CN112102047A - Virtual power plant optimization combination bidding method, device, equipment and storage medium - Google Patents
Virtual power plant optimization combination bidding method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a virtual power plant optimization combination bidding method, a virtual power plant optimization combination bidding device, virtual power plant optimization combination bidding equipment and a virtual power plant optimization combination bidding storage medium, wherein the virtual power plant optimization combination bidding method comprises the following steps: each auxiliary service variety and the predicted price are constrained based on preset bidding capacity constraint, and the bidding capacity of the virtual power plant is controlled within the range without influencing the clearing price of the market; combining standby resources of the virtual power plant based on preset combination constraints to enable the bidding capacity of the virtual power plant to be consistent with the performance requirements of corresponding auxiliary service varieties; and performing equivalent probability constraint on the standby resources meeting the performance requirements of the corresponding auxiliary service varieties based on preset availability equivalent constraint, and selecting the standby resources meeting the high availability requirement of an auxiliary service market to participate in bidding. Through planning the expected bid-winning capacity of different auxiliary service varieties, and considering the influence of the availability of the resource combination on the assessment cost, the bid capacity of the virtual power plant can meet the performance requirements of the corresponding auxiliary service varieties, and the virtual power plant can better participate in the auxiliary market service.
Description
Technical Field
The invention belongs to the technical field of virtual power plants, and particularly relates to a virtual power plant optimal combination bidding method, device, equipment and storage medium.
Background
The virtual power plant is a power supply coordination management system which realizes aggregation and coordination optimization of distributed energy sources (DER) such as Distributed Generators (DGs), energy storage systems, controllable loads, electric vehicles and the like through advanced information communication technology and software systems, and is used as a special power plant to participate in power market and power grid operation. The virtual power plants can aggregate DER to participate in the operation of the electric power market and the auxiliary service market, and management and auxiliary services are provided for the power distribution network and the power transmission network.
However, in the research of the current auxiliary service market, the main focus is on how to simulate the conventional power plant to participate in the electric energy market, and after the combination is determined, the electric energy market participates in the electric power spot transaction in a relatively determined form, so that the participation of the virtual power plant in the auxiliary service market is rarely related. Compared with a conventional power plant, the virtual power plant needs a more complex control strategy and a management mode facing a large number of objects, but has unique advantages of configurable capacity scale and adjustable external performance, so that the virtual power plant can be more flexibly and flexibly adapted to system requirements, and the integration period is much shorter than the construction period of the conventional power plant, so that how to enable the virtual power plant to better participate in the auxiliary service market becomes an urgent problem to be solved.
Disclosure of Invention
In order to solve the problem of how a virtual power plant participates in an auxiliary service market in the prior art, the invention provides a virtual power plant optimized combination bidding method, device, equipment and storage medium, which have the characteristics of enabling the virtual power plant to better participate in auxiliary market service, improving the utilization rate of energy and the like.
The technical scheme adopted by the invention is as follows:
a virtual power plant optimization combination bidding method comprises the following steps:
each auxiliary service variety and the predicted price are constrained based on preset bidding capacity constraint, and the bidding capacity of the virtual power plant is controlled within the range without influencing the clearing price of the market;
combining standby resources of the virtual power plant based on preset combination constraints to enable the bidding capacity of the standby resources to be consistent with the performance requirements of the corresponding auxiliary service varieties;
and performing equivalent probability constraint on the standby resources meeting the performance requirements of the corresponding auxiliary service varieties based on preset availability equivalent constraint, and selecting the standby resources meeting the high availability requirement of an auxiliary service market to participate in bidding.
Further, the constraining each auxiliary service variety and the predicted price based on the preset bidding capacity constraint, and controlling the bidding capacity of the virtual power plant within a range not affecting the market clearing price includes: the constraint is made based on the following formula,
maxZ＝∑[F(yj,t)-C(yj,t)-E(η)]；
F(yj,t)＝pj,tyj,t；
C(yj,t)＝∑γixi,j；
E(η)＝Aη；
wherein j is the serial number of the auxiliary service variety, i is the individual serial number of the integration unit, and F (y)j,t) Winning the bid for said auxiliary service type of jth in time period t, C (y)j,t) The integration cost of the jth auxiliary service type in the time period t, E (eta) is the potential assessment cost corresponding to the availability requirement eta, yj,tBidding capacity, x, for jth auxiliary service during time period ti,jCapacity, p, for an individual i to participate in a combination of a jth auxiliary service type during a time period tj,tPredicted price, gamma, for jth auxiliary service during time period tiIntegration cost per unit volume for an individual i; a is an assessment coefficient.
Further, the step of combining the standby resources of the virtual power plant based on the preset combination constraint to make the bidding capacity of the virtual power plant consistent with the performance requirement of the corresponding auxiliary service variety includes:
the availability of the standby resources is constrained based on a capacity availability formula:
P(∑i,txi,j≥yj,t)≥η；
0≤i,t≤1；
wherein the content of the first and second substances,i,tis the probability distribution of the available trait for individual i.
Further, the combining the standby resources of the virtual power plant based on the preset combination constraint to make the bidding capacity consistent with the performance requirement of the corresponding auxiliary service variety further comprises:
constraining a minimum response time of the corresponding secondary service type based on a response time constraint formula:
ri≤Rj；
wherein R isjCorresponding time requirement for the jth auxiliary service type, riIs the minimum response time of individual i.
Further, the combining the standby resources of the virtual power plant based on the preset combination constraint to make the bidding capacity consistent with the performance requirement of the corresponding auxiliary service variety further comprises:
and constraining the time for continuously outputting the electric energy based on the duration constraint:
∑lixi,j≥Ljyj,t；
wherein L isjDuration requirement for jth auxiliary service type, liThe maximum duration of the individual i.
Further, the combining the standby resources of the virtual power plant based on the preset combination constraint to make the bidding capacity consistent with the performance requirement of the corresponding auxiliary service variety further comprises:
the capacity of the monomer is constrained based on capacity constraints:
0≤xi,j≤Ci；
wherein, CiIs the maximum available capacity of the monomer resource i to be integrated.
Further, the performing, based on a preset availability equivalent constraint, an equivalent probability constraint on the standby resources meeting the performance requirements of the corresponding auxiliary service varieties, and selecting the standby resources meeting the high availability requirements of the auxiliary service market to participate in bidding includes:
the probability of availability is constrained based on opportunity constraints:
P(g(x,ξ)≥0)≥p；
0≤p≤1；
wherein x is a decision variable, ξ is a random variable, and g (x, ξ) is a constraint function of the two variables, which is the requirement of equivalently replacing high-availability conventional resources.
According to the specific implementation manner of the invention, the virtual power plant optimized combination bidding device comprises:
the bidding capacity constraint module is used for constraining each auxiliary service variety and the predicted price based on preset bidding capacity constraint and controlling the bidding capacity of the virtual power plant within a range not influencing the clearing price of the market;
the resource combination module is used for combining the standby resources of the virtual power plant based on preset combination constraint so that the bidding capacity of the virtual power plant is consistent with the performance requirement of the corresponding auxiliary service variety; and
and the equivalent constraint module is used for performing equivalent probability constraint on the standby resources meeting the performance requirements of the corresponding auxiliary service varieties based on preset availability equivalent constraint and selecting the standby resources meeting the high availability requirements of the auxiliary service market to participate in bidding.
According to the specific embodiment of the invention, the device comprises a processor and a memory, wherein the processor is connected with the memory through a communication bus; the processor is used for calling and executing the program stored in the memory; the storage is used for storing a program, and the program is at least used for executing the virtual power plant optimization combination bidding method.
According to the embodiment of the invention, a storage medium is provided, and a computer program is stored in the storage medium, and is at least used for the virtual power plant optimization combination bidding method.
The invention has the beneficial effects that: the virtual power plant with high availability probability is obtained by optimally combining the resources controlled by the virtual power plant, and the availability probability which can be compared with or even surpassed the power generation resources is achieved. The virtual power plants participate in bidding in the auxiliary service market, the bidding capacity of each auxiliary service in different periods needs to be decided, and the virtual power plants meeting the availability probability requirement are constructed in the corresponding period. Through planning the expected bid-winning capacity of different auxiliary service varieties, and considering the influence of the availability of the resource combination on the assessment cost, the bid capacity of the virtual power plant can meet the performance requirements of the corresponding auxiliary service varieties, the overall high availability probability of the virtual power plant is realized, and the virtual power plant can better participate in the auxiliary market service.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow diagram providing a virtual plant optimization portfolio bidding methodology in accordance with an exemplary embodiment;
FIG. 2 is a schematic diagram of a virtual plant optimization portfolio bidding appliance provided in accordance with an exemplary embodiment;
fig. 3 is a schematic diagram of an apparatus provided in accordance with an example embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a virtual power plant optimized combination bidding method, including the following steps:
101. each auxiliary service variety and the predicted price are constrained based on preset bidding capacity constraint, and the bidding capacity of the virtual power plant is controlled within the range without influencing the clearing price of the market;
102. combining standby resources of the virtual power plant based on preset combination constraints to enable the bidding capacity of the virtual power plant to be consistent with the performance requirements of corresponding auxiliary service varieties;
103. and performing equivalent probability constraint on the standby resources meeting the performance requirements of the corresponding auxiliary service varieties based on preset availability equivalent constraint, and selecting the standby resources meeting the high availability requirement of an auxiliary service market to participate in bidding.
Specifically, in the electric power auxiliary service market, the traditional power sources such as thermal power, hydropower and the like serving as main standby resources at present are mature in technology and stable in operation. The currently implemented ancillary service specifications and market rules are also initially based on the characteristics of these units. The availability requirements for the backup service are correspondingly high. Whether by backup market clearing or bilateral negotiation, once backup responsibility is determined, high availability of backup resources is required when scheduled, and call failure assessment is relatively strict.
In the mature electric power market, the auxiliary services acquired through bidding trading are mainly frequency modulation and standby services related to active balance. The constructed model comprises the following technical indexes: minimum capacity, response time, response accuracy, duration, direction of adjustment, availability, etc. The standby service is respectively as follows according to the requirement of response time from short to long: spinning standby, non-spinning standby, replenishing standby, and the like. As power systems increase in their demands for flexibility, a more subdivided variety of backup services may also occur. Taking the rotating standby service as an example, when the system standby capacity is insufficient in the peak load period, the virtual power plant can control the load to reduce the power consumption, and under the condition of no blockage, the effect of the virtual power plant is consistent with the effect of the system scheduling requiring a certain unit to increase the power generation output from the aspect of maintaining the power balance.
Then, firstly, each auxiliary service variety and the forecast price are restrained based on the preset bid capacity restraint, the bid capacity of the virtual power plant is controlled within the range without influencing the clearing price of the market, the virtual power plant needs to plan the expected bid capacities of different auxiliary service varieties, the prediction of various auxiliary service prices and the control of resource integration cost are depended on, the influence of the availability of resource combination on the assessment cost is considered, and the income participating in the electric auxiliary service market is maximized;
combining standby resources of the virtual power plant based on preset combination constraints to enable the bidding capacity of the standby resources to be consistent with the performance requirements of corresponding auxiliary service varieties, finishing the combination of the resources of the virtual power plant in the corresponding period of time of the mastered resources, and enabling the bidding capacity of the standby resources to reach the performance requirements of the corresponding auxiliary service varieties;
finally, the resources held by the virtual power plant are on the load side, including a large number of consumer electrical devices, and a portion of the electrical energy storage. The availability of the adjustable resource is influenced by the power utilization state of the adjustable resource, the cashing of the winning capacity needs to ensure that the part of the capacity is in the running state in the corresponding time period, and the load power can be reduced or the running can be stopped; the availability of the energy storage device is influenced by the charging and discharging state of the energy storage device, the successful bid amount to be redeemed needs to have enough charging and discharging capacity in a corresponding time period to meet the requirement of duration, but as mentioned above, the resources have the technical and economic characteristics of operation of the resources, and the availability of a single individual is difficult to compare with that of a traditional power supply.
As an implementation of the above embodiment, the availability equivalence probability is essentially an opportunity constraint, and can be expressed by the following formula:
the probability of availability is constrained based on opportunity constraints:
P(g(x,ξ)≥0)≥p；
0≤p≤1；
wherein x is a decision variable, ξ is a random variable, and g (x, ξ) is a constraint function of the two variables, which is the requirement of equivalently replacing high-availability conventional resources.
Specifically, the constraint can be described as a requirement that a plurality of demand-side resources equivalently replace high-availability conventional resources, the capacities of the demand-side resources participating in combination are respectively used as decision variables, and the probability distribution characteristics are reflected by random variables, so that the demand-side resources can be considered to have an availability probability requirement of more than 99%, for example, in a specific implementation process, at least 3 monomer capacities of 1MW need to be invested to replace the capacity provided by a 1MW thermal power generating unit with an availability probability, and the demand-side resources with an availability probability of 80% can reach an availability probability of 99.2% after being integrated. Therefore, the virtual power plant with high availability probability is obtained by optimally combining the resources controlled by the virtual power plant, and the availability probability exceeding the conventional power generation resources is achieved.
And because the virtual power plant participates in bidding in the auxiliary service market, the bidding capacity of each auxiliary service in different periods needs to be decided, how economically the virtual power plant meeting the availability probability requirement is constructed in the corresponding period is considered, and condition constraint is introduced for optimizing the virtual power plant to achieve the expected high availability probability.
Wherein, based on presetting the restriction of bid capacity to each auxiliary service variety and forecast price and retraining, the control of the bid capacity of virtual power plant does not influence the scope of market clearing price and includes: the constraint is made based on the following formula,
maxZ＝∑[F(yj,t)-C(yj,t)-E(η)]；
F(yj,t)＝pj,tyj,t；
C(yj,t)＝∑γixi,j；
E(η)＝Aη；
wherein j is the serial number of the auxiliary service variety, i is the individual serial number of the integration unit, and F (y)j,t) Winning the bid for said auxiliary service type of jth in time period t, C (y)j,t) The integration cost of the jth auxiliary service type in the time period t, E (eta) is the potential assessment cost corresponding to the availability requirement eta, yj,tBidding capacity, x, for jth auxiliary service during time period ti,jFor individuals i to participate in the j-th speciesCombined capacity, p, of auxiliary service type in time period tj,tPredicted price, gamma, for jth auxiliary service during time period tiIntegration cost per unit volume for an individual i; a is an assessment coefficient.
Because the virtual power plant as the receiver of the price participates in the auxiliary service market without affecting the marginal price of the market, and the expectation of the income participating in the electric auxiliary service market is the forecast price based on past transaction history data, the maximum bid demand is introduced as the constraint, so that the bid capacity is controlled within a range which does not affect the clearing price of the market, namely
0≤yj,t≤Dj,t,max(ii) a The revenue of participating in the electricity market ancillary services is maximized.
Furthermore, combining the standby resources of the virtual power plant based on the preset combination constraint to make the bidding capacity consistent with the performance requirement of the corresponding auxiliary service variety comprises:
the availability of the standby resources is constrained based on a capacity availability formula:
P(∑i,txi,j≥yj,t)≥η；
0≤i,t≤1；
wherein the content of the first and second substances,i,tis the probability distribution of the available trait for individual i.
The uncertainty of the actual available capacity of the non-power generation resources due to self reasons is reflected, and the probability distribution characteristics of the non-power generation resources can be obtained by carrying out brought-in analysis after data are collected.
Constraining a minimum response time of the corresponding secondary service type based on a response time constraint formula:
ri≤Rj；
wherein R isjCorresponding time requirement for the jth auxiliary service type, riIs the minimum response time of individual i. That is, the individual resources participating in the combination should meet the requirement of the minimum response time of the corresponding auxiliary service type.
And constraining the time for continuously outputting the electric energy based on the duration constraint:
∑lixi,j≥Ljyj,t；
wherein L isjDuration requirement for jth auxiliary service type, liThe maximum duration of the individual i. The duration is the requirement for the continuous output power capability after the capacity is called, and the total output electric energy of the individual standby resources in each period is not less than the output electric energy of the bidding capacity in the minimum duration, so as to ensure that reliable and continuous electric energy is provided for users to use, namely, the electric energy is sufficient.
The capacity of the monomer is constrained based on capacity constraints:
0≤xi,j≤Ci；
wherein, CiIs the maximum available capacity of the monomer resource i to be integrated. The range of cell capacities in a particular implementation is to meet set requirements to provide the amount of power and sustained power requirements to meet the power requirements.
The bidding strategy decision of the virtual power plant participating in the auxiliary service market is established based on the technical characteristics and economic characteristics of the controllable standby resources of the virtual power plant and the theory of the availability equivalent probability of the standby resources, the bidding capacities of different auxiliary service types are decided, and how each type of standby resources need to be matched in different auxiliary service bidding capacities, so that the income maximization of the power-assisted service market participating is realized, a basis is provided for the virtual power plant to participate in the power-assisted market service, and the virtual power plant can better participate in the market auxiliary service.
Referring to fig. 2 based on the same design concept, another specific embodiment of the present invention further provides a virtual power plant optimized combination bidding apparatus, including:
the bidding capacity constraint module is used for constraining each auxiliary service variety and the predicted price based on preset bidding capacity constraint and controlling the bidding capacity of the virtual power plant within a range not influencing the clearing price of the market;
the resource combination module is used for combining the standby resources of the virtual power plant based on preset combination constraint so that the bidding capacity of the virtual power plant is consistent with the performance requirement of the corresponding auxiliary service variety; and
and the equivalent constraint module is used for carrying out equivalent probability constraint on the standby resources meeting the performance requirements of the corresponding auxiliary service varieties based on preset availability equivalent constraint and selecting the standby resources meeting the high availability requirements of the auxiliary service market to participate in bidding.
The specific implementation manner of the virtual power plant optimized combination bidding method provided by the above embodiment can be referred to, and the detailed description of the method is omitted here.
Referring to fig. 3, an embodiment of the present invention further provides an apparatus, including a processor and a memory, the processor and the memory being connected by a communication bus; the processor is used for calling and executing the program stored in the memory; and a memory for storing a program for executing at least the virtual plant optimization combined bidding method according to the above embodiment.
In order to match with the virtual power plant optimized combination bidding device provided by the embodiment of the present invention, some embodiments of the present invention further provide a storage medium, where a computer program is stored in the storage medium, and the computer program is at least used to execute the virtual power plant optimized combination bidding method described in the above embodiments.
The storage medium is not limited to a floppy disk, a hard disk and a flash disk, and other memories may be used.
According to the virtual power plant optimized combination bidding method, device, equipment and storage medium provided by the embodiment of the invention, based on the economic characteristics of the adjustable resource technology, a resource integration bidding strategy of the virtual power plant is provided, on one hand, the decision of participating in the bidding capacity of different types of auxiliary service markets is considered, on the other hand, the influence of the integration cost of the adjustable resources with different performances on the bidding decision is considered, and the conditional probability constraint is introduced to realize the optimal combination of high-availability equivalent resources. By establishing a virtual power plant standby resource combination optimization model, decision on bidding capacity and combination of standby resources are realized, and support is provided for the virtual power plant to flexibly participate in an auxiliary service market.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A virtual power plant optimization combination bidding method is characterized by comprising the following steps:
each auxiliary service variety and the predicted price are constrained based on preset bidding capacity constraint, and the bidding capacity of the virtual power plant is controlled within the range without influencing the clearing price of the market;
combining standby resources of the virtual power plant based on preset combination constraints to enable the bidding capacity of the standby resources to be consistent with the performance requirements of the corresponding auxiliary service varieties;
and performing equivalent probability constraint on the standby resources meeting the performance requirements of the corresponding auxiliary service varieties based on preset availability equivalent constraint, and selecting the standby resources meeting the high availability requirement of an auxiliary service market to participate in bidding.
2. The virtual power plant optimization combination bidding method according to claim 1, wherein the constraining of each auxiliary service variety and the predicted price based on the preset bidding capacity constraint, the controlling of the bidding capacity of the virtual power plant within a range that does not affect the market clearing price comprises: the constraint is made based on the following formula,
maxZ＝∑[F(yj,t)-C(yj,t)-E(η)]；
F(yj,t)＝pj,tyj,t；
C(yj,t)＝∑γixi,j；
E(η)＝Aη；
wherein j is the serial number of the auxiliary service variety, i is the individual serial number of the integration unit, and F (y)j,t) Winning the bid for said auxiliary service type of jth in time period t, C (y)j,t) The integration cost of the jth auxiliary service type in the time period t, E (eta) is the potential assessment cost corresponding to the availability requirement eta, yj,tBidding capacity, x, for jth auxiliary service during time period ti,jCapacity, p, for an individual i to participate in a combination of a jth auxiliary service type during a time period tj,tPredicted price, gamma, for jth auxiliary service during time period tiIntegration cost per unit volume for an individual i; a is an assessment coefficient.
3. The method of claim 2, wherein the step of combining the standby resources of the virtual power plant based on the preset combination constraint to make the bidding capacity consistent with the performance requirement of the corresponding auxiliary service varieties comprises:
the availability of the standby resources is constrained based on a capacity availability formula:
P(∑i,txi,j≥yj,t)≥η；
0≤i,t≤1；
wherein the content of the first and second substances,i,tis the probability distribution of the available trait for individual i.
4. The method of claim 2, wherein the step of combining the standby resources of the virtual power plant based on the preset combination constraint to make the bidding capacity consistent with the performance requirement of the corresponding auxiliary service varieties further comprises:
constraining a minimum response time of the corresponding secondary service type based on a response time constraint formula:
ri≤Rj；
wherein R isjCorresponding time requirement for the jth auxiliary service type, riIs the minimum response time of individual i.
5. The method of claim 2, wherein the step of combining the standby resources of the virtual power plant based on the preset combination constraint to make the bidding capacity consistent with the performance requirement of the corresponding auxiliary service varieties further comprises:
and constraining the time for continuously outputting the electric energy based on the duration constraint:
∑lixi,j≥Ljyj,t；
wherein L isjDuration requirement for jth auxiliary service type, liThe maximum duration of the individual i.
6. The method of claim 2, wherein the step of combining the standby resources of the virtual power plant based on the preset combination constraint to make the bidding capacity consistent with the performance requirement of the corresponding auxiliary service varieties further comprises:
the capacity of the monomer is constrained based on capacity constraints:
0≤xi,j≤Ci；
wherein, CiIs the maximum available capacity of the monomer resource i to be integrated.
7. The virtual power plant optimization combination bidding method according to any one of claims 1 to 6, wherein the performing equivalent probability constraint on the standby resources meeting the performance requirement of the corresponding auxiliary service varieties based on preset availability equivalent constraint and selecting the standby resources meeting the high availability requirement of the auxiliary service market to participate in bidding comprises:
the probability of availability is constrained based on opportunity constraints:
P(g(x,ξ)≥0)≥p；
0≤p≤1；
wherein x is a decision variable, ξ is a random variable, and g (x, ξ) is a constraint function of the two variables, which is the requirement of equivalently replacing high-availability conventional resources.
8. A virtual power plant optimized combination bidding device, comprising:
the bidding capacity constraint module is used for constraining each auxiliary service variety and the predicted price based on preset bidding capacity constraint and controlling the bidding capacity of the virtual power plant within a range not influencing the clearing price of the market;
the resource combination module is used for combining the standby resources of the virtual power plant based on preset combination constraint so that the bidding capacity of the virtual power plant is consistent with the performance requirement of the corresponding auxiliary service variety; and
and the equivalent constraint module is used for performing equivalent probability constraint on the standby resources meeting the performance requirements of the corresponding auxiliary service varieties based on preset availability equivalent constraint and selecting the standby resources meeting the high availability requirements of the auxiliary service market to participate in bidding.
9. A device comprising a processor and a memory, the processor and the memory being connected by a communication bus; the processor is used for calling and executing the program stored in the memory; the memory for storing a program for performing at least the virtual plant optimization combined bidding method of any one of claims 1-7.
10. A storage medium having stored thereon a computer program for performing at least the virtual plant optimized combined bidding method according to any one of claims 1 to 7.
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