Distributed resource assessment aggregation method and device of virtual power plant and electronic equipment

Abstract

The invention relates to the field of power, and provides a distributed resource evaluation and aggregation method and device for a virtual power plant and electronic equipment, and the method comprises the steps: dynamically calculating the adjustment capability score and the close degree of each distributed resource according to the adjustment capability index of each distributed resource of the virtual power plant; based on the adjustment capability score and the close degree of each distributed resource, selecting a distributed resource meeting a threshold condition from each distributed resource to participate in aggregation scheduling; and optimizing the runnable boundary of each distributed resource participating in aggregation scheduling based on the power of each distributed resource participating in aggregation scheduling and a pre-established double-layer optimization model. According to the method, efficient aggregation and operation boundary optimization of distributed energy resources by the virtual power plant can be realized through a dynamic scoring mechanism and a hierarchical optimization architecture, and the method has the advantages of high efficiency, flexibility and accuracy.

Description

Technical Field

[0001] The present invention relates to the field of electric power, and particularly to a method, device and electronic equipment for evaluating and aggregating distributed resources of a virtual power plant. Background Art

[0002] A virtual power plant is a mainstream solution for source-network-load interaction and a market-oriented operation mode. In the aggregation of distributed resources by a virtual power plant, the evaluation of regulation capacity and the determination of operation boundaries are two core business processes. The connection and integration of these two processes are the key factors for the virtual power plant to improve resource utilization efficiency and achieve safe dispatching. However, in most current virtual power plant application cases, the evaluation of regulation capacity is not organically and dynamically integrated into the process of determining and updating resource boundaries, making it difficult to balance the efficiency and economy goals of the virtual power plant. That is, the current application of virtual power plants faces two main problems. One is the insufficient certification of regulation resource capacity, resulting in a low acceptance rate of declared capacity in the ancillary service market. The other is the lag in dynamic response and the high delay in boundary update, making it difficult to meet the requirements of minute-level response in the market and grid dispatching. Summary of the Invention

[0003] In view of the above problems, the present invention provides a method, device and electronic equipment for evaluating and aggregating distributed resources of a virtual power plant that overcome or at least partially solve the above problems.

[0004] In a first aspect, a method for evaluating and aggregating distributed resources of a virtual power plant includes:

[0005] Dynamically calculating the regulation capacity scores and closeness degrees of each distributed resource according to the regulation capacity indicators of each distributed resource of the virtual power plant, where the regulation capacity indicators of each distributed resource of the virtual power plant are pre-quantified and established indicators;

[0006] Based on the regulation capacity scores and closeness degrees of each distributed resource, selecting distributed resources that meet the threshold conditions from each distributed resource to participate in the aggregated scheduling, where the threshold conditions include: threshold conditions for regulation capacity scores and threshold conditions for closeness degrees;

[0007] Based on the power of each distributed resource participating in the aggregated scheduling and a pre-established two-layer optimization model, optimizing the operable boundaries of each distributed resource participating in the aggregated scheduling, where the two-layer optimization model includes: a local optimization model and a global aggregation model.

[0008] Optionally, in some alternative embodiments, before dynamically calculating the regulation capacity scores and closeness degrees of each distributed resource according to the regulation capacity indicators of each distributed resource of the virtual power plant, the method further includes:

[0009] Quantitative calculations are carried out based on economic factors, safety factors, and timeliness factors to establish the regulation ability indicators of each distributed resource in the virtual power plant. Among them, the regulation ability indicators include: adjustable range, regulation rate, sustainable time, regulation cost, and credibility. The adjustable range represents the adjustable power interval of the distributed resource per unit time. The regulation rate represents the sensitivity of the distributed resource to respond to the dispatching instruction. The sustainable time represents the sustainable time of the distributed resource at the target output power. The regulation cost represents the economic cost of the distributed resource when responding to the dispatching instruction. The credibility represents the prediction accuracy of the output of the distributed resource of the new energy power source type.

[0010] Optionally, in some alternative embodiments, the quantitative calculations based on economic factors, safety factors, and timeliness factors to establish the regulation ability indicators of each distributed resource in the virtual power plant include:

[0011] Based on the time interval, maximum power, and minimum power for the distributed resource to complete the dispatching instruction, establish the adjustable range in the regulation ability indicator of the corresponding distributed resource;

[0012] Based on the moment when the distributed resource triggers the power regulation instruction, establish the regulation rate in the regulation ability indicator of the corresponding distributed resource;

[0013] Based on the remaining energy of the energy storage type distributed resource and the set power of the energy storage type distributed resource, establish the sustainable time in the regulation ability indicator of the corresponding energy storage type distributed resource;

[0014] Based on the power regulated by the distributed resource in one dispatching action, establish the regulation cost in the regulation ability indicator of the corresponding distributed resource;

[0015] Based on the total number of time periods of the dispatching decision of the distributed resource of the new energy power source type within a single day, the predicted output values of each time period within a single day, the actual output of each time period within a single day, and the rated output value of the distributed resource of the new energy power source type, establish the credibility in the regulation ability indicator of the distributed resource of the new energy power source type.

[0016] Optionally, in some alternative embodiments, the dynamic calculation of the regulation ability scores and closeness degrees of each of the distributed resources according to the regulation ability indicators of each distributed resource in the virtual power plant includes:

[0017] For any type of distributed resource in the virtual power plant, use the analytic hierarchy process to determine the priority weights of the distributed resource in each regulation mode, where the sorting of the priority weights of each distributed resource in different regulation modes is different;

[0018] Based on the priority weights in the current adjustment mode and the switching moment of the adjustment mode, perform time smoothing processing on the priority weights of each distributed resource in the current adjustment mode, where the switching moment of the adjustment mode refers to the switching moment from the previous adjustment mode to the current adjustment mode;

[0019] Based on the quantity, value, maximum and minimum values of each adjustment ability index of each distributed resource and the priority weights after time smoothing processing, calculate the adjustment ability scores of each distributed resource;

[0020] For distributed resources of new energy power source type, based on the standard deviation of credibility of the distributed resources of new energy power source type and the maximum and minimum values of the credibility of each distributed resource of new energy power source type, calculate the closeness degree of the distributed resources of new energy power source type, where the maximum and minimum values include: the maximum value of credibility and the minimum value of credibility;

[0021] For other types of distributed resources, based on the maximum and minimum values of positive indicators and negative indicators of the other types of distributed resources, calculate the closeness degree of the other types of distributed resources, where the positive indicators include: adjustable range, adjustment rate and sustainable time, the negative indicator includes: adjustment cost, and the maximum and minimum values include: the maximum value and the minimum value.

[0022] Optionally, in some alternative embodiments, the step of calculating the closeness degree of the distributed resources of new energy power source type based on the standard deviation of credibility of the distributed resources of new energy power source type and the maximum and minimum values of the credibility of each distributed resource of new energy power source type includes:

[0023] For distributed resources of new energy power source type, based on the standard deviation of credibility of the distributed resources of new energy power source type, fuzzify the credibility of the distributed resources of new energy power source type to obtain the corresponding triangular fuzzy sequence;

[0024] According to the triangular fuzzy sequence and the maximum and minimum values of the credibility of each distributed resource of new energy power source type, calculate the fuzzy index, positive ideal solution and negative ideal solution of the distributed resources of new energy power source type;

[0025] According to the fuzzy index, the positive ideal solution and the negative ideal solution, calculate the distance from the positive ideal solution and the distance from the negative ideal solution of the distributed resources of new energy power source type;

[0026] According to the distance from the positive ideal solution and the distance from the negative ideal solution, calculate the grey relational grade coefficient of the distributed resources of new energy power source type;

[0027] Based on the grey relational grade coefficients of the new energy power source type distributed resources, the closeness degree of the new energy power source type distributed resources is calculated.

[0028] Optionally, in some alternative embodiments, for other types of distributed resources, based on the maximum and minimum values of the positive indicators and the maximum and minimum values of the negative indicators of the other types of distributed resources, calculating the closeness degree of the other types of distributed resources includes:

[0029] For other types of distributed resources, based on the maximum and minimum values of the positive indicators and the maximum and minimum values of the negative indicators of the other types of distributed resources, normalization processing is performed to obtain the normalized values of the positive indicators and the normalized values of the negative indicators of the other types of distributed resources, where the other types of distributed resources are distributed resources other than the new energy power source type distributed resources;

[0030] According to the normalized values of the positive indicators and the normalized values of the negative indicators of the other types of distributed resources, the positive ideal solution and the negative ideal solution of the other types of distributed resources are calculated;

[0031] According to the positive ideal solution and the negative ideal solution of the other types of distributed resources, the positive ideal solution distance and the negative ideal solution distance of the other types of distributed resources are calculated;

[0032] According to the positive ideal solution distance and the negative ideal solution distance of the other types of distributed resources, the closeness degree of the other types of distributed resources is calculated.

[0033] Optionally, in some alternative embodiments, based on the regulation ability scores and closeness degrees of the distributed resources, selecting the distributed resources that meet the threshold conditions from the distributed resources to participate in the aggregated scheduling includes:

[0034] From the distributed resources, select the distributed resources with a regulation ability score greater than a preset score threshold and a closeness degree greater than a preset closeness degree threshold to participate in the aggregated scheduling.

[0035] Optionally, in some alternative embodiments, based on the powers of the distributed resources participating in the aggregated scheduling and the pre-established two-layer optimization model, optimizing the operable boundaries of the distributed resources participating in the aggregated scheduling includes:

[0036] Based on the power constraints and energy constraints of the distributed resources participating in the aggregated scheduling, a two-layer optimization model is pre-established;

[0037] Based on the two-layer optimization model and the powers of the distributed resources participating in the aggregated scheduling, iterative optimization calculations are performed to obtain the operable boundaries of the distributed resources participating in the aggregated scheduling.

[0038] Second aspect, a distributed resource evaluation and aggregation device for a virtual power plant, comprising: a dynamic calculation unit, an aggregated resource selection unit, and an operating boundary optimization unit;

[0039] The dynamic calculation unit is configured to dynamically calculate the regulation ability scores and closeness degrees of the distributed resources according to the regulation ability indicators of the distributed resources of the virtual power plant, wherein the regulation ability indicators of the distributed resources of the virtual power plant are pre-quantified and established indicators;

[0040] The aggregated resource selection unit is configured to select the distributed resources that meet the threshold conditions from the distributed resources to participate in the aggregated scheduling based on the regulation ability scores and closeness degrees of the distributed resources, wherein the threshold conditions include: the threshold conditions of the regulation ability scores and the threshold conditions of the closeness degrees;

[0041] The operating boundary optimization unit is configured to optimize the operable boundaries of the distributed resources participating in the aggregated scheduling based on the powers of the distributed resources participating in the aggregated scheduling and a pre-established two-layer optimization model, wherein the two-layer optimization model includes: a local optimization model and a global aggregation model.

[0042] Third aspect, an electronic device, the electronic device includes at least one processor, and at least one memory and a bus connected to the processor; wherein, the processor and the memory complete communication with each other through the bus; the processor is configured to call program instructions in the memory to execute the distributed resource evaluation and aggregation method of the virtual power plant described in any one of the above.

[0043] With the above technical solution, a method, device, and electronic device for evaluating and aggregating distributed resources of a virtual power plant provided by the present invention can dynamically calculate the regulation ability scores and closeness degrees of each of the distributed resources of the virtual power plant, where the regulation ability indicators of each of the distributed resources of the virtual power plant are pre-quantified and established indicators; based on the regulation ability scores and closeness degrees of each of the distributed resources, select the distributed resources that meet the threshold conditions from each of the distributed resources to participate in the aggregation scheduling, where the threshold conditions include: the threshold conditions for the regulation ability scores and the threshold conditions for the closeness degrees; based on the power of each of the distributed resources participating in the aggregation scheduling and a pre-established two-layer optimization model, optimize the operable boundaries of each of the distributed resources participating in the aggregation scheduling, where the two-layer optimization model includes: a local optimization model and a global aggregation model. It can be seen from this that the present invention can achieve efficient aggregation and operation boundary optimization of distributed energy resources by the virtual power plant through a dynamic scoring mechanism and a hierarchical optimization architecture, and has the advantages of high efficiency (by setting a dynamic selection mechanism, enabling the virtual power plant to quickly determine the distributed resources to be aggregated), flexibility (adjusting the index weights in real time according to the regulation mode of the power grid to ensure a high degree of matching and accuracy between resource selection and the current target), and accuracy (accurately characterizing the complementary characteristics of distributed resources through hierarchical optimization, expanding the operable boundaries of the aggregated resources of the virtual power plant).

[0044] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are specifically exemplified below. Brief Description of the Drawings

[0045] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0046] Figure 1 Shows a flowchart of a method for evaluating and aggregating distributed resources of a virtual power plant provided by the present invention;

[0047] Figure 2 Shows a schematic structural diagram of a device for evaluating and aggregating distributed resources of a virtual power plant provided by the present invention;

[0048] Figure 3 Shows a schematic structural diagram of an electronic device provided by the present invention. Detailed Description of the Embodiments

[0051] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be completely conveyed to those skilled in the art.

[0052] As Figure 1 shown, the present invention provides a method for evaluating and aggregating distributed resources of a virtual power plant, including: S100, S200, and S300;

[0053] S100. Dynamically calculate the regulation ability scores and closeness degrees of each of the distributed resources according to the regulation ability indicators of each distributed resource of the virtual power plant, where the regulation ability indicators of each distributed resource of the virtual power plant are pre-quantified and established indicators;

[0054] Optionally, the regulation ability indicators described in the present invention can be pre-calculated by a quantification method for the influencing factors, and the present invention does not limit this. For example, optionally, in some alternative embodiments, before the S100, the method further includes: step 1.1;

[0055] Step 1.1. Perform quantification calculations based on economic factors, safety factors, and timeliness factors to establish the regulation ability indicators of each distributed resource of the virtual power plant, where the regulation ability indicators include: adjustable range, regulation rate, sustainable time, regulation cost, and credibility. The adjustable range represents the adjustable power interval of the distributed resource per unit time, the regulation rate represents the sensitivity of the distributed resource to respond to the scheduling instruction, the sustainable time represents the sustainable time of the distributed resource at the target output power, the regulation cost represents the economic cost of the distributed resource when responding to the scheduling instruction, and the credibility represents the prediction accuracy of the output of the distributed resource of the new energy power source type.

[0056] Specifically, when the present invention evaluates the regulation ability of the distributed resource, it can be quantified from three dimensions: economy, safety, and timeliness, so as to construct the following various regulation ability indicators.

[0057] (1) Adjustable range.

[0058] This indicator measures the adjustable power interval of the distributed resource per unit time, and is specifically defined as shown in the following formula 1:

[0059] Formula 1:

[0060] In the above formula, the subscript i in the present invention is defined to represent the number of distributed resources; represents the time interval to complete a scheduling instruction; and respectively represent the maximum and minimum power of the i-th distributed resource.

[0061] (2) Regulation rate.

[0062] This index mainly measures the sensitivity of distributed resources to respond to scheduling instructions, and the specific definition is shown in Formula 2 below:

[0063] Formula 2:

[0064] In the above formula, represents the moment when the distributed resource triggers the power regulation instruction.

[0065] (3) Sustainable time.

[0066] This index mainly measures the sustainable time of energy storage distributed resources under the target output power, and the specific definition is shown in Formula 3 below:

[0067] Formula 3:

[0068] In the above formula, represents the remaining energy of the energy storage distributed resource; represents the set power.

[0069] (4) Regulation cost.

[0070] This index measures the economic cost of distributed resources when responding to scheduling instructions. Since the regulation power of distributed resources can be regarded as an opportunity cost of power change, the present invention can refer to the power generation cost of the unit and describe it using a quadratic cost function. The specific definition is shown in Formula 4 below:

[0071] Formula 4:

[0072] In the above formula, represents the power regulated by the i-th distributed resource in one scheduling action; , , are respectively the quadratic term, linear term, and constant term coefficients in the quadratic cost function.

[0073] (5) Credibility.

[0074] In general, distributed power sources such as wind power and photovoltaic power are not regarded as adjustable resources due to the uncertainty of their output. However, in the aggregation decision-making of a virtual power plant, it is difficult to separate them from distributed resources for individual decision-making. Therefore, from another perspective, the present invention can use the prediction accuracy of new energy output as a kind of adjustment ability, and thus constructs a credibility index as shown in Formula 5 below.

[0075] Formula 5:

[0076] In the above formula, T represents the total number of time periods for intraday scheduling decisions; represents the predicted output value of distributed power source i at time period t; represents the actual output of distributed power source i at time period t; represents the rated output value of distributed power source i.

[0077] That is, in some alternative embodiments, the step 1.1 includes: step 2.1, step 2.2, step 2.3, step 2.4, and step 2.5;

[0078] Step 2.1: Establish an adjustable range in the adjustment ability index of the corresponding distributed resource according to the time interval, maximum power, and minimum power for the distributed resource to complete the scheduling instruction;

[0079] Step 2.2: Establish an adjustment rate in the adjustment ability index of the corresponding distributed resource according to the moment when the distributed resource triggers the power adjustment instruction;

[0080] Step 2.3: Establish a sustainable time in the adjustment ability index of the corresponding energy storage type distributed resource according to the remaining energy of the energy storage type distributed resource and the set power of the energy storage type distributed resource;

[0081] Step 2.4: Establish an adjustment cost in the adjustment ability index of the corresponding distributed resource according to the power adjusted by the distributed resource in a single scheduling action;

[0082] Step 2.5: Establish a credibility in the adjustment ability index of the new energy power source type distributed resource according to the total number of time periods for the intraday scheduling decision of the new energy power source type distributed resource, the predicted output values of each time period within a single day, the actual output of each time period within a single day, and the rated output value of the new energy power source type distributed resource.

[0083] Optionally, in some alternative embodiments, the S100 includes: step 3.1, step 3.2, step 3.3, step 3.4, and step 3.5;

[0084] Step 3.1: For any type of distributed resource in the virtual power plant, the analytic hierarchy process is used to determine the priority weights of the distributed resource under each regulation mode. Among them, the ranking of the priority weights of each distributed resource under different regulation modes is different;

[0085] Step 3.2: Based on the priority weights under the current regulation mode and the switching moment of the regulation mode, time smoothing processing is performed on the priority weights of each distributed resource under the current regulation mode, where the switching moment of the regulation mode refers to the switching moment from the previous regulation mode to the current regulation mode;

[0086] Step 3.3: Based on the quantity, value, maximum and minimum values of each regulation ability index of each distributed resource and the priority weights after time smoothing processing, the regulation ability scores of each distributed resource are calculated;

[0087] Step 3.4: For distributed resources of new energy power sources, based on the standard deviation of the credibility of the distributed resources of new energy power sources and the maximum and minimum values of the credibility of each distributed resource of new energy power sources, the closeness of the distributed resources of new energy power sources is calculated, where the maximum and minimum values include: the maximum credibility value and the minimum credibility value;

[0088] Optionally, in some alternative embodiments, Step 3.4 includes: Step 4.1, Step 4.2, Step 4.3, Step 4.4 and Step 4.5;

[0089] Step 4.1: For distributed resources of new energy power sources, based on the standard deviation of the credibility of the distributed resources of new energy power sources, the credibility of the distributed resources of new energy power sources is fuzzified to obtain the corresponding triangular fuzzy sequence;

[0090] Step 4.2: According to the triangular fuzzy sequence and the maximum and minimum values of the credibility of each distributed resource of new energy power sources, the fuzzy index, positive ideal solution and negative ideal solution of the distributed resources of new energy power sources are calculated;

[0091] Step 4.3: According to the fuzzy index, the positive ideal solution and the negative ideal solution, the positive ideal solution distance and the negative ideal solution distance of the distributed resources of new energy power sources are calculated;

[0092] Step 4.4: According to the positive ideal solution distance and the negative ideal solution distance, the grey correlation degree coefficient of the distributed resources of new energy power sources is calculated;

[0093] Step 4.5: Based on the grey correlation degree coefficient of the distributed resources of new energy power sources, the closeness of the distributed resources of new energy power sources is calculated.

[0094] Step 3.5: For other types of distributed resources, calculate the closeness degree of the other types of distributed resources based on the maximum and minimum values of the positive indicators and the maximum and minimum values of the negative indicators of the other types of distributed resources. Among them, the positive indicators include: adjustable range, adjustment rate, and sustainable time, and the negative indicator includes: adjustment cost, and the maximum and minimum values include: maximum value and minimum value.

[0095] Optionally, in some alternative embodiments, the step 3.5 includes: step 5.1, step 5.2, step 5.3, and step 5.4;

[0096] Step 5.1: For other types of distributed resources, perform normalization processing based on the maximum and minimum values of the positive indicators and the maximum and minimum values of the negative indicators of the other types of distributed resources to obtain the normalized values of the positive indicators and the normalized values of the negative indicators of the other types of distributed resources. Among them, the other types of distributed resources are distributed resources other than new energy power source type distributed resources;

[0097] Step 5.2: Calculate the positive ideal solution and the negative ideal solution of the other types of distributed resources according to the normalized values of the positive indicators and the normalized values of the negative indicators of the other types of distributed resources;

[0098] Step 5.3: Calculate the positive ideal solution distance and the negative ideal solution distance of the other types of distributed resources according to the positive ideal solution and the negative ideal solution of the other types of distributed resources;

[0099] Step 5.4: Calculate the closeness degree of the other types of distributed resources according to the positive ideal solution distance and the negative ideal solution distance of the other types of distributed resources.

[0100] Optionally, after constructing the adjustment ability indicators of the distributed resources, the present invention needs to comprehensively evaluate the specific adjustment ability of the distributed resources based on these indicators. Since in actual business, the virtual power plant faces different application scenarios and the priorities of the corresponding adjustment indicators are different, for this reason, the present invention constructs the index weight adjustment rules shown in Table 1 below based on different demand scenarios.

[0101] Table 1

[0102]

[0103] Optionally, the weights of each index of various types of distributed resources in different modes in Table 1 can be determined by methods such as the Delphi method, the expert experience method, and the analytic hierarchy process method. The present invention will not describe this in detail. For specific details, please refer to the relevant descriptions in this field. The present invention takes the determined weights as static weights.

[0104] Optionally, due to the need for the security and stability of the power system, when the application scenario changes, the power of distributed resources cannot be instantaneously switched, so the corresponding adjustment index weights cannot be instantaneously transformed. Therefore, the present invention introduces a time smoothing mechanism. When the adjustment mode changes, the adjustment index weights of distributed resources change as shown in Formula 6 below.

[0105] Formula 6:

[0106] In the above formula, represents the target weight value that the j-th adjustment index should adopt according to the scenario in Table 1; represents the smoothing coefficient; represents the trigger time point of mode switching.

[0107] Optionally, after determining the weight setting and change mechanism of each adjustment index of distributed resources, without considering the problem of uncertainty first, an evaluation index as shown in Formula 7 below is constructed.

[0108]

[0109] Wherein, represents the score of the adjustment ability of the i-th distributed resource; n represents the total number of adjustment indexes under the condition of not considering uncertainty, which is n = 4 in this article; represents the weight of the j-th type of adjustment index of the i-th distributed resource; and respectively represent the minimum value and the maximum value of the j-th type of adjustment index of the i-th distributed resource; represents the value of the j-th type of adjustment index of the i-th distributed resource.

[0110] After obtaining the comprehensive evaluation indexes of each distributed resource through Formula 7, the present invention can set a threshold. Under the condition of meeting the dispatching instruction, the distributed resources that meet the conditions shown in Formula 8 below are preferentially aggregated and resource-called.

[0111] Formula 8:

[0112] Wherein, represents the comprehensive score threshold of the adjustment ability for measuring the priority of distributed resource calling.

[0113] Optionally, through the above calculations, the present invention can perform a first-level screening on the priority of distributed resource calling, which can avoid the dimensional explosion easily caused by overall evaluation under the condition of multi-dimensional heterogeneous data.

[0114] Optionally, uncertainty is introduced next. In the above steps, the credibility index is not considered because the credibility indices of other distributed resources except new energy are all 0. Therefore, the present invention can incorporate new energy into the model.

[0115] First, the credibility index is fuzzified to obtain the triangular fuzzy sequence shown in Formula 9 below.

[0116] Formula 9:

[0117] In the above formula, and respectively represent the lower bound, midpoint, and upper bound values of the original sequence in the triangular fuzzy number; represents the credibility standard deviation.

[0118] Optionally, the present invention can take into account the triangular fuzzy numbers of all new energy power sources in the virtual power plant. Then, the a - cut set sequence of a single new energy source is shown in Formula 10 below:

[0119] Formula 10:

[0120] In the above formula, and respectively represent the maximum and minimum credibility values of all new energy power sources in the system.

[0121] According to Formula 10, the fuzzy index and positive and negative ideal solutions of new energy power source i are specifically shown in Formula 11 below:

[0122] Formula 11:

[0123] Optionally, is the fuzzy index of new energy power source i, is the positive ideal solution, is the negative ideal solution. Thus, the distances from new energy power source i to the positive and negative ideal solutions are shown in Formula 12 below:

[0124] Formula 12:

[0125] Optionally, is the distance from new energy power source i to the positive ideal solution, is the distance from new energy power source i to the negative ideal solution. Further, the grey relational grade coefficient of power source i is shown in Formula 13 below:

[0126] Formula 13:

[0127] In the above formula, and respectively represent the maximum and minimum distances from all new energy power sources in the system to the positive ideal solution; and respectively represent the maximum and minimum distances from all new energy power sources in the system to the negative ideal solution; represents the correlation coefficient, taking a decimal between 0 and 1.

[0128] Thus, the closeness degree of power source i is shown in Formula 14 as follows:

[0129] Formula 14:

[0130] Optionally, is the closeness degree of power source i, is the positive grey correlation degree, is the negative grey correlation degree. The above calculation process can be extended to other distributed resources, and the difference lies in the determination of the positive and negative ideal solutions and distances.

[0131] First, normalize the indicators of other distributed resources according to the method shown in Formula 15 as follows.

[0132] Formula 15:

[0133] Wherein, represents the jth type of regulation index of the ith distributed resource; and represent the minimum and maximum values of the jth type of regulation index of all distributed resources in the system; in this article, the positive indicators include the adjustable range, adjustment rate, and sustainable time; the negative indicator is the adjustment cost.

[0134] Optionally, the positive and negative ideal solutions of a single indicator of a deterministic distributed resource are determined according to Formula 16 as follows.

[0135] Formula 16:

[0136] Since, except for new energy, other types of distributed resources may include multiple types of regulation indicators, their distances to the positive and negative ideal solutions are respectively shown in Formula 17 as follows:

[0137] Formula 17:

[0138] Wherein, m represents the total number of types of regulation indicators included in this distributed resource.

[0139] Substitute the above Formula 17 into Formula 13 and Formula 14 to obtain the closeness degree of the distributed resource.

[0140] Optionally, for non-new energy distributed resources and new energy power sources screened by the system through comprehensive scoring, the present invention can calculate their closeness, and then set the selection criteria as shown in Formula 18 below.

[0141] Formula 18:

[0142] where is a threshold, whose value range and dimension are the same as those of the threshold in Formula 8, but the values are different.

[0143] Optionally, all distributed resources that meet Formula 18 can participate in the aggregation and scheduling tasks. Through the double-layer screening mechanism, it avoids the dimensional explosion and non-linear problems of traditional methods in dealing with multi-dimensional data and heterogeneous data, comprehensively and quantitatively evaluates the regulation capabilities of different distributed resources, makes the decision-making more explicit, and takes into account both economy and efficiency.

[0144] S200. Select distributed resources that meet the threshold conditions from each of the distributed resources to participate in the aggregation scheduling based on the regulation ability scores and closeness of each of the distributed resources, where the threshold conditions include: the threshold conditions of the regulation ability score and the threshold conditions of the closeness.

[0145] Optionally, in some alternative embodiments, S200 includes: Step 6.1;

[0146] Step 6.1. Select distributed resources from each of the distributed resources whose regulation ability scores are greater than the preset score threshold and whose closeness is greater than the preset closeness threshold to participate in the aggregation scheduling.

[0147] S300. Optimize the operable boundaries of each of the distributed resources participating in the aggregation scheduling based on the power of each of the distributed resources participating in the aggregation scheduling and the pre-established double-layer optimization model, where the double-layer optimization model includes: a local optimization model and a global aggregation model.

[0148] Optionally, in some alternative embodiments, S300 includes: Step 7.1 and Step 7.2;

[0149] Step 7.1. Pre-establish a double-layer optimization model based on the power constraints and energy constraints of each of the distributed resources participating in the aggregation scheduling.

[0150] Step 7.2. Perform iterative optimization calculations based on the double-layer optimization model and the power of each of the distributed resources participating in the aggregation scheduling to obtain the operable boundaries of each of the distributed resources participating in the aggregation scheduling.

[0151] Optionally, as described above, the present invention solves the problems of "how to select distributed resources" and "which distributed resources to select" in the aggregation and scheduling of virtual power plants. Next, the present invention can solve the problem of how to utilize these resources after selecting the distributed resources.

[0152] First, still following the traditional idea, the operation of distributed resources is subject to certain constraint conditions, which are listed in the following formulas 19 to 21 by the present invention.

[0153] (1) The power constraint condition of the distributed resource is as follows formula 19.

[0154] Formula 19:

[0155] (2) For energy storage type distributed resources, the energy constraint condition shown in the following formula 20 also needs to be considered.

[0156] Formula 20:

[0157] Wherein, and respectively represent the minimum remaining energy and the maximum remaining energy of energy storage i.

[0158] (3) For traditional power sources in distributed resources, such as gas turbines, etc., their ramp rate constraint conditions need to be considered, which can be specifically expressed as the following formula 21.

[0159] Formula 21:

[0160] Wherein, represents the maximum ramp rate of unit i.

[0161] In addition to the above conditions, the constraint conditions of the system also include power flow constraints, etc. The present invention does not list them one by one. Specifically, corresponding constraint conditions can be set according to actual needs.

[0162] Optionally, for constructing a two-layer model, the present invention can use formulas 19 to 20 as the local feasible region of (the i-th distributed resource), and construct the local model shown in the following formula 22.

[0163] Formula 22:

[0164] In the above formula, represents the power historical reference mean value of the i-th distributed resource; is the transpose of the Lagrange multiplier sequence, and the number of its elements depends on the number of local constraint conditions; represents the constraint condition expression related to the power of the i-th distributed resource.

[0165] The local model (lower-layer model) can be understood as the goal at the micro level. It is called the local model because the upper-layer model is at the overall level of the virtual power plant, that is, the dispatching center or other global operations are carried out on the host. It needs to interact with the external network (for example, the virtual power plant participates in market bidding), which can be regarded as a kind of server operation; while the local model is deployed locally and is used to calculate how much regulation power or load reduction should be given by each specific distributed resource.

[0166] Meanwhile, the present invention can construct a global aggregation model as shown in Formula 23 below as the upper-layer model.

[0167] Formula 23:

[0168] In the above formula, N represents the total number of distributed resources aggregated by the virtual power plant; represents the total power of the resources aggregated by the virtual power plant; represents the power regulation range of the i-th distributed resource in the feasible region. For different types of distributed resources, they are respectively as shown in Formula 24 below:

[0169] Formula 24:

[0170] In the above formula, represents the maximum charging power of the energy storage; represents the maximum discharging power of the energy storage; represents the load curtailment amount.

[0171] In addition, in Formula 23, represents the dynamic weight coefficient, which can be set in the form as shown in Formula 25 below.

[0172] Formula 25:

[0173] Among them, and Please refer to Formula 2 and Formula 4; represents a rational number generated to ensure that the weight does not exceed 1.

[0174] Optionally, the function of the upper-layer model is: the goal that the virtual power plant needs to achieve at the overall level , that is, the weighted flexibility regulation of all aggregated distributed resources is maximized under the given constraints , that is, the flexibility resources achieve the maximum utilization efficiency at the macro level,

[0175] The upper-layer model can be regarded as a macro objective function, and the present invention does not limit this.

[0176] For the local model, the present invention can set the iterative process shown in Formula 26 below.

[0177] Formula 26:

[0178] In the above formula, t represents the iteration step; is the coefficient for controlling the number of steps in the ADMM (Alternating Direction Method of Multipliers) algorithm.

[0179] Meanwhile, at the global end, the present invention can set the iterative process shown in Formula 27 below.

[0180] Formula 27:

[0181] In the above two iterative processes, the Lagrange multiplier is updated according to the following Formula 28.

[0182] Formula 28:

[0183] The convergence condition for the above process is as shown in Formula 29:

[0184] Formula 29:

[0185] In the above formula, represents the threshold coefficient in the ADMM algorithm.

[0186] Optionally, since the upper and lower layer models are coupled and not isolated, because the solution calculated by the lower layer needs to be balanced with the upper layer model, that is, the micro-aggregation equals the macro. For such a complex model, it cannot be calculated rigidly according to the traditional linear programming, so the solution needs to be iterated, that is, continuously search for a better solution. In this paper, the ADMM algorithm is used, which is carried out alternately. It does not find the global optimal solution in one step, but first gives an initial solution, and then iteratively optimizes until the convergence condition is met. At this time, the solution given is the optimal solution.

[0187] Through the above model and algorithm, the optimal global operating conditions of the virtual power plant under the given aggregated resources can be dynamically updated.

[0188] Due to the above technical solutions adopted by the present invention, it has the following advantages: Through the dynamic scoring mechanism and the hierarchical optimization architecture, the present solution realizes the efficient aggregation of distributed energy resources by the virtual power plant and the optimization of the operation boundary. The specific advantages are as follows:

[0189] (1) High efficiency: Set a dynamic selection mechanism to enable the virtual power plant to quickly determine the set of distributed resources to be aggregated; when determining the operation boundary of the aggregated resources, the algorithm adopted can quickly reduce the data dimension and improve the calculation efficiency.

[0190] (2) Flexibility: Adjust the index weights in real time according to the power grid demand patterns (peak shaving, frequency modulation, economic mode) to ensure a high degree of matching between resource selection and the current target; at the same time, optimize economy, regulation rate, and capacity utilization rate to avoid the loss of regulation potential caused by a single target.

[0191] (3) Precision: Introduce the grey correlation coefficient to replace the Euclidean distance to solve the heterogeneous fusion problem of fuzzy indicators and deterministic indicators; precisely characterize the complementary characteristics of distributed resources through hierarchical optimization, expanding the operable boundary of the aggregated resources of the virtual power plant.

[0192] The operating boundary of distributed resources has two meanings: Firstly, it is the constraint conditions subject to physical constraints and the safe operation constraints of the power grid, that is, the constraint conditions for the output, power, and load curtailment operation of these distributed resources. Secondly, due to the different performances and regulation capabilities of distributed resources, indicators such as their regulation power and load curtailment can only operate within a certain range.

[0193] Although the operations are depicted in a specific order, this should not be construed as requiring the operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous.

[0194] It should be understood that the various steps recited in the method embodiments of the present invention can be executed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.

[0195] As Figure 2 shown, the present invention provides a distributed resource evaluation and aggregation device for a virtual power plant, including: a dynamic calculation unit 100, an aggregated resource selection unit 200, and an operating boundary optimization unit 300;

[0196] The dynamic calculation unit 100 is configured to dynamically calculate the regulation ability scores and closeness degrees of the distributed resources according to the regulation ability indicators of the distributed resources of the virtual power plant, wherein the regulation ability indicators of the distributed resources of the virtual power plant are pre-quantified and established indicators;

[0197] The aggregated resource selection unit 200 is configured to select the distributed resources that meet the threshold conditions from the distributed resources to participate in the aggregated scheduling based on the regulation ability scores and closeness degrees of the distributed resources, wherein the threshold conditions include: the threshold conditions of the regulation ability scores and the threshold conditions of the closeness degrees;

[0198] The operation boundary optimization unit 300 is configured to optimize the operable boundaries of distributed resources participating in aggregated scheduling based on the power of each distributed resource participating in aggregated scheduling and a pre-established two-layer optimization model, where the two-layer optimization model includes: a local optimization model and a global aggregation model.

[0199] Optionally, in some alternative embodiments, the apparatus further includes: an index establishment unit;

[0200] The index establishment unit is configured to perform quantitative calculations based on economic factors, safety factors, and timeliness factors to establish the regulation ability indexes of each distributed resource of the virtual power plant before dynamically calculating the regulation ability scores and proximity degrees of each distributed resource according to the regulation ability indexes of each distributed resource of the virtual power plant, where the regulation ability indexes include: adjustable range, regulation rate, sustainable time, regulation cost, and credibility. The adjustable range represents the adjustable power interval of the distributed resource per unit time, the regulation rate represents the sensitivity of the distributed resource to respond to the scheduling instruction, the sustainable time represents the sustainable time of the distributed resource at the target output power, the regulation cost represents the economic cost of the distributed resource when responding to the scheduling instruction, and the credibility represents the prediction accuracy of the output of the distributed resource of the new energy power source type.

[0201] Optionally, in some alternative embodiments, the index establishment unit includes: a range index establishment subunit, a rate index establishment subunit, a time index establishment subunit, a cost index establishment subunit, and a credibility index establishment subunit;

[0202] The range index establishment subunit is configured to establish the adjustable range in the regulation ability index of the corresponding distributed resource according to the time interval, maximum power, and minimum power for the distributed resource to complete the scheduling instruction;

[0203] The rate index establishment subunit is configured to establish the regulation rate in the regulation ability index of the corresponding distributed resource according to the moment when the distributed resource triggers the power regulation instruction;

[0204] The time index establishment subunit is configured to establish the sustainable time in the regulation ability index of the corresponding energy storage type distributed resource according to the remaining energy of the energy storage type distributed resource and the set power of the energy storage type distributed resource;

[0205] The cost index establishment subunit is configured to establish the regulation cost in the regulation ability index of the corresponding distributed resource according to the power adjusted by the distributed resource in one scheduling action;

[0206] The credibility index establishing subunit is configured to establish the credibility in the regulation capacity index of the new energy power source type distributed resources according to the total number of scheduling decision time periods within a single day of the new energy power source type distributed resources, the predicted output values of each time period within a single day, the actual output of each time period within a single day, and the rated output value of the new energy power source type distributed resources.

[0207] Optionally, in some alternative embodiments, the dynamic calculation unit 100 includes: a weight determination subunit, a smoothing processing subunit, a scoring calculation subunit, a new energy proximity calculation subunit, and an other resource proximity calculation subunit;

[0208] The weight determination subunit is configured to use the analytic hierarchy process to determine the priority weights of any type of distributed resource of the virtual power plant in each regulation mode, wherein the sorting of the priority weights of each distributed resource in different regulation modes is different;

[0209] The smoothing processing subunit is configured to perform time smoothing processing on the priority weights of each distributed resource in the current regulation mode based on the priority weights in the current regulation mode and the switching moment of the regulation mode, wherein the switching moment of the regulation mode refers to the switching moment from the previous regulation mode to the current regulation mode;

[0210] The scoring calculation subunit is configured to calculate the regulation capacity scores of each distributed resource based on the quantity, value, maximum and minimum values of each regulation capacity index of each distributed resource and the priority weights after time smoothing processing;

[0211] The new energy proximity calculation subunit is configured to calculate the proximity of the new energy power source type distributed resources based on the standard deviation of the credibility of the new energy power source type distributed resources and the maximum and minimum values of the credibility of each new energy power source type distributed resource, wherein the maximum and minimum values include: the maximum credibility value and the minimum credibility value;

[0212] The other resource proximity calculation subunit is configured to calculate the proximity of the other type of distributed resources based on the maximum and minimum values of the positive indicators and the maximum and minimum values of the negative indicators of the other type of distributed resources, wherein the positive indicators include: adjustable range, adjustment rate, and sustainable time, the negative indicator includes: adjustment cost, and the maximum and minimum values include: maximum value and minimum value.

[0213] Optionally, in some alternative embodiments, the new energy proximity calculation subunit includes: a fuzzification processing subunit, a new energy ideal solution calculation subunit, a new energy distance calculation subunit, a grey coefficient calculation subunit, and a grey coefficient substitution subunit;

[0214] The fuzzification processing subunit is configured to perform fuzzification on the credibility of the new energy power source type distributed resources based on the standard deviation of the credibility of the new energy power source type distributed resources, so as to obtain a corresponding triangular fuzzy sequence;

[0215] The new energy ideal solution calculation subunit is configured to calculate the fuzzy index, the positive ideal solution and the negative ideal solution of the new energy power source type distributed resources according to the triangular fuzzy sequence and the maximum and minimum values of the credibility of each new energy power source type distributed resource;

[0216] The new energy distance calculation subunit is configured to calculate the positive ideal solution distance and the negative ideal solution distance of the new energy power source type distributed resources according to the fuzzy index, the positive ideal solution and the negative ideal solution;

[0217] The grey correlation coefficient calculation subunit is configured to calculate the grey correlation degree coefficient of the new energy power source type distributed resources according to the positive ideal solution distance and the negative ideal solution distance;

[0218] The grey coefficient substitution subunit is configured to calculate the closeness of the new energy power source type distributed resources based on the grey correlation degree coefficient of the new energy power source type distributed resources.

[0219] Optionally, in some alternative embodiments, the other resource closeness calculation subunit includes: a normalization processing subunit, an other resource ideal solution calculation subunit, an other resource distance calculation subunit and an other resource closeness obtaining subunit;

[0220] The normalization processing subunit is configured to perform normalization processing on other types of distributed resources based on the maximum and minimum values of the positive indicators and the maximum and minimum values of the negative indicators of the other types of distributed resources, so as to obtain the normalized values of the positive indicators and the normalized values of the negative indicators of the other types of distributed resources, where the other types of distributed resources are distributed resources other than the new energy power source type distributed resources;

[0221] The other resource ideal solution calculation subunit is configured to calculate the positive ideal solution and the negative ideal solution of the other types of distributed resources according to the normalized values of the positive indicators and the normalized values of the negative indicators of the other types of distributed resources;

[0222] The other resource distance calculation subunit is configured to calculate the positive ideal solution distance and the negative ideal solution distance of the other types of distributed resources according to the positive ideal solution and the negative ideal solution of the other types of distributed resources;

[0223] The other resource proximity obtaining subunit is configured to calculate the proximity of the other types of distributed resources according to the positive ideal solution distance and the negative ideal solution distance of the other types of distributed resources.

[0224] Optionally, in some alternative embodiments, the aggregated resource selection unit 200 includes: an aggregated resource selection subunit;

[0225] The aggregated resource selection subunit is configured to select, from each of the distributed resources, the distributed resources with an adjustment ability score greater than a preset score threshold and a proximity greater than a preset proximity threshold to participate in the aggregated scheduling.

[0226] Optionally, in some alternative embodiments, the operation boundary optimization unit 300 includes: an optimization model establishment subunit and an optimization model calculation subunit;

[0227] The optimization model establishment subunit is configured to pre-establish a two-layer optimization model based on the power constraints and energy constraints of each distributed resource participating in the aggregated scheduling;

[0228] The optimization model calculation subunit is configured to perform iterative optimization calculations based on the two-layer optimization model and the power of each distributed resource participating in the aggregated scheduling to obtain the operable boundaries of each distributed resource participating in the aggregated scheduling.

[0229] Regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0230] The distributed resource evaluation and aggregation device of the virtual power plant includes a processor and a memory. The above dynamic calculation unit 100, aggregated resource selection unit 200, operation boundary optimization unit 300, etc. are all stored in the memory as program units, and the processor executes the above program units stored in the memory to implement corresponding functions.

[0231] The processor contains a kernel, and the kernel retrieves the corresponding program units from the memory. One or more kernels can be set. By adjusting the kernel parameters, through the dynamic scoring mechanism and the hierarchical optimization architecture, the virtual power plant realizes the efficient aggregation and operation boundary optimization of distributed energy resources, and has the advantages of high efficiency (by setting a dynamic selection mechanism, enabling the virtual power plant to quickly determine the distributed resources to be aggregated), flexibility (adjusting the index weights in real time according to the regulation mode of the power grid to ensure a high degree of matching and accuracy of resource selection with the current target), and accuracy (precisely depicting the complementary characteristics of distributed resources through hierarchical optimization, expanding the operable boundaries of the aggregated resources of the virtual power plant).

[0232] An embodiment of the present invention provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the distributed resource evaluation and aggregation method of the virtual power plant is implemented.

[0233] An embodiment of the present invention provides a processor, which is used to run a program. When the program runs, the distributed resource evaluation and aggregation method of the virtual power plant is executed.

[0234] As Figure 3 As shown, an embodiment of the present invention provides an electronic device 70, which includes at least one processor 701, at least one memory 702 connected to the processor 701, and a bus 703. Among them, the processor 701 and the memory 702 complete communication with each other through the bus 703. The processor 701 is used to call program instructions in the memory 702 to execute the above-mentioned distributed resource evaluation and aggregation method of the virtual power plant. The electronic device herein can be a server, a PC, a PAD, a mobile phone, etc.

[0235] The present invention also provides a computer program product, which is suitable for executing a program initialized with the following method steps when executed on an electronic device:

[0236] A distributed resource evaluation and aggregation method for a virtual power plant includes:

[0237] According to the regulation ability indexes of the distributed resources of the virtual power plant, dynamically calculate the regulation ability scores and closeness degrees of the distributed resources, wherein the regulation ability indexes of the distributed resources of the virtual power plant are indexes established by pre-quantification.

[0238] Based on the regulation ability scores and closeness degrees of the distributed resources, select the distributed resources that meet the threshold conditions from the distributed resources to participate in the aggregation scheduling, wherein the threshold conditions include: the threshold conditions of the regulation ability scores and the threshold conditions of the closeness degrees.

[0239] Based on the power of the distributed resources participating in the aggregation scheduling and a pre-established two-layer optimization model, optimize the operable boundaries of the distributed resources participating in the aggregation scheduling, wherein the two-layer optimization model includes: a local optimization model and a global aggregation model.

[0240] Optionally, in some alternative embodiments, before calculating the regulation ability scores and closeness degrees of the distributed resources according to the regulation ability indexes of the distributed resources of the virtual power plant, the method further includes:

[0241] Quantitative calculations are carried out based on economic factors, safety factors and timeliness factors to establish the regulation ability indexes of various distributed resources in a virtual power plant. Among them, the regulation ability indexes include: adjustable range, regulation rate, sustainable time, regulation cost and credibility. The adjustable range represents the adjustable power interval of distributed resources per unit time. The regulation rate represents the sensitivity of distributed resources to respond to dispatch instructions. The sustainable time represents the sustainable time of distributed resources at the target output power. The regulation cost represents the economic cost of distributed resources when responding to dispatch instructions. The credibility represents the prediction accuracy of the output of distributed resources of new energy power source type.

[0242] Optionally, in some alternative embodiments, the quantitative calculations based on economic factors, safety factors and timeliness factors to establish the regulation ability indexes of various distributed resources in a virtual power plant include:

[0243] Based on the time interval, maximum power and minimum power for distributed resources to complete dispatch instructions, establish the adjustable range in the regulation ability indexes of the corresponding distributed resources;

[0244] Based on the moment when distributed resources trigger power regulation instructions, establish the regulation rate in the regulation ability indexes of the corresponding distributed resources;

[0245] Based on the remaining energy of energy storage type distributed resources and the set power of energy storage type distributed resources, establish the sustainable time in the regulation ability indexes of the corresponding energy storage type distributed resources;

[0246] Based on the power regulated by distributed resources in one dispatch action, establish the regulation cost in the regulation ability indexes of the corresponding distributed resources;

[0247] Based on the total number of time periods of the dispatch decision of distributed resources of new energy power source type within a single day, the predicted output values of each time period within a single day, the actual output of each time period within a single day, and the rated output value of the distributed resources of new energy power source type, establish the credibility in the regulation ability indexes of the distributed resources of new energy power source type.

[0248] Optionally, in some alternative embodiments, the dynamic calculation of the regulation ability scores and closeness degrees of each of the distributed resources according to the regulation ability indexes of the various distributed resources in a virtual power plant includes:

[0249] For any type of distributed resource in a virtual power plant, the analytic hierarchy process is used to determine the priority weights of the distributed resources in each regulation mode, where the sorting of the priority weights of the distributed resources in different regulation modes is different;

[0250] Based on the priority weights in the current adjustment mode and the switching moment of the adjustment mode, perform time smoothing processing on the priority weights of each distributed resource in the current adjustment mode, where the switching moment of the adjustment mode refers to the switching moment from the previous adjustment mode to the current adjustment mode;

[0251] Based on the quantity, value, maximum and minimum values of each adjustment ability index of each distributed resource and the priority weights after time smoothing processing, calculate the adjustment ability scores of each distributed resource;

[0252] For distributed resources of new energy power sources, based on the standard deviation of the credibility of the distributed resources of new energy power sources and the maximum and minimum values of the credibility of each distributed resource of new energy power sources, calculate the closeness degree of the distributed resources of new energy power sources, where the maximum and minimum values include: the maximum credibility value and the minimum credibility value;

[0253] For other types of distributed resources, based on the maximum and minimum values of the positive indicators and the maximum and minimum values of the negative indicators of the other types of distributed resources, calculate the closeness degree of the other types of distributed resources, where the positive indicators include: adjustable range, adjustment rate and sustainable time, the negative indicator includes: adjustment cost, and the maximum and minimum values include: maximum value and minimum value.

[0254] Optionally, in some alternative embodiments, the step of calculating the closeness degree of the distributed resources of new energy power sources based on the standard deviation of the credibility of the distributed resources of new energy power sources and the maximum and minimum values of the credibility of each distributed resource of new energy power sources includes:

[0255] For distributed resources of new energy power sources, based on the standard deviation of the credibility of the distributed resources of new energy power sources, fuzzify the credibility of the distributed resources of new energy power sources to obtain the corresponding triangular fuzzy sequence;

[0256] According to the triangular fuzzy sequence and the maximum and minimum values of the credibility of each distributed resource of new energy power sources, calculate the fuzzy index, positive ideal solution and negative ideal solution of the distributed resources of new energy power sources;

[0257] According to the fuzzy index, the positive ideal solution and the negative ideal solution, calculate the distance from the positive ideal solution and the distance from the negative ideal solution of the distributed resources of new energy power sources;

[0258] According to the distance from the positive ideal solution and the distance from the negative ideal solution, calculate the grey correlation degree coefficient of the distributed resources of new energy power sources;

[0259] Based on the grey correlation coefficients of the new energy power source type distributed resources, the closeness degree of the new energy power source type distributed resources is calculated.

[0260] Optionally, in some alternative embodiments, for other types of distributed resources, based on the maximum and minimum values of the positive indicators and the maximum and minimum values of the negative indicators of the other types of distributed resources, calculating the closeness degree of the other types of distributed resources includes:

[0261] For other types of distributed resources, based on the maximum and minimum values of the positive indicators and the maximum and minimum values of the negative indicators of the other types of distributed resources, perform normalization processing to obtain the normalized values of the positive indicators and the normalized values of the negative indicators of the other types of distributed resources, where the other types of distributed resources are distributed resources other than the new energy power source type distributed resources;

[0262] According to the normalized values of the positive indicators and the normalized values of the negative indicators of the other types of distributed resources, calculate the positive ideal solution and the negative ideal solution of the other types of distributed resources;

[0263] According to the positive ideal solution and the negative ideal solution of the other types of distributed resources, calculate the positive ideal solution distance and the negative ideal solution distance of the other types of distributed resources;

[0264] According to the positive ideal solution distance and the negative ideal solution distance of the other types of distributed resources, calculate the closeness degree of the other types of distributed resources.

[0265] Optionally, in some alternative embodiments, based on the regulation ability scores and the closeness degrees of the distributed resources, selecting the distributed resources that meet the threshold conditions from the distributed resources to participate in the aggregated scheduling includes:

[0266] Select the distributed resources with a regulation ability score greater than a preset score threshold and a closeness degree greater than a preset closeness degree threshold from the distributed resources to participate in the aggregated scheduling.

[0267] Optionally, in some alternative embodiments, based on the powers of the distributed resources participating in the aggregated scheduling and a pre-established two-layer optimization model, optimizing the operable boundaries of the distributed resources participating in the aggregated scheduling includes:

[0268] Based on the power constraints and energy constraints of the distributed resources participating in the aggregated scheduling, pre-establish a two-layer optimization model;

[0269] Based on the two-layer optimization model and the powers of the distributed resources participating in the aggregated scheduling, perform iterative optimization calculations to obtain the operable boundaries of the distributed resources participating in the aggregated scheduling.

[0270] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses, electronic devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable devices produce means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks of the block diagram.

[0271] In a typical configuration, an electronic device includes one or more processors (CPUs), a memory, and a bus. The electronic device may also include an input / output interface, a network interface, etc.

[0272] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one storage chip. The memory is an example of computer-readable media.

[0273] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tape disk storage, or other magnetic storage devices, or any other non-transitory media that can store information accessible by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0274] In the description of the present invention, it should be understood that if terms such as "upper", "lower", "front", "rear", "left" and "right" are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the drawings. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated position or element must have a specific orientation, be constructed and operate in a specific orientation. Therefore, it should not be construed as a limitation of the present invention.

[0275] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent in such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, commodity or device comprising the element.

[0276] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0277] The above are only the embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A distributed resource evaluation and aggregation method for a virtual power plant, characterized in that Including: According to the regulation ability indexes of various distributed resources of the virtual power plant, dynamically calculate the regulation ability scores and closeness degrees of each of the distributed resources, wherein the regulation ability indexes of various distributed resources of the virtual power plant are indexes established by pre - quantization; Based on the regulation ability scores and closeness degrees of each of the distributed resources, select the distributed resources that meet the threshold conditions from each of the distributed resources to participate in the aggregated scheduling, wherein the threshold conditions include: the threshold conditions of the regulation ability score and the threshold conditions of the closeness degree; Based on the powers of the distributed resources participating in the aggregated scheduling and the pre - established two - layer optimization model, optimize the operable boundaries of the distributed resources participating in the aggregated scheduling, wherein the two - layer optimization model includes: a local optimization model and a global aggregation model.

2. The method according to claim 1, characterized in that, Before the step of dynamically calculating the regulation ability scores and closeness degrees of each of the distributed resources according to the regulation ability indexes of various distributed resources of the virtual power plant, the method further includes: Based on economic factors, safety factors and timeliness factors, conduct quantitative calculations to establish the regulation ability indexes of various distributed resources of the virtual power plant, wherein the regulation ability indexes include: adjustable range, regulation rate, sustainable time, regulation cost and credibility. The adjustable range represents the adjustable power interval of the distributed resource per unit time, the regulation rate represents the sensitivity of the distributed resource to respond to the scheduling instruction, the sustainable time represents the sustainable time of the distributed resource at the target output power, the regulation cost represents the economic cost of the distributed resource when responding to the scheduling instruction, and the credibility represents the prediction accuracy of the output of the distributed resource of the new - energy power source type.

3. The method according to claim 2, wherein The step of establishing the regulation ability indexes of various distributed resources of the virtual power plant by conducting quantitative calculations based on economic factors, safety factors and timeliness factors includes: According to the time interval, maximum power and minimum power for the distributed resource to complete the scheduling instruction, establish the adjustable range in the regulation ability index of the corresponding distributed resource; According to the moment when the distributed resource triggers the power regulation instruction, establish the regulation rate in the regulation ability index of the corresponding distributed resource; According to the remaining energy of the energy - storage - type distributed resource and the set power of the energy - storage - type distributed resource, establish the sustainable time in the regulation ability index of the corresponding energy - storage - type distributed resource; According to the power regulated by the distributed resource in one scheduling action, establish the regulation cost in the regulation ability index of the corresponding distributed resource; According to the total number of time periods of the scheduling decision of the distributed resource of the new - energy power source type within a single day, the predicted output values of each time period within a single day, the actual output of each time period within a single day and the rated output value of the distributed resource of the new - energy power source type, establish the credibility in the regulation ability index of the distributed resource of the new - energy power source type.

4. The method according to claim 1, wherein The step of dynamically calculating the regulation ability scores and closeness degrees of each of the distributed resources according to the regulation ability indexes of various distributed resources of the virtual power plant includes: For any type of distributed resource of the virtual power plant, use the analytic hierarchy process to determine the priority weights of the distributed resource in each regulation mode, wherein the sorting of the priority weights of the distributed resources in different regulation modes is different; Based on the priority weights in the current adjustment mode and the switching moment of the adjustment mode, perform time smoothing processing on the priority weights of each distributed resource in the current adjustment mode, where the switching moment of the adjustment mode refers to the switching moment from the previous adjustment mode to the current adjustment mode; Based on the number, value, maximum and minimum values of each adjustment ability index of each distributed resource and the priority weights after time smoothing processing, calculate the adjustment ability scores of each distributed resource; For distributed resources of new energy power source type, based on the standard deviation of credibility of the distributed resources of new energy power source type and the maximum and minimum values of the credibility of each distributed resource of new energy power source type, calculate the closeness degree of the distributed resources of new energy power source type, where the maximum and minimum values include: the maximum value of credibility and the minimum value of credibility; For other types of distributed resources, based on the maximum and minimum values of positive indicators and the maximum and minimum values of negative indicators of the other types of distributed resources, calculate the closeness degree of the other types of distributed resources, where the positive indicators include: adjustable range, adjustment rate and sustainable time, the negative indicator includes: adjustment cost, and the maximum and minimum values include: the maximum value and the minimum value.

5. The method according to claim 4, wherein The step of calculating the closeness degree of the distributed resources of new energy power source type based on the standard deviation of credibility of the distributed resources of new energy power source type and the maximum and minimum values of the credibility of each distributed resource of new energy power source type includes: For distributed resources of new energy power source type, based on the standard deviation of credibility of the distributed resources of new energy power source type, fuzzify the credibility of the distributed resources of new energy power source type to obtain the corresponding triangular fuzzy sequence; According to the triangular fuzzy sequence and the maximum and minimum values of the credibility of each distributed resource of new energy power source type, calculate the fuzzy index, positive ideal solution and negative ideal solution of the distributed resources of new energy power source type; According to the fuzzy index, the positive ideal solution and the negative ideal solution, calculate the distance from the positive ideal solution and the distance from the negative ideal solution of the distributed resources of new energy power source type; According to the distance from the positive ideal solution and the distance from the negative ideal solution, calculate the grey correlation degree coefficient of the distributed resources of new energy power source type; Based on the grey correlation degree coefficient of the distributed resources of new energy power source type, calculate the closeness degree of the distributed resources of new energy power source type.

6. The method according to claim 4, wherein The step of calculating the closeness degree of the other types of distributed resources based on the maximum and minimum values of positive indicators and the maximum and minimum values of negative indicators of the other types of distributed resources includes: For other types of distributed resources, based on the maximum and minimum values of positive indicators and the maximum and minimum values of negative indicators of the other types of distributed resources, perform normalization processing to obtain the normalized values of positive indicators and the normalized values of negative indicators of the other types of distributed resources, where the other types of distributed resources are distributed resources other than those of new energy power source type; Calculate the positive ideal solution and negative ideal solution of the other types of distributed resources according to the normalized values of the positive indicators and negative indicators of the other types of distributed resources; Calculate the positive ideal solution distance and negative ideal solution distance of the other types of distributed resources according to the positive ideal solution and negative ideal solution of the other types of distributed resources; Calculate the closeness degree of the other types of distributed resources according to the positive ideal solution distance and negative ideal solution distance of the other types of distributed resources.

7. The method according to claim 1, wherein The step of selecting distributed resources that meet the threshold conditions from each of the distributed resources based on the regulation ability scores and closeness degrees of each of the distributed resources to participate in the aggregated scheduling includes: Select, from each of the distributed resources, distributed resources with a regulation ability score greater than a preset score threshold and a closeness degree greater than a preset closeness threshold to participate in the aggregated scheduling.

8. The method according to claim 1, wherein The step of optimizing the operable boundaries of the distributed resources participating in the aggregated scheduling based on the powers of the distributed resources participating in the aggregated scheduling and a pre-established two-layer optimization model includes: Pre-establish a two-layer optimization model based on the power constraints and energy constraints of the distributed resources participating in the aggregated scheduling; Based on the two-layer optimization model and the powers of the distributed resources participating in the aggregated scheduling, perform iterative optimization calculations to obtain the operable boundaries of the distributed resources participating in the aggregated scheduling.

9. A distributed resource evaluation and aggregation device for a virtual power plant, characterized in that including: A dynamic calculation unit, an aggregated resource selection unit, and an operable boundary optimization unit; The dynamic calculation unit is configured to dynamically calculate the regulation ability scores and closeness degrees of each of the distributed resources according to the regulation ability indicators of each of the distributed resources of the virtual power plant, where the regulation ability indicators of each of the distributed resources of the virtual power plant are pre-quantified and established indicators; The aggregated resource selection unit is configured to select, based on the regulation ability scores and closeness degrees of each of the distributed resources, distributed resources that meet the threshold conditions from each of the distributed resources to participate in the aggregated scheduling, where the threshold conditions include: threshold conditions for the regulation ability scores and threshold conditions for the closeness degrees; The operable boundary optimization unit is configured to optimize the operable boundaries of the distributed resources participating in the aggregated scheduling based on the powers of the distributed resources participating in the aggregated scheduling and a pre-established two-layer optimization model, where the two-layer optimization model includes: a local optimization model and a global aggregation model.

10. An electronic device, characterized in that, The electronic device includes at least one processor, at least one memory connected to the processor, and a bus; wherein, the processor and the memory communicate with each other through the bus; the processor is configured to call program instructions in the memory to execute the method for evaluating and aggregating distributed resources of a virtual power plant according to any one of claims 1 to 8.

Images