Classification and aggregation method and device of virtual power plant, electronic equipment and storage medium

Abstract

The invention provides a classification and aggregation method and device of a virtual power plant, electronic equipment and a storage medium. The method comprises the following steps: acquiring reliability index parameters of an energy power station; determining a frequency modulation performance index and a peak regulation performance index of the energy power station based on the reliability index parameter and the time domain response performance information of the energy power station; based on the frequency modulation performance index and the peak regulation performance index, classifying and aggregating the energy power station to obtain a classifying and aggregating model, and determining a target function of the classifying and aggregating model; and determining a classification aggregation result of the classification aggregation model based on the objective function. According to the classification and aggregation method and device of the virtual power plant, the electronic equipment and the storage medium, a better classification and aggregation result can be obtained, and the integrating degree of the virtual power plant and peak regulation and frequency modulation auxiliary services is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of virtual power plants, and in particular to a classification and aggregation method, device, electronic device and storage medium for virtual power plants. Background Art

[0002] In recent years, the reform of the power market and the construction of the energy Internet have also greatly promoted the development of virtual power plants (VPPs). Driven by the benefits of VPP classification and aggregation, VPP classifies and aggregates cross-regional and multi-type controllable distributed power sources through advanced information collection, control, communication and other technologies, thereby giving full play to the flexibility and complementarity of resource scheduling. Distributed power sources participating in classification and aggregation include wind power generation units, photovoltaic power generation units, etc., which have the characteristics of large number and large performance differences. Therefore, it is necessary to improve the aggregation capacity of VPP internal resources.

[0003] However, in related technologies, when aggregating and scheduling VPP resources, usually only the price differences of different power products are considered, which cannot meet the use needs of the power grid. Summary of the invention

[0004] In view of this, the purpose of the present disclosure is to propose a classification aggregation method, device, electronic device and storage medium for a virtual power plant to solve or at least partially solve the above-mentioned problems.

[0005] Based on the above objectives, the first aspect of the present disclosure provides a classification and aggregation method for a virtual power plant, including:

[0006] Obtain reliability index parameters of energy power stations;

[0007] Determine the frequency regulation performance index and peak regulation performance index of the energy power station based on the reliability index parameter and the time domain response performance information of the energy power station;

[0008] Based on the frequency regulation performance index and the peak regulation performance index, the energy power station is classified and aggregated to obtain a classification aggregation model, and an objective function of the classification aggregation model is determined;

[0009] A classification aggregation result of the classification aggregation model is determined based on the objective function.

[0010] In some embodiments, the energy power station includes a renewable energy power station and a conventional energy power station, and the reliability index parameter includes a first reliability index parameter;

[0011] The step of obtaining the reliability index parameters of the energy power station includes:

[0012] Determining a second reliability index parameter of the renewable energy power station by random sampling and statistics of the renewable energy power station;

[0013] Determining the confidence capacity of the renewable energy power station relative to the conventional energy power station based on the second reliability indicator parameter;

[0014] Based on the confidence capacity, a first reliability index parameter of the reliability assessment of the renewable energy power station based on the conventional energy power station is determined.

[0015] In some embodiments, the second reliability indicator parameter of the renewable energy power station is obtained based on power shortage expectation information.

[0016] In some embodiments, determining the confidence capacity of the renewable energy power station relative to the conventional energy power station based on the second reliability indicator parameter includes:

[0017] Determining a first linear relationship between the second reliability indicator parameter and the first capacity of the renewable energy power station based on the second reliability indicator parameter and the first capacity of the renewable energy power station;

[0018] Using the renewable energy power station to replace the conventional energy power station, obtaining a second capacity of the conventional energy power station, so as to determine a second linear relationship between a second reliability index parameter and the second capacity of the conventional energy power station replaced by the renewable energy power station;

[0019] A reliability index parameter value corresponding to the first linear relationship of the target capacity of the renewable energy power station is obtained, and a confidence capacity of the renewable energy power station is determined based on the reliability index parameter value and the second linear relationship.

[0020] In some embodiments, the time domain response performance information includes response time and responsive frequency; and determining the frequency regulation performance index and peak regulation performance index of the energy power station includes:

[0021] Determine the frequency modulation performance index based on the response time and the responsive frequency;

[0022] Determining the peak load performance index based on the response time;

[0023] Among them, the frequency modulation performance index and the peak shaving performance index meet preset constraints, the constraint of the frequency modulation performance index is determined based on the maximum allowable response time and the maximum allowable responsive frequency of the frequency modulation service, and the constraint of the peak shaving performance index is determined based on the maximum allowable response time of the peak shaving service.

[0024] In some embodiments, the classifying and aggregating the energy power plants based on the frequency regulation performance index and the peak regulation performance index includes:

[0025] Determine a first weight of a frequency regulation performance indicator of a target energy power station in the energy power station in the frequency regulation performance indicator set and a second weight of a peak regulation performance indicator of the target energy power station in the peak regulation performance indicator set;

[0026] Based on the magnitude relationship between the first weight and the second weight, the classification of the target energy power station is determined.

[0027] In some embodiments, determining the classification aggregation result of the classification aggregation model based on the objective function includes:

[0028] Taking the minimum expected value of insufficient power as the goal, the classification aggregation model is solved based on the objective function to obtain the classification aggregation result.

[0029] A second aspect of the present disclosure provides a classification and aggregation device for a virtual power plant.

[0030] The acquisition module is configured to: acquire reliability index parameters of the energy power station;

[0031] A determination module is configured to: determine a frequency regulation performance index and a peak regulation performance index of the energy power station based on the reliability index parameter and the time domain response performance information of the energy power station;

[0032] An aggregation module is configured to: classify and aggregate the energy power stations based on the frequency regulation performance index and the peak regulation performance index to obtain a classification aggregation model, and determine an objective function of the classification aggregation model;

[0033] The result generation module is configured to determine the classification aggregation result of the classification aggregation model based on the objective function.

[0034] A third aspect of the present disclosure provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the program.

[0035] A fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions are used to cause the computer to execute the method described in the first aspect.

[0036] From the above, it can be seen that the classification and aggregation method, device, electronic device and storage medium of the virtual power plant provided by the present invention take into account the reliability index parameters and time domain response performance information of each energy power station when classifying and aggregating the energy power stations of the virtual power plant, so that classification and aggregation can be performed based on the determined frequency regulation performance index and peak regulation performance index to obtain a classification aggregation model and the objective function of the classification aggregation model, thereby obtaining better classification and aggregation results and improving the compatibility of the virtual power plant with peak regulation and frequency regulation auxiliary services. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 A flowchart of an exemplary method provided by an embodiment of the present disclosure is shown.

[0039] Figure 2 A flowchart of an exemplary method provided by an embodiment of the present disclosure is shown.

[0040] Figure 3 A schematic diagram of an exemplary device provided by an embodiment of the present disclosure is shown.

[0041] Figure 4 A schematic diagram of the hardware structure of an exemplary computer device provided in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0042] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0043] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0044] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0045] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0046] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0047] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0048] As described in the background technology, in the related technology, when aggregating and scheduling VPP resources, usually only the price differences of different power products are considered, but it is not taken into account that in the power grid peak-shaving and frequency-regulating auxiliary services, different auxiliary services have differentiated requirements for the time domain response performance information of VPP power generation, which cannot meet the use requirements of the power grid.

[0049] In view of this, an embodiment of the present disclosure provides a classification aggregation method for a virtual power plant to solve the above problems.

[0050] like Figure 1 As shown, the classification and aggregation method of the virtual power plant includes:

[0051] Step S101, obtaining reliability index parameters of an energy power station.

[0052] In this embodiment, the energy power station may include various types of power stations such as renewable energy power stations and conventional energy power stations. Among them, the renewable energy power station may include a wind power station, a photovoltaic power station, etc., and the conventional energy power station may include a thermal power station, etc., which is not limited in this embodiment.

[0053] In this embodiment, the reliability index parameters of each energy power station can be obtained. The reliability index parameters can be determined based on the power shortage time probability, power shortage time expectation, and power shortage expectation value, which evaluate the reliability of the energy power station from the aspects of power outage probability, power outage time, and power outage amount.

[0054] Step S103, determining the frequency regulation performance index and the peak regulation performance index of the energy power station based on the reliability index parameter and the time domain response performance information of the energy power station.

[0055] In this embodiment, after the reliability index parameters of each energy power station are determined, each energy power station of the virtual power plant that meets the reliability index requirements of the power system can be used for the peak-shaving and frequency-regulating auxiliary services of the power system. In this embodiment, when facing the peak-shaving and frequency-regulating auxiliary services, the time domain response performance information of the energy power station is considered, and the frequency regulation performance index and the peak-shaving performance index of the energy power station are determined, so as to quantify the degree of fit between each energy power station of the virtual power plant and the performance requirements of the peak-shaving service and the frequency regulation service based on the frequency regulation performance index and the peak-shaving performance index of the energy power station.

[0056] Step S105, based on the frequency regulation performance index and the peak regulation performance index, classify and aggregate the energy power stations to obtain a classification aggregation model and determine the objective function of the classification aggregation model.

[0057] In this embodiment, after the frequency regulation performance index and the peak regulation performance index are determined, the energy power stations can be classified and aggregated based on the frequency regulation performance index and the peak regulation performance index, thereby obtaining a classification and aggregation model. At the same time, the objective function of the classification and aggregation model can be determined.

[0058] Step S107, determining the classification aggregation result of the classification aggregation model based on the objective function.

[0059] In this embodiment, the classification aggregation model is solved based on the objective function to obtain the classification aggregation result.

[0060] In this embodiment, when classifying and aggregating the energy power stations of the virtual power plant, the reliability index parameters and time domain response performance information of each energy power station are taken into consideration, so that classification and aggregation can be performed based on the determined frequency regulation performance indicators and peak regulation performance indicators to obtain a classification aggregation model and the objective function of the classification aggregation model, thereby obtaining better classification and aggregation results and improving the compatibility of the virtual power plant with peak regulation and frequency regulation auxiliary services.

[0061] In some embodiments, the energy power station includes a renewable energy power station and a conventional energy power station, wherein the renewable energy power station may include a wind power station, a photovoltaic power station, etc., and the conventional energy power station may include a thermal power station, etc.

[0062] Since wind power generation and photovoltaic power generation have different power generation characteristics and belong to different types of units, related technologies often roughly homogenize units that are not homogenous, resulting in a decrease in the calculation accuracy of reliability indicators and a waste of resources.

[0063] To solve the above problem, in this embodiment, the reliability index parameters of the energy power station are calculated by confidence capacity, and the wind power station and photovoltaic power station are equivalent to conventional power plants of the same type through the confidence capacity index. At the same time, the numerical value of the confidence capacity can reflect the capabilities of different wind power stations and photovoltaic power stations compared with conventional power plants.

[0064] In this embodiment, the reliability index parameter includes a first reliability index parameter; the step S101 of obtaining the reliability index parameter of the energy power station includes:

[0065] Step S201 : determining a second reliability index parameter of the renewable energy power station by random sampling and statistics of the renewable energy power station.

[0066] In this embodiment, the sequential Monte Carlo method can be used to calculate the second reliability index parameter δ of the wind power station and the photovoltaic power station. EENS .

[0067] In some embodiments, the second reliability indicator parameter of the renewable energy power station is obtained based on power shortage expectation information.

[0068] Affected by topography, climate conditions, and the generators themselves, the output of renewable energy presents a large uncertainty, resulting in low reliability of renewable energy power generation systems, which in turn leads to a significant decline in the enthusiasm of grid dispatchers for renewable energy dispatch. In related technologies, when classifying and aggregating virtual power plants, there is less reliability assessment of renewable energy, which leads to low peak and frequency regulation performance of VPPs.

[0069] In this embodiment, the second reliability index parameter of the renewable energy power station is obtained based on the power shortage expectation information. Since the power shortage expectation information is a comprehensive reliability index that comprehensively describes the number of power outages, average duration, and average power outage power of the power generation unit, in this embodiment, the power shortage expectation information is used as the main power generation reliability index to obtain the second reliability index parameter, which increases the reliability assessment of renewable energy and thus improves the peak and frequency regulation performance of the VPP.

[0070] In some embodiments, the time series state distribution of the power generation of a single wind power generation unit and photovoltaic power generation unit can be calculated first, and then the time series state distribution of the power generation of all wind power generation units and photovoltaic power generation units in the site can be accumulated to finally obtain the time series state distribution of the power generation of a single wind power station and photovoltaic power station, and the second reliability index parameter of the single wind power station or photovoltaic power station can be calculated based on the time series state distribution.

[0071] In some embodiments, the second reliability index parameter of a renewable energy power station, i.e., a single wind power station or a photovoltaic power station, includes:

[0072]

[0073] Among them, δ EENS (Z Y,o ) is the expected function of insufficient power, Z Y,o is the system state at the oth time point in the Yth simulation; D(Z Y,o ) is the system in Z Y,o Duration of the state; N o is the number of system states; N Y is the number of simulation calculations; Calculate the expected value of power shortage of the wind farm or photovoltaic power station for the Yth time.

[0074] Step S203: determining the confidence capacity of the renewable energy power station relative to the conventional energy power station based on the second reliability indicator parameter.

[0075] In this embodiment, under the premise of equal reliability, the wind power station or photovoltaic power station is used to replace the unit capacity of the conventional energy power station to evaluate the confidence capacity of the renewable energy power station. The confidence capacity index can be used to describe the capacity value of wind power stations and photovoltaic power stations, and can also quantify the contribution of wind power stations and photovoltaic power stations to the redundancy of the power system.

[0076] Step S205: determining, based on the confidence capacity, a first reliability index parameter for reliability evaluation of the renewable energy power station based on the conventional energy power station.

[0077] In this embodiment, the first reliability index parameter of the reliability evaluation of the renewable energy power station based on the conventional energy power station can be determined based on the confidence capacity.

[0078] In some embodiments, an auxiliary function method may be used to calculate the confidence capacity of a wind power station or a photovoltaic power station. Determining the confidence capacity of the renewable energy power station relative to the conventional energy power station based on the second reliability index parameter in step S203 includes:

[0079] Step S301: determining a first linear relationship between a second reliability indicator parameter and the first capacity of the renewable energy power station based on the second reliability indicator parameter and the first capacity of the renewable energy power station.

[0080] In this embodiment, the first capacity C of the wind power station or photovoltaic power station can be N The corresponding reliability indexes are obtained, and then the δ of wind power station and photovoltaic power station are plotted. EENS -C N Curve, that is, the second reliability index parameter δ of the renewable energy power station is obtained EENS With the first capacity C N The first linear relationship between . Where N is the number of samples.

[0081] Step S303, using the renewable energy power station to replace the conventional energy power station, obtaining the second capacity of the conventional energy power station, so as to determine the second linear relationship between the second reliability index parameter and the second capacity of the renewable energy power station replacing the conventional energy power station.

[0082] In this embodiment, a wind power station or a photovoltaic power station can be used to replace the generator set of a conventional energy power station. r The corresponding reliability index can be obtained by plotting the δ of wind power station or photovoltaic power station replacing conventional units. EENS -C r Curve, that is, the second reliability index parameter δ EENS With the second capacity C r The second linear relationship between .

[0083] Step S305: obtaining a reliability index parameter value corresponding to the first linear relationship of the target capacity of the renewable energy power station, and determining a confidence capacity of the renewable energy power station based on the reliability index parameter value and the second linear relationship.

[0084] In this embodiment, when the capacity of the wind power station or photovoltaic power station is the target capacity C N0When δ EENS -C N Find the target capacity C on the curve N0 The corresponding reliability index parameter value δ EENS0 , and then according to the reliability index parameter value through the second linear relationship, that is, in δ EENS -C r Find the corresponding capacity C on the curve r0 , C r0 This is the confidence capacity of the wind power station or photovoltaic power station.

[0085] Among them, the confidence capacity of renewable energy power stations, namely wind power stations and photovoltaic power stations, includes:

[0086] δ EENS (C N ,L s )=δ EENS (C r ,L s )

[0087] Among them, δ EENS (·,·) is the expected function of insufficient power; L s For system load.

[0088] In this way, the total confidence capacity of all wind power plants or photovoltaic power plants can be obtained:

[0089]

[0090] Among them, C all is the total confidence capacity of all wind power stations or photovoltaic power stations; M is the number of all wind power stations and photovoltaic power stations; C u is the confidence capacity of the u-th wind power station or photovoltaic power station.

[0091] Then, in step S205, based on the confidence capacity, determining a first reliability index parameter for reliability evaluation of the renewable energy power station based on the conventional energy power station includes:

[0092] δ EENS =δ EENS (C all )

[0093] but:

[0094] In some embodiments, Figure 2 As shown, the calculation process of the first reliability indicator parameter includes:

[0095] Step S401, inputting the original output data of each renewable energy power station.

[0096] Step S403: Simulate the annual output sequences of each renewable energy power station.

[0097] Step S405: When N year = 1, i.e., when the number of samples is taken as one year, correct the annual output sequences of each renewable energy power station by component state sampling.

[0098] Step S407: Input the annual load forecast data.

[0099] Step S409: Calculate the cumulative reliability index of the hourly operating state of each renewable energy power station.

[0100] Step S411: Calculate the confidence capacity of each renewable energy power station and sum them up.

[0101] Step S413: Judge the relationship between N year and N. If N year < N, then increase N year by 1 and continue the simulation for the next year; if N year > N, then stop the simulation and execute Step S415.

[0102] Wherein, N year < N means that the simulation may not have reached enough times or years to obtain stable or reliable statistical results. In this case, it may be necessary to continue the simulation and increase the value of Nyear until a certain stopping criterion is met (such as reaching the predetermined total number of simulations N).

[0103] N year > N means that the simulation has exceeded the necessary times, possibly because the predetermined stopping condition has been reached or some abnormal situation has occurred. In this case, some actions may need to be taken, such as stopping the simulation, outputting the current results, or performing additional analysis.

[0104] Step S415: Calculate the first reliability index parameter of the conventional energy power station corresponding to the total confidence capacity of each renewable energy power station, and output this first reliability index parameter.

[0105] In some embodiments, the time-domain response performance information includes response time and response frequency; determining the frequency modulation performance index and peak shaving performance index of the energy power station in Step S103 includes:

[0106] The frequency modulation performance index is determined based on the response time and the responsive frequency; the peak shaving performance index is determined based on the response time; wherein the frequency modulation performance index and the peak shaving performance index satisfy preset constraints, the constraints of the frequency modulation performance index are determined based on the maximum allowable response time and the maximum allowable responsive frequency of the frequency modulation service, and the constraints of the peak shaving performance index are determined based on the maximum allowable response time of the peak shaving service.

[0107] In this embodiment, after completing the VPP reliability index evaluation, the VPP that meets the power system reliability index requirements is used for the power system peak-shaving and frequency-regulating auxiliary services. For the time domain response requirements of different auxiliary services, the VPP peak-shaving performance index and frequency-regulating performance index are determined based on the VPP time domain response performance information to quantify the degree of fit between the VPP and the peak-shaving and frequency-regulating auxiliary performance requirements.

[0108] The time domain response performance information includes:

[0109] Response time τ respond , refers to the maximum time required for a renewable energy power station to respond to the output required by the dispatching instruction after receiving the instruction from the power dispatching;

[0110] Response frequency f respond , refers to the number of times renewable energy power station resources can be called upon within the time range required by power dispatch.

[0111] If a renewable energy power station is ready to participate in the peak-shaving and frequency-regulating auxiliary services, it is necessary to test its peak-shaving and frequency-regulating response capabilities, by issuing peak-shaving and frequency-regulating instructions to the renewable energy power station, and monitoring the power generation curve of the renewable energy power station participating in the peak-shaving and frequency-regulating. And by setting the discrimination conditions, analyze the power generation curve of the renewable energy power station and calculate the above two time-domain response performance information.

[0112] Setting frequency regulation performance indicators for renewable energy power plants Generally speaking, the VPP power generation capacity is E q The response time τ q,respond The shorter the frequency f, the more q,respond The higher it is, the more compatible it is with the frequency regulation service. The peak regulation service generally has a certain requirement for the response speed, but has a certain requirement for the duration. The calling frequency of the peak regulation service is generally low. Based on this, the peak regulation performance index of renewable energy power plants is defined. and The value is between 0 and 1. The larger the value, the higher the performance fit of the indicator.

[0113]

[0114] in, is τ respond The homogenized value of Yes respond The homogenized value of .

[0115] The constraints of frequency modulation performance indicators include:

[0116]

[0117] in, The maximum permissible response time for frequency modulation services, The maximum permissible responsive frequency for FM service.

[0118] The constraints of the peak load performance indicators include:

[0119]

[0120] in, The maximum allowed response time for peak load shaving services.

[0121] In some embodiments, the step S105 of classifying and aggregating the energy power plants based on the frequency regulation performance index and the peak regulation performance index includes:

[0122] Step S501, determining a first weight of a frequency regulation performance indicator of a target energy power station in the energy power station in the frequency regulation performance indicator set and a second weight of a peak regulation performance indicator of the target energy power station in the peak regulation performance indicator set.

[0123] In this embodiment, classification and aggregation are performed based on the frequency regulation performance index and the peak regulation performance index to form two types of energy power station sets for peak regulation services and frequency regulation services, where the set K AGC represents the frequency regulation service energy power station set, set K reg Represents a collection of energy power stations providing peak load services.

[0124] because and The calculation methods are different, so it is meaningless to compare the two indicators horizontally for the same energy power station. Therefore, this embodiment classifies energy power stations by comparing the weights of the two indicators. First, the weight of the indicator of a certain energy power station in the indicator set is calculated, and then the weight values ​​of the two indicators of the energy power station are compared, and the energy power station is classified into a set with a larger weight.

[0125] Among them, the frequency regulation performance index of the target energy power station is in the frequency regulation performance index set The first weight in include:

[0126]

[0127] The peak load performance index of the target energy power station is in the peak load performance index set The second weight in include:

[0128]

[0129] Step S503: determining the classification of the target energy power station based on the magnitude relationship between the first weight and the second weight.

[0130] In this embodiment, the first weight is compared and the second weight The size relationship between them: If the first weight If it is larger, the target energy power station is included in the set On the contrary, the collection

[0131] Since the response performance of the VPP will not change significantly, unless a new load is added, the above classification process only needs to be performed once.

[0132] In some embodiments, determining the classification aggregation result of the classification aggregation model based on the objective function in step S107 includes: solving the classification aggregation model based on the objective function with the goal of minimizing the expected value of insufficient power to obtain the classification aggregation result.

[0133] In this embodiment, based on the VPP reliability assessment, VPP is used for peak-shaving and frequency-regulating auxiliary services, and VPP is classified into two types of VPP sets for peak-shaving services and frequency-regulating services. At the same time, considering that the output of renewable energy is seasonal and volatile, power system accidents occur frequently, resulting in a decrease in the enthusiasm of the power grid to dispatch renewable energy. This embodiment further takes the minimum expected value of power shortage as the optimization goal of the VPP aggregation model.

[0134] Among them, the goal is to minimize the expected value of insufficient power, and use intelligent algorithms to find the optimal combination that meets the objective function. The objective function is as follows:

[0135]

[0136] Among them, S u is a 0-1 variable indicating whether the u-th renewable energy power station participates in the aggregation, S u =1 means participating in aggregation, S u =0 means no participation; Q cis the capacity requirement of the power system peak load / frequency regulation auxiliary service; k is the capacity margin requirement. In some embodiments, k=1.3.

[0137] In this embodiment, the target optimization problem can be solved by using an optimization algorithm to solve the VPP aggregation model, thereby obtaining an optimization result.

[0138] In this embodiment, confidence capacity is introduced to homogenize different types of generator sets, thereby improving the calculation accuracy of reliability indicators; based on the time domain response performance indicators of VPP, VPP peak-shaving performance indicators and frequency regulation performance indicators are defined, which improves the compatibility of VPP with peak-shaving and frequency regulation auxiliary services; the VPP classification aggregation model fully considers external environmental factors and the potential of renewable energy itself, and deeply explores the VPP aggregation scheme with the best reliability.

[0139] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0140] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0141] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a classification aggregation device for a virtual power plant.

[0142] refer to Figure 3 , the classification aggregation device of the virtual power plant includes:

[0143] The acquisition module 11 is configured to: acquire reliability index parameters of the energy power station;

[0144] The determination module 13 is configured to: determine the frequency regulation performance index and the peak regulation performance index of the energy power station based on the reliability index parameter and the time domain response performance information of the energy power station;

[0145] The aggregation module 15 is configured to: classify and aggregate the energy power plants based on the frequency regulation performance index and the peak regulation performance index to obtain a classification aggregation model, and determine an objective function of the classification aggregation model;

[0146] The result generating module 17 is configured to determine the classification aggregation result of the classification aggregation model based on the objective function.

[0147] In some embodiments, the energy power station includes a renewable energy power station and a conventional energy power station, and the reliability index parameter includes a first reliability index parameter;

[0148] The acquisition module 11 is further configured to:

[0149] Determining a second reliability index parameter of the renewable energy power station by random sampling and statistics of the renewable energy power station;

[0150] Determining the confidence capacity of the renewable energy power station relative to the conventional energy power station based on the second reliability indicator parameter;

[0151] Based on the confidence capacity, a first reliability index parameter of the reliability assessment of the renewable energy power station based on the conventional energy power station is determined.

[0152] In some embodiments, the second reliability indicator parameter of the renewable energy power station is obtained based on power shortage expectation information.

[0153] In some embodiments, determining the confidence capacity of the renewable energy power station relative to the conventional energy power station based on the second reliability indicator parameter includes:

[0154] Determining a first linear relationship between the second reliability indicator parameter and the first capacity of the renewable energy power station based on the second reliability indicator parameter and the first capacity of the renewable energy power station;

[0155] Using the renewable energy power station to replace the conventional energy power station, obtaining a second capacity of the conventional energy power station, so as to determine a second linear relationship between a second reliability index parameter and the second capacity of the conventional energy power station replaced by the renewable energy power station;

[0156] A reliability index parameter value corresponding to the first linear relationship of the target capacity of the renewable energy power station is obtained, and a confidence capacity of the renewable energy power station is determined based on the reliability index parameter value and the second linear relationship.

[0157] In some embodiments, the time domain response performance information includes response time and responsive frequency; the determination module 13 is further configured to:

[0158] Determine the frequency modulation performance index based on the response time and the responsive frequency;

[0159] Determining the peak load performance index based on the response time;

[0160] Among them, the frequency modulation performance index and the peak shaving performance index meet preset constraints, the constraint of the frequency modulation performance index is determined based on the maximum allowable response time and the maximum allowable responsive frequency of the frequency modulation service, and the constraint of the peak shaving performance index is determined based on the maximum allowable response time of the peak shaving service.

[0161] In some embodiments, the aggregation module 15 is further configured to:

[0162] Determine a first weight of a frequency regulation performance indicator of a target energy power station in the energy power station in the frequency regulation performance indicator set and a second weight of a peak regulation performance indicator of the target energy power station in the peak regulation performance indicator set;

[0163] Based on the magnitude relationship between the first weight and the second weight, the classification of the target energy power station is determined.

[0164] In some embodiments, the result generation module 17 is further configured to:

[0165] Taking the minimum expected value of insufficient power as the goal, the classification aggregation model is solved based on the objective function to obtain the classification aggregation result.

[0166] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0167] The device of the above embodiment is used to implement the classification aggregation method of the corresponding virtual power plant in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0168] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the classification aggregation method of the virtual power plant described in any of the above embodiments is implemented.

[0169] Figure 4A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0170] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0171] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0172] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0173] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0174] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0175] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0176] The electronic device of the above-mentioned embodiment is used to implement the classification aggregation method of the corresponding virtual power plant in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0177] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the classification aggregation method of the virtual power plant as described in any of the above embodiments.

[0178] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0179] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the classification and aggregation method of the virtual power plant as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0180] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0181] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the known power / ground connections to the integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0182] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0183] The embodiments of the present disclosure are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A classification and aggregation method for a virtual power plant, characterized in that: include: Obtain reliability index parameters of energy power stations; Determine the frequency regulation performance index and peak regulation performance index of the energy power station based on the reliability index parameter and the time domain response performance information of the energy power station; Based on the frequency regulation performance index and the peak regulation performance index, the energy power station is classified and aggregated to obtain a classification aggregation model, and an objective function of the classification aggregation model is determined; A classification aggregation result of the classification aggregation model is determined based on the objective function.

2. The method according to claim 1, characterized in that The energy power station includes a renewable energy power station and a conventional energy power station, and the reliability index parameter includes a first reliability index parameter; The step of obtaining the reliability index parameters of the energy power station includes: Determining a second reliability index parameter of the renewable energy power station by random sampling and statistics of the renewable energy power station; Determining the confidence capacity of the renewable energy power station relative to the conventional energy power station based on the second reliability indicator parameter; Based on the confidence capacity, a first reliability index parameter of the reliability assessment of the renewable energy power station based on the conventional energy power station is determined.

3. The method according to claim 2, characterized in that The second reliability indicator parameter of the renewable energy power station is obtained based on power shortage expectation information.

4. The method according to claim 2, characterized in that: The determining, based on the second reliability indicator parameter, the confidence capacity of the renewable energy power station relative to the conventional energy power station comprises: Determining a first linear relationship between the second reliability indicator parameter and the first capacity of the renewable energy power station based on the second reliability indicator parameter and the first capacity of the renewable energy power station; Using the renewable energy power station to replace the conventional energy power station, obtaining a second capacity of the conventional energy power station, so as to determine a second linear relationship between a second reliability index parameter and the second capacity of the conventional energy power station replaced by the renewable energy power station; A reliability index parameter value corresponding to the first linear relationship of the target capacity of the renewable energy power station is obtained, and a confidence capacity of the renewable energy power station is determined based on the reliability index parameter value and the second linear relationship.

5. The method according to claim 1, characterized in that The time domain response performance information includes response time and responsive frequency; the determination of the frequency regulation performance index and peak regulation performance index of the energy power station includes: Determine the frequency modulation performance index based on the response time and the responsive frequency; Determining the peak load performance index based on the response time; Among them, the frequency modulation performance index and the peak shaving performance index meet preset constraints, the constraint of the frequency modulation performance index is determined based on the maximum allowable response time and the maximum allowable responsive frequency of the frequency modulation service, and the constraint of the peak shaving performance index is determined based on the maximum allowable response time of the peak shaving service.

6. The method according to claim 1, characterized in that The classifying and aggregating the energy power stations based on the frequency regulation performance index and the peak regulation performance index includes: Determine a first weight of a frequency regulation performance indicator of a target energy power station in the energy power station in the frequency regulation performance indicator set and a second weight of a peak regulation performance indicator of the target energy power station in the peak regulation performance indicator set; Based on the magnitude relationship between the first weight and the second weight, the classification of the target energy power station is determined.

7. The method according to claim 1, characterized in that The determining the classification aggregation result of the classification aggregation model based on the objective function includes: Taking the minimum expected value of insufficient power as the goal, the classification aggregation model is solved based on the objective function to obtain the classification aggregation result.

8. A classification aggregation device for a virtual power plant, comprising: The acquisition module is configured to: acquire reliability index parameters of the energy power station; A determination module is configured to: determine a frequency regulation performance index and a peak regulation performance index of the energy power station based on the reliability index parameter and the time domain response performance information of the energy power station; An aggregation module is configured to: classify and aggregate the energy power stations based on the frequency regulation performance index and the peak regulation performance index to obtain a classification aggregation model, and determine an objective function of the classification aggregation model; The result generation module is configured to determine the classification aggregation result of the classification aggregation model based on the objective function.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the classification and aggregation method for a virtual power plant as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the classification aggregation method of the virtual power plant as described in any one of claims 1 to 7.

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