable capacity of the virtual power plant according to the peak regulation instruction issued by the auxiliary service market of the grid side, and finally reports the operation data of the virtual power plant and the decision of participating in peak regulation. As shown in fig. 3 and 4, where the capacity output value can be adjusted for the virtual power plant
Expressed as:
in the formula,
the number of resources can be adjusted for the interior of the virtual power plant; />
The number of uncertain resources in the virtual power plant is determined; />
Increasing/reducing the force output value after scheduling for the operation of the adjustable resource; />
Running a power output fluctuation value for the uncertain resources; />
The actual force output value of the resource can be adjusted after scheduling; />
A desired power output that is a translatable load or a conventional output of a controllable distributed power supply; />
The actual output value of the uncertain resource j in the virtual power plant at the moment t is obtained; />
And (4) the output predicted value of the uncertain resource j at the time t.
By equation (9), the virtual plant tunable capacity can be expressed as the actual operating characteristics of the virtual plant after the virtual plant is optimally scheduled
And the running base line reported day before>
The difference of (c).
Therefore, the formula for calculating the adjustable capacity of the virtual power plant is as follows:
in the formula,
actual operating characteristics of the virtual power plant after scheduling; />
The operation base line of the internal resources of the virtual power plant is obtained; />
Adjusting the number of resources for the virtual power plant; />
The number of uncertain resources in the virtual power plant is set; />
The actual force output value of the adjustable resource after scheduling; />
A set of output values for the uncertainty resource; />
A running baseline for the adjustable resource; />
A running baseline for the uncertainty resource; />
The output predicted value of the adjustable resource i at the moment t is obtained; />
The actual output value of the uncertain resource j in the virtual power plant at the moment t is obtained; />
And (4) the output predicted value of the uncertain resource j at the time t.
In summary, if modeling is performed on the adjustable capacity of a virtual power plant aggregating multiple resources, the modeling and calculation can be performed according to the following steps: the method includes the steps that internal resources of the virtual power plant are classified into two types of adjustable resources and uncertain resources, a predicted value of the two types of adjustable resources and uncertain resources is solved, and a next-day operation base line of the virtual power plant is obtained. And secondly, modeling and calculating the actual output condition of the uncertain resources by using dynamic robust constraint. The internal adjustable resource characteristic parameters are extracted and further expressed as the forms of the constraint expressions (6), (7) and (8), wherein
、/>
、/>
、/>
、/>
、
And (4) respectively superposing to obtain the output constraint set of all adjustable resources in the virtual power plant. And finally, solving the adjustable capacity of the virtual power plant according to the equations (10), (11) and (12) to guide and optimize the virtual power plant to participate in the day-ahead peak shaving auxiliary service.
The model has the advantages that uncertainty of virtual power plant aggregation resources is considered, model establishment under a certain aggregation mode is not limited, and the virtual power plant integrally participates in research of an optimized scheduling bidding strategy. Meanwhile, if the virtual power plant operator aggregates new resources (such as comprehensive energy, gas or heat energy and the like), classification is only carried out according to the operation characteristics. Further, if one virtual power plant operator manages a plurality of virtual power plants in different aggregation forms in subsequent research, the parameters of the virtual power plants can be directly superposed to obtain the adjustable capacity of the virtual power plant operator without modifying the whole model. The constraint set can be easily embedded into some optimization models, so that virtual power plant operators can conveniently participate in different markets to carry out overall follow-up operation.
The construction method of the adjustable capacity of the virtual power plant, which is provided by the invention, can be oriented to a peak regulation auxiliary service market, and has the beneficial effects that:
(1) The uncertainty of the virtual power plant aggregation resources is considered, the model is not limited to be established in a certain specific aggregation mode, and the virtual power plant can participate in the research of the optimized scheduling bidding strategy integrally. Meanwhile, if the virtual power plant operator aggregates new resources (such as comprehensive energy, gas or heat energy and the like), classification is only carried out according to the operation characteristics. Further, if one virtual power plant operator manages a plurality of virtual power plants in different aggregation forms in subsequent research, the parameters of the virtual power plants can be directly superposed to obtain the adjustable capacity of the virtual power plant operator without modeling the whole.
(2) In the process that the virtual power plant participates in the day-ahead auxiliary service optimization scheduling, the adjustability of the virtual power plant can be fully evaluated, the operating characteristics and the output condition of various resources can be fully considered, and the operation reliability and the economical efficiency of the virtual power plant are balanced.
Aiming at the fact that the internal aggregation resources of the virtual power plant are various, the adjustment capacities of the virtual power plant under different aggregation resources are different, the output of the internal resources has uncertainty, and the adjustment capacity under dynamic aggregation is analyzed. According to the construction method of the adjustable capacity of the virtual power plant, uncertainty analysis is firstly carried out on resources with different operation characteristics, and specifically, the uncertainty analysis and the adjustable resource operation characteristic analysis are based on robust uncertainty analysis. Secondly, unified modeling is carried out on each uncertain resource, a universal model is provided for the virtual power plant under dynamic aggregation to participate in optimized scheduling, the adjustable capacity of the virtual power plant is evaluated through the unified model, the adjustable capacity of the virtual power plant is finally obtained, and the difficulty brought by different aggregation modes of the virtual power plant for subsequent optimized scheduling is effectively solved.
Therefore, the uncertainty of the virtual power plant polymerization resources is considered, the method is not limited to model establishment under a certain specific polymerization mode, and the virtual power plant can participate in research of the optimized scheduling bidding strategy integrally. Meanwhile, if the virtual power plant operator aggregates new resources (such as comprehensive energy, gas or heat energy and the like), classification is only carried out according to the operation characteristics. Further, if one virtual power plant operator manages a plurality of virtual power plants in different aggregation forms in subsequent research, parameters of the virtual power plants can be directly superposed to obtain the adjustable capacity of the virtual power plant operator, and modeling is not required to be carried out on the whole. When the virtual power plant participates in the electric power market, the adjustability of the virtual power plant can be fully evaluated, the operation characteristics and the output condition of various resources can be fully considered, various resources can be fully scheduled, and the operation reliability and the economy of the virtual power plant are balanced.
Exemplary devices
Fig. 5 is a schematic structural diagram of a virtual power plant adjustable capacity building apparatus according to an exemplary embodiment of the present invention. As shown in fig. 5, the apparatus 500 includes:
a resource dividing module 510, configured to divide internal resources of a virtual power plant into an uncertain resource and an adjustable resource according to an operation characteristic of the internal resources of the virtual power plant;
a first determining module 520, configured to determine a running baseline of the uncertainty resource and a set of contribution values of the uncertainty resource;
a second determining module 530, configured to determine a running baseline of the adjustable resource, an output value of the adjustable resource, and a set of output constraints of the adjustable resource;
a third determining module 540, configured to determine an adjustable capacity of the virtual power plant according to the operation baseline of the uncertain resource, the output value set of the uncertain resource, the operation baseline of the adjustable resource, the output value of the adjustable resource, and the output constraint set of the adjustable resource.
Optionally, the first determining module 520 is specifically configured to: : determining a running baseline for the uncertain resource by the following formula
：
In the formula,
for the output predicted value of the uncertainty resource j at the moment t, the judgment is carried out>
And the number of uncertain resources in the virtual power plant is obtained.
Optionally, the first determining module 520 is specifically configured to:
modeling the predicted value and fluctuation interval of the uncertain resources by using a preset uncertainty analysis method, and preliminarily determining the output value set of the uncertain resources;
and optimizing the preliminarily determined output value set by considering the electricity price influence factor to obtain the output value set of the uncertain resources.
Optionally, the first determining module 520 is further specifically configured to:
modeling the predicted value and the fluctuation interval of the uncertain resources by using a dynamic robust constraint method, and preliminarily determining the output value set of the uncertain resources as follows:
in the formula,
、/>
、/>
actual output values of conventional load, wind power and photovoltaic in the virtual power plant at the moment t are respectively obtained; />
The actual output value of the uncertain resources in the virtual power plant at the moment t is obtained; />
The output predicted value of the uncertain resource j at the moment t is obtained; />
Predicting the maximum fluctuation deviation of the output of the uncertain resource j at the moment t;
、/>
when the variable is 0-1 and the value is 1, respectively indicating that the output of the uncertain resources reaches the upper bound/lower bound of the prediction interval, or else, indicating the output is an expected value; />
、/>
For uncertainty pre-calculated values, i.e. the maximum space and the maximum number of moments of the force value in a scheduling period in which the pre-calculated value fluctuates>
Acting on a fluctuating parameter, which is collected indefinitely on a spatial scale, for a demand response>
Outputting a fluctuation parameter of an uncertain set on a time scale for demand response, namely a maximum value of deviation between a predicted value and an actual value in a scheduling period;
and considering the electricity price influence factor, optimizing the preliminarily determined output value set to obtain the output value set of the uncertain resources as follows:
in the formula,
、/>
、/>
actual output values of conventional load, wind power and photovoltaic in the virtual power plant at the moment t are respectively obtained; />
The output predicted value of the uncertain resource j at the moment t is obtained; />
Predicting the maximum fluctuation deviation of the output of the uncertain resource j at the moment t; />
、/>
And the variable is a variable between 0 and 1, when the value is 1, the output of the uncertain resource is respectively expressed to reach the upper limit/lower limit of the prediction interval, and otherwise, the output is an expected value.
Optionally, the second determining module 530 is specifically configured to: the determination of the equationAdjusting a running baseline of a resource
：
In the formula,
for adjusting the output predicted value of the resource i at the moment t>
And adjusting the number of resources in the virtual power plant.
Optionally, the second determining module 530 is specifically configured to:
determining the increased/reduced output value of the scheduled operation of the adjustable resource as the output value of the adjustable resource;
and the increased/reduced output value of the scheduled operation of the adjustable resource is the difference value between the actual output value of the scheduled operation of the adjustable resource and the output predicted value of the scheduled operation of the adjustable resource.
Optionally, the second determining module 530 is specifically configured to:
determining an output boundary constraint for the adjustable resource;
determining a transmit power constraint for the adjustable resource;
determining the electric quantity recovery constraint of the adjustable resource after the scheduling period is finished;
and determining a contribution constraint set of the adjustable resource according to the contribution boundary constraint, the power generation and power consumption constraint and the power recovery constraint.
Optionally, the set of output constraints of the adjustable resource is:
in the formula,
、/>
respectively minimum adjustable power and maximum adjustable power; />
The actual force output value of the adjustable resource after scheduling; />
The initial electric quantity is the scheduling moment; />
The maximum available remaining power or the maximum power demand; />
Is the minimum available remaining power or the minimum power demand; />
Expected values of the expected power generation and consumption obtained through the uncertainty resource prediction curve; />
And (4) the output predicted value of the adjustable resource i at the moment t.
Optionally, the third determining module 540 is specifically configured to:
determining an operation baseline of the resources in the virtual power plant according to the operation baseline of the uncertain resources and the operation baseline of the adjustable resources;
determining the actual operation characteristics of the virtual power plant after scheduling according to the output value set of the uncertain resources, the output value of the adjustable resources and the output constraint set of the adjustable resources;
and solving the difference value between the actual operation characteristic after the virtual power plant is scheduled and the operation baseline of the internal resources of the virtual power plant to obtain the adjustable capacity of the virtual power plant.
Optionally, the calculation formula of the adjustable capacity of the virtual power plant is as follows:
in the formula,
actual operating characteristics of the virtual power plant after scheduling; />
The operation base line of the internal resources of the virtual power plant is obtained; />
Adjusting the number of resources for the virtual power plant; />
The number of uncertain resources in the virtual power plant is obtained; />
The actual force output value of the adjustable resource after scheduling; />
A set of contribution values for said uncertainty resources; />
A running baseline for the adjustable resource; />
A running baseline for the uncertainty resource; />
The output predicted value of the adjustable resource i at the moment t is obtained; />
The actual output value of the uncertain resource j in the virtual power plant at the moment t is obtained; />
And (4) predicting the output of the uncertain resource j at the time t.
The virtual power plant adjustable capacity construction apparatus 500 according to the embodiment of the present invention corresponds to the virtual power plant adjustable capacity construction method 100 according to another embodiment of the present invention, and details thereof are not repeated herein.
Exemplary electronic device
Fig. 6 is a structure of an electronic device according to an exemplary embodiment of the present invention. The electronic device may be either or both of the first device and the second device, or a stand-alone device separate from them, which stand-alone device may communicate with the first device and the second device to receive the acquired input signals therefrom. FIG. 6 illustrates a block diagram of an electronic device in accordance with an embodiment of the present invention. As shown in fig. 6, the electronic device 60 includes one or more processors 61 and a memory 62.
The processor 61 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
The input device 63 may also include, for example, a keyboard, a mouse, and the like.
The output device 64 can output various information to the outside. The output devices 64 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device that are relevant to the present invention are shown in fig. 6, omitting components such as buses, input/output interfaces, and the like. In addition, the electronic device may include any other suitable components, depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present invention may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method of information mining of historical change records according to various embodiments of the present invention described in the "exemplary methods" section above of this specification.
The computer program product may write program code for carrying out operations for embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present invention may also be a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the steps in the method of information mining of historical change records according to various embodiments of the present invention described in the "exemplary methods" section above of this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present invention have been described above with reference to specific embodiments, but it should be noted that the advantages, effects, etc. mentioned in the present invention are only examples and are not limiting, and the advantages, effects, etc. must not be considered to be possessed by various embodiments of the present invention. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the invention is not limited to the specific details described above.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
The block diagrams of devices, systems, apparatuses, and systems involved in the present invention are merely illustrative examples and are not intended to require or imply that the devices, systems, apparatuses, and systems must be connected, arranged, or configured in the manner shown in the block diagrams. These devices, systems, apparatuses, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
The method and system of the present invention may be implemented in a number of ways. For example, the methods and systems of the present invention may be implemented in software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustrative purposes only, and the steps of the method of the present invention are not limited to the order specifically described above unless specifically indicated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as a program recorded in a recording medium, the program including machine-readable instructions for implementing a method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
It should also be noted that in the systems, apparatus and methods of the present invention, individual components or steps may be broken down and/or re-combined. These decompositions and/or recombinations are to be regarded as equivalents of the present invention. The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the invention to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (16)
1. A method for constructing adjustable capacity of a virtual power plant is characterized in that uncertainty of virtual power plant polymerization resources is considered, model establishment under a certain specific polymerization mode is not limited, if a virtual power plant operator polymerizes new resources, classification is only needed according to the operation characteristics of the virtual power plant operator, if a subsequent virtual power plant operator manages a plurality of virtual power plants in different polymerization modes, parameters of the virtual power plants can be directly superposed to obtain the adjustable capacity of the virtual power plant operator, and the whole model does not need to be modified; the construction method of the adjustable capacity of the virtual power plant comprises the following steps:
dividing internal resources of a virtual power plant into uncertain resources and adjustable resources according to the operating characteristics of the internal resources of the virtual power plant, wherein the operating characteristics comprise that load output is influenced by market electricity price and the adjustable resources have definite capacity and output range, the uncertain resources comprise wind power, photovoltaic and conventional loads, and the adjustable resources comprise flexible loads, energy storage, gas turbines and electric vehicles;
determining a running baseline of the uncertain resources and a set of output values of the uncertain resources;
determining a running baseline of the adjustable resource and a force output value of the adjustable resource;
the main information needed to make decisions at the virtual plant operator is: the expected power utilization plan of the load can be translated, and the scheduling range and unit scheduling cost of each time interval can be reduced; the maximum or minimum value of the conventional output and the schedulable power of the controllable distributed power supply; the capacity, the maximum or minimum charge-discharge capacity and the initial capacity required by a scheduling period of the energy storage unit; it can be seen that such adjustable resources have clear parameters such as capacity, output range, etc., and the virtual power plant operator can determine its adjustable capability according to the aggregated resource parameters, which is specifically represented as increasing or decreasing output values after the operation scheduling of the adjustable resources, and is represented as a difference between an actual output value of the adjustable resources after scheduling and an expected power output of the translatable load or an output predicted value of the adjustable resources, so that the output constraint set of the adjustable resources is as follows:
in the formula,
、/>
respectively minimum adjustable power and maximum adjustable power; />
The actual force output value of the adjustable resource after scheduling; />
The initial electric quantity is the scheduling moment; />
The maximum power demand is met; />
The minimum power consumption requirement is met; />
The expected power consumption expected value is obtained through the uncertainty resource prediction curve; />
The output predicted value of the adjustable resource i at the moment t is obtained;
wherein,
representing an adjustable resource contribution boundary constraint;
indicating that the adjustable resource power consumption at any time interval needs to be within the adjustable capacity range; />
After the optimal scheduling period is finished, the resources can be adjusted to restore the electric quantity to an initial level or reach a target electric quantity;
determining an operation baseline of the resources in the virtual power plant according to the operation baseline of the uncertain resources and the operation baseline of the adjustable resources; determining the actual operation characteristics of the virtual power plant after scheduling according to the output value set of the uncertain resources, the output value of the adjustable resources and the output constraint set of the adjustable resources; calculating a difference value between the actual operation characteristic after the virtual power plant is scheduled and the operation baseline of the internal resources of the virtual power plant to obtain the adjustable capacity of the virtual power plant;
wherein the adjustable capacity of the virtual power plant
The calculation formula of (2) is as follows:
in the formula,
actual operating characteristics of the virtual power plant after scheduling; />
The operation base line of the internal resources of the virtual power plant is obtained; />
Adjusting the number of resources for the virtual power plant; />
The number of uncertain resources in the virtual power plant is obtained; />
The actual force output value of the adjustable resource after scheduling; />
A set of output values for the uncertainty resource; />
A running baseline for the adjustable resource; />
A running baseline for the uncertainty resource; />
The output predicted value of the adjustable resource i at the moment t is obtained; />
The actual output value of the uncertain resource j in the virtual power plant at the moment t is obtained; />
And (4) predicting the output of the uncertain resource j at the time t.
2. The method of claim 1, wherein determining a running baseline for the uncertainty resource comprises: determining a running baseline for the uncertain resource by the following formula
：
In the formula,
for the output predicted value of the uncertainty resource j at the moment t, the judgment is carried out>
And the number of uncertain resources in the virtual power plant is obtained.
3. The method of claim 1, wherein determining the set of contribution values for the uncertainty resource comprises:
modeling the predicted value and fluctuation interval of the uncertain resources by using a preset uncertainty analysis method, and preliminarily determining the output value set of the uncertain resources;
and optimizing the preliminarily determined output value set by considering the electricity price influence factors to obtain the output value set of the uncertain resources.
4. The method of claim 3,
modeling the predicted value and the fluctuation interval of the uncertain resources by using a preset uncertainty analysis method, and preliminarily determining the output value set of the uncertain resources, wherein the method comprises the following steps:
modeling the predicted value and the fluctuation interval of the uncertain resources by using a dynamic robust constraint method, and preliminarily determining the output value set of the uncertain resources as follows:
in the formula,
、/>
、/>
actual output values of conventional load, wind power and photovoltaic in the virtual power plant at the moment t are respectively obtained; />
The actual output value of the uncertain resources in the virtual power plant at the moment t is obtained; />
At time t for an uncertain resource jOutput predicted value; />
Predicting the maximum fluctuation deviation of the output of the uncertain resource j at the moment t; />
、/>
A variable from 0 to 1; />
、/>
For the precalculated value of uncertainty, i.e. the maximum space, the maximum number of times of fluctuation of the output value to the predicted value within a scheduling period, for the number of times of maximum time, for which the output value fluctuates>
Acting on a fluctuating parameter, which is collected indefinitely on a spatial scale, for a demand response>
Outputting a fluctuation parameter of an uncertain set on a time scale for demand response, namely a maximum value of deviation between a predicted value and an actual value in a scheduling period;
and considering the electricity price influence factor, optimizing the preliminarily determined output value set to obtain the output value set of the uncertain resources as follows:
in the formula,
、/>
、/>
actual output values of conventional load, wind power and photovoltaic in the virtual power plant at the moment t are respectively obtained; />
The output predicted value of the uncertain resource j at the moment t is obtained; />
Predicting the maximum fluctuation deviation of output at the moment t for the uncertain resources j; />
、/>
A variable from 0 to 1;
wherein, when the uncertainty resource j is the normal load
At that time, the normal load->
The predicted value of the force at the time->
Expressed as:
in the formula,
the self-elastic coefficient of the load at the moment t; />
The rate of change of electricity price at the time t; />
Predicting a value of a conventional load in the virtual power plant; />
And predicting the conventional load of the virtual power plant.
5. The method of claim 1, wherein determining a running baseline for the adjustable resource comprises: determining a running baseline for the adjustable resource by the following formula
：
In the formula,
for adjusting the output predicted value of the resource i at the moment t>
And adjusting the number of resources in the virtual power plant.
6. The method of claim 1, wherein determining the force-out value for the adjustable resource comprises:
determining the increased or reduced output value of the adjustable resource after the operation scheduling of the adjustable resource as the output value of the adjustable resource;
and the output value increased or reduced after the operation of the adjustable resource is scheduled is the difference value between the actual output value of the adjustable resource and the output predicted value of the adjustable resource after scheduling.
7. The method of claim 1, wherein determining the set of output constraints for the adjustable resource comprises:
determining an output boundary constraint for the adjustable resource;
determining a transmit power constraint for the adjustable resource;
determining the electric quantity recovery constraint of the adjustable resource after the scheduling period is finished;
and determining a contribution constraint set of the adjustable resource according to the contribution boundary constraint, the power generation and power consumption constraint and the power recovery constraint.
8. A virtual power plant adjustable capacity construction device is characterized in that uncertainty of virtual power plant aggregated resources is considered, model establishment under a certain specific aggregation mode is not limited, if a virtual power plant operator aggregates new resources, classification is only carried out according to the operation characteristics of the virtual power plant operator, if a subsequent virtual power plant operator manages a plurality of virtual power plants in different aggregation modes, parameters of the virtual power plants can be directly superposed to obtain the virtual power plant operator adjustable capacity, and the model is not required to be integrally modified; the adjustable capacity of virtual power plant founds the device and includes:
the resource dividing module is used for dividing internal resources of the virtual power plant into uncertain resources and adjustable resources according to the operating characteristics of the internal resources of the virtual power plant, wherein the operating characteristics comprise that load output is influenced by market electricity price and the adjustable resources have definite capacity and output range, the uncertain resources comprise wind power, photovoltaic and conventional loads, and the adjustable resources comprise flexible loads, energy storage, gas turbines and electric vehicles;
the first determining module is used for determining a running baseline of the uncertain resources and a contribution value set of the uncertain resources;
the second determination module is used for determining the running base line of the adjustable resource and the force output value of the adjustable resource; the main information needed to make decisions at the virtual plant operator is: the expected power utilization plan of the load can be translated, and the scheduling range and unit scheduling cost of each time interval can be reduced; the maximum or minimum value of the conventional output and schedulable power of the controllable distributed power supply; the capacity, the maximum or minimum charge-discharge capacity and the initial capacity required by a scheduling period of the energy storage unit; it can be seen that such adjustable resources have clear parameters such as capacity, output range, etc., and the virtual power plant operator can determine its adjustable capability according to the aggregated resource parameters, which is specifically represented as increasing or decreasing output values after the operation scheduling of the adjustable resources, and is represented as a difference between an actual output value of the adjustable resources after scheduling and an expected power output of the translatable load or an output predicted value of the adjustable resources, so that the output constraint set of the adjustable resources is as follows:
in the formula,
、/>
respectively minimum adjustable power and maximum adjustable power; />
The actual force output value of the adjustable resource after scheduling; />
The initial electric quantity is the scheduling moment; />
The maximum power demand is met; />
The minimum power consumption requirement is met; />
The expected power consumption expected value is obtained through the uncertainty resource prediction curve; />
The output predicted value of the adjustable resource i at the moment t is obtained;
wherein,
representing an adjustable resource contribution boundary constraint; />
Indicating that the adjustable resource electricity consumption in any time period needs to be within the adjustable capacity range; />
Indicating that the adjustable resource restores the electric quantity to the initial level or reaches the target electric quantity after the optimal scheduling period is finished;
the third determining module is used for determining the running base line of the resources in the virtual power plant according to the running base line of the uncertain resources and the running base line of the adjustable resources; determining the actual operation characteristics of the virtual power plant after scheduling according to the output value set of the uncertain resources, the output value of the adjustable resources and the output constraint set of the adjustable resources; calculating a difference value between the actual operation characteristic after the virtual power plant is scheduled and the operation baseline of the internal resources of the virtual power plant to obtain the adjustable capacity of the virtual power plant;
wherein the adjustable capacity of the virtual power plant
Is calculated by the formula is as follows:
in the formula,
actual operating characteristics of the virtual power plant after scheduling; />
The operation base line of the internal resources of the virtual power plant is obtained; />
Adjusting the number of resources for the virtual power plant; />
The number of uncertain resources in the virtual power plant is obtained; />
The actual force output value of the adjustable resource after scheduling; />
A set of output values for the uncertainty resource; />
A running baseline for the adjustable resource; />
A running baseline for the uncertainty resource; />
The output predicted value of the adjustable resource i at the moment t is obtained; />
The actual output value of the uncertain resource j in the virtual power plant at the moment t is obtained; />
And (4) the output predicted value of the uncertain resource j at the time t.
9. The apparatus of claim 8, wherein the first determining module is specifically configured to: determining a running baseline for the uncertain resource by the following formula
：
In the formula,
for the output predicted value of the uncertainty resource j at the moment t, the judgment is carried out>
And the number of uncertain resources in the virtual power plant.
10. The apparatus of claim 8, wherein the first determining module is specifically configured to:
modeling the predicted value and the fluctuation interval of the uncertain resources by using a preset uncertainty analysis method, and preliminarily determining the output value set of the uncertain resources;
and optimizing the preliminarily determined output value set by considering the electricity price influence factor to obtain the output value set of the uncertain resources.
11. The apparatus of claim 10,
the first determining module is further specifically configured to:
modeling the predicted value and the fluctuation interval of the uncertain resources by using a dynamic robust constraint method, and preliminarily determining the output value set of the uncertain resources as follows:
in the formula,
、/>
、/>
actual output values of conventional load, wind power and photovoltaic in the virtual power plant at the moment t are respectively obtained; />
The actual output value of the uncertain resources in the virtual power plant at the moment t is obtained; />
The output predicted value of the uncertain resource j at the moment t is obtained; />
Predicting the maximum fluctuation deviation of output at the moment t for the uncertain resources j; />
、/>
A variable from 0 to 1; />
、/>
For the precalculated value of uncertainty, i.e. the maximum space, the maximum number of times of fluctuation of the output value to the predicted value within a scheduling period, for the number of times of maximum time, for which the output value fluctuates>
Acting on a fluctuating parameter, which is collected indefinitely on a spatial scale, for a demand response>
Outputting a fluctuation parameter of an uncertain set on a time scale for demand response, namely a maximum value of deviation between a predicted value and an actual value in a scheduling period;
and considering the electricity price influence factor, optimizing the preliminarily determined output value set to obtain the output value set of the uncertain resources as follows:
in the formula,
、/>
、/>
actual output values of conventional load, wind power and photovoltaic in the virtual power plant at the moment t are respectively obtained; />
The output predicted value of the uncertain resource j at the moment t is obtained; />
Predicting the maximum fluctuation deviation of output at the moment t for the uncertain resources j; />
、/>
A variable from 0 to 1;
wherein, when the uncertainty resource j is the normal load
At that time, the normal load->
Force prediction at a time>
Expressed as:
in the formula,
the self-elastic coefficient of the load at the moment t; />
The rate of change of electricity price at the time t; />
Predicting a conventional load prediction value in the virtual power plant; />
And predicting the conventional load of the virtual power plant.
12. The apparatus of claim 8, wherein the second determining module is specifically configured to: determining a running baseline for the adjustable resource by the following formula
：
In the formula,
for adjusting the output predicted value of the resource i at the moment t>
And adjusting the number of resources in the virtual power plant.
13. The apparatus of claim 8, wherein the second determining module is specifically configured to:
determining the increased or reduced output value of the scheduled operation of the adjustable resource as the output value of the adjustable resource;
and the output value increased or reduced after the operation of the adjustable resource is scheduled is the difference value between the actual output value of the adjustable resource and the output predicted value of the adjustable resource after scheduling.
14. The apparatus of claim 8, wherein the second determining module is specifically configured to:
determining an output boundary constraint for the adjustable resource;
determining a transmit power constraint for the adjustable resource;
determining the electric quantity recovery constraint of the adjustable resource after the scheduling period is finished;
and determining a contribution constraint set of the adjustable resource according to the contribution boundary constraint, the power generation and power consumption constraint and the power recovery constraint.
15. A computer-readable storage medium, characterized in that the storage medium stores a computer program for performing the method of any of the preceding claims 1-7.
16. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any one of claims 1 to 7.
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